Gaboxadol Increases Resting Theta and Alpha Power without Affecting Evoked Responses in Fragile X Syndrome in a Home-based Setting

Gaboxadol Increases Resting Theta and Alpha Power without Affecting Evoked Responses in Fragile X Syndrome in a Home-based Setting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gaboxadol Increases Resting Theta and Alpha Power without Affecting Evoked Responses in Fragile X Syndrome in a Home-based Setting Lisa A. De Stefano, Hyeonseok Kim, Craig A. Erickson, Lauren M. Schmitt, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7871579/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: Fragile X syndrome (FXS) lacks FDA-approved treatments despite various small molecules contributing to phenotypic rescue in the FMR1 knockout (KO) mouse model. Translation from the mouse model has been hampered by phenotypic heterogeneity that contributes to participation barriers among participants who are most affected and may be unable to regularly visit the research laboratory. The current study utilized a crossover design to test the acute neural and behavioral effects of a single 10mg dose of gaboxadol and the reliability of electroencephalography (EEG) and behavioral data collected in participant homes compared to the clinic. Methods: Ten adult males with full mutation FXS completed four blinded dosing visits (two placebo, two gaboxadol), with two occurring in-home and two in-lab. Pre- and post-dose assessments included resting high-density EEG, an auditory chirp paradigm, RBANS List Learning, and NIH Toolbox Cognition Battery subtests. Results: No serious adverse events were reported. Compared with placebo, gaboxadol increased theta and alpha band power, with no interaction between collection environment (home vs. lab). Additionally, gaboxadol increased the proportion of electrodes with detectable low-frequency peaks and slowed the peak frequency. There were no effects on auditory-evoked measures or NIH Toolbox, with only a marginal effect on RBANS List Learning. An analysis of pre-dose EEG found reliability estimates across testing locations for all tested resting power and behavioral measures that were similar to in-lab reliability estimates found in the literature. Conclusions: Single-dose gaboxadol augmented theta and alpha power in FXS during resting EEG, similar to previous findings in the typically developing population and in the FMR1 KO, without normalizing gamma abnormalities, altering auditory-evoked responses, or contributing to behavioral change. These results did not significantly differ between the home and lab settings, supporting the feasibility of in-home data collection for clinical trials in FXS, including those that use complex measures such as EEG as endpoints. Clinical trial number: clinicaltrials.gov, NCT06334419, Registration Date: March 8, 2024 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 1. Introduction Fragile X syndrome (FXS) is the most common inherited form of intellectual disability and autism spectrum disorder, with a clinical phenotype that is additionally often marked by anxiety, attentional symptoms, sensory hypersensitivity and irritability, among other concerns. FXS is caused by a CGG triplet repeat expansion in the promoter region of the FMR1 gene resulting in gene methylation and subsequent silencing of gene transcription with resultant reduction or absence of fragile x messenger ribonucleoprotein (FMRP) expression. Despite phenotypic rescue with various small molecules in the Fmr1 knockout (KO) mouse model of FXS, there are no FDA-approved treatments for FXS, and translation from the mouse model has been largely unsuccessful. One potential reason for this lack of translation is the increase in phenotypic heterogeneity in participants with FXS, who do not have uniform repeat counts and methylation patterns like the Fmr1 KO mouse, a deletion animal model. This variability highlights the need to use biomarkers that are sensitive across a broad range of clinical severity, particularly in individuals with low FMRP expression who may more closely resemble the Fmr1 KO model. Many of our previous studies of drug response in patients with FXS have utilized a double-blind, randomized, single-dose crossover design, with participants visiting the clinic to engage in pre- and post-dose encephalography (EEG) in response to placebo and study drug. This approach allows us to assess target engagement utilizing cross-species validated biomarkers such as increased gamma power at rest (Goswami et al., 2019 ; Jonak et al., 2020 ; Lovelace et al., 2018 ; Pedapati et al., 2022 ; Wang et al., 2017 ) and altered intertrial phase coherence (ITC) during auditory stimulation (Ethridge et al., 2017 , 2019 ; Jonak et al., 2020 ; Lovelace et al., 2018 ; Miyakoshi et al., 2025 ; Norris et al., 2025 ). Recently published trials have found that baclofen reduced gamma power across-species (Jonak et al., 2022 ), while lovastatin and minocycline did not normalize EEG in humans (McKinney et al., 2024 ). This design holds promise for early, rapid screening of potential drugs that engage neural targets relevant to FXS prior to more costly, intensive chronic dosing trials. However, the requirement that participants travel to often distant clinic sites for repeated visits to engage in clinical trial research can provide a barrier to participation. This barrier may be particularly challenging to overcome among participants who present with significant impairment who may be best modeled by work with the Fmr1 KO mouse and could potentially benefit the most from clinical trial translational medicine efforts. For example, in our recent study of baclofen, follow-up analyses found the strongest reductions in gamma power in participants who had the lowest FMRP (Jonak et al., 2022 ), and thus were likely to be more severely affected (Boggs et al., 2022 ; Loesch et al., 2004 ). Home-based trial protocols may help overcome this barrier, improving access to clinical research and representation of more severely affected individuals. The current study aimed to expand upon our single-dose design by collecting pre- and post-dose EEG and behavioral measures both in home and in clinic in response to a candidate study drug, gaboxadol following a 10 mg single dose. Gaboxadol was primarily developed for the treatment of insomnia. Its clinical development involved over 4300 subjects across Phase I, II, and III studies, demonstrating efficacy in improving sleep maintenance and initiation, increasing slow-wave sleep (SWS), and showing a generally favorable safety and tolerability profile with no significant next-day residual or withdrawal effects at therapeutic doses (up to 15 mg for adults and 10 mg for the elderly administered as a single dose before sleep to insomnia patients). However, the development for the insomnia indication was terminated in March 2007 due to its effect size for sleep onset not being considered competitive and an unattractive risk-benefit ratio at higher doses. The dosing of 10 mg per occasion in this current study was therefore deemed appropriate and safe. Gaboxadol is an orthosteric agonist at GABA A receptors, with high selectivity for extrasynaptic receptors containing δ subunits (Stórustovu & Ebert, 2006 ), which contribute to tonic inhibition in multiple neuronal populations (Semyanov et al., 2004 ). Previous examinations of the effects of gaboxadol on EEG power spectra have chiefly occurred during non-REM sleep, with most studies reporting an increase in slow wave (or delta, 1-4.5 Hz) and theta activity (4-5-7 Hz) in a dose-dependent manner (Lundahl et al., 2012 ; Ma et al., 2011 ; Mathias et al., 2005 ; Walsh et al., 2007 ). Mixed results have been found in the alpha band, with one study reporting a decrease in alpha power (Ma et al., 2011 ) while another found increased alpha power at the highest studied dose (20mg; Lundahl et al., 2012 ). Interestingly, multiple studies that examined sex found that these effects were strongest in females, potentially due to altered δ subunit density (Dijk et al., 2010 ; Ma et al., 2011 ), although several studies conducted in both young and elderly subjects suggested a modest but significant increase (approximately 10 to 20%) in weight adjusted plasma exposure between female and male subjects. Previous studies of gaboxadol in the Fmr1 KO mouse found it to rescue inhibitory tone and increase action potential thresholds to that of wild-type (WT) mice in amygdalar slices (Olmos-Serrano et al., 2010 ). Behaviorally, gaboxadol has been shown to reduce aberrant pre-pulse inhibition at low decibels in vivo (Olmos-Serrano et al., 2011 ) and normalize a variety of behaviors, particularly at a low dose (Cogram et al., 2019 ). However, a recent attempt to find electrophysiological correlates in Fmr1 KO mice did not find a normalizing impact of single-dose gaboxadol on gamma power or visually- or auditory-evoked potentials, though gaboxadol at its higher dose was associated with increased power broadly from 1–30 Hz (Janz et al., 2025 ). In humans, a clinical trial of gaboxadol in 23 individuals with FXS evaluated three fixed doses in parallel groups across a 12-week dosing period and found that 60% of participants showed clinical improvement (Budimirovic et al., 2021 ), though no neural biomarkers were assessed and there was no control group. As a result, it remains unclear whether gaboxadol engages relevant neural targets in humans with FXS. In the current study, we sought to determine not only the effect of single-dose gaboxadol on EEG biomarkers commonly found in FXS, but also the feasibility and consistency of collecting these biomarkers in the participants’ home environment. By extending the single-dose crossover design into participant homes, we aimed to assess whether decentralized EEG collection could support early-phase biomarker-driven drug screening in FXS, which has the potential to reduce participant burden, improve participation rates, and better represent the FXS phenotype in clinical research. 2. Methods and Materials 2.1 Study design This study consisted of two in-home and two in-clinic dosing visits with a two-week washout period between visits. Participants always began the sequence with a clinic visit at which inclusion and exclusion criteria were assessed prior to the first dose, followed by a home visit, another in-clinic visit, and a final home visit. All participants received two doses of placebo (one of each at each location) and two doses of 10 mg gaboxadol (one of each at each location) with all investigators and participants blinded to treatment status. The study protocol was conducted in accordance with the Declaration of Helsinki, approved by the Cincinnati Children’s Hospital Medical Center Institutional Review Board, and registered at clinicaltrials.gov with the identifier NCT06334419. Potential participants were deemed eligible if they: (1) were male, aged 18–40; (2) had confirmed full Fmr1 mutation (> 200 CGG repeats); (3) were in generally good health at the time of the initial clinic visit, as determined by a study physician; (4) if receiving serotonin-selective reuptake inhibitor (SSRIs), serotonin-norepinephrine reuptake inhibitor (SNRI), or serotonin antagonist and reuptake inhibitor (SARI), were on a stable, well-tolerated dose for the previous three months with no further changes anticipated; (5) were not sexually active or confirmed at least one form of contraceptive. Study exclusion criteria are described in Supplement S1. At the baseline visit, which served as the first dosing day, participants completed demographic, phenotypic, and health history forms, received a physical examination and Clinical Global Impressions – Severity score from a study physician, and completed the Abbreviated Stanford Binet 5 (Roid & Pomplun, 2012 ). At each clinic visit, participants received a blood draw and urinalysis to assess health status and optionally donated a post-dose pharmacokinetic blood sample to assess the amount of gaboxadol in the blood. Laboratory work results were reviewed prior to dosing at first visit. At every dosing visit, including both clinic and home visits, participants completed the following measures pre- and post-dose: EEG, Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) List Learning (Randolph et al., 1998 ), and portions of the NIH Toolbox Cognition Battery (Weintraub et al., 2013 ; Oral Reading Recognition, Picture Vocabulary, Speeded Matching, Pattern Comparison Processing Speed, Flanker Inhibitory Control and Attention, and Dimensional Change Card Sort subtests). Post-dose measures were collected 60–90 minutes after administration of the drug in alignment with the Tmax of gaboxadol (Ovid Therapeutics, Inc, 2022 ). 2.2 Procedures Electroencephalography. EEG recordings were obtained using saline-based 128-channel HydroCel Geodesic Sensor Nets connected to an EGI Net Amps 400 amplifier (Magstim EGI, Eugene, OR). Data were sampled at 1000 Hz at collection. All EEG assessments were collected pre- and post-dose at each visit. Rest. Ten minutes of resting-state EEG data were collected while participants sat in a quiet room and watched a silent movie of their choosing on an iPad to facilitate participation. Chirp. Participants additionally listened to the “chirp” stimulus, a 2-second white noise carrier stimulus that is amplitude modulated by a sinusoid that linearly increases in frequency from 0-100 Hz over its duration. Chirp stimuli were presented 200 times at 65 db SPL through headphones with an intertrial interval that randomly varied between 1.5-2s. EEG preprocessing. EEGLAB was used for EEG preprocessing (Delorme & Makeig, 2004 ). After data import, resting data were downsampled to 250 Hz while chirp data were not downsampled. Following this step, a high-pass filter was applied with a 0.5 Hz cutoff frequency, a 1.0 Hz transition band, and a Blackman window. Line noise at 60 Hz and its harmonics were removed using the CleanLine plugin (Bigdely-Shamlo et al., 2015 ; Miyakoshi et al., 2021 ; Mullen, 2012 ). Then, the first and last 3 seconds of each recording were trimmed. To detect and reject artifactual channels, the continuous data were first segmented into 1,000 equal-length epochs. To ensure equal epoch length, a small number of trailing samples were discarded from the end of each recording. For each channel, the maximum absolute amplitude was computed across time within each epoch. To model channel-wise amplitude distributions, a generalized extreme value (GEV) distribution was fit to the maximum absolute amplitude across epochs. A per-channel threshold was then defined as the 90th percentile of the fitted GEV distribution. Epochs exceeding this threshold were marked as high-amplitude outliers. Standard deviations were then computed across time within each epoch, and epochs with high-amplitude outliers were excluded from further estimation. Channels whose median standard deviation across non-outlier epochs fell below 0.1 µV were classified as flat and excluded from the montage. Subsequently, the remaining data were re-referenced to the median (computed after excluding outlier time points). Standard deviation was again calculated per epoch per channel, and a robust threshold was applied to the median SD values to identify channels with excessively large amplitude fluctuations. Specifically, channels whose median standard deviation exceeded the group-level median by more than 20 times the scaled median absolute deviation (MAD × 1.4826) were rejected. A similar approach was used in our previous study (Miyakoshi et al., 2025 ). Artifact Subspace Reconstruction (ASR) was applied to remove transient high-amplitude artifacts (Kothe & Makeig, 2013 ; Miyakoshi, 2023 ; T. R. Mullen et al., 2015 ). ASR was run with a burst criterion of 25, using Euclidean distance. Calibration was restricted to clean data segments by setting BurstCriterionRefMaxBadChns to 0, ensuring that no channels exceeded the artifact threshold during calibration (Kim, Chang, et al., 2025 ). Previously rejected channels were restored by spherical spline interpolation. The original reference channel (Cz), which is not recorded separately in the EGI system, was restored as a zero-filled channel to preserve its position in the montage. All channels, including the reconstructed Cz, were then re-referenced to the average reference (Kim et al., 2023 ). NIH Toolbox Cognition Battery (NIH Toolbox). Participants completed six subtests from the NIH Toolbox that have previously been assessed for use in FXS (Shields et al., 2020 ). For each measure, the pre-dose change sensitive score was subtracted from the post-dose change sensitive score to form a difference score that was then assessed statistically. The number of participants included in each assessment varies across visit date, as some participants were nonverbal (n = 2) or refused to participate in subtests during at least one testing occasion (n = variable). Dimensional Change Card Sort Test. In this test of attention and cognitive flexibility, participants are required to match a pair of pictures on one dimension (shape, color), before shifting to another dimension later in the task. Flanker Inhibitory Control and Attention Test. Participants are required to attend to a central stimulus and ignore flanking stimuli. Oral Reading Recognition Test. Participants are required to read letters and words out loud. Pattern Comparison Processing Speed Test. Participants must make determinations about whether two stimuli are the same or different. Picture Vocabulary Test. Participants listen to a word and select the corresponding picture from an array of four pictures. Speeded Matching Test. Participants are presented with a target picture along with four pictures beneath it and must select the picture that matches the target from the array. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Participants completed the List Learning subtests of the RBANS both pre- and post-dose, which is a measure of memory for a list of words that are read to them. Each list contains ten words and four attempts at recall, for a total potential score of 40. To evaluate drug effects, the pre-dose score was subtracted from the post-dose score, indicating the additional number of words remembered post-dose in each condition. All eight verbal participants completed all testing occasions of the RBANS List Learning subtest. Clinical Global Impressions – Improvement (CGI-I). The CGI-I is a physician rating of improvement on a Likert scale, with a score of “1” indicating “very much improved” and a score of “7” indicating “very much worse.” A score of “4” centers the scale, indicating “no change.” 2.3 Data analysis Behavioral data. Intraclass correlation (ICC). Reliability of pre-dose measures across all four visits was examined in R (version 4.4.0; R Core Team, 2024 ) using the irr package (version 0.84.1; Gamer et al., 2019). ICC was calculated for a single measurement using a two-way random-effects model with absolute agreement [ICC(A,1)]. ICC analysis utilized change sensitive scores for NIH Toolbox Cognition Battery subtests and raw scores (total words remembered) for RBANS List Learning. Linear mixed-effects models (LME). LME were implemented using PROC MIXED in SAS® version 9.4 (SAS Institute Inc., Cary, NC) to compare outcomes between placebo and gaboxadol. Subject was included as a random effect, and treatment was modeled as a fixed effect. To examine the impact of assessment location (clinic vs. home) models initially included location and an interaction between treatment and location as predictors, which were removed if the terms were not significant. P -values < 0.05 were considered significant. There were no adjustments made for multiple comparisons, as this is considered a pilot study. Rest. Resting power. Power spectral density (PSD) was estimated using MATLAB’s spectrogram function with a 1 s Hamming window, 50% overlap, and frequency range of 2-100 Hz. For each channel, the median power across time windows was computed for robustness (Melman & Victor, 2016 ) and converted to decibels (dB). The resulting log-transformed PSD was stored per channel for further analysis. To estimate the spectral exponent (SPEX), a log-transformed PSD was modeled with an exponential curve using the FOOOF algorithm (Donoghue et al., 2020 ; Gao et al., 2017 ). The fit was restricted to the 2 to 100 Hz range and used a fixed (no-knee) aperiodic model. Periodic peaks were limited to a maximum of one per spectrum, and a threshold of 2 standard deviations was applied to detect candidate peaks. The spectral exponent, defined as the slope of the aperiodic fit, was extracted from each channel for further analysis. To examine treatment-related changes in spontaneous EEG activity, PSD data were organized into a 2 × 2 repeated-measures design with the factors Dose (pre- vs. post-dose) and Treatment (Drug vs. Placebo), resulting in four condition-specific PSD sets. A two-way repeated-measures ANOVA was performed at each channel-frequency bin, focusing on the interaction between Dose and Treatment to identify frequency- and region-specific effects of the drug. The resulting interaction-term p-values were mapped across all channels and frequencies to identify potential regions of interest for follow-up analysis. To extract the most relevant frequency bin, a significance mask was first generated by thresholding the p-values at p < 0.05. This binary mask was summed across electrodes and smoothed using a 1-Hz moving average. The frequency bin corresponding to the peak of this smoothed sum was selected for further modeling. For each subject and session, spectral power at the selected frequency was averaged across all electrodes and entered into a linear mixed-effects model with fixed effects of Dose, Treatment, and their interaction, and random slopes and intercepts grouped by subject. Subsequently, linear mixed-effects models were applied to PSD values averaged across all channels and within each predefined frequency band: delta (2 to 4 Hz), theta (4 to 8 Hz), alpha (8 to 13 Hz), beta (13 to 30 Hz), gamma (30 to 55 Hz), and high gamma (65 Hz and above). Each model included fixed effects for Dose, Treatment, and their interaction, along with random intercepts and slopes for each subject. Model fitting was performed using maximum likelihood estimation in MATLAB R2023a ( fitlme ). A second analysis focused on post-dose sessions to assess treatment-related differences in spontaneous EEG activity across recording environments. PSD data were grouped by Treatment (Drug vs. Placebo) and Environment (lab vs. home), and a two-way repeated-measures ANOVA was performed at each channel-frequency bin with Treatment and Environment as within-subject factors. To extract a representative frequency bin for follow-up analysis, p-values from the main effect of Treatment were thresholded at p < 0.05 to create a binary significance mask. This mask was summed across electrodes at each frequency and smoothed using a 1-Hz smoothing, and the frequency bin with the maximum value was selected for subsequent modeling. For follow-up modeling, post-dose spectral power at the previously selected frequency bin was averaged across all electrodes. Data were grouped by Treatment (Drug vs. Placebo) and recording Environment (lab vs. home). A linear mixed-effects model was then fit to the data with fixed effects for Treatment, Environment, and their interaction, and with random intercepts and slopes specified per subject. In a separate analysis, PSD values were averaged within each predefined frequency band and across all electrodes using only post-dose sessions. For each frequency band, a linear mixed-effects model was fit with fixed effects for Treatment, Environment, and their interaction, and with random intercepts and slopes specified per subject. Peak frequency. To assess drug-related changes in the frequency of spontaneous alpha oscillations, the spectral peak within the 4 to 13 Hz range was identified for each subject and electrode during pre- and post-dose sessions in the Drug condition. For each session, the frequency bin with the maximum power in the alpha range was extracted per channel and subject. Peaks located at the lowest frequency bin were treated as invalid and excluded from further analysis. Two metrics were computed. First, for each channel, the proportion of subjects with valid alpha peaks in both sessions was calculated to quantify detection consistency. Second, for each channel, the average frequency shift from pre- to post-dose was computed using only subjects with valid peaks in both sessions. This yielded a topographic map of alpha peak frequency shifts potentially attributable to the drug. To evaluate group-level effects, a single representative (“champion”) channel was selected for statistical analysis. For each channel, a composite score was calculated by multiplying the detection consistency by the mean frequency shift. The channel with the maximum absolute value of this score was selected. At the champion channel, subjects with valid alpha peaks in both sessions were identified, and peak frequency values were extracted. These values were analyzed using a linear mixed-effects model with dose as a fixed effect and random intercepts and slopes for each subject. This model tested whether the alpha peak frequency was significantly modulated by drug administration. SPEX. To examine treatment-related changes in the aperiodic component of the EEG power spectrum, SPEX values were extracted per subject and channel for each condition: pre-dose Placebo, post-dose Placebo, pre-dose Drug, and post-dose Drug. A linear mixed-effects model was applied separately at each electrode to test for the interaction between Dose (Pre vs. Post) and Treatment (Placebo vs. Drug). The model included fixed effects for Dose, Treatment, and their interaction, and random intercepts and slopes by subject. The resulting interaction terms were used to assess the spatial distribution of drug-related effects on SPEX. A second analysis focused on post-dose sessions to evaluate whether SPEX differed between Drug and Placebo across recording environments. For each channel, a linear mixed-effects model was fit with fixed effects for Treatment (Drug vs. Placebo), Environment (lab vs. home), and their interaction, and with random intercepts and slopes by subject. Statistical terms corresponding to the main effect of Treatment and the Treatment × Environment interaction were extracted per channel. ICC. To assess the test-retest reliability of our measures, we computed ICC using the ICC(A,1) formulation, which estimates absolute agreement for single measurements, for resting power spectra and SPEX. Analyses were restricted to pre-dose sessions. Three comparisons were performed: Lab-Lab, Home-Home, and Lab-Home. For resting power, at each electrode and frequency, PSD values from the two sessions were paired within subjects, and ICCs were computed across subjects. To summarize spectral reliability, ICC values were averaged across electrodes at each frequency. Scalp topographies were generated for the frequencies with the highest and lowest mean ICC values in each comparison. In a separate analysis, PSDs were first averaged within canonical frequency bands (delta: 2 to 4 Hz, theta: 4 to 8 Hz, alpha: 8 to 13 Hz, beta: 13 to 30 Hz, gamma: 30 to 55 Hz, and high gamma: 65 Hz and above), and ICCs were computed on these band-averaged values. For SPEX, values were extracted at each electrode and paired within subjects. The resulting ICC values were mapped across the scalp to assess the reliability of SPEX across repeated measures in different environments. Chirp. Evoked activity. Epochs were extracted from − 1000 ms to + 3000 ms relative to the onset of the chirp stimulus (t = 0). Event-related potentials (ERPs), event-related spectral perturbations (ERSPs), and inter-trial coherence (ITC) were computed for each epoch. For ERSP analysis, baseline correction was performed using the pre-stimulus interval from − 1000 ms to 0 ms. We also computed event-related spatio-spectral perturbations (ERSSP) to quantify the spatiotemporal extent of stimulus-evoked activity, with a particular focus on potential extralemniscal involvement (Kim et al., 2025 , under review). For each subject, the median power and ITC values were computed across trials at each time-frequency bin. For each frequency and channel, a normal distribution was defined using the mean and standard deviation of the baseline period (-1000 to 0 ms). ERSP values falling above the 97.5th percentile or below the 2.5th percentile of this distribution were marked as significant, corresponding to event-related synchronization (ERS) or desynchronization (ERD), respectively. For ITC, only bins in the top 5% were considered significant. These binary significance masks were then summed across channels, yielding per-subject time-frequency matrices that reflect the number of significant channels at each bin. These matrices were used to characterize the spatiotemporal distribution of stimulus-related activity in both power and phase domains. To quantify stimulus-related spatiotemporal dynamics, we defined a region of interest (ROI) in the ERSSP matrix spanning 0-500 ms and 2–13 Hz. This window was selected to capture early low-frequency activity, including theta and alpha band responses. For each subject, ERSSP values within this ROI were averaged to yield a single summary measure per condition. These values were used for subsequent statistical comparisons. A linear mixed-effects model was used to analyze the ROI-averaged EEG measure. The model included fixed effects for dose, treatment group, and their interaction. A random intercept was included for each subject to account for repeated measurements. To assess group differences in ERP amplitude at Cz, independent-samples t-tests were performed at each time point between the two experimental groups, separately for pre-dose and post-dose sessions. A two-tailed t-threshold corresponding to α = 0.05 was calculated using the median degrees of freedom from the pre-dose comparison. One subject was excluded from the pre-dose session of one group due to missing data. ICC. We assessed the test-retest reliability of multiple EEG-derived measures across repeated pre-dose sessions using ICCs. Three comparisons were performed: (1) Lab-Lab, (2) Home-Home, and (3) Lab-Home. The measures included: (1) the N1-P1 peak-to-peak amplitude at Cz, (2) ERSSP-ERS values averaged within a predefined time-frequency region of interest (ROI; 0-500 ms, 2–13 Hz) corresponding to the vertex potential (VP), (3) ERSSP-ITC values within the same ROI, and (4) ERSSP-ITC values at the frequency and latency corresponding to the expected auditory steady-state response (ASSR). For the ERP analysis, the waveform was segmented to a 0-300 ms post-stimulus window, corresponding to the typical latency range of the N1-P1 complex. The peak-to-peak amplitude was calculated as the difference between the maximum and minimum voltage within this interval. Subjects with missing or invalid data were excluded pairwise for each comparison. To define the ROI for ASSR-related ITC, binary significance maps (significant = 1, non-significant = 0) were computed for ERSSP-ITC across four conditions (pre- and post-dose sessions for both treatment groups). Subject-level maps were averaged within each condition, then across conditions, yielding a group-level map reflecting the proportion of subjects showing significant ITC at each time-frequency bin. This map was summed across channels to generate a 40 Hz time series, and the time point with the highest value was selected. The resulting time-frequency bin (40 Hz at peak latency) was used to extract ERSSP-ITC values for each subject and session. For all ERSSP-based metrics, ROI values were calculated as the average number of significant channels within the specified bins. ICCs were computed using the ICC(A,1) formulation, which estimates absolute agreement for single measurements. This method was implemented in MATLAB and applied separately to each of the three comparisons using pre-dose data only. 3. Results 3.1 Demographics Ten individuals completed all four dosing visits; there was no early withdrawal from the study. See Fig. 1 for a consort diagram detailing recruitment and eligibility. Three participants did not use any relevant concomitant medications during the study, while the remaining 7 participants used at least one medication. Demographics, baseline characteristics, and concomitant medications can be found in Table 1 . Three participants in the placebo condition reported adverse events (AE), including elevated heart rate, fatigue, and emotional disturbance (anxiety, aggression, crying). No participants receiving placebo in-home reported AE. In the laboratory, four participants receiving gaboxadol reported fatigue. An additional one participant reported fever that was deemed unrelated. In-home, two participants receiving gaboxadol reported fatigue. Table 1 Demographic Information and Baseline Characteristics Measure M (SD) or % (N) N 10 Age (years) 29.92 (6.95) % male 100% (10) Race White 100% (10) Ethnicity Hispanic or Latino 20% (2) Not Hispanic or Latino 80% (8) Aberrant Behavior Checklist (ABC-FXS) Subscale 1: Irritability/Aggression 14.00 (10.60) Subscale 2: Lethargy 5.80 (4.42) Subscale 3: Stereotypy 5.50 (3.57) Subscale 4: Hyperactivity 7.60 (5.82) Subscale 5: Inappropriate Speech 4.70 (2.63) Subscale 6: Social Avoidance 3.40 (3.72) Clinical Global Impressions - Severity 3.80 (0.63) Stanford Binet-5 (SB-5) Deviation IQ (Abbreviated) 24.16 (22.73) Concomitant Medications No psychiatric medications 30% (3) Methylphenidate 10% (1) 2nd generation antipsychotics 60% (6) SSRI or SNRIs 30% (3) Valproic acid 10% (1) Carbamazepine 10% (1) Clomipramine 10% (1) Atomexitine 10% (1) N, sample size; M, mean; SD, standard deviation; IQ, intelligence quotient. 3.2 Behavior ICC. ICC estimates can be found in Table 2, along with previously published estimates of ICC for these measures in FXS (Berry-Kravis et al., 2008; Shields et al., 2020). In general, comparable ICC values were found across pre-dose behavioral measures in our sample, despite lower sample sizes than those found in the literature. Notably, Shields et al. 2020 contains varying sample sizes for each measure, as they completed a data validation step prior to including data in the ICC analysis, while our data includes anyone for whom a score could be generated via the application across all testing occasions. While this adds an element of noise to our data, it reduces subjectivity associated with examiner determinations of validity. We found ICC for behavior measures to be relatively in line with the estimates in the literature, particularly with regard to Oral Reading and Pattern Comparison. Similar to Shields et al. (2020), ICC was lowest for the DCCS, both within and across environments. Our Picture Vocabulary ICC was higher than Shields et al. (2020), while our Flanker ICC was lower, though this effect did not differ between Lab-Lab and Home- Home. Our RBANS ICC was higher within Home-Home than Lab-Lab, but both measures were at least as high as found in Berry-Kravis et al. (2008). LME. Boxplots reflecting behavioral Table 2. Comparison of Intraclass Correlation Coefficients (ICC) Across Study Visits Across All Visits Mean Home – Mean Lab Lab – Lab Home - Home Shields et al. (2020) Berry-Kravis et al. (2008) n ICC (95% CI) n ICC (95% CI) n ICC (95% CI) n ICC (95% CI) n ICC (95% CI) n ICC (95% CI) DCCS 9 0.62 (0.30-0.88) 9 0.79 (0.31-0.95) 9 0.50 (-0.09-0.89) 9 0.62 (-0.05-0.90) 23 0.41 (0.01-0.69) - - Flanker 9 0.68 (0.39-0.90) 9 0.82 (0.38-0.96) 9 0.71 (0.05-0.93) 9 0.63 (0.04-0.90) 37 0.84 (0.70-0.91) - - Oral Reading 7 0.76 (0.46-0.94) 9 0.93 (0.73-0.99) 8 0.95 (0.69-0.99) 7 0.95 (0.78-0.99) 56 0.96 (0.93-0.98) - - Pattern Comparison 8 0.78 (0.45-0.94) 9 0.90 (0.44-0.98) 8 0.69 (0.04-0.93) 9 0.84 (0.04-0.97) 40 0.71 (0.50-0.84) - - Picture Vocabulary 9 0.94 (0.85-0.98) 10 0.98 (0.93-1.00) 9 0.91 (0.58-0.98) 10 0.97 (0.88-0.99) 57 0.79 (0.66-0.87) - - Speeded Matching 9 0.86 (0.67-0.96) 9 0.92 (0.69-0.98) 9 0.88 (0.58-0.97) 9 0.86 (0.50-0.97) - - - - RBANS 8 0.86 (0.60-0.97) 8 0.94 (0.69-0.99) 8 0.76 (-0.02-0.95) 8 0.93 (0.72-0.99) - - 41 0.70 RBANS List Learning. There were no significant effects of location or interaction between treatment and location, thus these factors were removed from the model. Examining treatment alone, with each dose considered a replicate, there was a marginal main effect of treatment, such that gaboxadol use was associated with an increase in words remembered after dose, while placebo had a slight decrease ( F (1, 23) = 3.36, p = .08, gaboxadol M = 2.38, placebo M = -0.50). However, this trend did not reach statistical significance and should not necessarily be interpreted as evidence of a treatment effect. To further understand the dynamics of the scores on this measure, a follow-up analysis was conducted to model the effect of treatment on pre- and post-dose scores separately. This analysis resulted in a main effect of treatment, such that scores in gaboxadol were higher than in the placebo condition overall, regardless of dose ( F (1,53) = 4.50, p = .04, gaboxadol M = 16.88, placebo M = 14.88; see Fig. 3 ). Though the interaction between treatment and dose was not significant ( F (1,53) = 2.37, p = .13), to further understand this pilot data, we examined uncorrected post-hoc tests comparing least squares means. We found that post-dose scores in the gaboxadol condition ( M = 18.06) significantly differed from pre- and post-dose scores in the placebo condition ( t (53) = 2.23, p = .03, placebo pre-dose M = 15.13; t (53) = 2.60, p = .01, placebo post-dose M = 14.63), but only marginally differed from pre-dose scores in the gaboxadol condition ( t (53) = 1.80, p = .08, gaboxadol pre-dose M = 15.69) due to slightly improved pre-dose performance in the gaboxadol condition. Thus, though gaboxadol was related to an increase in 2.38 words remembered, and placebo was associated with a .5 reduction in words remembered, these post-hoc results should be interpreted cautiously given the small sample size and increased risk of Type I error. Speeded Matching Test. There were no main effects of treatment or location, but there was a significant interaction between treatment and location, F (1, 24) = 8.89, p < .01. Comparison of least squares means indicated that performance improved post-dose in placebo in-lab ( M = 7.67) relative to placebo in-home ( M = -4.56, t (24) = 2.88, p < .01) and gaboxadol in-lab ( M = -3.00, t (24) = -2.51, p = .02), but not compared to gaboxadol in-home ( M = 2.67, t (24) = 1.18, p = .25). Flanker Inhibitory Control and Attention Test. There were no main effects of treatment or location, or interaction between treatment and location (all p s > .24). Dimensional Change Card Sort Test. There were no main effects of treatment or location on difference scores in the DCCS. There was a significant interaction between treatment and location, F (1,23.9) = 5.55, p = .03. Uncorrected post-hoc tests comparing differences of least squares means found a significant difference between difference scores in gaboxadol treatment in-clinic and in-home, t (24.4) = 2.39, p = .02, such that participants improved their scores post-dose in-home with gaboxadol more than in-clinic (gaboxadol in-home M = 7.14, gaboxadol in-clinic M = -1.78). There was also a marginal difference between difference scores in-clinic with gaboxadol and in-clinic with placebo treatment, t (23.5) = 1.75, p = .09, such that participants had larger difference scores in-clinic in the placebo condition than in-clinic with gaboxadol (placebo in-clinic M = 4.56, gaboxadol in-clinic M = -1.78). Pattern Comparison Processing Speed Test. There were no main effects of treatment or location, or interaction between treatment and location (all p s > .47). Oral Reading Recognition Test. There were no main effects of treatment or location, or interaction between treatment and location (all p s > .16). Picture Vocabulary Test. There was no main effect of treatment or interaction between treatment and location, and thus these factors were removed from the statistical model. Looking only at the effect of treatment, and thus utilizing each dose as a replicate, there was a marginal main effect of treatment, F (1, 28.6) = 3.09, p = .09, with gaboxadol having a reduction in score post-dose (gaboxadol M = -4.00), while placebo had a modest increase (placebo M = 0.75). To understand this further, we conducted a model utilizing treatment and dose (pre and post) as factors. Here, we found no main effect of treatment or dose, but a marginal interaction between treatment and dose, F (1, 65) = 3.27, p = .08. Uncorrected post-hoc tests examining differences of least squares means found that scores in the gaboxadol condition significantly differed between pre- and post-dose (gaboxadol pre-dose M = 475.8, post-dose M = 471.8) but these scores did not otherwise significantly differ from placebo’s pre- or post-dose scores (placebo pre-dose M = 472.85, post-dose M = 473.60). Clinical Global Impressions – Improvement. Most dosing occasions received a physician rating of “4” for “no change,” though three occasions saw a “3” indicating that the participant had “minimally improved.” All three occasions were in the gaboxadol condition and represented three distinct participants. Two of the occasions were at in-home visits and one was in-clinic. 3.3 EEG 3.3.1 Power Spectral Density (PSD) and Spectral Exponent (SPEX) analysis 3.3.1.1 The drug increases EEG power at 6 Hz. We calculated PSD of resting-state EEG data to compare Pre-Post conditions between the Placebo and Drug conditions, which employed a 2 x 2 factorial design. The results are shown in Fig. 4 . The targeted 2 x 2 interaction showed a characteristic frequency-dependent pattern peaking in the theta band (4–8 Hz), which was confirmed across the majority of electrodes. The peak detection on electrode-averaged results identified that the peak of the statistical results was localized at 6.8 Hz, where the interaction calculated within the LME model was statistically significant, F (1, 76) = 13.8, p = 0.00039. Next, we separated these estimates into six conventional frequency bands (Delta, 1–4 Hz; Theta, 4–8 Hz; Alpha, 8–13 Hz; Beta, 13–30 Hz; Gamma, 30–55 Hz; High Gamma, 65–100 Hz) and applied the same test for the mean EEG power of each frequency bands. The results are shown in Fig. 5 . A significant interaction was observed in the theta band ( F (1,76) = 12.29, p = 0.0008) and the alpha band ( F (1,76) = 7.43, p = 0.0080). Other frequency bands did not reach statistical significance. We conclude that the drug increases EEG power in the theta and alpha bands, with the effect centered at 6.4 Hz. 3.3.1.2 No significant difference between Lab and Home. Based on the results confirmed on 3.3.1.1, we asked whether there was a difference between Lab and Home conditions only in the Post-dose condition. This analysis also employed 2 x 2 design, but the interaction was not significant. Instead, we observed a clear main effect of Drug > Placebo. Thus, we focus on the main effect. The results are shown in Fig. 6 . Unlike Fig. 4 , the electrode x frequency plot as well as the p-value scalp topography show the main effect of Drug. The mean power across all electrodes showed a peak statistical value at 7.4 Hz again where the main effect Drug > Placebo was significant, F (1, 36) = 12.7, p = 0.0011, while the interaction was not significant. Next, we separated these estimates into six conventional frequency bands and applied the same test for the mean EEG power of each frequency band. The results are shown in Fig. 7 . Both the theta band and alpha band showed statistical significance: F (1, 36) = 6.87, p = 0.013 and F (1, 36) = 5.04, p = 0.031, respectively. Other frequency bands did not reach statistical significance. We conclude that the Lab-Home difference was statistically insignificant; theta and alpha power augmentation was found in both environments. 3.3.1.3 The drug slows alpha peak frequency. We examined the drug's impact on alpha peak frequency by comparing data in the Drug condition Pre- and Post-dose. In conducting this analysis, we ensured not to overgeneralize the assumption that, while an alpha peak is present in most grand-mean PSD plots in general, it does not guarantee that every individual exhibits a detectable alpha peak. Thus, we first quantified the rate of peak alpha presence across subjects and electrodes. The results are shown in Fig. 8 . On average, only 30.2% of datasets showed the alpha peak (SD 13.2) in a given electrode. For an additional comparison of this value with that of a resting EEG database, see Supplement S2. Next, we quantified the frequency shift for each electrode that showed a pair of alpha peaks in both Pre- and Post-dose conditions. Most electrodes showed a negative shift: on average, the peak frequency was shifted 0.53 Hz (SD 0.32) down. We evaluated histograms of the peak frequencies between Pre and Post conditions. The drug increased the number of detectable peaks from 836 to 1349, with a prominent peak around 7 Hz. This comparison also revealed that the peak frequency distribution in the Pre condition was not centered at the conventional alpha range (8–13 Hz) but the distribution is centered between 7 and 8 Hz. Finally, a representative electrode was selected that maximized the balance between the number of datasets with detectable peaks and the amount of peak frequency shift. This representative electrode analysis confirmed a significant downshift of peak frequency by 1.1 Hz. 3.3.1.4 The Drug biases the E/I balance toward inhibition. We evaluated the effect of the drug on estimated E/I balance by calculating SPEX for all scalp electrodes. We compared Pre-Post conditions between the Placebo and Drug conditions, which employed a 2 x 2 factorial design. The results are shown in Fig. 9 . None of the electrodes showed significant interaction. However, only one electrode showed a trend toward significance, confirming SPEX coefficient increase in the Drug Post condition, indicating a E/I balance shift toward inhibition. Although this finding had a limitation in statistical significance, the Drug > Placebo pattern was more consistently observed in the next analysis. 3.3.1.5 Intraclass Correlation Coefficients (ICCs) between Lab and Home are comparable. We tested the effect of location by comparing data in the Pre-dose condition between Lab and Home. The results are shown in Fig. 11 and presented in Table 3 . Across the three contrasts, ICCs were generally similar across the frequency spectrum. All three comparisons showed that peak ICC values were identified in the 7–10 Hz range. Visual inspection of the maximum and minimum ICC for each condition showed disparate patterns. In addition, to allow comparison with previous studies (Liu et al., 2023 ), we generated ICCs by frequency band across the three contrasts. We conclude that reproducibility between Lab-Home recordings is comparable to those of Lab-Lab and Home-Home. Table 3 Test-Retest Reliability (ICC) of Pre-dose Resting-State PSDs by Frequency Band (Mean ICC across electrodes, SD in parentheses) Frequency Band Lab-Lab Home-Home Lab-Home Liu et al. ( 2023 ), Mean Delta (2–4 Hz) 0.776 (0.127) 0.752 (0.144) 0.769 (0.108) 0.803 Theta (4–8 Hz) 0.818 (0.085) 0.837 (0.120) 0.828 (0.093) 0.771 Alpha (8–13 Hz) 0.812 (0.115) 0.847 (0.129) 0.864 (0.097) 0.852 Beta (13–30 Hz) 0.699 (0.244) 0.742 (0.173) 0.746 (0.165) 0.785 Gamma (30–55 Hz) 0.675 (0.213) 0.737 (0.134) 0.672 (0.140) 0.676 Hi-Gamma (65 + Hz) 0.703 (0.165) 0.747 (0.125) 0.683 (0.106) For interest, we conducted the same ICC analysis on the SPEX data. The results are shown in Fig. 12 . The mean ICC values across the electrodes were in the range of 0.70–0.81, which were comparable to the ICC values from the PSD test. Visual inspection did not confirm a similar topographical pattern across locations. 3.4.1 Auditory-evoked event-related potential (ERP) analysis in time and time-frequency domains. 3.4.1.1 Vertex potential (VP) is insensitive to the drug effect. To start the auditory-evoked ERP analysis, we first evaluated a large-amplitude phenomenon with a transient response to the onset and offset of the auditory stimuli that showed a central scalp distribution. The results are shown in Fig. 13 . We identified this evoked response as a classical VP that consists of biphasic N1-P1 components. Between the onset- and offset-evoked VPs, we can observe the oscillatory waves in response to the chirp-modulated stimulus with a plateau between 500 and 2000 ms. In the initial time-domain analysis, we targeted the onset-evoked VP. We compared the effect of Pre-Post conditions between the Placebo and Drug conditions on the VP, which employed a 2 x 2 factorial design. The results are shown in Fig. 14 . The difference between Pre and Post did not reach statistical significance. We conclude that the drug did not affect VP amplitudes. 3.4.1.2 Power modulations on VP are insensitive to the drug effect. We applied ERSSP analysis on the ERSP to compare the effect of Pre-Post conditions between the Placebo and Drug conditions on the VP, which employed a 2 x 2 factorial design. The target time-frequency ROI was from 0 to 500 ms and from 2 to 13 Hz. The results are shown in Fig. 15 . Neither the interaction nor the main effect of Drug reached statistical significance. 3.4.1.3 ITC modulations on VP and 40-Hz peak are insensitive to the drug effect. We applied ERSSP analysis on the ITC to compare the effect of Pre-Post conditions between the Placebo and Drug conditions on the VP and 40-Hz peak, which employed a 2 x 2 factorial design. The target time-frequency ROI was from 0 to 500 ms and from 2 to 13 Hz for the VP and 824 ms at 40 Hz where the maximum ITC-ERSSP was observed. The results are shown in Fig. 16. Neither the interaction nor the main effect of Drug reached statistical significance in VPs or the 40-Hz ITC peak. Figure 16. ERSSP analysis on ITC for Pre-Post comparison between the Placebo and Drug conditions using a 2 x 2 design. Two ROIs were set. For log-scaled plots, time-frequency ROI was set from 0 to 500 ms and from 2 to 13 Hz to test 2 x 2 interaction for increase (ERS) and decrease (ERD). For linear-scaled plots, time-frequency ROI was set at 40 Hz and 824 ms, which showed the maximum ITC at 40 Hz across time. None of the results reached statistical significance. 3.4.1.4 Lab-Lab showed the highest Intraclass Correlation Coefficients (ICCs) in auditory ERPs. We tested ICC between Lab and Home conditions, Pre-dose only, for four EEG measures using ICC(A,1), which estimates absolute agreement for single measurements. The results are summarized in Table 4 . The VP (N1-P1) peak amplitude difference yielded higher ICCs than other measures in most cases, followed by ERSSP-ITC VP . These two measures showed consistent values across comparisons. These two measures showed ICC values that ranged from 0.64 to 0.85, with Lab-Lab being generally higher than other pairs. We conclude that Lab-Lab reproducibility is better than Home-Home or Lab-Home pairs within our auditory-evoked data. Table 4 Test-Retest Reliability (ICC) of Pre-dose Auditory EEG Measures (ICC, confidence intervals in brackets) Measure Lab-Lab Home-Home Lab-Home VP (N1-P1) peak difference 0.85 [0.51 0.96], p = 0.0006 0.64 [0.05 0.90], p = 0.0185 0.72 [0.41 0.88], p = 0.0001 ERSSP-ERS VP (ROI: 0-500 ms, 2–13 Hz) 0.65 [0.10 0.90], p = 0.0115 0.34 [-0.29 0.79], p = 0.1465 0.66 [0.32 0.85], p = 0.0006 ERSSP-ITC VP (ROI: 0-500 ms, 2–13 Hz) 0.75 [0.08 0.94], p = 0.0151 0.66 [0.01 0.91], p = 0.0241 0.67 [0.33 0.86], p = 0.0005 ERSSP-ITC 40Hz (ROI: 824 ms, 40 Hz) 0.23 [-0.47 0.74], p = 0.2573 0.74 [0.13 0.94], p = 0.0106 0.37 [-0.10 0.70], p = 0.0591 3.5 Performance changes in Flanker and Dimension tasks correlated with drug-induced power increases. We conducted a correlation analysis between change in cognitive performance and change in EEG power at the frequency of maximal drug effect, 7.2 Hz. The results are shown in Fig. 17 . The analysis revealed that the improvement in performance in Flanker and Dimension tasks were correlated with increase in power at 7.2 Hz. The mean Pearson’s correlation coefficients across the significant electrode sites for Flanker and Dimension tasks were r = 0.34 and 0.21 (SD 0.36 and 0.32), respectively. Note that these results need to be interpreted with caution. Correlating a biomarker change with a non-significant behavioural change is known to be susceptible to spurious findings (Kriegeskorte et al., 2009 ) and may be best used for hypothesis generation. 4. Discussion This study provides initial evidence that utilizing decentralized, home-based cognitive assessment and EEG is both feasible and reliable in adult males with FXS, while also evaluating the acute behavioral and neural effects of single-dose gaboxadol. Our results expand upon previous single-site biomarker-based clinical trials in FXS by demonstrating that critical measures, including cognitive batteries and relevant EEG measures such as resting-state spectral power and auditory-evoked potentials, can be collected consistently outside the controlled clinic setting, with between-location ICC values generally consistent with those found in the literature (see Tables 2– 4 ). This finding is particularly important because challenges in traveling to a traditional research setting provide a barrier to broader participation in clinical trials among individuals with significant impairment, for whom upcoming treatments may provide the most benefit. Pharmacodynamically, like in the Fmr1 KO mouse (Janz et al., 2025 ) and similar to sleep studies in humans that found augmented theta power (Lundahl et al., 2012 ; Ma et al., 2011 ; Mathias et al., 2005 ; Walsh et al., 2007 ), we found that gaboxadol was associated with increased theta and alpha power, with no significant effect on the gamma band. This result did not differ between home and lab, suggesting it is a robust indicator of gaboxadol’s effect on the brain and supporting the reliability of resting state EEG as a biomarker that can be collected both in-lab and in-home. Specifically, gaboxadol’s maximal effect was an augmentation of power in the 6–8 Hz range, with a concomitant shift in individual peak frequency in those for whom peaks could be detected, and a shift in the spectral exponent (SPEX) that suggests gaboxadol augmented inhibition in the brain. Behaviorally, we observed a marginal increase in words remembered in the RBANS List Learning subtest following single-dose gaboxadol. This result suggests a potential effect of gaboxadol on social engagement, attention or working memory, all of which are required for successful completion of the RBANS LL. Among these, a previous clinical trial of gaboxadol in participants with FXS found improvements in lethargy/social withdrawal (Budimirovic et al., 2021 ), though no cognitive domains were assessed for comparison and there was no control group. While the RBANS findings suggest a numerical trend, the absence of statistical significance and potential baseline confounding suggest the results are best viewed as exploratory and hypothesis-generating. There were no effects of gaboxadol on the NIH Toolbox cognitive measures. Because of this, the reported correlation between the EEG power increase and performance on the Flanker and DCCS tasks should be presented with some caution. Correlating a biomarker change with a non-significant behavioral change in a small, exploratory study is potentially susceptible to spurious findings and should be considered, like with the RBANS, to be hypothesis-generating. Our lack of NIH Toolbox findings may be the result of increased noise in our data: while many studies screen datapoints on the examiner’s assessment of validity, our study did not. Notably, the NIH Toolbox in populations of patients with neurodevelopmental disorders has never been demonstrated to be sensitive to acute drug effects. The EEG results present a potential paradox. The data demonstrates that the drug engages its CNS target (evidenced by the robust increase in theta/alpha power) but fails to normalize the gamma band EEG abnormality that has been demonstrated repeatedly to characterize FXS neurophysiology. Additionally, acute single dose gaboxadol did not alter auditory-evoked EEG by any measure (ERP, ERSSP, ITC). This contrast suggests modulation of general tonic thalamo-cortical network activity, rather than correction of transient, stimulus-evoked processes known to be impaired in FXS. Gaboxadol operates on extrasynaptic GABA A δ-subunit–containing receptors and particularly the \(\:{{\alpha\:}}_{4}{{\beta\:}}_{3}{\delta\:}\) subunit, which are predominantly expressed in the thalamus (Pirker et al., 2000 ) and mediate tonic inhibition (Semyanov et al., 2004 ; Stórustovu & Ebert, 2006 ). Though the specific mechanism at work is undetermined, a recent preprint demonstrated that gaboxadol preferentially affected the mediodorsal thalamic nuclei (MD) during sleep (Redondo et al., 2025 , under review), which has dense connections with the posterior orbitofrontal cortex (Jones, 1985 ; Rose & Woolsey, 1948 ). The MD is involved in a broadly tuned olfactory pathway that is responsible for integrating input (Yarita et al., 1980 ), distinct from the more finely tuned non-thalamic olfactory pathway (Tanabe et al., 1975 ). The MD also has a connection to the amygdala, which provides emotional and reward-related value associated with sensory input (Jones, 1985 ; Yarita et al., 1980 ) and has previously shown hyperexcitability that was rescued by gaboxadol in a mouse model of FXS (Olmos-Serrano et al., 2010 , 2011 ). We recently proposed a hyper-extralemniscal model of FXS (Miyakoshi et al., 2025 ), which highlights the role of supramodal, broadly-tuned extralemniscal thalamic pathway with diffuse cortical projections, in contrast to sharply-tuned, modality-specific lemniscal thalamic pathways. In line with this framework, our current findings may relate to the auditory extralemniscal thalamic pathway, which is primarily mediated by the medial division of the medial geniculate body (MGm). Like the MD’s olfactory pathway, the MGm’s auditory pathway is broadly tuned, terminates within the cortex, forms a dual pathway with a counterpart that is finely tuned, and has connections to the amygdala. From a dual system perspective (Miyakoshi et al., 2025 ; Somervail et al., 2025 ), the MD, if at work in the gaboxadol response, has more in common with the extralemniscal system, rather than the sensory-tuned lemniscal system. This would provide a potential anatomical basis for the observed increases in resting state power without changes to auditory-evoked activity. We interpret these findings as supporting a model in which gaboxadol alters tonic activity in the thalamocortical loop, potentially via the MD, specifically engaging broadly tuned extralemniscal- (also known as non-specific-) like pathways. Although we do not have direct evidence to determine a specific mechanistic pathway, several observations converge: (1) modulation in the low-frequency band indicates the recruitment of thalamo-cortical loop; (2) the broad distribution of the maximum power increase across the whole scalp regions indicates recruitment of the extralemniscal thalamic system which is known to have spatially diffuse cortical and subcortical projections; (3) the absence of the modulation in auditory event-related potential indicates that the ‘saliency-encoding’ VPs remained intact (Miyakoshi et al., 2025 ; Mouraux et al., 2011 ; Mouraux & Iannetti, 2009 ; Somervail et al., 2021 , 2022 , 2025 ). From these observations, we conclude that the spatiotemporal distribution of the effect of gaboxadol is broad and tonic. It modulates EEG states i.e., baseline, without affecting the transient ‘saliency network’ that is the source of the VPs (Menon & Uddin, 2010 ; Mouraux et al., 2011 ). In this study, we quantified the empirical probability of observing an alpha peak in PSD across datasets and electrodes. We found that 30.2% (SD 13.2) of datasets show an alpha peak at a given electrode site. To validate this rate, we conducted an additional analysis on resting-state EEG data from the MPI Leipzig Mind-Brain-Body database (Babayan et al., 2019 ). We obtained a rate of 47.1% (SD 10.5). The details are reported in Supplement S2. The lower rate in our results may be explained by several factors: (1) participants watched silent movies during the eyes-open resting session in our data, while the participants in the LEMON dataset did not watch them; (2) our participants were a clinical population and half of the datasets were recorded at Home, while their participants were normal adults and were recorded in the Lab. Thus, the eyes-open resting session does not reliably produce a detectable alpha peak in a given electrode. This rate also depends on electrode locations. Our data confirmed that central and parietal regions show higher rates. Overall, we emphasize that Gaboxadol noticeably increased (161%) the number of electrodes with a detectable alpha peak, indicating the drug’s ability to increase oscillatory EEG signals. Taken together, the behavioral, EEG, and anatomical data support a model in which gaboxadol acts as a modulator of tonic thalamocortical activity in FXS. Importantly, these effects were observable both in-lab and in-home, suggesting that in-home data collection can provide an adequate replacement for clinic visits in participants who are unable to travel to a trial site. At the same time, these results do not provide strong evidence for a pro-cognitive or disease-modifying effect of single-dose gaboxadol in FXS. Furthermore, the interpretation of these acute, single-dose findings must be tempered by the potential effects of chronic administration. The acute effects may reflect transient arousal changes unlikely to persist with chronic dosing. While gaboxadol may have a unique profile that spares it from developing tolerance to its sleep-promoting effects, there is direct preclinical evidence that tolerance does develop for other functional effects, such as motor impairment. Declarations Ethics approval and consent to participate: This study was conducted in accordance with the Declaration of Helsinki and approved by the Cincinnati Children’s Hospital Institutional Review Board. Consent for publication: Not applicable Availability of data and materials: Due to the size and scope of the dataset, the data are not publicly hosted. The data are available from the corresponding authors upon reasonable request and in consultation with the study sponsor, HealX. Competing interests: This work was sponsored by HealX pharmaceuticals who both supplied the gaboxadol and matching placebo and paid for the work to be conducted at Cincinnati Children’s Hospital Medical Center. Funding: Support for this work was provided by HealX pharmaceuticals. Author contributions: L.A.D., H.K., and M.M. analyzed the data and prepared the manuscript. C.A.E, L.M.S., K.C.D., E.V.P., D.L.R., and M.N.N. assisted with the conceptualization and design of the study. L.A.D, L.M.S., W.S.M., and A.D. participated in the coordination and implementation of the study. R.H. and R.W. provided feedback on the manuscript and assisted with interpretation of the data. P.S.H. provided feedback on statistical analysis of behavioral measures. Trial registration: clinicaltrials.gov, NCT06334419, Registration Date: March 8, 2024 References Babayan A, Erbey M, Kumral D, Reinelt JD, Reiter AMF, Röbbig J, Schaare HL, Uhlig M, Anwander A, Bazin P-L, Horstmann A, Lampe L, Nikulin VV, Okon-Singer H, Preusser S, Pampel A, Rohr CS, Sacher J, Thöne-Otto A, Villringer A. A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Sci Data. 2019;6(1):180308. https://doi.org/10.1038/sdata.2018.308 . Berry-Kravis E, Sumis A, Kim O-K, Lara R, Wuu J. Characterization of Potential Outcome Measures for Future Clinical Trials in Fragile X Syndrome. J Autism Dev Disord. 2008;38(9):1751–7. https://doi.org/10.1007/s10803-008-0564-8 . Bigdely-Shamlo N, Mullen T, Kothe C, Su K-M, Robbins KA. (2015). The PREP pipeline: Standardized preprocessing for large-scale EEG analysis. Frontiers in Neuroinformatics , 9 . https://doi.org/10.3389/fninf.2015.00016 Boggs AE, Schmitt LM, McLane RD, Adayev T, LaFauci G, Horn PS, Dominick KC, Gross C, Erickson CA. Optimization, validation and initial clinical implications of a Luminex-based immunoassay for the quantification of Fragile X Protein from dried blood spots. Sci Rep. 2022;12(1):5617. https://doi.org/10.1038/s41598-022-09633-8 . Budimirovic DB, Dominick KC, Gabis LV, Adams M, Adera M, Huang L, Ventola P, Tartaglia NR, Berry-Kravis E. Gaboxadol in Fragile X Syndrome: A 12-Week Randomized, Double-Blind, Parallel-Group, Phase 2a Study. Front Pharmacol. 2021;12:757825. https://doi.org/10.3389/fphar.2021.757825 . Cogram P, Deacon RMJ, Warner-Schmidt JL, Schimmelmann V, Abrahams MJ, B. S., During MJ. Gaboxadol Normalizes Behavioral Abnormalities in a Mouse Model of Fragile X Syndrome. Front Behav Neurosci. 2019;13:141. https://doi.org/10.3389/fnbeh.2019.00141 . Delorme A, Makeig S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009 . Dijk D, James L, Peters S, Walsh J, Deacon S. Sex differences and the effect of gaboxadol and zolpidem on EEG power spectra in NREM and REM sleep. J Psychopharmacol. 2010;24(11):1613–8. https://doi.org/10.1177/0269881109105788 . Donoghue T, Haller M, Peterson EJ, Varma P, Sebastian P, Gao R, Noto T, Lara AH, Wallis JD, Knight RT, Shestyuk A, Voytek B. Parameterizing neural power spectra into periodic and aperiodic components. Nat Neurosci. 2020;23(12):1655–65. https://doi.org/10.1038/s41593-020-00744-x . Ethridge LE, De Stefano LA, Schmitt LM, Woodruff NE, Brown KL, Tran M, Wang J, Pedapati EV, Erickson CA, Sweeney JA. Auditory EEG Biomarkers in Fragile X Syndrome: Clinical Relevance. Front Integr Nuerosci. 2019;13:60. https://doi.org/10.3389/fnint.2019.00060 . Ethridge LE, White SP, Mosconi MW, Wang J, Pedapati EV, Erickson CA, Byerly MJ, Sweeney JA. Neural synchronization deficits linked to cortical hyper-excitability and auditory hypersensitivity in fragile X syndrome. Mol Autism. 2017;8(1):22. https://doi.org/10.1186/s13229-017-0140-1 . Gamer M, Lemon J. & IFPS. (2019). Irr: Various Coefficients of Interrater Reliability and Agreement. R package version 0.84.1 [Computer software]. https://CRAN.R-project.org/package=irr Gao R, Peterson EJ, Voytek B. Inferring synaptic excitation/inhibition balance from field potentials. NeuroImage. 2017;158:70–8. https://doi.org/10.1016/j.neuroimage.2017.06.078 . Goswami S, Cavalier S, Sridhar V, Huber KM, Gibson JR. Local cortical circuit correlates of altered EEG in the mouse model of Fragile X syndrome. Neurobiol Dis. 2019;124:563–72. https://doi.org/10.1016/j.nbd.2019.01.002 . Janz P, Bainier M, Marashli S, Gross S, Redondo RL. Clinically-probed mechanisms of action in Fragile-X syndrome fail to normalize translational EEG phenotypes in Fmr1 knockout mice. Neuropharmacology. 2025;262:110182. https://doi.org/10.1016/j.neuropharm.2024.110182 . Jonak CR, Lovelace JW, Ethell IM, Razak KA, Binder DK. Multielectrode array analysis of EEG biomarkers in a mouse model of Fragile X Syndrome. Neurobiol Dis. 2020;138:104794. https://doi.org/10.1016/j.nbd.2020.104794 . Jonak CR, Pedapati EV, Schmitt LM, Assad SA, Sandhu MS, DeStefano L, Ethridge L, Razak KA, Sweeney JA, Binder DK, Erickson CA. Baclofen-associated neurophysiologic target engagement across species in fragile X syndrome. J Neurodevelopmental Disorders. 2022;14(1):52. https://doi.org/10.1186/s11689-022-09455-9 . Jones EG. The Thalamus. Plenum; 1985. https://doi.org/10.1007/978-1-4615-1749-8 . Kim H, Chang C-Y, Kothe C, Iversen JR, Miyakoshi M. Juggler’s ASR: Unpacking the principles of artifact subspace reconstruction for revision toward extreme MoBI. J Neurosci Methods. 2025;420:110465. https://doi.org/10.1016/j.jneumeth.2025.110465 . Kim H, Ethridge LE, De Stefano LA, Schmitt LM, Pedapati EV, Erickson CA, Miyakoshi M. (2025). Event-Related Spatio-Spectral Perturbation (ERSSP): A Scalp-Wide EEG Analysis Framework for Capturing Extralemniscal Thalamocortical Activity, Validated Using a Fragile X Syndrome Model. Kim H, Luo J, Chu S, Cannard C, Hoffmann S, Miyakoshi M. ICA’s bug: How ghost ICs emerge from effective rank deficiency caused by EEG electrode interpolation and incorrect re-referencing. Front Signal Process. 2023;3. https://doi.org/10.3389/frsip.2023.1064138 . Kothe CA, Makeig S. BCILAB: A platform for brain–computer interface development. J Neural Eng. 2013;10(5):056014. https://doi.org/10.1088/1741-2560/10/5/056014 . Kriegeskorte N, Simmons WK, Bellgowan PSF, Baker CI. Circular analysis in systems neuroscience: The dangers of double dipping. Nat Neurosci. 2009;12(5):535–40. https://doi.org/10.1038/nn.2303 . Liu R, Pedapati EV, Schmitt LM, Shaffer RC, Smith EG, Dominick KC, DeStefano LA, Westerkamp G, Horn P, Sweeney JA, Erickson CA. Reliability of resting-state electrophysiology in fragile X syndrome. Biomarkers Neuropsychiatry. 2023;9:100070. https://doi.org/10.1016/j.bionps.2023.100070 . Loesch DZ, Huggins RM, Hagerman RJ. Phenotypic variation and FMRP levels in fragile X. Ment Retard Dev Disabil Res Rev. 2004;10(1):31–41. https://doi.org/10.1002/mrdd.20006 . Lovelace JW, Ethell IM, Binder DK, Razak KA. Translation-relevant EEG phenotypes in a mouse model of Fragile X Syndrome. Neurobiol Dis. 2018;115:39–48. https://doi.org/10.1016/j.nbd.2018.03.012 . Lundahl J, Deacon S, Maurice D, Staner L. EEG spectral power density profiles during NREM sleep for gaboxadol and zolpidem in patients with primary insomnia. J Psychopharmacol. 2012;26(8):1081–7. https://doi.org/10.1177/0269881111424457 . Ma J, Dijk D-J, Svetnik V, Tymofyeyev Y, Ray S, Walsh JK, Deacon S. EEG Power Spectra Response to a 4-h Phase Advance and Gaboxadol Treatment in 822 Men and Women. J Clin Sleep Med. 2011;07(05):493–501. https://doi.org/10.5664/JCSM.1316 . Mathias S, Zihl J, Steiger A, Lancel M. Effect of Repeated Gaboxadol Administration on Night Sleep and Next-Day Performance in Healthy Elderly Subjects. Neuropsychopharmacology. 2005;30(4):833–41. https://doi.org/10.1038/sj.npp.1300641 . McKinney WS, Schmitt LM, De Stefano LA, Ethridge L, Norris JE, Horn PS, Dauterman S, Rosselot H, Pedapati EV, Reisinger DL, Dominick KC, Shaffer RC, Chin D, Friedman NR, Hong M, Sweeney JA, Erickson C. Results from a Double-Blind, Randomized, Placebo-Controlled, Single-Dose, Crossover Trial of Lovastatin or Minocycline in Fragile X Syndrome. J Child Adolesc Psychopharmacol. 2024. https://doi.org/10.1089/cap.2024.0103 . cap.2024.0103. Melman T, Victor JD. Robust power spectral estimation for EEG data. J Neurosci Methods. 2016;268:14–22. https://doi.org/10.1016/j.jneumeth.2016.04.015 . Menon V, Uddin LQ. Saliency, switching, attention and control: A network model of insula function. Brain Struct Function. 2010;214(5–6):655–67. https://doi.org/10.1007/s00429-010-0262-0 . Miyakoshi M. Artifact subspace reconstruction: A candidate for a dream solution for EEG studies, sleep or awake. Sleep. 2023;46(12). https://doi.org/10.1093/sleep/zsad241 . Miyakoshi M, Kim H, De Stefano LA, Schmitt LM, Norris JE, Ethridge LE, Erickson CA, Pedapati EV. Hyper-extralemniscal model of Fragile X syndrome. Cereb Cortex. 2025;35(6). https://doi.org/10.1093/cercor/bhaf141 . Miyakoshi M, Schmitt LM, Erickson CA, Sweeney JA, Pedapati EV. (2021). Can We Push the Quasi-Perfect Artifact Rejection Even Closer to Perfection? Frontiers in Neuroinformatics , 14 . https://doi.org/10.3389/fninf.2020.597079 Mouraux A, Diukova A, Lee MC, Wise RG, Iannetti GD. A multisensory investigation of the functional significance of the pain matrix. NeuroImage. 2011;54(3):2237–49. https://doi.org/10.1016/j.neuroimage.2010.09.084 . Mouraux A, Iannetti GD. Nociceptive Laser-Evoked Brain Potentials Do Not Reflect Nociceptive-Specific Neural Activity. J Neurophysiol. 2009;101(6):3258–69. https://doi.org/10.1152/jn.91181.2008 . Mullen T. (2012). CleanLine . https://nitrc.org/projects/cleanline Mullen TR, Kothe CAE, Chi YM, Ojeda A, Kerth T, Makeig S, Jung T-P, Cauwenberghs G. Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Trans Biomed Eng. 2015;62(11):2553–67. https://doi.org/10.1109/tbme.2015.2481482 . Norris JE, De Stefano LA, McKinney WS, Schmitt LM, Miyakoshi M, Gross C, Piloto S, Heald B, Pedapati EV, Erickson CA, Sweeney JA, Ethridge LE. Auditory steady-state response deficits in Fragile X Syndrome implicate deficits in stimulus representation maintenance and GABAergic modulation. Neurology. 2025. https://doi.org/10.1101/2025.01.29.25321365 . Olmos-Serrano JL, Corbin JG, Burns MP. The GABA A Receptor Agonist THIP Ameliorates Specific Behavioral Deficits in the Mouse Model of Fragile X Syndrome. Dev Neurosci. 2011;33(5):395–403. https://doi.org/10.1159/000332884 . Olmos-Serrano JL, Paluszkiewicz SM, Martin BS, Kaufmann WE, Corbin JG, Huntsman MM. Defective GABAergic Neurotransmission and Pharmacological Rescue of Neuronal Hyperexcitability in the Amygdala in a Mouse Model of Fragile X Syndrome. J Neurosci. 2010;30(29):9929–38. https://doi.org/10.1523/JNEUROSCI.1714-10.2010 . Ovid Therapeutics, Inc. Investigator’s Brochure. Ovid Therapeutics, Inc; 2022. Pedapati EV, Schmitt LM, Ethridge LE, Miyakoshi M, Sweeney JA, Liu R, Smith E, Shaffer RC, Dominick KC, Gilbert DL, Wu SW, Horn PS, Binder DK, Lamy M, Axford M, Erickson CA. Neocortical localization and thalamocortical modulation of neuronal hyperexcitability contribute to Fragile X Syndrome. Commun Biology. 2022;5(1):442. https://doi.org/10.1038/s42003-022-03395-9 . Pirker S, Schwarzer C, Wieselthaler A, Sieghart W, Sperk G. GABAA receptors: Immunocytochemical distribution of 13 subunits in the adult rat brain. Neuroscience. 2000;101(4):815–50. https://doi.org/10.1016/s0306-4522(00)00442-5 . R Core Team. (2024). R: A Language and Environment for Statistical Computing [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org Randolph C, Tierney MC, Mohr E, Chase TN. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): Preliminary clinical validity. J Clin Exp Neuropsychol. 1998;20(3):310–9. https://doi.org/10.1076/jcen.20.3.310.823 . Redondo R, Duarte JM, Janz P, Bruns A, Gross S, Bainier M, Debilly S, Schöppenthau S, Bielser T, Kuennecke B, Schulz J. Hyperactivation of distinct thalamic nuclei differentially impairs sleep physiology in rats. Springer Science and Business Media LLC; 2025. https://doi.org/10.21203/rs.3.rs-6809066/v1 . Roid GH, Pomplun M. The stanford-binet intelligence scales. Volume 654. New York, NY, USA: The Guilford Press; 2012. Rose J, Woolsey C. The orbitofrontal cortex and its connections with the mediodorsal nucleus in rabbit, sheep and cat. Res Publ Assoc Res Nerv Ment Dis. 1948;27(1):210–32. PubMed. Semyanov A, Walker MC, Kullmann DM, Silver RA. Tonically active GABAA receptors: Modulating gain and maintaining the tone. Trends Neurosci. 2004;27(5):262–9. https://doi.org/10.1016/j.tins.2004.03.005 . Shields RH, Kaat AJ, McKenzie FJ, Drayton A, Sansone SM, Coleman J, Michalak C, Riley K, Berry-Kravis E, Gershon RC, Widaman KF, Hessl D. Validation of the NIH Toolbox Cognitive Battery in intellectual disability. Neurology. 2020;94(12):e1229–40. https://doi.org/10.1212/WNL.0000000000009131 . Somervail R, Bufacchi RJ, Salvatori C, Neary-Zajiczek L, Guo Y, Novembre G, Iannetti GD. Brain Responses to Surprising Stimulus Offsets: Phenomenology and Functional Significance. Cereb Cortex. 2022;32(10):2231–44. https://doi.org/10.1093/cercor/bhab352 . Somervail R, Perovic S, Bufacchi RJ, Caminiti R, Iannetti D. (2025). A two-system theory of sensory-evoked brain responses . Somervail R, Zhang F, Novembre G, Bufacchi RJ, Guo Y, Crepaldi M, Hu L, Iannetti GD. Waves of Change: Brain Sensitivity to Differential, not Absolute, Stimulus Intensity is Conserved Across Humans and Rats. Cereb Cortex. 2021;31(2):949–60. https://doi.org/10.1093/cercor/bhaa267 . Stórustovu SÍ, Ebert B. Pharmacological Characterization of Agonists at δ-Containing GABAA Receptors: Functional Selectivity for Extrasynaptic Receptors Is Dependent on the Absence of γ2. J Pharmacol Exp Ther. 2006;316(3):1351–9. https://doi.org/10.1124/jpet.105.092403 . Tanabe T, Iino M, Takagi SF. Discrimination of odors in olfactory bulb, pyriform-amygdaloid areas, and orbitofrontal cortex of the monkey. J Neurophysiol. 1975;38(5):1284–96. Walsh JK, Deacon S, Dijk D-J, Lundahl J. The Selective Extrasynaptic GABAA Agonist, Gaboxadol, Improves Traditional Hypnotic Efficacy Measures and Enhances Slow Wave Activity in a Model of Transient Insomnia. Sleep. 2007;30(5):593–602. https://doi.org/10.1093/sleep/30.5.593 . Wang J, Ethridge LE, Mosconi MW, White SP, Binder DK, Pedapati EV, Erickson CA, Byerly MJ, Sweeney JA. A resting EEG study of neocortical hyperexcitability and altered functional connectivity in fragile X syndrome. J Neurodevelopmental Disorders. 2017;9(1):11. https://doi.org/10.1186/s11689-017-9191-z . Weintraub S, Bauer PJ, Zelazo PD, Wallner-Allen K, Dikmen SS, Heaton RK, Tulsky DS, Slotkin J, Blitz DL, Carlozzi NE, Havlik RJ, Beaumont JL, Mungas D, Manly JJ, Borosh BG, Nowinski CJ, Gershon RC. I. NIH Toolbox Cognition Battery (CB): Introduction and pediatric data. Monogr Soc Res Child Dev. 2013;78(4):1–15. https://doi.org/10.1111/mono.12031 . Yarita H, Iino M, Tanabe T, Kogure S, Takagi SF. A transthalamic olfactory pathway to orbitofrontal cortex in the monkey. J Neurophysiol. 1980;43(1):69–85. https://doi.org/10.1152/jn.1980.43.1.69 . Additional Declarations Competing interest reported. This work was sponsored by HealX pharmaceuticals who both supplied the gaboxadol and matching placebo and paid for the work to be conducted at Cincinnati Children’s Hospital Medical Center. Supplementary Files healxsupplement.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Jan, 2026 Reviews received at journal 09 Jan, 2026 Reviews received at journal 09 Jan, 2026 Reviewers agreed at journal 02 Dec, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers invited by journal 26 Oct, 2025 Editor assigned by journal 20 Oct, 2025 Submission checks completed at journal 20 Oct, 2025 First submitted to journal 15 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7871579","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532986154,"identity":"dcf01ece-1b4e-4c1f-a284-049cc6b84bcb","order_by":0,"name":"Lisa A. De Stefano","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACPmYQyQYiGB8ACRsGBgkCWthgWngYmA2ArDQitMBIqJbDRGhhZ3/4gaHMxt6evZnxc0HFeXn+2d2JHxgq7tk14HQYj7EEw7m0xB6ew8zSM87cNpxx5+xmCYYzxcl4tDBIMLYdTuCRyD8gzdt2O8FAIncbA2NbQjJOhzGzP/7B2Pbfnkf+MfNv3rZzxGhhMAPacoCxR4KZDWjLAbgWO9xaeMwsEs4lJ/acSWaz5jmTbDjjRu5miYQzCQm4tPDzH39840OZnT17+2Hm2zwVdvL8M3I3fvhQkWCPSwsYYBoIFElswKsHG8BvyygYBaNgFIwkAAAwVEqS9a9TaQAAAABJRU5ErkJggg==","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Lisa","middleName":"A.","lastName":"De Stefano","suffix":""},{"id":532986155,"identity":"1e96a461-6a79-4db7-af2c-02381034a271","order_by":1,"name":"Hyeonseok Kim","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Hyeonseok","middleName":"","lastName":"Kim","suffix":""},{"id":532986157,"identity":"c7d7f5d3-e730-4d1f-b3cc-8dc95ef1c2fc","order_by":2,"name":"Craig A. Erickson","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Craig","middleName":"A.","lastName":"Erickson","suffix":""},{"id":532986159,"identity":"372a7cc7-3868-416e-8910-1a1f7909dd3b","order_by":3,"name":"Lauren M. Schmitt","email":"","orcid":"","institution":"Phelan-McDermid Syndrome Foundation","correspondingAuthor":false,"prefix":"","firstName":"Lauren","middleName":"M.","lastName":"Schmitt","suffix":""},{"id":532986160,"identity":"38a7fad8-2288-483e-b09e-a6e06d5c4c2a","order_by":4,"name":"Kelli C. Dominick","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Kelli","middleName":"C.","lastName":"Dominick","suffix":""},{"id":532986161,"identity":"660756b0-ab83-471b-9846-37186797787f","order_by":5,"name":"Ernest V. Pedapati","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Ernest","middleName":"V.","lastName":"Pedapati","suffix":""},{"id":532986162,"identity":"db9801c8-36e3-4301-908e-f4f75a811ad1","order_by":6,"name":"Richard Huckle","email":"","orcid":"","institution":"HealX Ltd","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Huckle","suffix":""},{"id":532986163,"identity":"63a56a6b-d650-4bfb-bd00-3d92572f7491","order_by":7,"name":"Robert Wilson","email":"","orcid":"","institution":"HealX Ltd","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Wilson","suffix":""},{"id":532986164,"identity":"aae4edad-d80e-4065-a59f-ba5b1bb97cad","order_by":8,"name":"Walker S. McKinney","email":"","orcid":"","institution":"Children’s Mercy Hospital","correspondingAuthor":false,"prefix":"","firstName":"Walker","middleName":"S.","lastName":"McKinney","suffix":""},{"id":532986165,"identity":"f6876b75-d945-46f3-a1a0-14ae506a12f7","order_by":9,"name":"Debra L. Reisinger","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Debra","middleName":"L.","lastName":"Reisinger","suffix":""},{"id":532986166,"identity":"b03cb91a-ce48-4c99-b8a9-e76680a15450","order_by":10,"name":"Meredith N. Nelson","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Meredith","middleName":"N.","lastName":"Nelson","suffix":""},{"id":532986167,"identity":"4d9f8e46-118a-47ba-a2ad-f2015171a81d","order_by":11,"name":"Ashley E. Dapore","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Ashley","middleName":"E.","lastName":"Dapore","suffix":""},{"id":532986169,"identity":"3eb63c3b-461e-40df-8a3f-54929a84caa9","order_by":12,"name":"Paul S. Horn","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"S.","lastName":"Horn","suffix":""},{"id":532986170,"identity":"aa18cf14-d5df-449b-bc5a-804230502594","order_by":13,"name":"Makoto Miyakoshi","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Makoto","middleName":"","lastName":"Miyakoshi","suffix":""}],"badges":[],"createdAt":"2025-10-15 20:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7871579/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7871579/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94354700,"identity":"54924dff-b8fc-4311-83a0-681e56de0673","added_by":"auto","created_at":"2025-10-27 12:56:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6386189,"visible":true,"origin":"","legend":"","description":"","filename":"drafthealxv14inline.docx","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/5fc3741b6955979119aa2dcb.docx"},{"id":94354694,"identity":"6b6a2e07-2f4f-4b93-8316-3fd25dfdc21f","added_by":"auto","created_at":"2025-10-27 12:56:10","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14886,"visible":true,"origin":"","legend":"","description":"","filename":"66456b5032b24c9f9becf5b859c7aa1f.json","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/43e0f339a8cb4c8684b5ff26.json"},{"id":94355027,"identity":"c128b2d9-0d01-43ef-a69b-6c9a964fe4b0","added_by":"auto","created_at":"2025-10-27 12:56:58","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":464456,"visible":true,"origin":"","legend":"","description":"","filename":"healxsupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/cafbe7db4865c27c5f61d6bd.docx"},{"id":94355286,"identity":"4376f0c2-6c0c-48d3-9598-3b85ef889a75","added_by":"auto","created_at":"2025-10-27 12:57:30","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":221242,"visible":true,"origin":"","legend":"","description":"","filename":"66456b5032b24c9f9becf5b859c7aa1f1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/9a7248181165601255c63e7c.xml"},{"id":94355170,"identity":"00653322-ce7b-44ee-b6a8-5b0f04ded511","added_by":"auto","created_at":"2025-10-27 12:57:20","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":19941,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/42f5b619e9dfffcc3b1c2497.png"},{"id":94355212,"identity":"c3e54323-e922-4bd3-9387-425495d74ff3","added_by":"auto","created_at":"2025-10-27 12:57:25","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":722662,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/7acaf37b0d70b77bcb4d33de.jpeg"},{"id":94354908,"identity":"e50d858d-4672-463d-83ab-e5f63f669644","added_by":"auto","created_at":"2025-10-27 12:56:42","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":892018,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/84d41bc792ac3b5b27df42be.jpeg"},{"id":94354847,"identity":"01f8269d-da28-4329-a703-f51c2bb5bbc2","added_by":"auto","created_at":"2025-10-27 12:56:34","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132090,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/2329a2ec04f14fb4875e7d6b.jpeg"},{"id":94355345,"identity":"3377c890-7db1-4d14-ba51-460d265140f3","added_by":"auto","created_at":"2025-10-27 12:57:36","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":215687,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/e91f74f9360a6b4759b9b44f.jpeg"},{"id":94355141,"identity":"5f75d907-1479-4d4f-a84d-fb45b02033eb","added_by":"auto","created_at":"2025-10-27 12:57:16","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":234285,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage14.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/766d64720f1773cf89dac0db.jpeg"},{"id":94355068,"identity":"ebeefd85-c1b4-4334-aa97-e21a9c09d670","added_by":"auto","created_at":"2025-10-27 12:57:07","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1533097,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/5eca15508bb182fa6299daac.jpeg"},{"id":94489099,"identity":"60860d7c-1447-4812-a27d-ffa01dfe5a82","added_by":"auto","created_at":"2025-10-27 17:03:09","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120489,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/618ae7615d082b0629745cf2.jpeg"},{"id":94354953,"identity":"b274de34-623f-4641-b5a0-4d0147a54572","added_by":"auto","created_at":"2025-10-27 12:56:50","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87774,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/8aa37b86428810464c67cad9.png"},{"id":94489107,"identity":"891d65fa-bf2c-424f-a5fc-59c26068fc0a","added_by":"auto","created_at":"2025-10-27 17:03:15","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":334585,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/a07d6640009d88002b49771a.jpeg"},{"id":94354674,"identity":"d3d87426-b97c-48af-9e48-876782277253","added_by":"auto","created_at":"2025-10-27 12:56:07","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173084,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/fcc06ac649458364b6a60f03.jpeg"},{"id":94354969,"identity":"bea76b26-3e87-415e-82aa-770af4b5d75e","added_by":"auto","created_at":"2025-10-27 12:56:54","extension":"jpeg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":343946,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/cf44de4f6db73939448019fb.jpeg"},{"id":94355074,"identity":"1837d525-b4b6-4259-b95e-8daf0e62ee3b","added_by":"auto","created_at":"2025-10-27 12:57:08","extension":"jpeg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195973,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/c7e9b456b94b01eaea5a5f58.jpeg"},{"id":94355071,"identity":"e7d8de0e-59a9-4b8e-ba99-4ed0cb172e25","added_by":"auto","created_at":"2025-10-27 12:57:08","extension":"jpeg","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":382171,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/1fca65d56a972a5a3e53bf6d.jpeg"},{"id":94354922,"identity":"6cd09c8d-c429-4062-a9ab-404096b0cf7d","added_by":"auto","created_at":"2025-10-27 12:56:46","extension":"jpeg","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79599,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/0b5c31c63ae657c47ff0e477.jpeg"},{"id":94354677,"identity":"1b7b2a2b-7d58-409d-b662-14178b07a6ac","added_by":"auto","created_at":"2025-10-27 12:56:07","extension":"jpeg","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74660,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/bdb06e0c7d5ebe9788e87e09.jpeg"},{"id":94354798,"identity":"87abb921-58d9-473c-bb46-d626cf1a91e3","added_by":"auto","created_at":"2025-10-27 12:56:25","extension":"jpeg","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52643,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/c135c43b06d13b7bee14fc68.jpeg"},{"id":94354934,"identity":"beafdab0-3b18-46cf-bd1e-663fcac47f4b","added_by":"auto","created_at":"2025-10-27 12:56:49","extension":"jpeg","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45995,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/7bd48bf73770038820489ec7.jpeg"},{"id":94489537,"identity":"d1b357ea-ffe3-49cf-b1e5-4e408d4adb3d","added_by":"auto","created_at":"2025-10-27 17:05:02","extension":"jpeg","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94016,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/484889a7fa6e8404b1ab08a1.jpeg"},{"id":94354844,"identity":"95cd178c-e4a4-411b-8156-3260553b853d","added_by":"auto","created_at":"2025-10-27 12:56:33","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9006,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/74382ab4b4a60f2246bc4f88.png"},{"id":94355077,"identity":"b71b9274-0970-4537-8785-d341014a143b","added_by":"auto","created_at":"2025-10-27 12:57:09","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163743,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/2589b8790b89414832ea30f6.png"},{"id":94355067,"identity":"1d3b9f85-d546-45b4-a3d7-7c7d41d2dcfe","added_by":"auto","created_at":"2025-10-27 12:57:07","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173853,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/844a54e9f3097b4bcecb6f9d.png"},{"id":94355385,"identity":"52393364-0f59-4eda-8fcc-509bf5ded09a","added_by":"auto","created_at":"2025-10-27 12:57:40","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41239,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/57720b9964e6d4f4a6bca3bb.png"},{"id":94354801,"identity":"7f2278b8-e4fb-4598-8c14-ee3f6138b80f","added_by":"auto","created_at":"2025-10-27 12:56:25","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":72258,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/d1422a18a34a5d6224e0a7dd.png"},{"id":94355146,"identity":"f7554100-a423-4aa3-9c41-3725ea734427","added_by":"auto","created_at":"2025-10-27 12:57:16","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87888,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/bc033a964f757864eb44ce81.png"},{"id":94354846,"identity":"0b04da94-194f-4862-b745-23c9114aa53f","added_by":"auto","created_at":"2025-10-27 12:56:34","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":322640,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/028b40e50f9b78d8aedfc357.png"},{"id":94354730,"identity":"114e1a50-e108-49f7-8e55-fb8319220a33","added_by":"auto","created_at":"2025-10-27 12:56:16","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23415,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/2984c26b68601cc79398c1d6.png"},{"id":94355215,"identity":"9dc62d66-699b-4234-8a32-2f5b401348d9","added_by":"auto","created_at":"2025-10-27 12:57:25","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24970,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/8fab5679edf01afc39f0cd68.png"},{"id":94354701,"identity":"22008533-fcb9-4f33-ae9d-779e17d79830","added_by":"auto","created_at":"2025-10-27 12:56:12","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98346,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/bbe414af301890a8bf33ecd0.png"},{"id":94355285,"identity":"29c10644-d887-4d96-96d3-277e7a582003","added_by":"auto","created_at":"2025-10-27 12:57:30","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49417,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/83d6c681763ea4028409fe4d.png"},{"id":94355184,"identity":"4a90fd7c-d056-41c3-8718-626adc8531eb","added_by":"auto","created_at":"2025-10-27 12:57:22","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98294,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/e850d882b247f26eb9ac92c6.png"},{"id":94354880,"identity":"85546f3d-b7db-48c4-80d8-8aad3e6e98aa","added_by":"auto","created_at":"2025-10-27 12:56:41","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":50117,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/a7276d346237eb9fb97f7fd0.png"},{"id":94355392,"identity":"7f9274ed-b251-4d2b-876e-3b2167f591a3","added_by":"auto","created_at":"2025-10-27 12:57:40","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":124610,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/63cc228e3b0cb9f8b0bba1f7.png"},{"id":94355064,"identity":"163281e7-686c-4f2c-a007-37946033eb1f","added_by":"auto","created_at":"2025-10-27 12:57:07","extension":"png","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23711,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/0da660d2df599a37cc8d31f1.png"},{"id":94355346,"identity":"ade59036-af60-4213-a38a-c081e4d9a577","added_by":"auto","created_at":"2025-10-27 12:57:37","extension":"png","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32715,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/0d6631c73f1ad48569192164.png"},{"id":94354915,"identity":"cff96ebe-e869-4e4a-8e6f-12be024cf7b8","added_by":"auto","created_at":"2025-10-27 12:56:43","extension":"png","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25479,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/cb5aa55b6c02ae76e7eff362.png"},{"id":94355183,"identity":"1b860e64-5c28-45c4-a4d8-be1eed6fa338","added_by":"auto","created_at":"2025-10-27 12:57:22","extension":"png","order_by":40,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21276,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/907ef3d866615180c823d28d.png"},{"id":94354863,"identity":"e4a92d35-bead-450e-8ae6-17d85a5aecf0","added_by":"auto","created_at":"2025-10-27 12:56:39","extension":"png","order_by":41,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":34899,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/9cfcc5128ee383a21742cf94.png"},{"id":94355278,"identity":"15b60bea-8924-453e-8a35-4e690b3f714c","added_by":"auto","created_at":"2025-10-27 12:57:29","extension":"xml","order_by":42,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":219759,"visible":true,"origin":"","legend":"","description":"","filename":"66456b5032b24c9f9becf5b859c7aa1f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/ca181b9cfd6f2dbb16ff038f.xml"},{"id":94355079,"identity":"3256ecfe-8b6b-4f5a-bf8a-a0e16b551ffa","added_by":"auto","created_at":"2025-10-27 12:57:09","extension":"html","order_by":43,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":239285,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/bfaeab76a82c6273ae64ef35.html"},{"id":94355053,"identity":"ea9f775c-9b7b-455b-afc4-6e5d385c9eb0","added_by":"auto","created_at":"2025-10-27 12:57:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19941,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConsort diagram displaying the flow of recruitment and enrollment. Participants were recruited via an examination of historical clinic records.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/63656fcef2bda104af66ea4f.png"},{"id":94354697,"identity":"04a53457-3475-4535-be55-4f2d17693d6c","added_by":"auto","created_at":"2025-10-27 12:56:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":119705,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of pre- to post-dose change scores on each NIH Toolbox subtest as well as RBANS List Learning, for placebo (red) and gaboxadol (blue). The box spans the interquartile range (IQR), the horizontal bar marks the median, and the whiskers extend to 1.5 x IQR, with overlaid points for each individual at each location. Positive values reflect improvement relative to pre-dose.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRBANS LL, Repeatable Battery for the Assessment of Neuropsychiatric Status List Learning subtest; NIH Toolbox measures: Oral Reading, Oral Reading Recognition Test; Picture Vocab, Picture Vocabulary Test; Speeded Match, Speeded Matching Test; Flanker, Flanker Inhibitory Control and Attention Test; Dim. Card Sort, Dimensional Change Card Sort Test; Pattern Comp., Pattern Comparison Test.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/119330b9925a2908c7b8cf7c.png"},{"id":94354713,"identity":"be0e4e91-8144-453f-ac2c-1c1c5976018d","added_by":"auto","created_at":"2025-10-27 12:56:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87774,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePre- and post-dose numbers of words remembered on the RBANS List Learning subtest for placebo (red) and gaboxadol (blue). The box spans the interquartile range (IQR), the horizontal bar marks the median, the heavier bar marks the mean, and the whiskers extend to 1.5 x IQR, with overlaid points for each individual at each location. Scores were overall significantly higher in the gaboxadol condition, and post-dose gaboxadol significantly differed from pre-dose placebo (p = .03), post-dose placebo (p = .01), and marginally differed from pre-dose gaboxadol (p = .08).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/46c4fcc46c0111214d64bf52.png"},{"id":94354920,"identity":"946f54f3-2efa-420d-9746-8a2362219f8f","added_by":"auto","created_at":"2025-10-27 12:56:45","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":334585,"visible":true,"origin":"","legend":"\u003cp\u003ePSD analysis for Pre-Post comparison between the Placebo and Drug conditions using a 2 x 2 design. \u003cem\u003eTop left\u003c/em\u003e: representative data recorded at Cz. \u003cem\u003eTop right\u003c/em\u003e: results from the 2 x 2 test across 129 electrodes and 491 frequency bins. Uncorrected p-values are plotted. \u003cem\u003eBottom left\u003c/em\u003e: a scalp topography showing uncorrected p-values at 6.8 Hz in which the most electrodes showed significant interactions. \u003cem\u003eBottom right\u003c/em\u003e: results from the interaction test on mean powers across 129 electrodes at 6.8 Hz. The edges of the box plots indicate quartiles, the horizontal lines indicate median values, and the dots indicate individual data points.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/2a897aea758b44e8d2ee5466.jpeg"},{"id":94354910,"identity":"34ff21de-131a-415c-aef6-5fe8cdab8a73","added_by":"auto","created_at":"2025-10-27 12:56:42","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":173084,"visible":true,"origin":"","legend":"\u003cp\u003eThe 2 x 2 interactions tested for power bands. Both theta-band (4-8 Hz) and alpha-band (8-13 Hz) power showed significant interactions in which post-Drug condition demonstrated prominent power increase.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/3cfe885a082d40f3c05b34f0.jpeg"},{"id":94355202,"identity":"3b807d5e-b570-4e69-bd4d-6e33e89f3f7b","added_by":"auto","created_at":"2025-10-27 12:57:24","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":343946,"visible":true,"origin":"","legend":"\u003cp\u003ePSD analysis for Lab-Home comparison between Placebo and Drug conditions using a 2 x 2 design. \u003cem\u003eTop left\u003c/em\u003e: a representative data recorded at Cz. \u003cem\u003eTop right\u003c/em\u003e: results showing the main effect of Drug across 129 electrodes and 491 frequency bins. Uncorrected p-values are plotted. Neither the 2 x 2 interaction tests nor the main effect of Location showed interpretable results. \u003cem\u003eBottom left\u003c/em\u003e: a scalp topography of the uncorrected p-values at 7.4 Hz in which the most electrodes showed the significant main effect of Drug. \u003cem\u003eBottom right\u003c/em\u003e: results from the interaction test on mean powers across 129 electrodes at 6.4 Hz. The edges of the box plots indicate quartiles, the horizontal lines indicate median values, and the dots indicate individual data points.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/d0f43a5c2cd76be65ef98639.jpeg"},{"id":94355214,"identity":"0876fbb9-20cf-4824-9ccf-6a0d08c86d6c","added_by":"auto","created_at":"2025-10-27 12:57:25","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":195973,"visible":true,"origin":"","legend":"\u003cp\u003eThe 2 x 2 interactions tested for power bands. Both theta-band (4-8 Hz) and alpha-band (8-13 Hz) power showed a significant main effect of Drug. None of Lab vs. Home contrasts or interactions showed significant results.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/6e29ff68b719c6c26c6179f9.jpeg"},{"id":94355277,"identity":"8b33a304-851c-42b1-b2b1-867aa77a1e59","added_by":"auto","created_at":"2025-10-27 12:57:29","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":382171,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of the drug on alpha peak frequency. \u003cem\u003eTop left:\u003c/em\u003e a histogram of sample probability of alpha peak presence across electrodes. \u003cem\u003eTop right:\u003c/em\u003e a scalp topography of the sample probability of alpha peak presence. \u003cem\u003eSecond row left:\u003c/em\u003e a histogram of the amount of alpha peak shift. Most of them show a negative shift. \u003cem\u003eSecond row right:\u003c/em\u003e a scalp topography of the alpha peak frequency shift. \u003cem\u003eThird row, \u003c/em\u003ehistograms of peak ‘alpha’ frequencies before (left) and after (right) administering the drug.\u003cem\u003e Bottom left\u003c/em\u003e, an illustration of an electrode site that showed the maximum product of the percent datasets and the amount of the peak shift. \u003cem\u003eBottom right\u003c/em\u003e, PSD comparison at the selected electrode site where 45% of datasets showed a peak downshift of 1.1 Hz on average.\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/5e5397a1507cec266d505229.jpeg"},{"id":94355168,"identity":"973bf805-4005-4aff-9ced-e414020618d1","added_by":"auto","created_at":"2025-10-27 12:57:20","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":290002,"visible":true,"origin":"","legend":"\u003cp\u003eSpectral exponent (SPEX) analysis for Pre-Post comparison for Placebo and Drug conditions using a 2 x 2 design. \u003cem\u003eTop\u003c/em\u003e, SPEX topographies for the 2 x 2 factorial design. \u003cem\u003eBottom left:\u003c/em\u003e a right occipito-temporal electrode location showed trends toward significance\u003cem\u003eBottom middle\u003c/em\u003e: Grand-mean fitted SPEX models for the midline electrode. \u003cem\u003eBottom right\u003c/em\u003e: grand-mean SPEX values for each condition. The edges of the box plots indicate quartiles, the horizontal lines indicate median values, and the dots indicate individual data points.\u003c/p\u003e","description":"","filename":"image10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/b1c2548a5042a2dca69c38fd.jpeg"},{"id":94354696,"identity":"b83130b9-0b46-4cac-9acc-978712aa0793","added_by":"auto","created_at":"2025-10-27 12:56:11","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":253616,"visible":true,"origin":"","legend":"\u003cp\u003eSpectral exponent (SPEX) analysis for Lab-Home comparison for Placebo and Drug conditions using a 2 x 2 design. \u003cem\u003eTop\u003c/em\u003e, SPEX topographies for the 2 x 2 factorial design. \u003cem\u003eMiddle left:\u003c/em\u003e three electrodes showed the main effect of Location at uncorrected \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. \u003cem\u003eMiddle right\u003c/em\u003e: All three electrodes showed Drug \u0026gt; Placebo. \u003cem\u003eBottom\u003c/em\u003e: Grand-mean fitted SPEX models for electrodes showing significant main effect. The edges of the box plots indicate quartiles, the horizontal lines indicate median values, and the dots indicate individual data points.\u003c/p\u003e","description":"","filename":"image11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/4ded9acf1fab2fde260c45f9.jpeg"},{"id":94354698,"identity":"18925781-0577-4d43-9d90-bc3f68387477","added_by":"auto","created_at":"2025-10-27 12:56:11","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":275944,"visible":true,"origin":"","legend":"\u003cp\u003eICCs of PSDs calculated for Lab-Lab, Home-Home, and Lab-Home conditions across all the electrodes and frequency bins. \u003cem\u003eA\u003c/em\u003e, \u003cem\u003eTop left\u003c/em\u003e: Frequency-domain plot of average ICCs across all electrodes for the Lab-Lab comparison. A shade represents +/- 1 SD across electrodes. A broken vertical line indicates a frequency where ICC was maximal, while a dotted vertical line indicates a frequency where ICC was minimal. \u003cem\u003eTop right\u003c/em\u003e: scalp topographies of ICCs at the maximum (left) and the minimum (right) frequency. The color scale is common across all the topographies. \u003cem\u003eMiddle:\u003c/em\u003e the same plot for the Home-Home comparison. Botton: the same plot for the Lab-Home comparison. \u003cem\u003eB\u003c/em\u003e: Band-separate ICC for three comparisons. The edges of the box plots indicate quartiles, the horizontal lines indicate median values, and the dots indicate grand-mean ICC for each electrode. Delta, 1-4 Hz; Theta, 4-8 Hz; Alpha, 8-13 Hz; Beta, 13-30 Hz; Gamma, 30-55 Hz; High Gamma, 65-100 Hz.\u003c/p\u003e","description":"","filename":"image12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/293b0a351bd598668ce2a9c4.jpeg"},{"id":94354845,"identity":"01fed2c7-31d0-4320-9e23-e3bc72739acd","added_by":"auto","created_at":"2025-10-27 12:56:34","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":205026,"visible":true,"origin":"","legend":"\u003cp\u003eICCs of SPEX calculated for Lab-Lab, Home-Home, and Lab-Home conditions across all the electrodes. \u003cem\u003eLeft\u003c/em\u003e: ICCs of SPEX for the Lab-Lab comparison. The mean and standard deviation values were calculated across electrodes. \u003cem\u003eMiddle\u003c/em\u003e: ICCs of SPEX for the Lab-Lab comparison. \u003cem\u003eRight\u003c/em\u003e: ICCs of SPEX for the Lab-Home comparison.\u003c/p\u003e","description":"","filename":"image14.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/2799cce16786c75e9db95fe4.jpeg"},{"id":94354699,"identity":"b8001da9-a40f-4804-91e7-4af390196393","added_by":"auto","created_at":"2025-10-27 12:56:11","extension":"jpeg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":175180,"visible":true,"origin":"","legend":"\u003cp\u003eGrand-mean auditory-evoked ERP recorded at vertex (Cz). Onset and offset of the auditory chirp stimuli are indicated by broken lines at 0 and 2000 ms, respectively. Onset- and offset-evoked vertex potentials (VPs) are present. For each negative and positive peaks (N1 and P1, respectively), scalp topographies of potentials are plotted. All scalp topographies share the same color scale.\u003c/p\u003e","description":"","filename":"image16.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/6b71a883993fcc899a995dfc.jpeg"},{"id":94355137,"identity":"6661f70f-e821-4543-9a17-26bae362c6d5","added_by":"auto","created_at":"2025-10-27 12:57:16","extension":"jpeg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":132090,"visible":true,"origin":"","legend":"\u003cp\u003eDetail view of the onset-evoked VPs at Cz for Pre-Post comparison between the Placebo and Drug conditions using a 2 x 2 design.\u003c/p\u003e","description":"","filename":"image18.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/187b9854ffe19bc1fcd05f74.jpeg"},{"id":94354911,"identity":"e5072419-ee5a-47a3-bf60-3d83bb3fedc5","added_by":"auto","created_at":"2025-10-27 12:56:43","extension":"jpeg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":387898,"visible":true,"origin":"","legend":"\u003cp\u003eERSSP analysis on power for Pre-Post comparison between the Placebo and Drug conditions using a 2 x 2 design. For the log-scaled plots, time-frequency ROI was set from 0 to 500 ms and from 2 to 13 Hz to test 2 x 2 interaction for increase (ERS) and decrease (ERD). None of the results reached statistical significance.\u003c/p\u003e","description":"","filename":"image19.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/ae9d4d38f0ca05bb7df2ada3.jpeg"},{"id":94355447,"identity":"4537ca92-5bbb-4269-a59d-506e79b92011","added_by":"auto","created_at":"2025-10-27 12:57:45","extension":"jpeg","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":215687,"visible":true,"origin":"","legend":"\u003cp\u003eERSSP analysis on ITC for Pre-Post comparison between the Placebo and Drug conditions using a 2 x 2 design. Two ROIs were set. For log-scaled plots, time-frequency ROI was set from 0 to 500 ms and from 2 to 13 Hz to test 2 x 2 interaction for increase (ERS) and decrease (ERD). For linear-scaled plots, time-frequency ROI was set at 40 Hz and 824 ms, which showed the maximum ITC at 40 Hz across time. None of the results reached statistical significance.\u003c/p\u003e","description":"","filename":"image21.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/d52046854cd82bfc6b2eddcb.jpeg"},{"id":94354714,"identity":"19f39fe5-9b3b-47db-83e6-2a1ff13f7d1d","added_by":"auto","created_at":"2025-10-27 12:56:16","extension":"jpeg","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":234285,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation test between Post-Pre peak EEG power and Post-Pre cognitive test performance. Higher performances in the Flanker task and Dimension task were correlated with drug-induced peak power increase.\u003c/p\u003e","description":"","filename":"image22.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/a838a06afb2d4fd0c8df2765.jpeg"},{"id":94491148,"identity":"2e948134-7dca-484a-b8f8-251b8adbfc9e","added_by":"auto","created_at":"2025-10-27 17:23:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5666375,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/71db9e5b-da5f-481a-b2ec-c894e44e2855.pdf"},{"id":94355051,"identity":"2c456bc8-dbf7-4233-9357-a333bf9951be","added_by":"auto","created_at":"2025-10-27 12:57:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":464456,"visible":true,"origin":"","legend":"","description":"","filename":"healxsupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-7871579/v1/eae1e3a4d3b569249a55ff18.docx"}],"financialInterests":"Competing interest reported. This work was sponsored by HealX pharmaceuticals who both supplied the gaboxadol and matching placebo and paid for the work to be conducted at Cincinnati Children’s Hospital Medical Center.","formattedTitle":"Gaboxadol Increases Resting Theta and Alpha Power without Affecting Evoked Responses in Fragile X Syndrome in a Home-based Setting","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFragile X syndrome (FXS) is the most common inherited form of intellectual disability and autism spectrum disorder, with a clinical phenotype that is additionally often marked by anxiety, attentional symptoms, sensory hypersensitivity and irritability, among other concerns. FXS is caused by a CGG triplet repeat expansion in the promoter region of the FMR1 gene resulting in gene methylation and subsequent silencing of gene transcription with resultant reduction or absence of fragile x messenger ribonucleoprotein (FMRP) expression. Despite phenotypic rescue with various small molecules in the \u003cem\u003eFmr1\u003c/em\u003e knockout (KO) mouse model of FXS, there are no FDA-approved treatments for FXS, and translation from the mouse model has been largely unsuccessful. One potential reason for this lack of translation is the increase in phenotypic heterogeneity in participants with FXS, who do not have uniform repeat counts and methylation patterns like the \u003cem\u003eFmr1\u003c/em\u003e KO mouse, a deletion animal model. This variability highlights the need to use biomarkers that are sensitive across a broad range of clinical severity, particularly in individuals with low FMRP expression who may more closely resemble the \u003cem\u003eFmr1\u003c/em\u003e KO model.\u003c/p\u003e\u003cp\u003eMany of our previous studies of drug response in patients with FXS have utilized a double-blind, randomized, single-dose crossover design, with participants visiting the clinic to engage in pre- and post-dose encephalography (EEG) in response to placebo and study drug. This approach allows us to assess target engagement utilizing cross-species validated biomarkers such as increased gamma power at rest (Goswami et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jonak et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lovelace et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pedapati et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and altered intertrial phase coherence (ITC) during auditory stimulation (Ethridge et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jonak et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lovelace et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Miyakoshi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Norris et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Recently published trials have found that baclofen reduced gamma power across-species (Jonak et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while lovastatin and minocycline did not normalize EEG in humans (McKinney et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This design holds promise for early, rapid screening of potential drugs that engage neural targets relevant to FXS prior to more costly, intensive chronic dosing trials.\u003c/p\u003e\u003cp\u003eHowever, the requirement that participants travel to often distant clinic sites for repeated visits to engage in clinical trial research can provide a barrier to participation. This barrier may be particularly challenging to overcome among participants who present with significant impairment who may be best modeled by work with the \u003cem\u003eFmr1\u003c/em\u003e KO mouse and could potentially benefit the most from clinical trial translational medicine efforts. For example, in our recent study of baclofen, follow-up analyses found the strongest reductions in gamma power in participants who had the lowest FMRP (Jonak et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and thus were likely to be more severely affected (Boggs et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Loesch et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Home-based trial protocols may help overcome this barrier, improving access to clinical research and representation of more severely affected individuals.\u003c/p\u003e\u003cp\u003eThe current study aimed to expand upon our single-dose design by collecting pre- and post-dose EEG and behavioral measures both in home and in clinic in response to a candidate study drug, gaboxadol following a 10 mg single dose. Gaboxadol was primarily developed for the treatment of insomnia. Its clinical development involved over 4300 subjects across Phase I, II, and III studies, demonstrating efficacy in improving sleep maintenance and initiation, increasing slow-wave sleep (SWS), and showing a generally favorable safety and tolerability profile with no significant next-day residual or withdrawal effects at therapeutic doses (up to 15 mg for adults and 10 mg for the elderly administered as a single dose before sleep to insomnia patients). However, the development for the insomnia indication was terminated in March 2007 due to its effect size for sleep onset not being considered competitive and an unattractive risk-benefit ratio at higher doses. The dosing of 10 mg per occasion in this current study was therefore deemed appropriate and safe.\u003c/p\u003e\u003cp\u003eGaboxadol is an orthosteric agonist at GABA\u003csub\u003eA\u003c/sub\u003e receptors, with high selectivity for extrasynaptic receptors containing δ subunits (St\u0026oacute;rustovu \u0026amp; Ebert, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), which contribute to tonic inhibition in multiple neuronal populations (Semyanov et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Previous examinations of the effects of gaboxadol on EEG power spectra have chiefly occurred during non-REM sleep, with most studies reporting an increase in slow wave (or delta, 1-4.5 Hz) and theta activity (4-5-7 Hz) in a dose-dependent manner (Lundahl et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mathias et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Walsh et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Mixed results have been found in the alpha band, with one study reporting a decrease in alpha power (Ma et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) while another found increased alpha power at the highest studied dose (20mg; Lundahl et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Interestingly, multiple studies that examined sex found that these effects were strongest in females, potentially due to altered δ subunit density (Dijk et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), although several studies conducted in both young and elderly subjects suggested a modest but significant increase (approximately 10 to 20%) in weight adjusted plasma exposure between female and male subjects.\u003c/p\u003e\u003cp\u003ePrevious studies of gaboxadol in the \u003cem\u003eFmr1\u003c/em\u003e KO mouse found it to rescue inhibitory tone and increase action potential thresholds to that of wild-type (WT) mice in amygdalar slices (Olmos-Serrano et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Behaviorally, gaboxadol has been shown to reduce aberrant pre-pulse inhibition at low decibels \u003cem\u003ein vivo\u003c/em\u003e (Olmos-Serrano et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and normalize a variety of behaviors, particularly at a low dose (Cogram et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, a recent attempt to find electrophysiological correlates in \u003cem\u003eFmr1\u003c/em\u003e KO mice did not find a normalizing impact of single-dose gaboxadol on gamma power or visually- or auditory-evoked potentials, though gaboxadol at its higher dose was associated with increased power broadly from 1\u0026ndash;30 Hz (Janz et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In humans, a clinical trial of gaboxadol in 23 individuals with FXS evaluated three fixed doses in parallel groups across a 12-week dosing period and found that 60% of participants showed clinical improvement (Budimirovic et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), though no neural biomarkers were assessed and there was no control group. As a result, it remains unclear whether gaboxadol engages relevant neural targets in humans with FXS.\u003c/p\u003e\u003cp\u003eIn the current study, we sought to determine not only the effect of single-dose gaboxadol on EEG biomarkers commonly found in FXS, but also the feasibility and consistency of collecting these biomarkers in the participants\u0026rsquo; home environment. By extending the single-dose crossover design into participant homes, we aimed to assess whether decentralized EEG collection could support early-phase biomarker-driven drug screening in FXS, which has the potential to reduce participant burden, improve participation rates, and better represent the FXS phenotype in clinical research.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design\u003c/h2\u003e\u003cp\u003eThis study consisted of two in-home and two in-clinic dosing visits with a two-week washout period between visits. Participants always began the sequence with a clinic visit at which inclusion and exclusion criteria were assessed prior to the first dose, followed by a home visit, another in-clinic visit, and a final home visit. All participants received two doses of placebo (one of each at each location) and two doses of 10 mg gaboxadol (one of each at each location) with all investigators and participants blinded to treatment status. The study protocol was conducted in accordance with the Declaration of Helsinki, approved by the Cincinnati Children\u0026rsquo;s Hospital Medical Center Institutional Review Board, and registered at clinicaltrials.gov with the identifier NCT06334419.\u003c/p\u003e\u003cp\u003ePotential participants were deemed eligible if they: (1) were male, aged 18\u0026ndash;40; (2) had confirmed full \u003cem\u003eFmr1\u003c/em\u003e mutation (\u0026gt;\u0026thinsp;200 CGG repeats); (3) were in generally good health at the time of the initial clinic visit, as determined by a study physician; (4) if receiving serotonin-selective reuptake inhibitor (SSRIs), serotonin-norepinephrine reuptake inhibitor (SNRI), or serotonin antagonist and reuptake inhibitor (SARI), were on a stable, well-tolerated dose for the previous three months with no further changes anticipated; (5) were not sexually active or confirmed at least one form of contraceptive. Study exclusion criteria are described in Supplement S1.\u003c/p\u003e\u003cp\u003eAt the baseline visit, which served as the first dosing day, participants completed demographic, phenotypic, and health history forms, received a physical examination and Clinical Global Impressions \u0026ndash; Severity score from a study physician, and completed the Abbreviated Stanford Binet 5 (Roid \u0026amp; Pomplun, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). At each clinic visit, participants received a blood draw and urinalysis to assess health status and optionally donated a post-dose pharmacokinetic blood sample to assess the amount of gaboxadol in the blood. Laboratory work results were reviewed prior to dosing at first visit. At every dosing visit, including both clinic and home visits, participants completed the following measures pre- and post-dose: EEG, Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) List Learning (Randolph et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and portions of the NIH Toolbox Cognition Battery (Weintraub et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Oral Reading Recognition, Picture Vocabulary, Speeded Matching, Pattern Comparison Processing Speed, Flanker Inhibitory Control and Attention, and Dimensional Change Card Sort subtests). Post-dose measures were collected 60\u0026ndash;90 minutes after administration of the drug in alignment with the Tmax of gaboxadol (Ovid Therapeutics, Inc, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Procedures\u003c/h2\u003e\u003cp\u003e\u003cem\u003eElectroencephalography.\u003c/em\u003e EEG recordings were obtained using saline-based 128-channel HydroCel Geodesic Sensor Nets connected to an EGI Net Amps 400 amplifier (Magstim EGI, Eugene, OR). Data were sampled at 1000 Hz at collection. All EEG assessments were collected pre- and post-dose at each visit.\u003c/p\u003e\u003cp\u003eRest. Ten minutes of resting-state EEG data were collected while participants sat in a quiet room and watched a silent movie of their choosing on an iPad to facilitate participation.\u003c/p\u003e\u003cp\u003eChirp. Participants additionally listened to the \u0026ldquo;chirp\u0026rdquo; stimulus, a 2-second white noise carrier stimulus that is amplitude modulated by a sinusoid that linearly increases in frequency from 0-100 Hz over its duration. Chirp stimuli were presented 200 times at 65 db SPL through headphones with an intertrial interval that randomly varied between 1.5-2s.\u003c/p\u003e\u003cp\u003eEEG preprocessing. EEGLAB was used for EEG preprocessing (Delorme \u0026amp; Makeig, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). After data import, resting data were downsampled to 250 Hz while chirp data were not downsampled. Following this step, a high-pass filter was applied with a 0.5 Hz cutoff frequency, a 1.0 Hz transition band, and a Blackman window. Line noise at 60 Hz and its harmonics were removed using the CleanLine plugin (Bigdely-Shamlo et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Miyakoshi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mullen, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Then, the first and last 3 seconds of each recording were trimmed. To detect and reject artifactual channels, the continuous data were first segmented into 1,000 equal-length epochs. To ensure equal epoch length, a small number of trailing samples were discarded from the end of each recording. For each channel, the maximum absolute amplitude was computed across time within each epoch. To model channel-wise amplitude distributions, a generalized extreme value (GEV) distribution was fit to the maximum absolute amplitude across epochs. A per-channel threshold was then defined as the 90th percentile of the fitted GEV distribution. Epochs exceeding this threshold were marked as high-amplitude outliers. Standard deviations were then computed across time within each epoch, and epochs with high-amplitude outliers were excluded from further estimation. Channels whose median standard deviation across non-outlier epochs fell below 0.1 \u0026micro;V were classified as flat and excluded from the montage. Subsequently, the remaining data were re-referenced to the median (computed after excluding outlier time points). Standard deviation was again calculated per epoch per channel, and a robust threshold was applied to the median SD values to identify channels with excessively large amplitude fluctuations. Specifically, channels whose median standard deviation exceeded the group-level median by more than 20 times the scaled median absolute deviation (MAD \u0026times; 1.4826) were rejected. A similar approach was used in our previous study (Miyakoshi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Artifact Subspace Reconstruction (ASR) was applied to remove transient high-amplitude artifacts (Kothe \u0026amp; Makeig, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Miyakoshi, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; T. R. Mullen et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). ASR was run with a burst criterion of 25, using Euclidean distance. Calibration was restricted to clean data segments by setting BurstCriterionRefMaxBadChns to 0, ensuring that no channels exceeded the artifact threshold during calibration (Kim, Chang, et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Previously rejected channels were restored by spherical spline interpolation. The original reference channel (Cz), which is not recorded separately in the EGI system, was restored as a zero-filled channel to preserve its position in the montage. All channels, including the reconstructed Cz, were then re-referenced to the average reference (Kim et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eNIH Toolbox Cognition Battery\u003c/em\u003e (NIH Toolbox). Participants completed six subtests from the NIH Toolbox that have previously been assessed for use in FXS (Shields et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For each measure, the pre-dose change sensitive score was subtracted from the post-dose change sensitive score to form a difference score that was then assessed statistically. The number of participants included in each assessment varies across visit date, as some participants were nonverbal (n\u0026thinsp;=\u0026thinsp;2) or refused to participate in subtests during at least one testing occasion (n\u0026thinsp;=\u0026thinsp;variable).\u003c/p\u003e\u003cp\u003eDimensional Change Card Sort Test. In this test of attention and cognitive flexibility, participants are required to match a pair of pictures on one dimension (shape, color), before shifting to another dimension later in the task.\u003c/p\u003e\u003cp\u003eFlanker Inhibitory Control and Attention Test. Participants are required to attend to a central stimulus and ignore flanking stimuli.\u003c/p\u003e\u003cp\u003eOral Reading Recognition Test. Participants are required to read letters and words out loud.\u003c/p\u003e\u003cp\u003ePattern Comparison Processing Speed Test. Participants must make determinations about whether two stimuli are the same or different.\u003c/p\u003e\u003cp\u003ePicture Vocabulary Test. Participants listen to a word and select the corresponding picture from an array of four pictures.\u003c/p\u003e\u003cp\u003eSpeeded Matching Test. Participants are presented with a target picture along with four pictures beneath it and must select the picture that matches the target from the array.\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe Repeatable Battery for the Assessment of Neuropsychological Status (RBANS).\u003c/em\u003e Participants completed the List Learning subtests of the RBANS both pre- and post-dose, which is a measure of memory for a list of words that are read to them. Each list contains ten words and four attempts at recall, for a total potential score of 40. To evaluate drug effects, the pre-dose score was subtracted from the post-dose score, indicating the additional number of words remembered post-dose in each condition. All eight verbal participants completed all testing occasions of the RBANS List Learning subtest.\u003c/p\u003e\u003cp\u003e\u003cem\u003eClinical Global Impressions \u0026ndash; Improvement (CGI-I).\u003c/em\u003e The CGI-I is a physician rating of improvement on a Likert scale, with a score of \u0026ldquo;1\u0026rdquo; indicating \u0026ldquo;very much improved\u0026rdquo; and a score of \u0026ldquo;7\u0026rdquo; indicating \u0026ldquo;very much worse.\u0026rdquo; A score of \u0026ldquo;4\u0026rdquo; centers the scale, indicating \u0026ldquo;no change.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data analysis\u003c/h2\u003e\u003cp\u003e\u003cb\u003eBehavioral data.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eIntraclass correlation (ICC).\u003c/em\u003e Reliability of pre-dose measures across all four visits was examined in \u003cem\u003eR\u003c/em\u003e (version 4.4.0; R Core Team, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) using the \u003cem\u003eirr\u003c/em\u003e package (version 0.84.1; Gamer et al., 2019). ICC was calculated for a single measurement using a two-way random-effects model with absolute agreement [ICC(A,1)]. ICC analysis utilized change sensitive scores for NIH Toolbox Cognition Battery subtests and raw scores (total words remembered) for RBANS List Learning.\u003c/p\u003e\u003cp\u003e\u003cem\u003eLinear mixed-effects models (LME).\u003c/em\u003e LME were implemented using PROC MIXED in SAS\u0026reg; version 9.4 (SAS Institute Inc., Cary, NC) to compare outcomes between placebo and gaboxadol. Subject was included as a random effect, and treatment was modeled as a fixed effect. To examine the impact of assessment location (clinic vs. home) models initially included location and an interaction between treatment and location as predictors, which were removed if the terms were not significant. \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant. There were no adjustments made for multiple comparisons, as this is considered a pilot study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRest.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eResting power.\u003c/em\u003e Power spectral density (PSD) was estimated using MATLAB\u0026rsquo;s \u003cem\u003espectrogram\u003c/em\u003e function with a 1 s Hamming window, 50% overlap, and frequency range of 2-100 Hz. For each channel, the median power across time windows was computed for robustness (Melman \u0026amp; Victor, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and converted to decibels (dB). The resulting log-transformed PSD was stored per channel for further analysis. To estimate the spectral exponent (SPEX), a log-transformed PSD was modeled with an exponential curve using the FOOOF algorithm (Donoghue et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The fit was restricted to the 2 to 100 Hz range and used a fixed (no-knee) aperiodic model. Periodic peaks were limited to a maximum of one per spectrum, and a threshold of 2 standard deviations was applied to detect candidate peaks. The spectral exponent, defined as the slope of the aperiodic fit, was extracted from each channel for further analysis.\u003c/p\u003e\u003cp\u003eTo examine treatment-related changes in spontaneous EEG activity, PSD data were organized into a 2 \u0026times; 2 repeated-measures design with the factors Dose (pre- vs. post-dose) and Treatment (Drug vs. Placebo), resulting in four condition-specific PSD sets. A two-way repeated-measures ANOVA was performed at each channel-frequency bin, focusing on the interaction between Dose and Treatment to identify frequency- and region-specific effects of the drug. The resulting interaction-term p-values were mapped across all channels and frequencies to identify potential regions of interest for follow-up analysis. To extract the most relevant frequency bin, a significance mask was first generated by thresholding the p-values at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This binary mask was summed across electrodes and smoothed using a 1-Hz moving average. The frequency bin corresponding to the peak of this smoothed sum was selected for further modeling. For each subject and session, spectral power at the selected frequency was averaged across all electrodes and entered into a linear mixed-effects model with fixed effects of Dose, Treatment, and their interaction, and random slopes and intercepts grouped by subject. Subsequently, linear mixed-effects models were applied to PSD values averaged across all channels and within each predefined frequency band: delta (2 to 4 Hz), theta (4 to 8 Hz), alpha (8 to 13 Hz), beta (13 to 30 Hz), gamma (30 to 55 Hz), and high gamma (65 Hz and above). Each model included fixed effects for Dose, Treatment, and their interaction, along with random intercepts and slopes for each subject. Model fitting was performed using maximum likelihood estimation in MATLAB R2023a (\u003cem\u003efitlme\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eA second analysis focused on post-dose sessions to assess treatment-related differences in spontaneous EEG activity across recording environments. PSD data were grouped by Treatment (Drug vs. Placebo) and Environment (lab vs. home), and a two-way repeated-measures ANOVA was performed at each channel-frequency bin with Treatment and Environment as within-subject factors. To extract a representative frequency bin for follow-up analysis, p-values from the main effect of Treatment were thresholded at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to create a binary significance mask. This mask was summed across electrodes at each frequency and smoothed using a 1-Hz smoothing, and the frequency bin with the maximum value was selected for subsequent modeling. For follow-up modeling, post-dose spectral power at the previously selected frequency bin was averaged across all electrodes. Data were grouped by Treatment (Drug vs. Placebo) and recording Environment (lab vs. home). A linear mixed-effects model was then fit to the data with fixed effects for Treatment, Environment, and their interaction, and with random intercepts and slopes specified per subject. In a separate analysis, PSD values were averaged within each predefined frequency band and across all electrodes using only post-dose sessions. For each frequency band, a linear mixed-effects model was fit with fixed effects for Treatment, Environment, and their interaction, and with random intercepts and slopes specified per subject.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePeak frequency.\u003c/em\u003e To assess drug-related changes in the frequency of spontaneous alpha oscillations, the spectral peak within the 4 to 13 Hz range was identified for each subject and electrode during pre- and post-dose sessions in the Drug condition. For each session, the frequency bin with the maximum power in the alpha range was extracted per channel and subject. Peaks located at the lowest frequency bin were treated as invalid and excluded from further analysis. Two metrics were computed. First, for each channel, the proportion of subjects with valid alpha peaks in both sessions was calculated to quantify detection consistency. Second, for each channel, the average frequency shift from pre- to post-dose was computed using only subjects with valid peaks in both sessions. This yielded a topographic map of alpha peak frequency shifts potentially attributable to the drug. To evaluate group-level effects, a single representative (\u0026ldquo;champion\u0026rdquo;) channel was selected for statistical analysis. For each channel, a composite score was calculated by multiplying the detection consistency by the mean frequency shift. The channel with the maximum absolute value of this score was selected. At the champion channel, subjects with valid alpha peaks in both sessions were identified, and peak frequency values were extracted. These values were analyzed using a linear mixed-effects model with dose as a fixed effect and random intercepts and slopes for each subject. This model tested whether the alpha peak frequency was significantly modulated by drug administration.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSPEX.\u003c/em\u003e To examine treatment-related changes in the aperiodic component of the EEG power spectrum, SPEX values were extracted per subject and channel for each condition: pre-dose Placebo, post-dose Placebo, pre-dose Drug, and post-dose Drug. A linear mixed-effects model was applied separately at each electrode to test for the interaction between Dose (Pre vs. Post) and Treatment (Placebo vs. Drug). The model included fixed effects for Dose, Treatment, and their interaction, and random intercepts and slopes by subject. The resulting interaction terms were used to assess the spatial distribution of drug-related effects on SPEX. A second analysis focused on post-dose sessions to evaluate whether SPEX differed between Drug and Placebo across recording environments. For each channel, a linear mixed-effects model was fit with fixed effects for Treatment (Drug vs. Placebo), Environment (lab vs. home), and their interaction, and with random intercepts and slopes by subject. Statistical terms corresponding to the main effect of Treatment and the Treatment \u0026times; Environment interaction were extracted per channel.\u003c/p\u003e\u003cp\u003e\u003cem\u003eICC.\u003c/em\u003e To assess the test-retest reliability of our measures, we computed ICC using the ICC(A,1) formulation, which estimates absolute agreement for single measurements, for resting power spectra and SPEX. Analyses were restricted to pre-dose sessions. Three comparisons were performed: Lab-Lab, Home-Home, and Lab-Home. For resting power, at each electrode and frequency, PSD values from the two sessions were paired within subjects, and ICCs were computed across subjects. To summarize spectral reliability, ICC values were averaged across electrodes at each frequency. Scalp topographies were generated for the frequencies with the highest and lowest mean ICC values in each comparison. In a separate analysis, PSDs were first averaged within canonical frequency bands (delta: 2 to 4 Hz, theta: 4 to 8 Hz, alpha: 8 to 13 Hz, beta: 13 to 30 Hz, gamma: 30 to 55 Hz, and high gamma: 65 Hz and above), and ICCs were computed on these band-averaged values. For SPEX, values were extracted at each electrode and paired within subjects. The resulting ICC values were mapped across the scalp to assess the reliability of SPEX across repeated measures in different environments.\u003c/p\u003e\u003cp\u003e\u003cb\u003eChirp.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eEvoked activity.\u003c/em\u003e Epochs were extracted from \u0026minus;\u0026thinsp;1000 ms to +\u0026thinsp;3000 ms relative to the onset of the chirp stimulus (t\u0026thinsp;=\u0026thinsp;0). Event-related potentials (ERPs), event-related spectral perturbations (ERSPs), and inter-trial coherence (ITC) were computed for each epoch. For ERSP analysis, baseline correction was performed using the pre-stimulus interval from \u0026minus;\u0026thinsp;1000 ms to 0 ms. We also computed event-related spatio-spectral perturbations (ERSSP) to quantify the spatiotemporal extent of stimulus-evoked activity, with a particular focus on potential extralemniscal involvement (Kim et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, under review). For each subject, the median power and ITC values were computed across trials at each time-frequency bin. For each frequency and channel, a normal distribution was defined using the mean and standard deviation of the baseline period (-1000 to 0 ms). ERSP values falling above the 97.5th percentile or below the 2.5th percentile of this distribution were marked as significant, corresponding to event-related synchronization (ERS) or desynchronization (ERD), respectively. For ITC, only bins in the top 5% were considered significant. These binary significance masks were then summed across channels, yielding per-subject time-frequency matrices that reflect the number of significant channels at each bin. These matrices were used to characterize the spatiotemporal distribution of stimulus-related activity in both power and phase domains. To quantify stimulus-related spatiotemporal dynamics, we defined a region of interest (ROI) in the ERSSP matrix spanning 0-500 ms and 2\u0026ndash;13 Hz. This window was selected to capture early low-frequency activity, including theta and alpha band responses. For each subject, ERSSP values within this ROI were averaged to yield a single summary measure per condition. These values were used for subsequent statistical comparisons. A linear mixed-effects model was used to analyze the ROI-averaged EEG measure. The model included fixed effects for dose, treatment group, and their interaction. A random intercept was included for each subject to account for repeated measurements.\u003c/p\u003e\u003cp\u003eTo assess group differences in ERP amplitude at Cz, independent-samples t-tests were performed at each time point between the two experimental groups, separately for pre-dose and post-dose sessions. A two-tailed t-threshold corresponding to α\u0026thinsp;=\u0026thinsp;0.05 was calculated using the median degrees of freedom from the pre-dose comparison. One subject was excluded from the pre-dose session of one group due to missing data.\u003c/p\u003e\u003cp\u003e\u003cem\u003eICC.\u003c/em\u003e We assessed the test-retest reliability of multiple EEG-derived measures across repeated pre-dose sessions using ICCs. Three comparisons were performed: (1) Lab-Lab, (2) Home-Home, and (3) Lab-Home. The measures included: (1) the N1-P1 peak-to-peak amplitude at Cz, (2) ERSSP-ERS values averaged within a predefined time-frequency region of interest (ROI; 0-500 ms, 2\u0026ndash;13 Hz) corresponding to the vertex potential (VP), (3) ERSSP-ITC values within the same ROI, and (4) ERSSP-ITC values at the frequency and latency corresponding to the expected auditory steady-state response (ASSR). For the ERP analysis, the waveform was segmented to a 0-300 ms post-stimulus window, corresponding to the typical latency range of the N1-P1 complex. The peak-to-peak amplitude was calculated as the difference between the maximum and minimum voltage within this interval. Subjects with missing or invalid data were excluded pairwise for each comparison. To define the ROI for ASSR-related ITC, binary significance maps (significant\u0026thinsp;=\u0026thinsp;1, non-significant\u0026thinsp;=\u0026thinsp;0) were computed for ERSSP-ITC across four conditions (pre- and post-dose sessions for both treatment groups). Subject-level maps were averaged within each condition, then across conditions, yielding a group-level map reflecting the proportion of subjects showing significant ITC at each time-frequency bin. This map was summed across channels to generate a 40 Hz time series, and the time point with the highest value was selected. The resulting time-frequency bin (40 Hz at peak latency) was used to extract ERSSP-ITC values for each subject and session. For all ERSSP-based metrics, ROI values were calculated as the average number of significant channels within the specified bins. ICCs were computed using the ICC(A,1) formulation, which estimates absolute agreement for single measurements. This method was implemented in MATLAB and applied separately to each of the three comparisons using pre-dose data only.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Demographics\u003c/h2\u003e\n \u003cp\u003eTen individuals completed all four dosing visits; there was no early withdrawal from the study. See Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for a consort diagram detailing recruitment and eligibility. Three participants did not use any relevant concomitant medications during the study, while the remaining 7 participants used at least one medication. Demographics, baseline characteristics, and concomitant medications can be found in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Three participants in the placebo condition reported adverse events (AE), including elevated heart rate, fatigue, and emotional disturbance (anxiety, aggression, crying). No participants receiving placebo in-home reported AE. In the laboratory, four participants receiving gaboxadol reported fatigue. An additional one participant reported fever that was deemed unrelated. In-home, two participants receiving gaboxadol reported fatigue.\u003c/p\u003e\n \u003cdiv\u003e\n \u003cdiv align=\"left\"\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Demographic Information and Baseline Characteristics\u003c/div\u003e\n \u003cdiv align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMeasure\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eM (SD) or % (N)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e29.92 (6.95)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% male\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e100% (10)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e100% (10)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHispanic or Latino\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e20% (2)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNot Hispanic or Latino\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e80% (8)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAberrant Behavior Checklist (ABC-FXS)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubscale 1: Irritability/Aggression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e14.00 (10.60)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubscale 2: Lethargy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5.80 (4.42)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubscale 3: Stereotypy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5.50 (3.57)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubscale 4: Hyperactivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7.60 (5.82)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubscale 5: Inappropriate Speech\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4.70 (2.63)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubscale 6: Social Avoidance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3.40 (3.72)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClinical Global Impressions - Severity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3.80 (0.63)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStanford Binet-5 (SB-5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDeviation IQ (Abbreviated)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e24.16 (22.73)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConcomitant Medications\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo psychiatric medications\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e30% (3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethylphenidate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10% (1)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2nd generation antipsychotics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e60% (6)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSSRI or SNRIs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e30% (3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValproic acid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10% (1)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCarbamazepine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10% (1)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClomipramine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10% (1)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAtomexitine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10% (1)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eN, sample size; M, mean; SD, standard deviation; IQ, intelligence quotient.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Behavior\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eICC.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eICC estimates can be found in Table 2, along with previously published estimates of ICC for these measures in FXS (Berry-Kravis et al., 2008; Shields et al., 2020). In general, comparable ICC values were found across pre-dose behavioral measures in our sample, despite lower sample sizes than those found in the literature. Notably, Shields et al. 2020 contains varying sample sizes for each measure, as they completed a data validation step prior to including data in the ICC analysis, while our data includes anyone for whom a score could be generated via the application across all testing occasions. While this adds an element of noise to our data, it reduces subjectivity associated with examiner determinations of validity. We found ICC for behavior measures to be relatively in line with the estimates in the literature, particularly with regard to Oral Reading and Pattern Comparison. Similar to Shields et al. (2020), ICC was lowest for the DCCS, both within and across environments. Our Picture Vocabulary ICC was higher than Shields et al. (2020), while our Flanker ICC was lower, though this effect did not differ between Lab-Lab and Home- Home. Our RBANS ICC was higher within Home-Home than Lab-Lab, but both measures were at least as high as found in Berry-Kravis et al. (2008).\u003c/p\u003e\u003cstrong\u003e\u003cem\u003eLME.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eBoxplots reflecting behavioral\u0026nbsp;\u003cp\u003e\u003cstrong\u003eTable 2. Comparison of Intraclass Correlation Coefficients (ICC) Across Study Visits\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"852\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAcross All Visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMean Home \u0026ndash; Mean Lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003eLab \u0026ndash; Lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003eHome - Home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003eShields et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003eBerry-Kravis et al. (2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eICC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eICC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eICC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eICC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eICC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003eICC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDCCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.62\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.30-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003cp\u003e(0.31-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.50\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.09-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.62\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.05-0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.41\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.01-0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eFlanker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.68\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.39-0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003cp\u003e(0.38-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.71\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.05-0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.63\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.04-0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.84\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.70-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eOral Reading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.76\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.46-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003cp\u003e(0.73-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.95\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.69-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.78-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.96\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.93-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003ePattern Comparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.78\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.45-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003cp\u003e(0.44-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.69\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.04-0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.84\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.04-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.71\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.50-0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003ePicture Vocabulary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.94\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.85-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.93-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.91\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.58-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.97\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.88-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.79\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.66-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSpeeded Matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.86\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.67-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003cp\u003e(0.69-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.88\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.58-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003cp\u003e(0.50-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRBANS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.60-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e(0.69-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.76\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.02-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003cp\u003e(0.72-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cp\u003e\u003cem\u003eRBANS List Learning.\u003c/em\u003e There were no significant effects of location or interaction between treatment and location, thus these factors were removed from the model. Examining treatment alone, with each dose considered a replicate, there was a marginal main effect of treatment, such that gaboxadol use was associated with an increase in words remembered after dose, while placebo had a slight decrease (\u003cem\u003eF\u003c/em\u003e(1, 23)\u0026thinsp;=\u0026thinsp;3.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.08, gaboxadol \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.38, placebo \u003cem\u003eM\u003c/em\u003e = -0.50). However, this trend did not reach statistical significance and should not necessarily be interpreted as evidence of a treatment effect.\u003c/p\u003e\u003cp\u003eTo further understand the dynamics of the scores on this measure, a follow-up analysis was conducted to model the effect of treatment on pre- and post-dose scores separately. This analysis resulted in a main effect of treatment, such that scores in gaboxadol were higher than in the placebo condition overall, regardless of dose (\u003cem\u003eF\u003c/em\u003e(1,53)\u0026thinsp;=\u0026thinsp;4.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.04, gaboxadol \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.88, placebo \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14.88; see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Though the interaction between treatment and dose was not significant (\u003cem\u003eF\u003c/em\u003e(1,53)\u0026thinsp;=\u0026thinsp;2.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.13), to further understand this pilot data, we examined uncorrected post-hoc tests comparing least squares means. We found that post-dose scores in the gaboxadol condition (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18.06) significantly differed from pre- and post-dose scores in the placebo condition (\u003cem\u003et\u003c/em\u003e(53)\u0026thinsp;=\u0026thinsp;2.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.03, placebo pre-dose \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15.13; \u003cem\u003et\u003c/em\u003e(53)\u0026thinsp;=\u0026thinsp;2.60, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01, placebo post-dose \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14.63), but only marginally differed from pre-dose scores in the gaboxadol condition (\u003cem\u003et\u003c/em\u003e(53)\u0026thinsp;=\u0026thinsp;1.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.08, gaboxadol pre-dose \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15.69) due to slightly improved pre-dose performance in the gaboxadol condition. Thus, though gaboxadol was related to an increase in 2.38 words remembered, and placebo was associated with a .5 reduction in words remembered, these post-hoc results should be interpreted cautiously given the small sample size and increased risk of Type I error.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSpeeded Matching Test.\u003c/em\u003e There were no main effects of treatment or location, but there was a significant interaction between treatment and location, \u003cem\u003eF\u003c/em\u003e(1, 24)\u0026thinsp;=\u0026thinsp;8.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01. Comparison of least squares means indicated that performance improved post-dose in placebo in-lab (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.67) relative to placebo in-home (\u003cem\u003eM\u003c/em\u003e = -4.56, \u003cem\u003et\u003c/em\u003e(24)\u0026thinsp;=\u0026thinsp;2.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01) and gaboxadol in-lab (\u003cem\u003eM\u003c/em\u003e = -3.00, \u003cem\u003et\u003c/em\u003e(24) = -2.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02), but not compared to gaboxadol in-home (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.67, \u003cem\u003et\u003c/em\u003e(24)\u0026thinsp;=\u0026thinsp;1.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.25).\u003c/p\u003e\u003cp\u003e\u003cem\u003eFlanker Inhibitory Control and Attention Test.\u003c/em\u003e There were no main effects of treatment or location, or interaction between treatment and location (all \u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026gt;\u0026thinsp;.24).\u003c/p\u003e\u003cp\u003e\u003cem\u003eDimensional Change Card Sort Test.\u003c/em\u003e There were no main effects of treatment or location on difference scores in the DCCS. There was a significant interaction between treatment and location, \u003cem\u003eF\u003c/em\u003e(1,23.9)\u0026thinsp;=\u0026thinsp;5.55, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.03. Uncorrected post-hoc tests comparing differences of least squares means found a significant difference between difference scores in gaboxadol treatment in-clinic and in-home, \u003cem\u003et\u003c/em\u003e(24.4)\u0026thinsp;=\u0026thinsp;2.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02, such that participants improved their scores post-dose in-home with gaboxadol more than in-clinic (gaboxadol in-home \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.14, gaboxadol in-clinic \u003cem\u003eM\u003c/em\u003e = -1.78). There was also a marginal difference between difference scores in-clinic with gaboxadol and in-clinic with placebo treatment, \u003cem\u003et\u003c/em\u003e(23.5)\u0026thinsp;=\u0026thinsp;1.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.09, such that participants had larger difference scores in-clinic in the placebo condition than in-clinic with gaboxadol (placebo in-clinic \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.56, gaboxadol in-clinic \u003cem\u003eM\u003c/em\u003e = -1.78).\u003c/p\u003e\u003cp\u003e\u003cem\u003ePattern Comparison Processing Speed Test.\u003c/em\u003e There were no main effects of treatment or location, or interaction between treatment and location (all \u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026gt;\u0026thinsp;.47).\u003c/p\u003e\u003cp\u003e\u003cem\u003eOral Reading Recognition Test.\u003c/em\u003e There were no main effects of treatment or location, or interaction between treatment and location (all \u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026gt;\u0026thinsp;.16).\u003c/p\u003e\u003cp\u003e\u003cem\u003ePicture Vocabulary Test.\u003c/em\u003e There was no main effect of treatment or interaction between treatment and location, and thus these factors were removed from the statistical model. Looking only at the effect of treatment, and thus utilizing each dose as a replicate, there was a marginal main effect of treatment, \u003cem\u003eF\u003c/em\u003e(1, 28.6)\u0026thinsp;=\u0026thinsp;3.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.09, with gaboxadol having a reduction in score post-dose (gaboxadol \u003cem\u003eM\u003c/em\u003e = -4.00), while placebo had a modest increase (placebo \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.75).\u003c/p\u003e\u003cp\u003eTo understand this further, we conducted a model utilizing treatment and dose (pre and post) as factors. Here, we found no main effect of treatment or dose, but a marginal interaction between treatment and dose, \u003cem\u003eF\u003c/em\u003e(1, 65)\u0026thinsp;=\u0026thinsp;3.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.08. Uncorrected post-hoc tests examining differences of least squares means found that scores in the gaboxadol condition significantly differed between pre- and post-dose (gaboxadol pre-dose \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;475.8, post-dose \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;471.8) but these scores did not otherwise significantly differ from placebo\u0026rsquo;s pre- or post-dose scores (placebo pre-dose \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;472.85, post-dose \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;473.60).\u003c/p\u003e\u003cp\u003e\u003cem\u003eClinical Global Impressions \u0026ndash; Improvement.\u003c/em\u003e Most dosing occasions received a physician rating of \u0026ldquo;4\u0026rdquo; for \u0026ldquo;no change,\u0026rdquo; though three occasions saw a \u0026ldquo;3\u0026rdquo; indicating that the participant had \u0026ldquo;minimally improved.\u0026rdquo; All three occasions were in the gaboxadol condition and represented three distinct participants. Two of the occasions were at in-home visits and one was in-clinic.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 EEG\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Power Spectral Density (PSD) and Spectral Exponent (SPEX) analysis\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section4\"\u003e\u003ch2\u003e3.3.1.1 The drug increases EEG power at 6 Hz.\u003c/h2\u003e\u003cp\u003eWe calculated PSD of resting-state EEG data to compare Pre-Post conditions between the Placebo and Drug conditions, which employed a 2 x 2 factorial design. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The targeted 2 x 2 interaction showed a characteristic frequency-dependent pattern peaking in the theta band (4\u0026ndash;8 Hz), which was confirmed across the majority of electrodes. The peak detection on electrode-averaged results identified that the peak of the statistical results was localized at 6.8 Hz, where the interaction calculated within the LME model was statistically significant, \u003cem\u003eF\u003c/em\u003e(1, 76)\u0026thinsp;=\u0026thinsp;13.8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00039.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNext, we separated these estimates into six conventional frequency bands (Delta, 1\u0026ndash;4 Hz; Theta, 4\u0026ndash;8 Hz; Alpha, 8\u0026ndash;13 Hz; Beta, 13\u0026ndash;30 Hz; Gamma, 30\u0026ndash;55 Hz; High Gamma, 65\u0026ndash;100 Hz) and applied the same test for the mean EEG power of each frequency bands. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. A significant interaction was observed in the theta band (\u003cem\u003eF\u003c/em\u003e(1,76)\u0026thinsp;=\u0026thinsp;12.29, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0008) and the alpha band (\u003cem\u003eF\u003c/em\u003e(1,76)\u0026thinsp;=\u0026thinsp;7.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0080). Other frequency bands did not reach statistical significance. We conclude that the drug increases EEG power in the theta and alpha bands, with the effect centered at 6.4 Hz.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section4\"\u003e\u003ch2\u003e3.3.1.2 No significant difference between Lab and Home.\u003c/h2\u003e\u003cp\u003eBased on the results confirmed on 3.3.1.1, we asked whether there was a difference between Lab and Home conditions only in the Post-dose condition. This analysis also employed 2 x 2 design, but the interaction was not significant. Instead, we observed a clear main effect of Drug\u0026thinsp;\u0026gt;\u0026thinsp;Placebo. Thus, we focus on the main effect. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Unlike Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the electrode x frequency plot as well as the p-value scalp topography show the main effect of Drug. The mean power across all electrodes showed a peak statistical value at 7.4 Hz again where the main effect Drug\u0026thinsp;\u0026gt;\u0026thinsp;Placebo was significant, \u003cem\u003eF\u003c/em\u003e(1, 36)\u0026thinsp;=\u0026thinsp;12.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0011, while the interaction was not significant.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNext, we separated these estimates into six conventional frequency bands and applied the same test for the mean EEG power of each frequency band. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Both the theta band and alpha band showed statistical significance: \u003cem\u003eF\u003c/em\u003e(1, 36)\u0026thinsp;=\u0026thinsp;6.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013 and \u003cem\u003eF\u003c/em\u003e(1, 36)\u0026thinsp;=\u0026thinsp;5.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031, respectively. Other frequency bands did not reach statistical significance. We conclude that the Lab-Home difference was statistically insignificant; theta and alpha power augmentation was found in both environments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section4\"\u003e\u003ch2\u003e3.3.1.3 The drug slows alpha peak frequency.\u003c/h2\u003e\u003cp\u003eWe examined the drug's impact on alpha peak frequency by comparing data in the Drug condition Pre- and Post-dose. In conducting this analysis, we ensured not to overgeneralize the assumption that, while an alpha peak is present in most grand-mean PSD plots in general, it does not guarantee that every individual exhibits a detectable alpha peak. Thus, we first quantified the rate of peak alpha presence across subjects and electrodes. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. On average, only 30.2% of datasets showed the alpha peak (SD 13.2) in a given electrode. For an additional comparison of this value with that of a resting EEG database, see Supplement S2. Next, we quantified the frequency shift for each electrode that showed a pair of alpha peaks in both Pre- and Post-dose conditions. Most electrodes showed a negative shift: on average, the peak frequency was shifted 0.53 Hz (SD 0.32) down. We evaluated histograms of the peak frequencies between Pre and Post conditions. The drug increased the number of detectable peaks from 836 to 1349, with a prominent peak around 7 Hz. This comparison also revealed that the peak frequency distribution in the Pre condition was not centered at the conventional alpha range (8\u0026ndash;13 Hz) but the distribution is centered between 7 and 8 Hz. Finally, a representative electrode was selected that maximized the balance between the number of datasets with detectable peaks and the amount of peak frequency shift. This representative electrode analysis confirmed a significant downshift of peak frequency by 1.1 Hz.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section4\"\u003e\u003ch2\u003e3.3.1.4 The Drug biases the E/I balance toward inhibition.\u003c/h2\u003e\u003cp\u003eWe evaluated the effect of the drug on estimated E/I balance by calculating SPEX for all scalp electrodes. We compared Pre-Post conditions between the Placebo and Drug conditions, which employed a 2 x 2 factorial design. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. None of the electrodes showed significant interaction. However, only one electrode showed a trend toward significance, confirming SPEX coefficient increase in the Drug Post condition, indicating a E/I balance shift toward inhibition. Although this finding had a limitation in statistical significance, the Drug\u0026thinsp;\u0026gt;\u0026thinsp;Placebo pattern was more consistently observed in the next analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section4\"\u003e\u003ch2\u003e3.3.1.5 Intraclass Correlation Coefficients (ICCs) between Lab and Home are comparable.\u003c/h2\u003e\u003cp\u003eWe tested the effect of location by comparing data in the Pre-dose condition between Lab and Home. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e and presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Across the three contrasts, ICCs were generally similar across the frequency spectrum. All three comparisons showed that peak ICC values were identified in the 7\u0026ndash;10 Hz range. Visual inspection of the maximum and minimum ICC for each condition showed disparate patterns. In addition, to allow comparison with previous studies (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we generated ICCs by frequency band across the three contrasts. We conclude that reproducibility between Lab-Home recordings is comparable to those of Lab-Lab and Home-Home.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eTest-Retest Reliability (ICC) of Pre-dose Resting-State PSDs by Frequency Band\u003c/b\u003e \u003cem\u003e(Mean ICC across electrodes, SD in parentheses)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency Band\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLab-Lab\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHome-Home\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLab-Home\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLiu et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Mean\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelta (2\u0026ndash;4 Hz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.776 (0.127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.752 (0.144)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.769 (0.108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.803\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTheta (4\u0026ndash;8 Hz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.818 (0.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.837 (0.120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.828 (0.093)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.771\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlpha (8\u0026ndash;13 Hz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.812 (0.115)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.847 (0.129)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.864 (0.097)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.852\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeta (13\u0026ndash;30 Hz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.699 (0.244)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.742 (0.173)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.746 (0.165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGamma (30\u0026ndash;55 Hz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.675 (0.213)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.737 (0.134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.672 (0.140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.676\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHi-Gamma (65\u0026thinsp;+\u0026thinsp;Hz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.703 (0.165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.747 (0.125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.683 (0.106)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor interest, we conducted the same ICC analysis on the SPEX data. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e. The mean ICC values across the electrodes were in the range of 0.70\u0026ndash;0.81, which were comparable to the ICC values from the PSD test. Visual inspection did not confirm a similar topographical pattern across locations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.4.1 Auditory-evoked event-related potential (ERP) analysis in time and time-frequency domains.\u003c/h2\u003e\u003cdiv id=\"Sec17\" class=\"Section4\"\u003e\u003ch2\u003e3.4.1.1 Vertex potential (VP) is insensitive to the drug effect.\u003c/h2\u003e\u003cp\u003eTo start the auditory-evoked ERP analysis, we first evaluated a large-amplitude phenomenon with a transient response to the onset and offset of the auditory stimuli that showed a central scalp distribution. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e. We identified this evoked response as a classical VP that consists of biphasic N1-P1 components. Between the onset- and offset-evoked VPs, we can observe the oscillatory waves in response to the chirp-modulated stimulus with a plateau between 500 and 2000 ms.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the initial time-domain analysis, we targeted the onset-evoked VP. We compared the effect of Pre-Post conditions between the Placebo and Drug conditions on the VP, which employed a 2 x 2 factorial design. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e. The difference between Pre and Post did not reach statistical significance. We conclude that the drug did not affect VP amplitudes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section4\"\u003e\u003ch2\u003e3.4.1.2 Power modulations on VP are insensitive to the drug effect.\u003c/h2\u003e\u003cp\u003eWe applied ERSSP analysis on the ERSP to compare the effect of Pre-Post conditions between the Placebo and Drug conditions on the VP, which employed a 2 x 2 factorial design. The target time-frequency ROI was from 0 to 500 ms and from 2 to 13 Hz. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e. Neither the interaction nor the main effect of Drug reached statistical significance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.4.1.3 ITC modulations on VP and 40-Hz peak are insensitive to the drug effect.\u003c/b\u003eWe applied ERSSP analysis on the ITC to compare the effect of Pre-Post conditions between the Placebo and Drug conditions on the VP and 40-Hz peak, which employed a 2 x 2 factorial design. The target time-frequency ROI was from 0 to 500 ms and from 2 to 13 Hz for the VP and 824 ms at 40 Hz where the maximum ITC-ERSSP was observed. The results are shown in Fig.\u0026nbsp;16. Neither the interaction nor the main effect of Drug reached statistical significance in VPs or the 40-Hz ITC peak.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 16.\u003c/b\u003e ERSSP analysis on ITC for Pre-Post comparison between the Placebo and Drug conditions using a 2 x 2 design. Two ROIs were set. For log-scaled plots, time-frequency ROI was set from 0 to 500 ms and from 2 to 13 Hz to test 2 x 2 interaction for increase (ERS) and decrease (ERD). For linear-scaled plots, time-frequency ROI was set at 40 Hz and 824 ms, which showed the maximum ITC at 40 Hz across time. None of the results reached statistical significance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section4\"\u003e\u003ch2\u003e3.4.1.4 Lab-Lab showed the highest Intraclass Correlation Coefficients (ICCs) in auditory ERPs.\u003c/h2\u003e\u003cp\u003eWe tested ICC between Lab and Home conditions, Pre-dose only, for four EEG measures using ICC(A,1), which estimates absolute agreement for single measurements. The results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The VP (N1-P1) peak amplitude difference yielded higher ICCs than other measures in most cases, followed by ERSSP-ITC\u003csub\u003eVP\u003c/sub\u003e. These two measures showed consistent values across comparisons. These two measures showed ICC values that ranged from 0.64 to 0.85, with Lab-Lab being generally higher than other pairs. We conclude that Lab-Lab reproducibility is better than Home-Home or Lab-Home pairs within our auditory-evoked data.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eTest-Retest Reliability (ICC) of Pre-dose Auditory EEG Measures\u003c/b\u003e \u003cem\u003e(ICC, confidence intervals in brackets)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLab-Lab\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHome-Home\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLab-Home\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVP (N1-P1) peak difference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.85 [0.51 0.96], p\u0026thinsp;=\u0026thinsp;0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.64 [0.05 0.90], p\u0026thinsp;=\u0026thinsp;0.0185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72 [0.41 0.88], p\u0026thinsp;=\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eERSSP-ERS\u003csub\u003eVP\u003c/sub\u003e (ROI: 0-500 ms, 2\u0026ndash;13 Hz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.65 [0.10 0.90], p\u0026thinsp;=\u0026thinsp;0.0115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34 [-0.29 0.79], p\u0026thinsp;=\u0026thinsp;0.1465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66 [0.32 0.85], p\u0026thinsp;=\u0026thinsp;0.0006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eERSSP-ITC\u003csub\u003eVP\u003c/sub\u003e (ROI: 0-500 ms, 2\u0026ndash;13 Hz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.75 [0.08 0.94], p\u0026thinsp;=\u0026thinsp;0.0151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.66 [0.01 0.91], p\u0026thinsp;=\u0026thinsp;0.0241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67 [0.33 0.86], p\u0026thinsp;=\u0026thinsp;0.0005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eERSSP-ITC\u003csub\u003e40Hz\u003c/sub\u003e (ROI: 824 ms, 40 Hz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.23 [-0.47 0.74], p\u0026thinsp;=\u0026thinsp;0.2573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.74 [0.13 0.94], p\u0026thinsp;=\u0026thinsp;0.0106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37 [-0.10 0.70], p\u0026thinsp;=\u0026thinsp;0.0591\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Performance changes in Flanker and Dimension tasks correlated with drug-induced power increases.\u003c/h2\u003e\u003cp\u003eWe conducted a correlation analysis between change in cognitive performance and change in EEG power at the frequency of maximal drug effect, 7.2 Hz. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e17\u003c/span\u003e. The analysis revealed that the improvement in performance in Flanker and Dimension tasks were correlated with increase in power at 7.2 Hz. The mean Pearson\u0026rsquo;s correlation coefficients across the significant electrode sites for Flanker and Dimension tasks were \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.34 and 0.21 (SD 0.36 and 0.32), respectively. Note that these results need to be interpreted with caution. Correlating a biomarker change with a non-significant behavioural change is known to be susceptible to spurious findings (Kriegeskorte et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and may be best used for hypothesis generation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides initial evidence that utilizing decentralized, home-based cognitive assessment and EEG is both feasible and reliable in adult males with FXS, while also evaluating the acute behavioral and neural effects of single-dose gaboxadol. Our results expand upon previous single-site biomarker-based clinical trials in FXS by demonstrating that critical measures, including cognitive batteries and relevant EEG measures such as resting-state spectral power and auditory-evoked potentials, can be collected consistently outside the controlled clinic setting, with between-location ICC values generally consistent with those found in the literature (see Tables\u0026nbsp;2\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This finding is particularly important because challenges in traveling to a traditional research setting provide a barrier to broader participation in clinical trials among individuals with significant impairment, for whom upcoming treatments may provide the most benefit.\u003c/p\u003e\u003cp\u003ePharmacodynamically, like in the \u003cem\u003eFmr1\u003c/em\u003e KO mouse (Janz et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and similar to sleep studies in humans that found augmented theta power (Lundahl et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mathias et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Walsh et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), we found that gaboxadol was associated with increased theta and alpha power, with no significant effect on the gamma band. This result did not differ between home and lab, suggesting it is a robust indicator of gaboxadol\u0026rsquo;s effect on the brain and supporting the reliability of resting state EEG as a biomarker that can be collected both in-lab and in-home. Specifically, gaboxadol\u0026rsquo;s maximal effect was an augmentation of power in the 6\u0026ndash;8 Hz range, with a concomitant shift in individual peak frequency in those for whom peaks could be detected, and a shift in the spectral exponent (SPEX) that suggests gaboxadol augmented inhibition in the brain.\u003c/p\u003e\u003cp\u003eBehaviorally, we observed a marginal increase in words remembered in the RBANS List Learning subtest following single-dose gaboxadol. This result suggests a potential effect of gaboxadol on social engagement, attention or working memory, all of which are required for successful completion of the RBANS LL. Among these, a previous clinical trial of gaboxadol in participants with FXS found improvements in lethargy/social withdrawal (Budimirovic et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), though no cognitive domains were assessed for comparison and there was no control group. While the RBANS findings suggest a numerical trend, the absence of statistical significance and potential baseline confounding suggest the results are best viewed as exploratory and hypothesis-generating. There were no effects of gaboxadol on the NIH Toolbox cognitive measures. Because of this, the reported correlation between the EEG power increase and performance on the Flanker and DCCS tasks should be presented with some caution. Correlating a biomarker change with a non-significant behavioral change in a small, exploratory study is potentially susceptible to spurious findings and should be considered, like with the RBANS, to be hypothesis-generating. Our lack of NIH Toolbox findings may be the result of increased noise in our data: while many studies screen datapoints on the examiner\u0026rsquo;s assessment of validity, our study did not. Notably, the NIH Toolbox in populations of patients with neurodevelopmental disorders has never been demonstrated to be sensitive to acute drug effects.\u003c/p\u003e\u003cp\u003eThe EEG results present a potential paradox. The data demonstrates that the drug engages its CNS target (evidenced by the robust increase in theta/alpha power) but fails to normalize the gamma band EEG abnormality that has been demonstrated repeatedly to characterize FXS neurophysiology. Additionally, acute single dose gaboxadol did not alter auditory-evoked EEG by any measure (ERP, ERSSP, ITC). This contrast suggests modulation of general tonic thalamo-cortical network activity, rather than correction of transient, stimulus-evoked processes known to be impaired in FXS.\u003c/p\u003e\u003cp\u003eGaboxadol operates on extrasynaptic GABA\u003csub\u003eA\u003c/sub\u003e δ-subunit\u0026ndash;containing receptors and particularly the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\alpha\\:}}_{4}{{\\beta\\:}}_{3}{\\delta\\:}\\)\u003c/span\u003e\u003c/span\u003e subunit, which are predominantly expressed in the thalamus (Pirker et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and mediate tonic inhibition (Semyanov et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; St\u0026oacute;rustovu \u0026amp; Ebert, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Though the specific mechanism at work is undetermined, a recent preprint demonstrated that gaboxadol preferentially affected the mediodorsal thalamic nuclei (MD) during sleep (Redondo et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, under review), which has dense connections with the posterior orbitofrontal cortex (Jones, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Rose \u0026amp; Woolsey, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1948\u003c/span\u003e). The MD is involved in a broadly tuned olfactory pathway that is responsible for integrating input (Yarita et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1980\u003c/span\u003e), distinct from the more finely tuned non-thalamic olfactory pathway (Tanabe et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). The MD also has a connection to the amygdala, which provides emotional and reward-related value associated with sensory input (Jones, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Yarita et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) and has previously shown hyperexcitability that was rescued by gaboxadol in a mouse model of FXS (Olmos-Serrano et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe recently proposed a hyper-extralemniscal model of FXS (Miyakoshi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which highlights the role of supramodal, broadly-tuned extralemniscal thalamic pathway with diffuse cortical projections, in contrast to sharply-tuned, modality-specific lemniscal thalamic pathways. In line with this framework, our current findings may relate to the auditory extralemniscal thalamic pathway, which is primarily mediated by the medial division of the medial geniculate body (MGm). Like the MD\u0026rsquo;s olfactory pathway, the MGm\u0026rsquo;s auditory pathway is broadly tuned, terminates within the cortex, forms a dual pathway with a counterpart that is finely tuned, and has connections to the amygdala. From a dual system perspective (Miyakoshi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Somervail et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), the MD, if at work in the gaboxadol response, has more in common with the extralemniscal system, rather than the sensory-tuned lemniscal system. This would provide a potential anatomical basis for the observed increases in resting state power without changes to auditory-evoked activity.\u003c/p\u003e\u003cp\u003eWe interpret these findings as supporting a model in which gaboxadol alters tonic activity in the thalamocortical loop, potentially via the MD, specifically engaging broadly tuned extralemniscal- (also known as non-specific-) like pathways. Although we do not have direct evidence to determine a specific mechanistic pathway, several observations converge: (1) modulation in the low-frequency band indicates the recruitment of thalamo-cortical loop; (2) the broad distribution of the maximum power increase across the whole scalp regions indicates recruitment of the extralemniscal thalamic system which is known to have spatially diffuse cortical and subcortical projections; (3) the absence of the modulation in auditory event-related potential indicates that the \u0026lsquo;saliency-encoding\u0026rsquo; VPs remained intact (Miyakoshi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mouraux et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mouraux \u0026amp; Iannetti, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Somervail et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). From these observations, we conclude that the spatiotemporal distribution of the effect of gaboxadol is broad and tonic. It modulates EEG states i.e., baseline, without affecting the transient \u0026lsquo;saliency network\u0026rsquo; that is the source of the VPs (Menon \u0026amp; Uddin, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mouraux et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, we quantified the empirical probability of observing an alpha peak in PSD across datasets and electrodes. We found that 30.2% (SD 13.2) of datasets show an alpha peak at a given electrode site. To validate this rate, we conducted an additional analysis on resting-state EEG data from the MPI Leipzig Mind-Brain-Body database (Babayan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We obtained a rate of 47.1% (SD 10.5). The details are reported in Supplement S2. The lower rate in our results may be explained by several factors: (1) participants watched silent movies during the eyes-open resting session in our data, while the participants in the LEMON dataset did not watch them; (2) our participants were a clinical population and half of the datasets were recorded at Home, while their participants were normal adults and were recorded in the Lab. Thus, the eyes-open resting session does not reliably produce a detectable alpha peak in a given electrode. This rate also depends on electrode locations. Our data confirmed that central and parietal regions show higher rates. Overall, we emphasize that Gaboxadol noticeably increased (161%) the number of electrodes with a detectable alpha peak, indicating the drug\u0026rsquo;s ability to increase oscillatory EEG signals.\u003c/p\u003e\u003cp\u003eTaken together, the behavioral, EEG, and anatomical data support a model in which gaboxadol acts as a modulator of tonic thalamocortical activity in FXS. Importantly, these effects were observable both in-lab and in-home, suggesting that in-home data collection can provide an adequate replacement for clinic visits in participants who are unable to travel to a trial site. At the same time, these results do not provide strong evidence for a pro-cognitive or disease-modifying effect of single-dose gaboxadol in FXS. Furthermore, the interpretation of these acute, single-dose findings must be tempered by the potential effects of chronic administration. The acute effects may reflect transient arousal changes unlikely to persist with chronic dosing. While gaboxadol may have a unique profile that spares it from developing tolerance to its sleep-promoting effects, there is direct preclinical evidence that tolerance does develop for other functional effects, such as motor impairment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Cincinnati Children\u0026rsquo;s Hospital Institutional Review Board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eDue to the size and scope of the dataset, the data are not publicly hosted. The data are available from the corresponding authors upon reasonable request and in consultation with the study sponsor, HealX.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThis work was sponsored by HealX pharmaceuticals who both supplied the gaboxadol and matching placebo and paid for the work to be conducted at Cincinnati Children\u0026rsquo;s Hospital Medical Center.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eSupport for this work was provided by HealX pharmaceuticals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eL.A.D., H.K., and M.M. analyzed the data and prepared the manuscript. C.A.E, L.M.S., K.C.D., E.V.P., D.L.R., and M.N.N. assisted with the conceptualization and design of the study. L.A.D, L.M.S., W.S.M., and A.D. participated in the coordination and implementation of the study. R.H. and R.W. provided feedback on the manuscript and assisted with interpretation of the data. P.S.H. provided feedback on statistical analysis of behavioral measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u0026nbsp;\u003c/strong\u003eclinicaltrials.gov, NCT06334419, Registration Date: March 8, 2024\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBabayan A, Erbey M, Kumral D, Reinelt JD, Reiter AMF, R\u0026ouml;bbig J, Schaare HL, Uhlig M, Anwander A, Bazin P-L, Horstmann A, Lampe L, Nikulin VV, Okon-Singer H, Preusser S, Pampel A, Rohr CS, Sacher J, Th\u0026ouml;ne-Otto A, Villringer A. A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Sci Data. 2019;6(1):180308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/sdata.2018.308\u003c/span\u003e\u003cspan address=\"10.1038/sdata.2018.308\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerry-Kravis E, Sumis A, Kim O-K, Lara R, Wuu J. Characterization of Potential Outcome Measures for Future Clinical Trials in Fragile X Syndrome. J Autism Dev Disord. 2008;38(9):1751\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10803-008-0564-8\u003c/span\u003e\u003cspan address=\"10.1007/s10803-008-0564-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBigdely-Shamlo N, Mullen T, Kothe C, Su K-M, Robbins KA. (2015). The PREP pipeline: Standardized preprocessing for large-scale EEG analysis. \u003cem\u003eFrontiers in Neuroinformatics\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fninf.2015.00016\u003c/span\u003e\u003cspan address=\"10.3389/fninf.2015.00016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoggs AE, Schmitt LM, McLane RD, Adayev T, LaFauci G, Horn PS, Dominick KC, Gross C, Erickson CA. Optimization, validation and initial clinical implications of a Luminex-based immunoassay for the quantification of Fragile X Protein from dried blood spots. Sci Rep. 2022;12(1):5617. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-022-09633-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-09633-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBudimirovic DB, Dominick KC, Gabis LV, Adams M, Adera M, Huang L, Ventola P, Tartaglia NR, Berry-Kravis E. Gaboxadol in Fragile X Syndrome: A 12-Week Randomized, Double-Blind, Parallel-Group, Phase 2a Study. Front Pharmacol. 2021;12:757825. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphar.2021.757825\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2021.757825\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCogram P, Deacon RMJ, Warner-Schmidt JL, Schimmelmann V, Abrahams MJ, B. S., During MJ. Gaboxadol Normalizes Behavioral Abnormalities in a Mouse Model of Fragile X Syndrome. Front Behav Neurosci. 2019;13:141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnbeh.2019.00141\u003c/span\u003e\u003cspan address=\"10.3389/fnbeh.2019.00141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDelorme A, Makeig S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jneumeth.2003.10.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jneumeth.2003.10.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDijk D, James L, Peters S, Walsh J, Deacon S. Sex differences and the effect of gaboxadol and zolpidem on EEG power spectra in NREM and REM sleep. J Psychopharmacol. 2010;24(11):1613\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0269881109105788\u003c/span\u003e\u003cspan address=\"10.1177/0269881109105788\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDonoghue T, Haller M, Peterson EJ, Varma P, Sebastian P, Gao R, Noto T, Lara AH, Wallis JD, Knight RT, Shestyuk A, Voytek B. Parameterizing neural power spectra into periodic and aperiodic components. Nat Neurosci. 2020;23(12):1655\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41593-020-00744-x\u003c/span\u003e\u003cspan address=\"10.1038/s41593-020-00744-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEthridge LE, De Stefano LA, Schmitt LM, Woodruff NE, Brown KL, Tran M, Wang J, Pedapati EV, Erickson CA, Sweeney JA. Auditory EEG Biomarkers in Fragile X Syndrome: Clinical Relevance. Front Integr Nuerosci. 2019;13:60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnint.2019.00060\u003c/span\u003e\u003cspan address=\"10.3389/fnint.2019.00060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEthridge LE, White SP, Mosconi MW, Wang J, Pedapati EV, Erickson CA, Byerly MJ, Sweeney JA. Neural synchronization deficits linked to cortical hyper-excitability and auditory hypersensitivity in fragile X syndrome. Mol Autism. 2017;8(1):22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13229-017-0140-1\u003c/span\u003e\u003cspan address=\"10.1186/s13229-017-0140-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGamer M, Lemon J. \u0026amp; IFPS. (2019). \u003cem\u003eIrr: Various Coefficients of Interrater Reliability and Agreement. R package version 0.84.1\u003c/em\u003e [Computer software]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=irr\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=irr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao R, Peterson EJ, Voytek B. Inferring synaptic excitation/inhibition balance from field potentials. NeuroImage. 2017;158:70\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2017.06.078\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2017.06.078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoswami S, Cavalier S, Sridhar V, Huber KM, Gibson JR. Local cortical circuit correlates of altered EEG in the mouse model of Fragile X syndrome. Neurobiol Dis. 2019;124:563\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nbd.2019.01.002\u003c/span\u003e\u003cspan address=\"10.1016/j.nbd.2019.01.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJanz P, Bainier M, Marashli S, Gross S, Redondo RL. Clinically-probed mechanisms of action in Fragile-X syndrome fail to normalize translational EEG phenotypes in Fmr1 knockout mice. Neuropharmacology. 2025;262:110182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuropharm.2024.110182\u003c/span\u003e\u003cspan address=\"10.1016/j.neuropharm.2024.110182\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJonak CR, Lovelace JW, Ethell IM, Razak KA, Binder DK. Multielectrode array analysis of EEG biomarkers in a mouse model of Fragile X Syndrome. Neurobiol Dis. 2020;138:104794. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nbd.2020.104794\u003c/span\u003e\u003cspan address=\"10.1016/j.nbd.2020.104794\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJonak CR, Pedapati EV, Schmitt LM, Assad SA, Sandhu MS, DeStefano L, Ethridge L, Razak KA, Sweeney JA, Binder DK, Erickson CA. Baclofen-associated neurophysiologic target engagement across species in fragile X syndrome. J Neurodevelopmental Disorders. 2022;14(1):52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s11689-022-09455-9\u003c/span\u003e\u003cspan address=\"10.1186/s11689-022-09455-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones EG. The Thalamus. Plenum; 1985. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-4615-1749-8\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4615-1749-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim H, Chang C-Y, Kothe C, Iversen JR, Miyakoshi M. Juggler\u0026rsquo;s ASR: Unpacking the principles of artifact subspace reconstruction for revision toward extreme MoBI. J Neurosci Methods. 2025;420:110465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jneumeth.2025.110465\u003c/span\u003e\u003cspan address=\"10.1016/j.jneumeth.2025.110465\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim H, Ethridge LE, De Stefano LA, Schmitt LM, Pedapati EV, Erickson CA, Miyakoshi M. (2025). Event-Related Spatio-Spectral Perturbation (ERSSP): A Scalp-Wide EEG Analysis Framework for Capturing Extralemniscal Thalamocortical Activity, Validated Using a Fragile X Syndrome Model.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim H, Luo J, Chu S, Cannard C, Hoffmann S, Miyakoshi M. ICA\u0026rsquo;s bug: How ghost ICs emerge from effective rank deficiency caused by EEG electrode interpolation and incorrect re-referencing. Front Signal Process. 2023;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/frsip.2023.1064138\u003c/span\u003e\u003cspan address=\"10.3389/frsip.2023.1064138\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKothe CA, Makeig S. BCILAB: A platform for brain\u0026ndash;computer interface development. J Neural Eng. 2013;10(5):056014. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1741-2560/10/5/056014\u003c/span\u003e\u003cspan address=\"10.1088/1741-2560/10/5/056014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKriegeskorte N, Simmons WK, Bellgowan PSF, Baker CI. Circular analysis in systems neuroscience: The dangers of double dipping. Nat Neurosci. 2009;12(5):535\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nn.2303\u003c/span\u003e\u003cspan address=\"10.1038/nn.2303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu R, Pedapati EV, Schmitt LM, Shaffer RC, Smith EG, Dominick KC, DeStefano LA, Westerkamp G, Horn P, Sweeney JA, Erickson CA. Reliability of resting-state electrophysiology in fragile X syndrome. Biomarkers Neuropsychiatry. 2023;9:100070. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bionps.2023.100070\u003c/span\u003e\u003cspan address=\"10.1016/j.bionps.2023.100070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoesch DZ, Huggins RM, Hagerman RJ. Phenotypic variation and FMRP levels in fragile X. Ment Retard Dev Disabil Res Rev. 2004;10(1):31\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/mrdd.20006\u003c/span\u003e\u003cspan address=\"10.1002/mrdd.20006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLovelace JW, Ethell IM, Binder DK, Razak KA. Translation-relevant EEG phenotypes in a mouse model of Fragile X Syndrome. Neurobiol Dis. 2018;115:39\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nbd.2018.03.012\u003c/span\u003e\u003cspan address=\"10.1016/j.nbd.2018.03.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLundahl J, Deacon S, Maurice D, Staner L. EEG spectral power density profiles during NREM sleep for gaboxadol and zolpidem in patients with primary insomnia. J Psychopharmacol. 2012;26(8):1081\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0269881111424457\u003c/span\u003e\u003cspan address=\"10.1177/0269881111424457\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa J, Dijk D-J, Svetnik V, Tymofyeyev Y, Ray S, Walsh JK, Deacon S. EEG Power Spectra Response to a 4-h Phase Advance and Gaboxadol Treatment in 822 Men and Women. J Clin Sleep Med. 2011;07(05):493\u0026ndash;501. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5664/JCSM.1316\u003c/span\u003e\u003cspan address=\"10.5664/JCSM.1316\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMathias S, Zihl J, Steiger A, Lancel M. Effect of Repeated Gaboxadol Administration on Night Sleep and Next-Day Performance in Healthy Elderly Subjects. Neuropsychopharmacology. 2005;30(4):833\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/sj.npp.1300641\u003c/span\u003e\u003cspan address=\"10.1038/sj.npp.1300641\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcKinney WS, Schmitt LM, De Stefano LA, Ethridge L, Norris JE, Horn PS, Dauterman S, Rosselot H, Pedapati EV, Reisinger DL, Dominick KC, Shaffer RC, Chin D, Friedman NR, Hong M, Sweeney JA, Erickson C. Results from a Double-Blind, Randomized, Placebo-Controlled, Single-Dose, Crossover Trial of Lovastatin or Minocycline in Fragile X Syndrome. J Child Adolesc Psychopharmacol. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/cap.2024.0103\u003c/span\u003e\u003cspan address=\"10.1089/cap.2024.0103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. cap.2024.0103.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMelman T, Victor JD. Robust power spectral estimation for EEG data. J Neurosci Methods. 2016;268:14\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jneumeth.2016.04.015\u003c/span\u003e\u003cspan address=\"10.1016/j.jneumeth.2016.04.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMenon V, Uddin LQ. Saliency, switching, attention and control: A network model of insula function. Brain Struct Function. 2010;214(5\u0026ndash;6):655\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00429-010-0262-0\u003c/span\u003e\u003cspan address=\"10.1007/s00429-010-0262-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiyakoshi M. Artifact subspace reconstruction: A candidate for a dream solution for EEG studies, sleep or awake. Sleep. 2023;46(12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/sleep/zsad241\u003c/span\u003e\u003cspan address=\"10.1093/sleep/zsad241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiyakoshi M, Kim H, De Stefano LA, Schmitt LM, Norris JE, Ethridge LE, Erickson CA, Pedapati EV. Hyper-extralemniscal model of Fragile X syndrome. Cereb Cortex. 2025;35(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cercor/bhaf141\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhaf141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiyakoshi M, Schmitt LM, Erickson CA, Sweeney JA, Pedapati EV. (2021). Can We Push the Quasi-Perfect Artifact Rejection Even Closer to Perfection? \u003cem\u003eFrontiers in Neuroinformatics\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fninf.2020.597079\u003c/span\u003e\u003cspan address=\"10.3389/fninf.2020.597079\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMouraux A, Diukova A, Lee MC, Wise RG, Iannetti GD. A multisensory investigation of the functional significance of the pain matrix. NeuroImage. 2011;54(3):2237\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2010.09.084\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2010.09.084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMouraux A, Iannetti GD. Nociceptive Laser-Evoked Brain Potentials Do Not Reflect Nociceptive-Specific Neural Activity. J Neurophysiol. 2009;101(6):3258\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/jn.91181.2008\u003c/span\u003e\u003cspan address=\"10.1152/jn.91181.2008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMullen T. (2012). \u003cem\u003eCleanLine\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nitrc.org/projects/cleanline\u003c/span\u003e\u003cspan address=\"https://nitrc.org/projects/cleanline\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMullen TR, Kothe CAE, Chi YM, Ojeda A, Kerth T, Makeig S, Jung T-P, Cauwenberghs G. Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Trans Biomed Eng. 2015;62(11):2553\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/tbme.2015.2481482\u003c/span\u003e\u003cspan address=\"10.1109/tbme.2015.2481482\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNorris JE, De Stefano LA, McKinney WS, Schmitt LM, Miyakoshi M, Gross C, Piloto S, Heald B, Pedapati EV, Erickson CA, Sweeney JA, Ethridge LE. Auditory steady-state response deficits in Fragile X Syndrome implicate deficits in stimulus representation maintenance and GABAergic modulation. Neurology. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2025.01.29.25321365\u003c/span\u003e\u003cspan address=\"10.1101/2025.01.29.25321365\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlmos-Serrano JL, Corbin JG, Burns MP. The GABA\u003csub\u003eA\u003c/sub\u003e Receptor Agonist THIP Ameliorates Specific Behavioral Deficits in the Mouse Model of Fragile X Syndrome. Dev Neurosci. 2011;33(5):395\u0026ndash;403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000332884\u003c/span\u003e\u003cspan address=\"10.1159/000332884\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlmos-Serrano JL, Paluszkiewicz SM, Martin BS, Kaufmann WE, Corbin JG, Huntsman MM. Defective GABAergic Neurotransmission and Pharmacological Rescue of Neuronal Hyperexcitability in the Amygdala in a Mouse Model of Fragile X Syndrome. J Neurosci. 2010;30(29):9929\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1523/JNEUROSCI.1714-10.2010\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.1714-10.2010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOvid Therapeutics, Inc. Investigator\u0026rsquo;s Brochure. Ovid Therapeutics, Inc; 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePedapati EV, Schmitt LM, Ethridge LE, Miyakoshi M, Sweeney JA, Liu R, Smith E, Shaffer RC, Dominick KC, Gilbert DL, Wu SW, Horn PS, Binder DK, Lamy M, Axford M, Erickson CA. Neocortical localization and thalamocortical modulation of neuronal hyperexcitability contribute to Fragile X Syndrome. Commun Biology. 2022;5(1):442. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s42003-022-03395-9\u003c/span\u003e\u003cspan address=\"10.1038/s42003-022-03395-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePirker S, Schwarzer C, Wieselthaler A, Sieghart W, Sperk G. GABAA receptors: Immunocytochemical distribution of 13 subunits in the adult rat brain. Neuroscience. 2000;101(4):815\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0306-4522(00)00442-5\u003c/span\u003e\u003cspan address=\"10.1016/s0306-4522(00)00442-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR Core Team. (2024). \u003cem\u003eR: A Language and Environment for Statistical Computing\u003c/em\u003e [Computer software]. R Foundation for Statistical Computing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org\u003c/span\u003e\u003cspan address=\"https://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRandolph C, Tierney MC, Mohr E, Chase TN. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): Preliminary clinical validity. J Clin Exp Neuropsychol. 1998;20(3):310\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1076/jcen.20.3.310.823\u003c/span\u003e\u003cspan address=\"10.1076/jcen.20.3.310.823\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRedondo R, Duarte JM, Janz P, Bruns A, Gross S, Bainier M, Debilly S, Sch\u0026ouml;ppenthau S, Bielser T, Kuennecke B, Schulz J. Hyperactivation of distinct thalamic nuclei differentially impairs sleep physiology in rats. Springer Science and Business Media LLC; 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-6809066/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-6809066/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoid GH, Pomplun M. The stanford-binet intelligence scales. Volume 654. New York, NY, USA: The Guilford Press; 2012.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRose J, Woolsey C. The orbitofrontal cortex and its connections with the mediodorsal nucleus in rabbit, sheep and cat. Res Publ Assoc Res Nerv Ment Dis. 1948;27(1):210\u0026ndash;32. PubMed.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSemyanov A, Walker MC, Kullmann DM, Silver RA. Tonically active GABAA receptors: Modulating gain and maintaining the tone. Trends Neurosci. 2004;27(5):262\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tins.2004.03.005\u003c/span\u003e\u003cspan address=\"10.1016/j.tins.2004.03.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShields RH, Kaat AJ, McKenzie FJ, Drayton A, Sansone SM, Coleman J, Michalak C, Riley K, Berry-Kravis E, Gershon RC, Widaman KF, Hessl D. Validation of the NIH Toolbox Cognitive Battery in intellectual disability. Neurology. 2020;94(12):e1229\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/WNL.0000000000009131\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000009131\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSomervail R, Bufacchi RJ, Salvatori C, Neary-Zajiczek L, Guo Y, Novembre G, Iannetti GD. Brain Responses to Surprising Stimulus Offsets: Phenomenology and Functional Significance. Cereb Cortex. 2022;32(10):2231\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cercor/bhab352\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhab352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSomervail R, Perovic S, Bufacchi RJ, Caminiti R, Iannetti D. (2025). \u003cem\u003eA two-system theory of sensory-evoked brain responses\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSomervail R, Zhang F, Novembre G, Bufacchi RJ, Guo Y, Crepaldi M, Hu L, Iannetti GD. Waves of Change: Brain Sensitivity to Differential, not Absolute, Stimulus Intensity is Conserved Across Humans and Rats. Cereb Cortex. 2021;31(2):949\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cercor/bhaa267\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhaa267\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSt\u0026oacute;rustovu S\u0026Iacute;, Ebert B. Pharmacological Characterization of Agonists at δ-Containing GABAA Receptors: Functional Selectivity for Extrasynaptic Receptors Is Dependent on the Absence of γ2. J Pharmacol Exp Ther. 2006;316(3):1351\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1124/jpet.105.092403\u003c/span\u003e\u003cspan address=\"10.1124/jpet.105.092403\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTanabe T, Iino M, Takagi SF. Discrimination of odors in olfactory bulb, pyriform-amygdaloid areas, and orbitofrontal cortex of the monkey. J Neurophysiol. 1975;38(5):1284\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalsh JK, Deacon S, Dijk D-J, Lundahl J. The Selective Extrasynaptic GABAA Agonist, Gaboxadol, Improves Traditional Hypnotic Efficacy Measures and Enhances Slow Wave Activity in a Model of Transient Insomnia. Sleep. 2007;30(5):593\u0026ndash;602. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/sleep/30.5.593\u003c/span\u003e\u003cspan address=\"10.1093/sleep/30.5.593\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, Ethridge LE, Mosconi MW, White SP, Binder DK, Pedapati EV, Erickson CA, Byerly MJ, Sweeney JA. A resting EEG study of neocortical hyperexcitability and altered functional connectivity in fragile X syndrome. J Neurodevelopmental Disorders. 2017;9(1):11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s11689-017-9191-z\u003c/span\u003e\u003cspan address=\"10.1186/s11689-017-9191-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeintraub S, Bauer PJ, Zelazo PD, Wallner-Allen K, Dikmen SS, Heaton RK, Tulsky DS, Slotkin J, Blitz DL, Carlozzi NE, Havlik RJ, Beaumont JL, Mungas D, Manly JJ, Borosh BG, Nowinski CJ, Gershon RC. I. NIH Toolbox Cognition Battery (CB): Introduction and pediatric data. Monogr Soc Res Child Dev. 2013;78(4):1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mono.12031\u003c/span\u003e\u003cspan address=\"10.1111/mono.12031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYarita H, Iino M, Tanabe T, Kogure S, Takagi SF. A transthalamic olfactory pathway to orbitofrontal cortex in the monkey. J Neurophysiol. 1980;43(1):69\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/jn.1980.43.1.69\u003c/span\u003e\u003cspan address=\"10.1152/jn.1980.43.1.69\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neurodevelopmental-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jndd","sideBox":"Learn more about [Journal of Neurodevelopmental Disorders](http://jneurodevdisorders.biomedcentral.com/)","snPcode":"11689","submissionUrl":"https://submission.nature.com/new-submission/11689/3","title":"Journal of Neurodevelopmental Disorders","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7871579/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7871579/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eFragile X syndrome (FXS) lacks FDA-approved treatments despite various small molecules contributing to phenotypic rescue in the \u003cem\u003eFMR1\u003c/em\u003e knockout (KO) mouse model. Translation from the mouse model has been hampered by phenotypic heterogeneity that contributes to participation barriers among participants who are most affected and may be unable to regularly visit the research laboratory. The current study utilized a crossover design to test the acute neural and behavioral effects of a single 10mg dose of gaboxadol and the reliability of electroencephalography (EEG) and behavioral data collected in participant homes compared to the clinic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eTen adult males with full mutation FXS completed four blinded dosing visits (two placebo, two gaboxadol), with two occurring in-home and two in-lab. Pre- and post-dose assessments included resting high-density EEG, an auditory chirp paradigm, RBANS List Learning, and NIH Toolbox Cognition Battery subtests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eNo serious adverse events were reported. Compared with placebo, gaboxadol increased theta and alpha band power, with no interaction between collection environment (home vs. lab). Additionally, gaboxadol increased the proportion of electrodes with detectable low-frequency peaks and slowed the peak frequency. There were no effects on auditory-evoked measures or NIH Toolbox, with only a marginal effect on RBANS List Learning. An analysis of pre-dose EEG found reliability estimates across testing locations for all tested resting power and behavioral measures that were similar to in-lab reliability estimates found in the literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eSingle-dose gaboxadol augmented theta and alpha power in FXS during resting EEG, similar to previous findings in the typically developing population and in the \u003cem\u003eFMR1 \u003c/em\u003eKO, without normalizing gamma abnormalities, altering auditory-evoked responses, or contributing to behavioral change. These results did not significantly differ between the home and lab settings, supporting the feasibility of in-home data collection for clinical trials in FXS, including those that use complex measures such as EEG as endpoints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: \u003c/strong\u003eclinicaltrials.gov, NCT06334419, Registration Date: March 8, 2024\u003c/p\u003e","manuscriptTitle":"Gaboxadol Increases Resting Theta and Alpha Power without Affecting Evoked Responses in Fragile X Syndrome in a Home-based Setting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-24 14:54:51","doi":"10.21203/rs.3.rs-7871579/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-10T00:16:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-09T21:38:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-09T19:47:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161825580156059625870894804580644181505","date":"2025-12-02T15:10:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328832970854249530344238092934357507504","date":"2025-11-08T02:12:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-26T18:51:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-20T13:38:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-20T13:36:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neurodevelopmental Disorders","date":"2025-10-15T20:38:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neurodevelopmental-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jndd","sideBox":"Learn more about [Journal of Neurodevelopmental Disorders](http://jneurodevdisorders.biomedcentral.com/)","snPcode":"11689","submissionUrl":"https://submission.nature.com/new-submission/11689/3","title":"Journal of Neurodevelopmental Disorders","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5378a65-2e42-48e8-95a1-75e891d243ca","owner":[],"postedDate":"October 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-25T07:40:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-24 14:54:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7871579","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7871579","identity":"rs-7871579","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}