Methylphenidate enhances a frontoparietal-dominant brain state improving cognitive performance

Methylphenidate enhances a frontoparietal-dominant brain state improving cognitive performance | 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 Methylphenidate enhances a frontoparietal-dominant brain state improving cognitive performance Weizheng Yan, Şükrü Barış Demiral, Dardo Tomasi, Rui Zhang, Peter Manza, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4096379/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Methylphenidate (MP) is a widely used stimulant medication for the treatment of attention deficit hyperactivity disorder (ADHD) that enhances brain dopamine signaling and improves attention. However, how dopamine stimulation alters brain state dynamics to support improved attention during task performance is still unclear. Methods We employed a multimodal neuroimaging approach combining positron emission tomography, functional magnetic resonance imaging, and behavioral tests, to discover associations between dopamine signaling, brain activity, and cognition. Multimodal images were collected from 37 healthy adults under a single-blind, counterbalanced, placebo-controlled crossover study. Dynamic functional analysis was used to compare the alterations in dynamic features of brain states before and after MP. Subsequently, we analyzed the correlation between these brain state changes and baseline striatal D1 and D2 dopamine receptor (D1R, D2R) availability. We then examined alterations in dynamic brain states and their effects on attention performances. Results The results showed that MP primarily affected frontoparietal-dominant activated (FPN+), somatomotor-dominant activated (SOM+), and visual-dominant suppressed (VIS-) brain states. Specifically, the dwell time and fractional occupancy exhibited significant increases within the FPN + and VIS- while an opposite trend within the SOM+. Furthermore, the increase of dwell time in FPN+, which was positively correlated with baseline striatal D1R availability, was also associated with quicker response in the 2-ball-track task, but not significant for the 3-ball-track task. Conclusions The findings suggest that MP’s enhancement of brain states with FPN + and VIS- while decreasing SOM+, in part through D1R signaling might underlie the MP’s improvement of attention for low cognitive effort tasks in healthy populations. Psychiatry Nuclear Medicine & Medical Imaging Personalized Medicine Computational Neuroscience Methlyphenidate dynamic functional analysis attention fMRI PET dopamine Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Methylphenidate (MP) is a stimulant drug that elevates extracellular dopamine (DA) levels in the brain by blocking the DA transporters ( 1 – 6 ). MP is primarily prescribed for Attention-Deficit/Hyperactivity Disorder (ADHD), but it is also misused as a cognitive enhancer by individuals seeking to improve cognitive performance ( 7 ). However, the actual capabilities and mechanisms of MP as a cognitive-enhancing drug remain a subject of debate ( 8 – 10 ). Bowman et al. recently observed that the so-called "smart drugs" such as MP boost motivation but reduce the quality of effort needed for solving complex problems ( 9 ). Indeed, PET imaging studies of brain dopamine activity showed that MP increased cognitive motivation, particularly in individuals with lower dopamine signaling (measured as synthesis capacity) ( 8 ). In individuals with ADHD, impaired dopaminergic signaling, which is associated with reduced motivation ( 11 ), was ameliorated by MP-induced enhancement of DA, concomitantly with behavioral improvement ( 12 ). These studies collectively suggest that dopamine stimulants such as MP might be most effective in individuals with dopamine deficiencies. However, these studies have not provided a thorough characterization of MP’s downstream effects on brain functional activity. This limitation has resulted in the lack of a clear, evidence-based pathway linking dopamine levels to brain function and, subsequently, cognitive performance. Our previous multimodal study in healthy controls ( 13 ) revealed that after stimulation with MP, brain activity (as measured by the fractional amplitude of low-frequency fluctuations (fALFF)) increased in association cortices and decreased in sensorimotor cortices. Additionally, the within-network resting state functional connectivity strength decreased more in sensorimotor than in association cortices. These results suggest that MP activates association networks while inhibiting sensorimotor networks. However, the measures of fALFF or of static connectivity assume that brain functional connectivity is constant over time, potentially missing additional dynamic patterns of brain function. Incorporating dynamic functional connectivity analysis allows for a more comprehensive elucidation of MP’s impact on brain function. Several studies have provided evidence that brain state dynamics are hierarchically organized in time ( 14 – 16 ). Therefore, a dynamic analytical approach tailored to capturing the brain network’s reorganization and transitions provides relevant information, such as fractional occupancy and dwell time( 16 ), pertinent to brain activity alterations. There is growing interest in utilizing dynamic models ( 17 , 18 ) to examine the impact of MP on modifying brain dynamic processes. For example, Cai et al. used Bayesian dynamical system modeling of resting-state fMRI and revealed that abnormal brain dynamics observed in ADHD were remediated by MP ( 17 ). In this multimodal study, we aimed to employ dynamic analytical methods to explore how DA receptor (D1R and D2R) availability and striatal DA levels impact brain dynamics and consequently affect cognitive and attention performance in a healthy population. The study has three specific aims: ( 1 ) to analyze the effects of MP on functional dynamics in healthy individuals; ( 2 ) to examine the relationship between changes in dynamic features and attention performance; and ( 3 ) to investigate the correlation between alterations in functional dynamics and baseline DA receptor availability. MATERIALS and METHODS As shown in Fig. 1 , we used PET and fMRI, along with behavioral tests, to investigate the relationships between neurotransmitters, brain activity, and cognitive performance. Thirty-seven participants were enrolled in a single-blind, placebo-controlled, crossover study. Each participant underwent imaging sessions before and after oral administration of 60 mg MP or placebo in a randomly counterbalanced order. We analyzed changes in dynamic brain features (e.g., fractional occupancy, dwell time, and appearance rate) associated with MP administration. In addition, we examined the relationship between these dynamic changes, DA receptor availability, and performance on a visual attention task. Figure 1 . Study design and pipeline for functional dynamic analysis. Participants and study design Data from 37 healthy adults (24 males, 13 females, aged 22–64 years) were included. All participants provided written informed consent, and the study was approved by the Institutional Review Board Committee of the National Institutes of Health. Participants were excluded if they had a history of substance misuse or dependence (other than nicotine), psychiatric disorders, neurological disease, medical conditions that may alter cerebral function (i.e., cardiovascular, endocrinological, oncological, or autoimmune diseases), current use of prescribed or over-the-counter medications, and/or head trauma with loss of consciousness > 30 minutes. Detailed demographic information is shown in Table 1 . Table 1. The demographic characteristics of the participants MP administration and PET acquisition : PET scans were used to measure the availability of D1R using [ 11 C]NNC-112 and D2R using [ 11 C]raclopride. Participants were imaged using two scanners based on availability: a Siemens AG High Resolution Research Tomography (HRRT) scanner used for 17 individuals (including 7 females), and a Siemens AG Biograph PET/CT scanner used for 20 individuals (including 6 females). Specifically, all [ 11 C]NNC-112 scans were scheduled at 10 AM under baseline conditions. [ 11 C]raclopride scans were conducted on two separate occasions for each participant: one performed one hour after oral placebo pill and the other performed one hour after 60 mg oral MP, in a single-blind manner with counterbalanced session order. The two [ 11 C]raclopride scans were uniformly scheduled at 1 pm and performed on the same scanner for each individual to ensure consistency. The technical details for scan procedures were as previously published ( 19 ). Briefly, the [ 11 C]NNC-112 imaging began immediately after injection of a maximum dose of 555 MBq and was followed by a series of 21 dynamic emission scans from the time of injection to 90 minutes post-injection. The [ 11 C]raclopride imaging was started after an injection of a maximum dose of 370 MBq followed by a series of 22 dynamic emission scans performed from the time of injection to 60 minutes post-injection. Before analysis, all dynamic emission scan images underwent a rigorous evaluation by one of the investigators (SBD) to ensure the exclusion of any images compromised by motion artifacts or misplacement. PET modeling, harmonization, and striatum extraction The MAGIA toolbox( 20 ) was employed for PET modeling. MAGIA consisted of frame-alignment (motion-correction, using the middle image of the image volume time series as master image for the alignment) and co-registration with the individual brain MRI (or a PET template if MRI image is missing), and a quality report for image quality evaluation. To mitigate scanner-specific effects between HRRT and PET/CT, we used ComBat ( 21 ), a data harmonization approach, to perform voxel-level harmonization independently for each tracer in the PET assessments to ensure data consistency between the two PET scanners. The ComBat model included age, gender, and race as covariates. For voxel-wise analysis, a subcortical atlas ( 22 ) consisting of 32 subcortical regions was used to extract voxels in the striatum, including voxels in the putamen, nucleus accumbens (NAc), and caudate. For ROI analysis, the same subcortical atlas was used to extract and average striatal ROIs. Details of the 32-ROI subcortical atlas and the striatum mask are displayed in Supplementary File S1 . MRI acquisition : Approximately 180 to 300 minutes after MP or placebo administration, participants underwent structural and resting-state fMRI using a 3.0T 32-channel Siemens Prisma scanner. To acquire resting fMRI time series, a multi-echo, multiband EPI sequence was used: multiband factor = 3, anterior-posterior phase encoding, TR = 891 ms, echo times = 16, 33, and 48 ms, flip angle = 57 deg, 45 slices with 2.9 × 2.9 × 3.0mm voxels and 520 time points while the participant relaxed with their eyes open (total acquisition time = 8 min). A fixation cross was presented on a black background under dimmed room lighting using a liquid-crystal display screen (BOLDscreen 32, Cambridge Research Systems; UK). The 3D MP-RAGE (TR/TE = 2400/2.24 ms, FA = 8 deg) and variable flip angle turbo spin-echo (Siemens SPACE; TR/TE = 3200/564 ms) pulse sequences were used to acquire high-resolution anatomical brain images with 0.8mm isotropic voxels field-of-view (FOV) = 240 × 256 mm, matrix = 300 × 320, and 208 sagittal slices. Functional MRI processing : Resting-state fMRI was preprocessed using fMRIPrep ( 23 ) and in-house codes. Specifically, the fMRIPrep pipeline was used for multi-echo-multi-band optimization( 24 ), gradient distortion correction, rigid body realignment, field map processing, and spatial normalization to standard MNI space. For post-processing steps, spatial smoothing was applied with a Full Width at Half Maximum (FWHM) of 5 mm. The fMRI data were filtered with a bandpass filter ranging from 0.01 to 0.08 Hz. The data also underwent a rigorous detrending process and regression analysis to mitigate the influence of six motion-related and three anatomical-related nuisance variables. Time points were excluded if the volume-to-volume BOLD signal met the following criteria: DVARS > 150 or framewise displacement (FD) > 0.5 mm. Time series for 200 cortical regions ( 25 ) and 32 subcortical regions ( 22 ) were then extracted and demeaned. Details of the two atlases are shown in Supplementary File S1. Dynamic functional analysis We followed the methodology established by Cornblath et al. ( 16 , 26 ) in which the fMRI time courses for all subjects under the MP and placebo were concatenated in time. A k-means clustering algorithm was then used to identify clusters of brain activation patterns or states. The Pearson correlation coefficient was used as a metric to measure the distance in the k-means. The clustering process was repeated 50 times with random initialization to select the best partitions of the data. The optimal number of clusters (k) was selected based on the elbow criterion. Specifically, we plotted the explained variance curve from k = 3 to k = 22 and identified the inflection point or "elbow". The inflection point was consistently observed between 4 and 6 for different partitions ( Supplementary file S2 ). Given the increased k beyond 6 resulted in less than 1% variance gain, the k in this study was set at 6, taking into account its balanced and interpretable nature. To strengthen the robustness of the partitions, we independently replicated the clustering process ten times and compared the Adjusted Mutual Information (AMI) across the ten resulting partitions. The partition with the highest cumulative AMI relative to the others was selected for subsequent analysis ( Supplementary file S3 ). N-ball-track task We used a blocked visual attention paradigm ( 27 ) focusing on sustained attention and visual indexing. Specifically, a limited set of visual objects is marked for rapid attentional processing. Each TRACK epoch, lasting one minute, consists of five tracking and response intervals ( Supplementary File S4 ). Briefly, a subset of balls (2 or 3 out of 10) is highlighted, followed by their random movement in the visual field. Participants were required to fixate on the central cross and to track these target balls as they moved. At the end of the tracking periods, the balls stopped moving, a new set of balls was highlighted, and participants were instructed to press a button if the highlighted balls were the target balls. Each "DO NOT TRACK" or control epoch consisted of similar one-minute intervals with no ball highlighting. Participants passively watched the balls move and stop. Each task variant (with 2 or 3 balls) contains three cycles of "TRACK" and "DO NOT TRACK" epochs, totaling 6 minutes and 10 seconds. The tasks were displayed using MRI-compatible goggles connected to a computer. We recorded response time and hitting accuracy via button presses and synchronized the paradigm with the fMRI acquisition using a scanner trigger signal. Of note, the n-ball-track task was performed during fMRI, but here we only use the behavioral data from this task. All dynamic functional analysis was performed on resting state fMRI data that were acquired immediately after the VA task. RESULTS MP changes brain dynamics in healthy adults As shown in the radial plots of Fig. 2 a and b, six cluster centroids, each representing a brain state, were identified. The name of each brain state was assigned by calculating the cosine similarity with eight predefined resting state networks: Frontal Parietal Network (FPN), Limbic Network (LIM), Ventral Attention Network (VAT), Dorsal Attention Network (DAT), Somatomotor Network (SOM), Visual Network (VIS), Default Mode Network (DMN), and Subcortical Network (SUB) ( 16 , 26 , 28 ). Given the regional time courses of each scan were demeaned during fMRI preprocessing, positive centroid values reflect higher than average activation, while negative centroid values reflect lower than average activation. Three pairs of anticorrelated brain states were discovered: FPN+, FPN-, SOM+, SOM-, VIS+, and VIS-. Specifically, the FPN + brain state consists mainly of high amplitude of FPN and DAT, accompanied by low amplitude of VIS. The SOM + brain state is mainly composed of the high amplitude of SOM and low amplitude of DMN. The VIS- brain state is mainly formed by the low amplitude of VIS, and high amplitude of DMN. To further assess the hierarchical relationships between these brain states, Pearson correlation coefficients were calculated for all centroid pairs ( Supplementary file S5 ). After identifying the brain states, we assessed the changes in several dynamic metrics between placebo and MP administration. These metrics include transition probability which refers to the probability of transitioning from one brain state to another over time; fractional occupancy , which describes the proportion of time that the brain spends in a particular state out of the total observation time; dwell time , which refers to the duration that the brain remains in a single state before transitioning to another; and appearance rate , which indicates how frequently a particular state appears throughout the observation period. As shown in Fig. 2 c, after MP administration, there was a higher tendency for brain states transiting into FPN + and VIS-, with a lower probability of transiting into the SOM+. Figure 2 d shows the change in dynamic characteristics after MP administration. Specifically, after MP, fractional occupancy increased in FPN+ (P < 0.05, uncorrected) and VIS- (P < 0.05, uncorrected) whereas SOM + decreased (P < 0.05, uncorrected). dwell time increased for the FPN+ (P < 0.05, uncorrected) and VIS- (P < 0.05, corrected). There were no significant differences in the appearance rate between placebo and MP administration. Figure 2 . Dynamic functional analysis results MP-induced FPN + fractional occupancy and dwell time changes were associated with faster response in ball-track tasks Among the 37 subjects, 29 participants completed 2-ball-track task and 31 participants completed 3-ball-track task under both placebo and MP conditions. We used response time and hitting accuracy as metrics to assess the speed and accuracy of task completion. As shown in Fig. 3 a, generally, after MP administration, there was a trend towards improvement in accuracy and a reduction in response time. The improvements in accuracy reached significance for the 2-ball-track task ( mean ± sd : PL: 90.3% ± 14.4%, MP: 97.0% ± 4.9% p = 0.023) though for the more demanding 3-ball-track task they did not reach significance ( mean ± sd : PL:89.0% ± 16.4%, MP: 94.8% ± 10.1%, p = 0.133). We further analyzed how changes in brain dynamics caused by MP affected performance in the n-ball-track task. Given that significant changes in brain dynamics were observed after MP administration, we conducted a correlation analysis between changes in task performance and the dynamic characteristics significantly altered by MP (including fractional occupancy of FPN+, SOM+, VIS-, and dwell time of FPN + and VIS-). The results indicated a significant correlation only between changes in FPN + and changes in response time. Specifically, as shown in Fig. 3 b, in the 2-ball-track task, increases of dwell time (r=-0.42, p = 0.02) and fractional occupancy (r=-0.47, p = 0.01) in FPN + significantly correlated with the changes in response time. However, in the 3-ball-track task, the correlation between FPN + change and response time change was not significant. Detailed correlation coefficients and p-values can be found in Supplementary Table S1 . Figure 3 . Analysis of the relationship between brain dynamics, performance on ball-track tasks, and dopamine receptor availability. Baseline D1R availability in putamen was correlated with dwell time changes in FPN + induced by MP We hypothesized that MP alters brain functional activity by increasing striatal dopamine levels thus affecting behavioral performance and thus predicted that individual differences in DA receptor availability would modulate the responses to MP. To test this, we first conducted a voxel-level correlation analysis between striatal DA receptor availability features and fMRI-derived dynamic features. The DA receptor availability features are striatal D1R availability after placebo (baseline), striatal D2R availability after placebo (baseline), and after MP. The fMRI-derived dynamic features are FPN + dwell time, FPN + fractional occupancy, VIS- dwell time, VIS- fractional occupancy, and SOM + fractional occupancy. These dynamic features were selected because they exhibited significant group differences. As shown in Fig. 3 c, the dwell time change of FPN + exhibited strong positive correlation with striatal D1R availability. The fractional occupancy change of VIS- exhibited positive correlation with D2R availability after placebo whereas the fractional occupancy change of FPN + exhibited a positive correlation with D2R after MP. Detailed voxel-based correlation can be found in Supplementary Figure S6-7. The correlations between the ROI-level measures of D1,2R availability extracted using a subcortical atlas ( 22 ) and the dynamic features are shown in Fig. 3 d (details in Supplementary Table S2-5 ). The results showed significant correlation between FPN + dwell time change and placebo D1R availability in the right posterior putamen (r = 0.38, p = 0.027), left NAc-shell (r = 0.34, p = 0.037), and right posterior caudate (r = 0.33, p = 0.05). Although not significant, the correlations in the opposite hemisphere showed the same trend with left posterior putamen (r = 0.31, p = 0.06), right NAc-shell (r = 0.26, p = 0.11), and left posterior caudate (r = 0.23, p = 0.16). Additionally, the FPN + fractional occupancy change showed significant correlation with MP D2R availability in the left NAc-shell (r = 0.32, p = 0.05). DISCUSSION Establishing the links between DA signaling, brain functional dynamics, and cognitive performance allowed us to reveal the effects of MP on brain at multiple levels. In this research, we employed a comprehensive set of measurements including PET, fMRI, and cognitive performance. Dynamic functional analysis revealed a significant enhancement of FPN + and VIS- following oral administration of MP. The change of dynamic features after MP showed significant correlations with striatal DA receptor availability. Furthermore, the change in FPN + dwell time showed a significant positive correlation with response speed in the 2-ball-track task, though the correlation was not significant with response speed in the 3-ball-track task, consistent with the limited benefits of MP in cognition in healthy individuals ( 29 ). In summary, the use of dynamic functional methodologies allowed us to establish the connection of striatal D1R availability to task performances after MP. Different from traditional functional connectivity methods that primarily emphasize the synchronicity between brain regions, dynamic functional connectivity reveals changes in characteristics such as dwell time, appearance rate, and fractional occupancy in different functional network activities before and after drug administration. From a dynamical perspective, the most salient feature observed was an increase of fractional occupancy and dwell time in FPN + and VIS-, and a decrease in fractional occupancy of SOM+. These findings align with our prior findings revealing increased activity in association networks but decreased activity in sensorimotor networks after MP ( 13 ). Regarding the specifics of dynamic functional states, the DMN consistently appeared in an opposing direction to the SOM and VIS networks. Specifically, when the amplitude of the DMN network was high, the corresponding amplitude of the SOM or VIS was low, and vice versa. These brain states might be influenced by our use of resting-state data for analysis and different brain states might emerge while engaged in various tasks. The FPN + brain state primarily comprises high-amplitude FPN and DAT networks in juxtaposition with the VIS network. Since the FPN and DAT are associated with cognitive processing, an increase of dwell time and fractional occupancy in FPN + suggests a brain state more likely to be conducive to cognitive efforts and one that is less distracted by visual stimuli. Dixon et al investigated the FPN’s functional connectivity under varying cognitive demands and identified two distinct FPN subsystems. One subsystem showed stronger connectivity with the DMN, indicating greater involvement in introspective processes. The other subsystem had stronger connections with the DAT, suggesting a key role in visuospatial perceptual attention ( 30 ). Our study used the n-ball-track tasks to differentiate task complexity. Following MP administration, participants showed improved performance in the 2-ball-track task (significant for accuracy and trend for response time), which correlated positively with increased FPN + dwell time. However, this correlation wasn't significant in the more complex 3-ball-track task, suggesting that while MP-induced brain changes can boost cognitive effort ( 31 ), their effectiveness in highly demanding cognitive tasks is limited. Notably, the placebo accuracy with the 3-ball-track task was almost 100% which could have limited our ability to detect any improvement whereas the placebo accuracy for the 2-ball-track evinced lower performance for some participants which might have reflected their disinterest in this very simple task. This is consistent with Bowman et al.'s findings, where 'smart drugs' increased motivation but not the quality of effort in complex problem-solving ( 9 ), and with studies showing that stimulants are particularly effective in improving performance by enhancing motivation to perform the task ( 32 ). MP’s therapeutic effects are in part due to amplification of DA signals and the inter-subject variability reported in the response to MP is due in part to differences in DA tone and receptor availability between individuals ( 33 ). Thus, measuring baseline dopamine receptor availability could theoretically predict changes in brain activity caused by MP, a hypothesis that our experiment confirmed. Our correlation analysis showed that the baseline level of striatal D1R availability positively correlated with the increase in MP-induced FPN + dwell time. Interestingly, the increase in FPN + was also associated with striatal D2R availability (left NAc-shell, r = 0.32, p = 0.05) but only for the measure after MP (not after placebo). Because MP reduces striatal D2R availability by increasing binding competition with [ 11 C]raclopride, as synaptic dopamine is increased ( 34 ), the correlation with D2R after MP was significantly correlated with MP-induced increase in FPN + fractional occupancy but not for placebo suggests an opposing effect for D2R with DA stimulation. These results pinpoint to a counteracting role of striatal D1R and D2R in MP-induced increase in the predominance of the FPN + brain state. Similarly opposing patterns for striatal D1R and D2R have been noted for other behaviors ( 35 ). The association of MP effects in brain state dynamics with dopamine receptor availability could explain why MP is beneficial for ADHD, which is characterized by deficits in dopaminergic singling ( 31 ). Our results also align with previous findings that participants with higher striatal DA synthesis capacity showed greater willingness to exert effort, while MP increased cognitive motivation more in participants with lower synthesis capacity ( 8 ). Our experiment established a significant relationship between D1R receptor availability after placebo and D2R receptor availability after MP and the predominance of FPN + brain state, providing valuable guidance for selecting individuals who would be more likely to benefit from MP to advance personalized treatment approaches. The study has limitations and potential directions for future research. First, our multimodal study was conducted on healthy adults, and considering MP is a frontline medication for ADHD, it's crucial to collect and compare multimodal data in ADHD participants. Second, due to equipment limitations, our data collection for PET and fMRI post-MP or placebo administration was conducted separately. This led to the fMRI data being collected approximately 180–300 minutes after medication, potentially when drug effects were reduced. Further studies using simultaneous PET/MR imaging might address this issue. Third, our study predominantly focuses on the D1R and D2R in the striatum whereas cortical dopamine receptors are engaged in the attention process and involved with MP’s effects. This will require studies with PET radioligands with stronger signals in cortical regions. CONCLUSION This study investigated the relationship between dopamine D1R and D2R availability, the effects of MP on performance in a visual attention task, and brain state dynamics. The experiment established a correlation between baseline D1R and measures of D2R after MP and the changes induced by MP in brain functional dynamics that favor FPN+, a brain state characterized by dominance of FPN and DAT and VIS suppression, as well as a correlation between MP-induced changes in FPN + and performance on an attention task. This study also presents a multi-level neuroimaging approach to investigate the effect of drugs, which has the potential for personalizing medication interventions by predicting individual variations in drug responses such as MP. Declarations COMPETING INTERESTS The authors declare no competing interests. FUNDING This work was accomplished with the support of the National Institute of Alcohol Abuse and Alcoholism (ZIAAA000550). AUTHOR CONTRIBUTIONS N.D.V., G.W., D.T. designed and implemented the original methylphenidate experiments and data collection. W.Y., S.B.D., R.Z., and P.M. preprocessed the fMRI and PET data. W.Y. and N.D.V. conceptualized the present study, performed the data analysis, and drafted the manuscript. W.Y. produced all the figures. W.Y., N.D.V, G.W., S.B.D., R.Z., D.T., P.M., contributed to the interpretation and discussion of the results. All authors reviewed, edited, and approved the final version. CODE AVAILABILITY All codes related to the dynamic analysis and regression analysis are shared on GitHub https://github.com/WizardYan/MP_PET_fMRI . References Butcher S, Liptrot J, Aburthnott G (1991) Characterisation of methylphenidate and nomifensine induced dopamine release in rat striatum using in vivo brain microdialysis. Neurosci Lett 122:245–248 Woods SK, Meyer JS (1991) Exogenous tyrosine potentiates the methylphenidate-induced increase in extracellular dopamine in the nucleus accumbens: a microdialysis study. Brain Res 560:97–105 During MJ, Bean AJ, Roth RH (1992) Effects of CNS stimulants on the in vivo release of the colocalized transmitters, dopamine and neurotensin, from rat prefrontal cortex. Neurosci Lett 140:129–133 Kuczenski R, Segal DS (1997) Effects of methylphenidate on extracellular dopamine, serotonin, and norepinephrine: comparison with amphetamine. J Neurochem 68:2032–2037 Volkow ND, Wang G-J, Fowler JS, Logan J, Gerasimov M, Maynard L et al (2001) Therapeutic Doses of Oral Methylphenidate Significantly Increase Extracellular Dopamine in the Human Brain. J Neurosci 21:RC121–RC121 Volkow ND, Wang G-J, Tomasi D, Kollins SH, Wigal TL, Newcorn JH et al (2012) Methylphenidate-Elicited Dopamine Increases in Ventral Striatum Are Associated with Long-Term Symptom Improvement in Adults with Attention Deficit Hyperactivity Disorder. J Neurosci 32:841–849 Teter CJ, McCabe SE, LaGrange K, Cranford JA, Boyd CJ (2006) Illicit Use of Specific Prescription Stimulants Among College Students: Prevalence, Motives, and Routes of Administration. Pharmacotherapy: J Hum Pharmacol Drug Therapy 26:1501–1510 Westbrook A, van den Bosch R, Määttä JI, Hofmans L, Papadopetraki D, Cools R et al (2020) Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work. Science 367:1362–1366 Bowman E, Coghill D, Murawski C, Bossaerts P (2023) Not so smart? Smart drugs increase the level but decrease the quality of cognitive effort. Sci Adv 9:eadd4165 Kortekaas-Rijlaarsdam AF, Luman M, Sonuga-Barke E, Oosterlaan J (2019) Does methylphenidate improve academic performance? A systematic review and meta-analysis. Eur Child Adolesc Psychiatry 28:155–164 Volkow ND, Wang GJ, Newcorn JH, Kollins SH, Wigal TL, Telang F et al (2011) Motivation deficit in ADHD is associated with dysfunction of the dopamine reward pathway. Mol Psychiatry 16:1147–1154 Nora DV, Gene-Jack W, Dardo T, Scott HK, Tim LW, Jeffrey HN et al (2012) Methylphenidate-Elicited Dopamine Increases in Ventral Striatum Are Associated with Long-Term Symptom Improvement in Adults with Attention Deficit Hyperactivity Disorder. J Neurosci 32:841 Manza P, Shokri-Kojori E, Demiral SB, Wiers CE, Zhang R, Giddens N et al (2022) Cortical D1 and D2 dopamine receptor availability modulate methylphenidate-induced changes in brain activity and functional connectivity. Commun Biol 5:514 Vidaurre D, Smith SM, Woolrich MW (2017) : Brain network dynamics are hierarchically organized in time. Proceedings of the National Academy of Sciences . 114:12827–12832 Breakspear M (2017) Dynamic models of large-scale brain activity. Nat Neurosci 20:340–352 Cornblath EJ, Ashourvan A, Kim JZ, Betzel RF, Ciric R, Adebimpe A et al (2020) Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands. Commun Biol 3:261 Cai W, Mizuno Y, Tomoda A, Menon V (2023) : Bayesian dynamical system analysis of the effects of methylphenidate in children with attention-deficit/hyperactivity disorder: a randomized trial. Neuropsychopharmacology Mizuno Y, Cai W, Supekar K, Makita K, Takiguchi S, Tomoda A et al (2022) Methylphenidate remediates aberrant brain network dynamics in children with attention-deficit/hyperactivity disorder: A randomized controlled trial. NeuroImage 257:119332 Zhang R, Manza P, Tomasi D, Kim SW, Shokri-Kojori E, Demiral SB et al (2021) : Dopamine D1 and D2 receptors are distinctly associated with rest-activity rhythms and drug reward. J Clin Investig. 131 Karjalainen T, Tuisku J, Santavirta S, Kantonen T, Bucci M, Tuominen L et al (2020) Magia: Robust Automated Image Processing and Kinetic Modeling Toolbox for PET Neuroinformatics. Front Neuroinform 14:3 Fortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA et al (2018) Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167:104–120 Tian Y, Margulies DS, Breakspear M, Zalesky A (2020) Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat Neurosci 23:1421–1432 Esteban O, Markiewicz CJ, Blair RW, Moodie CA, Isik AI, Erramuzpe A et al (2019) fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 16:111–116 DuPre E, Salo T, Ahmed Z, Bandettini PA, Bottenhorn KL, Caballero-Gaudes C et al (2021) TE-dependent analysis of multi-echo fMRI with* tedana. J Open Source Softw 6:3669 Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo X-N, Holmes AJ et al (2018) Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex 28:3095–3114 Singleton SP, Luppi AI, Carhart-Harris RL, Cruzat J, Roseman L, Nutt DJ et al (2022) Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain's control energy landscape. Nat Commun 13:5812 Rustichini A, Tomasi D, Volkow ND, Wang R, Telang F, Wang G-J et al (2009) : Dopamine Transporters in Striatum Correlate with Deactivation in the Default Mode Network during Visuospatial Attention. PLoS ONE. 4 Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M et al (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106:1125–1165 Repantis D, Bovy L, Ohla K, Kühn S, Dresler M (2021) Cognitive enhancement effects of stimulants: a randomized controlled trial testing methylphenidate, modafinil, and caffeine. Psychopharmacology 238:441–451 Dixon ML, De La Vega A, Mills C, Andrews-Hanna J, Spreng RN, Cole MW et al (2018) : Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proceedings of the National Academy of Sciences . 115:E1598-E1607 Zametkin AJ (2010) Dopamine Reward Pathway in Adult ADHD. JAMA 303:232–234 Ilieva IP, Farah MJ (2013) Enhancement stimulants: perceived motivational and cognitive advantages. Front Neurosci 7:198 Volkow ND, Fowler JS, Wang G-J, Ding Y-S, Gatley SJ (2002) Role of dopamine in the therapeutic and reinforcing effects of methylphenidate in humans: results from imaging studies. Eur Neuropsychopharmacol 12:557–566 Volkow ND, Wang G-J, Fowler JS, Logan J, Schlyer D, Hitzemann R et al (1994) Imaging endogenous dopamine competition with [11C]raclopride in the human brain. Synapse 16:255–262 Shen W, Flajolet M, Greengard P, Surmeier DJ (2008) Dichotomous Dopaminergic Control of Striatal Synaptic Plasticity. Science 321:848–851 Tables Table 1. The demographic characteristics of the participants Scanner 1: HRRT Scanner 2: PET/CT t(dt), P Age Min-Max 33–64 22–61 2.43( 35 ), 0.021 Mean ± SD 48.4(9.6) 37.6(12.5) Sex n, Female (%) 7(41%) 6(30%) 0.5037( 1 ), 0.48 BMI Min-Max 21–39 21–33 0.49( 35 ), 0.63 Mean ± SD 27.2(5.1) 27.9(3.5) IQ Min-Max 79–139 97–129 2.85( 35 ),0.007 Mean ± SD 122.4 109.6 Race n, White (%) 11 (65%) 5 (25%) - n, Black (%) 6 (35%) 10 (50%) - n, Asian(%) 0 (0%) 3 (15%) - n, Other(%) 0 (0%) 2 (10%) - BMI: body-mass index; IQ: intelligence quotient; SD: standard deviation. Additional Declarations The authors declare no competing interests. Supplementary Files Supplementaryfiles.docx Cite Share Download PDF Status: Posted Version 1 posted 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-4096379","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":279379344,"identity":"1c40e6d4-c7c3-45f8-b856-4c544c13feab","order_by":0,"name":"Weizheng Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYFACxgZmOPsDg0QCaVoYZxCnhYEBroWZh4GBsBaD483Nnwsq7tg1SCQ/e2y7wyKPgX/xMQm8Ws4cbDCeceZZcoNEmrlx7hmJYgaJZ2l4tZjdSGxI5m07nAz0hZl0bptEYoPEGWMDvFruP2w4zPsPpCX9m7QlUVpuMDY28zYctmOQyDGTZgRp4e8xfIBPi/2ZxGZmnmOHE9h43pRJ9gK1tEmwJeLVItl+/PFnnprD9vzs6dskfrbVJfbzHz5wAJ8WGEhsE0iAsNiIjE0GewZ+mNFwxigYBaNgFIwCCAAAbRBKJKlKJ9QAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0885-5631","institution":"National Institute on Alcohol Abuse and Alcoholism","correspondingAuthor":true,"prefix":"","firstName":"Weizheng","middleName":"","lastName":"Yan","suffix":""},{"id":279379345,"identity":"55478638-4695-403b-b932-6dd175857fe7","order_by":1,"name":"Şükrü Barış Demiral","email":"","orcid":"https://orcid.org/0000-0002-5288-603X","institution":"National Institute on Alcohol Abuse and Alcoholism","correspondingAuthor":false,"prefix":"","firstName":"Şükrü","middleName":"Barış","lastName":"Demiral","suffix":""},{"id":279379346,"identity":"b8a94721-b02c-4a8f-b1d1-be9230048683","order_by":2,"name":"Dardo Tomasi","email":"","orcid":"https://orcid.org/0000-0003-0183-5678","institution":"National Institute on Alcohol Abuse and Alcoholism","correspondingAuthor":false,"prefix":"","firstName":"Dardo","middleName":"","lastName":"Tomasi","suffix":""},{"id":279379347,"identity":"26e3f264-4ee9-4f45-97ad-29a5deafe297","order_by":3,"name":"Rui Zhang","email":"","orcid":"","institution":"National Institute on Alcohol Abuse and Alcoholism","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Zhang","suffix":""},{"id":279379348,"identity":"295cc9cf-1aa3-437f-a08a-5e205b620940","order_by":4,"name":"Peter Manza","email":"","orcid":"https://orcid.org/0000-0002-0791-357X","institution":"National Institute on Alcohol Abuse and Alcoholism","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Manza","suffix":""},{"id":279379349,"identity":"900d5667-7c80-41f2-b23d-e3d4f9fdc5d5","order_by":5,"name":"Gene-Jack Wang","email":"","orcid":"","institution":"National Institute on Alcohol Abuse and Alcoholism","correspondingAuthor":false,"prefix":"","firstName":"Gene-Jack","middleName":"","lastName":"Wang","suffix":""},{"id":279379350,"identity":"37f1082c-b2fd-4011-b78d-f148f4ad13a1","order_by":6,"name":"Nora D. Volkow","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3RIQvCQBTA8TcGswirL+lXOBno/DZ3CDPNIohJBOHSwLruF5gY1LbjQMuBVVjRYtZm0xOLlnM2wfvDCy/8eOEB2Gy/mSPGADVwgegFaSnzIMH3hOl5EPhMWtjJRTqU3WXFnZ+dVdgDf7ZBE2mnERWZkvF64vXRUdgHPEVGQpQi4siLOJNVgg5HNkYVkDKkS2Q1uJYj24SIOS+oJs3nFT9pHMyEU5GqWyOT3iBkmnD0mJlIVxyTYVQnO7nYX/iITX2Z5ybyHtXjlfzma/4XN2w2m+0vugPHbVDYVvKOoQAAAABJRU5ErkJggg==","orcid":"","institution":"National Institute on Alcohol Abuse and Alcoholism","correspondingAuthor":true,"prefix":"","firstName":"Nora","middleName":"D.","lastName":"Volkow","suffix":""}],"badges":[],"createdAt":"2024-03-14 00:46:22","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4096379/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4096379/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52785699,"identity":"8e2888e2-27f0-4720-b632-a56fff48b179","added_by":"auto","created_at":"2024-03-15 18:39:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":315138,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design and pipeline for functional dynamic analysis. \u003cstrong\u003e(A)\u003c/strong\u003e Study design. On one day, participants underwent a [\u003csup\u003e11\u003c/sup\u003eC]NNC-112 scan to assess D1R availability at baseline. Then, participants took the oral medication (either 60 mg MP or placebo) and underwent a [\u003csup\u003e11\u003c/sup\u003eC]raclopride scan to assess D2R availability, a resting state fMRI scan to assess brain activity, and a ball-track visual attention task. On the second day, the D2R, resting-state fMRI scans, and ball-track tasks were repeated after the other oral drug was given (MP/placebo session order was counterbalanced). \u003cstrong\u003e(B)\u003c/strong\u003e fMRI dynamic analysis pipeline: multi-echo-multi-band fMRI was preprocessed using fMRIPrep (23) and post-processed using in-house codes (see Methods section). The ROI time series were extracted from 7 cortical networks (200 ROIs (25)) and 1 subcortical network (32 ROIs (22)). The extracted ROI time series were concatenated across time and clustered using the k-means algorithm for identifying brain state centroids. Then, the dynamic characteristics are then derived and compared between pre- and post-MP.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4096379/v1/3f4717a688a21103d513c52d.png"},{"id":52785696,"identity":"cff97951-cfa7-4750-9b96-adffc55e2342","added_by":"auto","created_at":"2024-03-15 18:39:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":441860,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic analysis results. \u003cstrong\u003e(A)\u003c/strong\u003e Group average recurrent brain states are represented by the mean activation pattern across all subjects and conditions for each of the six clusters. \u003cstrong\u003e(B)\u003c/strong\u003e The rendered brain volumes of the six brain states. \u003cstrong\u003e(C)\u003c/strong\u003e T-test group comparison of transition probability between MP and PL. \u003cstrong\u003e(D)\u003c/strong\u003e Dynamic features comparison. \u003cem\u003eNotes\u003c/em\u003e: Comparisons in (c) and (d) were made using two-sided t-tests and p-values were corrected for Benjamini-Hochberg multiple comparisons where * represents P\u0026lt; 0.05, uncorrected, and ** represents p \u0026lt; 0.05, corrected.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4096379/v1/451d63239f7f8753b577266f.png"},{"id":52786131,"identity":"e65056bb-db01-497c-834f-73be31b3359f","added_by":"auto","created_at":"2024-03-15 18:47:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":152519,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the relationship between brain dynamics, performance on ball-track tasks, and dopamine receptor availability. \u003cstrong\u003e(A)\u003c/strong\u003e Comparison of ball-track performances between MP and Placebo groups. The response time and hitting accuracy were metrics for comparison. Values correspond to means and standard deviations. \u003cstrong\u003e(B)\u003c/strong\u003e Correlation between dynamic changes induced by MP with the ball-track task response time. \u003cstrong\u003e(C)\u003c/strong\u003e Voxel-based correlation analysis displays the voxels in the striatal that had significant correlations (p\u0026lt;0.05 with a cluster size of at least 30 spatially contiguous voxels) with the dynamic characteristic change of FPN+ induced by MP. The color bar represents the value of the correlation coefficients. (\u003cstrong\u003eD\u003c/strong\u003e) ROI-based correlation analysis showed that D1R availability in the striatum was positively correlated with the dwell time change of FPN+ after MP. The shaded area represents three times the variance. \u003cem\u003eNotes\u003c/em\u003e: pCAU-rh: right hemisphere posterior caudate; NAc-shell-lh: left hemisphere nucleus accumbens shell; pPUT-lh: left hemisphere posterior putamen.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4096379/v1/866214dc354d1ba920aed5d8.png"},{"id":52786575,"identity":"2bf6a5fd-cf66-4db9-84f6-02c79b10d7aa","added_by":"auto","created_at":"2024-03-15 18:52:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1442791,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4096379/v1/9c3026b5-3f6c-49c4-aacb-f39aeb07dcee.pdf"},{"id":52785700,"identity":"3daf8eeb-92bc-4edc-8450-114b2ff7f0fb","added_by":"auto","created_at":"2024-03-15 18:39:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7136401,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-4096379/v1/4d52802bcd329e54973ea235.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMethylphenidate enhances a frontoparietal-dominant brain state improving cognitive performance\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMethylphenidate (MP) is a stimulant drug that elevates extracellular dopamine (DA) levels in the brain by blocking the DA transporters (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). MP is primarily prescribed for Attention-Deficit/Hyperactivity Disorder (ADHD), but it is also misused as a cognitive enhancer by individuals seeking to improve cognitive performance (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, the actual capabilities and mechanisms of MP as a cognitive-enhancing drug remain a subject of debate (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Bowman et al. recently observed that the so-called \"smart drugs\" such as MP boost motivation but reduce the quality of effort needed for solving complex problems (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Indeed, PET imaging studies of brain dopamine activity showed that MP increased cognitive motivation, particularly in individuals with lower dopamine signaling (measured as synthesis capacity) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In individuals with ADHD, impaired dopaminergic signaling, which is associated with reduced motivation (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), was ameliorated by MP-induced enhancement of DA, concomitantly with behavioral improvement (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These studies collectively suggest that dopamine stimulants such as MP might be most effective in individuals with dopamine deficiencies. However, these studies have not provided a thorough characterization of MP\u0026rsquo;s downstream effects on brain functional activity. This limitation has resulted in the lack of a clear, evidence-based pathway linking dopamine levels to brain function and, subsequently, cognitive performance.\u003c/p\u003e \u003cp\u003eOur previous multimodal study in healthy controls (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) revealed that after stimulation with MP, brain activity (as measured by the fractional amplitude of low-frequency fluctuations (fALFF)) increased in association cortices and decreased in sensorimotor cortices. Additionally, the within-network resting state functional connectivity strength decreased more in sensorimotor than in association cortices. These results suggest that MP activates association networks while inhibiting sensorimotor networks. However, the measures of fALFF or of static connectivity assume that brain functional connectivity is constant over time, potentially missing additional dynamic patterns of brain function. Incorporating dynamic functional connectivity analysis allows for a more comprehensive elucidation of MP\u0026rsquo;s impact on brain function. Several studies have provided evidence that brain state dynamics are hierarchically organized in time (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Therefore, a dynamic analytical approach tailored to capturing the brain network\u0026rsquo;s reorganization and transitions provides relevant information, such as fractional occupancy and dwell time(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), pertinent to brain activity alterations. There is growing interest in utilizing dynamic models (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) to examine the impact of MP on modifying brain dynamic processes. For example, Cai et al. used Bayesian dynamical system modeling of resting-state fMRI and revealed that abnormal brain dynamics observed in ADHD were remediated by MP (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this multimodal study, we aimed to employ dynamic analytical methods to explore how DA receptor (D1R and D2R) availability and striatal DA levels impact brain dynamics and consequently affect cognitive and attention performance in a healthy population. The study has three specific aims: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to analyze the effects of MP on functional dynamics in healthy individuals; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) to examine the relationship between changes in dynamic features and attention performance; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) to investigate the correlation between alterations in functional dynamics and baseline DA receptor availability.\u003c/p\u003e"},{"header":"MATERIALS and METHODS","content":"\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, we used PET and fMRI, along with behavioral tests, to investigate the relationships between neurotransmitters, brain activity, and cognitive performance. Thirty-seven participants were enrolled in a single-blind, placebo-controlled, crossover study. Each participant underwent imaging sessions before and after oral administration of 60 mg MP or placebo in a randomly counterbalanced order. We analyzed changes in dynamic brain features (e.g., fractional occupancy, dwell time, and appearance rate) associated with MP administration. In addition, we examined the relationship between these dynamic changes, DA receptor availability, and performance on a visual attention task.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Study design and pipeline for functional dynamic analysis.\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\u003e \u003cstrong\u003eParticipants and study design\u003c/strong\u003e\u003c/p\u003e \u003cp\u003eData from 37 healthy adults (24 males, 13 females, aged 22\u0026ndash;64 years) were included. All participants provided written informed consent, and the study was approved by the Institutional Review Board Committee of the National Institutes of Health. Participants were excluded if they had a history of substance misuse or dependence (other than nicotine), psychiatric disorders, neurological disease, medical conditions that may alter cerebral function (i.e., cardiovascular, endocrinological, oncological, or autoimmune diseases), current use of prescribed or over-the-counter medications, and/or head trauma with loss of consciousness\u0026thinsp;\u0026gt;\u0026thinsp;30 minutes. Detailed demographic information is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e The demographic characteristics of the participants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/br\u003e\u003cp\u003e \u003cb\u003eMP administration and PET acquisition\u003c/b\u003e: PET scans were used to measure the availability of D1R using [\u003csup\u003e11\u003c/sup\u003eC]NNC-112 and D2R using [\u003csup\u003e11\u003c/sup\u003eC]raclopride. Participants were imaged using two scanners based on availability: a Siemens AG High Resolution Research Tomography (HRRT) scanner used for 17 individuals (including 7 females), and a Siemens AG Biograph PET/CT scanner used for 20 individuals (including 6 females). Specifically, all [\u003csup\u003e11\u003c/sup\u003eC]NNC-112 scans were scheduled at 10 AM under baseline conditions. [\u003csup\u003e11\u003c/sup\u003eC]raclopride scans were conducted on two separate occasions for each participant: one performed one hour after oral placebo pill and the other performed one hour after 60 mg oral MP, in a single-blind manner with counterbalanced session order. The two [\u003csup\u003e11\u003c/sup\u003eC]raclopride scans were uniformly scheduled at 1 pm and performed on the same scanner for each individual to ensure consistency. The technical details for scan procedures were as previously published (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Briefly, the [\u003csup\u003e11\u003c/sup\u003eC]NNC-112 imaging began immediately after injection of a maximum dose of 555 MBq and was followed by a series of 21 dynamic emission scans from the time of injection to 90 minutes post-injection. The [\u003csup\u003e11\u003c/sup\u003eC]raclopride imaging was started after an injection of a maximum dose of 370 MBq followed by a series of 22 dynamic emission scans performed from the time of injection to 60 minutes post-injection. Before analysis, all dynamic emission scan images underwent a rigorous evaluation by one of the investigators (SBD) to ensure the exclusion of any images compromised by motion artifacts or misplacement.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePET modeling, harmonization, and striatum extraction\u003c/strong\u003e \u003cp\u003eThe MAGIA toolbox(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) was employed for PET modeling. MAGIA consisted of frame-alignment (motion-correction, using the middle image of the image volume time series as master image for the alignment) and co-registration with the individual brain MRI (or a PET template if MRI image is missing), and a quality report for image quality evaluation. To mitigate scanner-specific effects between HRRT and PET/CT, we used ComBat (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), a data harmonization approach, to perform voxel-level harmonization independently for each tracer in the PET assessments to ensure data consistency between the two PET scanners. The ComBat model included age, gender, and race as covariates. For voxel-wise analysis, a subcortical atlas (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) consisting of 32 subcortical regions was used to extract voxels in the striatum, including voxels in the putamen, nucleus accumbens (NAc), and caudate. For ROI analysis, the same subcortical atlas was used to extract and average striatal ROIs. Details of the 32-ROI subcortical atlas and the striatum mask are displayed in \u003cem\u003eSupplementary File S1\u003c/em\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMRI acquisition\u003c/b\u003e: Approximately 180 to 300 minutes after MP or placebo administration, participants underwent structural and resting-state fMRI using a 3.0T 32-channel Siemens Prisma scanner. To acquire resting fMRI time series, a multi-echo, multiband EPI sequence was used: multiband factor\u0026thinsp;=\u0026thinsp;3, anterior-posterior phase encoding, TR\u0026thinsp;=\u0026thinsp;891 ms, echo times\u0026thinsp;=\u0026thinsp;16, 33, and 48 ms, flip angle\u0026thinsp;=\u0026thinsp;57 deg, 45 slices with 2.9 \u0026times; 2.9 \u0026times; 3.0mm voxels and 520 time points while the participant relaxed with their eyes open (total acquisition time\u0026thinsp;=\u0026thinsp;8 min). A fixation cross was presented on a black background under dimmed room lighting using a liquid-crystal display screen (BOLDscreen 32, Cambridge Research Systems; UK). The 3D MP-RAGE (TR/TE\u0026thinsp;=\u0026thinsp;2400/2.24 ms, FA\u0026thinsp;=\u0026thinsp;8 deg) and variable flip angle turbo spin-echo (Siemens SPACE; TR/TE\u0026thinsp;=\u0026thinsp;3200/564 ms) pulse sequences were used to acquire high-resolution anatomical brain images with 0.8mm isotropic voxels field-of-view (FOV)\u0026thinsp;=\u0026thinsp;240 \u0026times; 256 mm, matrix\u0026thinsp;=\u0026thinsp;300 \u0026times; 320, and 208 sagittal slices.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional MRI processing\u003c/b\u003e: Resting-state fMRI was preprocessed using fMRIPrep (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and in-house codes. Specifically, the fMRIPrep pipeline was used for multi-echo-multi-band optimization(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), gradient distortion correction, rigid body realignment, field map processing, and spatial normalization to standard MNI space. For post-processing steps, spatial smoothing was applied with a Full Width at Half Maximum (FWHM) of 5 mm. The fMRI data were filtered with a bandpass filter ranging from 0.01 to 0.08 Hz. The data also underwent a rigorous detrending process and regression analysis to mitigate the influence of six motion-related and three anatomical-related nuisance variables. Time points were excluded if the volume-to-volume BOLD signal met the following criteria: DVARS\u0026thinsp;\u0026gt;\u0026thinsp;150 or framewise displacement (FD)\u0026thinsp;\u0026gt;\u0026thinsp;0.5 mm. Time series for 200 cortical regions (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) and 32 subcortical regions (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) were then extracted and demeaned. Details of the two atlases are shown in \u003cem\u003eSupplementary File S1.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDynamic functional analysis\u003c/strong\u003e \u003cp\u003eWe followed the methodology established by Cornblath et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) in which the fMRI time courses for all subjects under the MP and placebo were concatenated in time. A k-means clustering algorithm was then used to identify clusters of brain activation patterns or states. The Pearson correlation coefficient was used as a metric to measure the distance in the k-means. The clustering process was repeated 50 times with random initialization to select the best partitions of the data. The optimal number of clusters (k) was selected based on the elbow criterion. Specifically, we plotted the explained variance curve from k\u0026thinsp;=\u0026thinsp;3 to k\u0026thinsp;=\u0026thinsp;22 and identified the inflection point or \"elbow\". The inflection point was consistently observed between 4 and 6 for different partitions (\u003cem\u003eSupplementary file S2\u003c/em\u003e). Given the increased k beyond 6 resulted in less than 1% variance gain, the k in this study was set at 6, taking into account its balanced and interpretable nature. To strengthen the robustness of the partitions, we independently replicated the clustering process ten times and compared the Adjusted Mutual Information (AMI) across the ten resulting partitions. The partition with the highest cumulative AMI relative to the others was selected for subsequent analysis (\u003cem\u003eSupplementary file S3\u003c/em\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eN-ball-track task\u003c/strong\u003e \u003cp\u003eWe used a blocked visual attention paradigm (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) focusing on sustained attention and visual indexing. Specifically, a limited set of visual objects is marked for rapid attentional processing. Each TRACK epoch, lasting one minute, consists of five tracking and response intervals (\u003cem\u003eSupplementary File S4\u003c/em\u003e). Briefly, a subset of balls (2 or 3 out of 10) is highlighted, followed by their random movement in the visual field. Participants were required to fixate on the central cross and to track these target balls as they moved. At the end of the tracking periods, the balls stopped moving, a new set of balls was highlighted, and participants were instructed to press a button if the highlighted balls were the target balls. Each \"DO NOT TRACK\" or control epoch consisted of similar one-minute intervals with no ball highlighting. Participants passively watched the balls move and stop. Each task variant (with 2 or 3 balls) contains three cycles of \"TRACK\" and \"DO NOT TRACK\" epochs, totaling 6 minutes and 10 seconds. The tasks were displayed using MRI-compatible goggles connected to a computer. We recorded response time and hitting accuracy via button presses and synchronized the paradigm with the fMRI acquisition using a scanner trigger signal. Of note, the n-ball-track task was performed during fMRI, but here we only use the behavioral data from this task. All dynamic functional analysis was performed on resting state fMRI data that were acquired immediately after the VA task.\u003c/p\u003e \u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMP changes brain dynamics in healthy adults\u003c/h2\u003e \u003cp\u003eAs shown in the radial plots of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and b, six cluster centroids, each representing a brain state, were identified. The name of each brain state was assigned by calculating the cosine similarity with eight predefined resting state networks: Frontal Parietal Network (FPN), Limbic Network (LIM), Ventral Attention Network (VAT), Dorsal Attention Network (DAT), Somatomotor Network (SOM), Visual Network (VIS), Default Mode Network (DMN), and Subcortical Network (SUB) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Given the regional time courses of each scan were demeaned during fMRI preprocessing, positive centroid values reflect higher than average activation, while negative centroid values reflect lower than average activation. Three pairs of anticorrelated brain states were discovered: FPN+, FPN-, SOM+, SOM-, VIS+, and VIS-. Specifically, the FPN\u0026thinsp;+\u0026thinsp;brain state consists mainly of high amplitude of FPN and DAT, accompanied by low amplitude of VIS. The SOM\u0026thinsp;+\u0026thinsp;brain state is mainly composed of the high amplitude of SOM and low amplitude of DMN. The VIS- brain state is mainly formed by the low amplitude of VIS, and high amplitude of DMN. To further assess the hierarchical relationships between these brain states, Pearson correlation coefficients were calculated for all centroid pairs (\u003cem\u003eSupplementary file S5\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter identifying the brain states, we assessed the changes in several dynamic metrics between placebo and MP administration. These metrics include \u003cem\u003etransition probability\u003c/em\u003e which refers to the probability of transitioning from one brain state to another over time; \u003cem\u003efractional occupancy\u003c/em\u003e, which describes the proportion of time that the brain spends in a particular state out of the total observation time; \u003cem\u003edwell time\u003c/em\u003e, which refers to the duration that the brain remains in a single state before transitioning to another; and \u003cem\u003eappearance rate\u003c/em\u003e, which indicates how frequently a particular state appears throughout the observation period. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, after MP administration, there was a higher tendency for brain states transiting into FPN\u0026thinsp;+\u0026thinsp;and VIS-, with a lower probability of transiting into the SOM+. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed shows the change in dynamic characteristics after MP administration. Specifically, after MP, fractional occupancy increased in FPN+ (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, uncorrected) and VIS- (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, uncorrected) whereas SOM\u0026thinsp;+\u0026thinsp;decreased (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, uncorrected). dwell time increased for the FPN+ (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, uncorrected) and VIS- (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, corrected). There were no significant differences in the appearance rate between placebo and MP administration.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Dynamic functional analysis results\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\u003e \u003cb\u003eMP-induced FPN\u0026thinsp;+\u0026thinsp;fractional occupancy and dwell time changes were associated with faster response in ball-track tasks\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAmong the 37 subjects, 29 participants completed 2-ball-track task and 31 participants completed 3-ball-track task under both placebo and MP conditions. We used response time and hitting accuracy as metrics to assess the speed and accuracy of task completion. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, generally, after MP administration, there was a trend towards improvement in accuracy and a reduction in response time. The improvements in accuracy reached significance for the 2-ball-track task (\u003cem\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/em\u003e: PL: 90.3%\u003cem\u003e\u0026plusmn;\u003c/em\u003e14.4%, MP: 97.0%\u003cem\u003e\u0026plusmn;\u003c/em\u003e4.9% p\u0026thinsp;=\u0026thinsp;0.023) though for the more demanding 3-ball-track task they did not reach significance (\u003cem\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/em\u003e: PL:89.0%\u003cem\u003e\u0026plusmn;\u003c/em\u003e16.4%, MP: 94.8%\u003cem\u003e\u0026plusmn;\u003c/em\u003e10.1%, p\u0026thinsp;=\u0026thinsp;0.133).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further analyzed how changes in brain dynamics caused by MP affected performance in the n-ball-track task. Given that significant changes in brain dynamics were observed after MP administration, we conducted a correlation analysis between changes in task performance and the dynamic characteristics significantly altered by MP (including fractional occupancy of FPN+, SOM+, VIS-, and dwell time of FPN\u0026thinsp;+\u0026thinsp;and VIS-). The results indicated a significant correlation only between changes in FPN\u0026thinsp;+\u0026thinsp;and changes in response time. Specifically, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, in the 2-ball-track task, increases of dwell time (r=-0.42, p\u0026thinsp;=\u0026thinsp;0.02) and fractional occupancy (r=-0.47, p\u0026thinsp;=\u0026thinsp;0.01) in FPN\u0026thinsp;+\u0026thinsp;significantly correlated with the changes in response time. However, in the 3-ball-track task, the correlation between FPN\u0026thinsp;+\u0026thinsp;change and response time change was not significant. Detailed correlation coefficients and p-values can be found in \u003cem\u003eSupplementary Table S1\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Analysis of the relationship between brain dynamics, performance on ball-track tasks, and dopamine receptor availability.\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\u003e \u003cb\u003eBaseline D1R availability in putamen was correlated with dwell time changes in FPN\u0026thinsp;+\u0026thinsp;induced by MP\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe hypothesized that MP alters brain functional activity by increasing striatal dopamine levels thus affecting behavioral performance and thus predicted that individual differences in DA receptor availability would modulate the responses to MP. To test this, we first conducted a voxel-level correlation analysis between striatal DA receptor availability features and fMRI-derived dynamic features. The DA receptor availability features are striatal D1R availability after placebo (baseline), striatal D2R availability after placebo (baseline), and after MP. The fMRI-derived dynamic features are FPN\u0026thinsp;+\u0026thinsp;dwell time, FPN\u0026thinsp;+\u0026thinsp;fractional occupancy, VIS- dwell time, VIS- fractional occupancy, and SOM\u0026thinsp;+\u0026thinsp;fractional occupancy. These dynamic features were selected because they exhibited significant group differences. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, the dwell time change of FPN\u0026thinsp;+\u0026thinsp;exhibited strong positive correlation with striatal D1R availability. The fractional occupancy change of VIS- exhibited positive correlation with D2R availability after placebo whereas the fractional occupancy change of FPN\u0026thinsp;+\u0026thinsp;exhibited a positive correlation with D2R after MP. Detailed voxel-based correlation can be found in \u003cem\u003eSupplementary Figure S6-7.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe correlations between the ROI-level measures of D1,2R availability extracted using a subcortical atlas (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and the dynamic features are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed (details in \u003cem\u003eSupplementary Table S2-5\u003c/em\u003e). The results showed significant correlation between FPN\u0026thinsp;+\u0026thinsp;dwell time change and placebo D1R availability in the right posterior putamen (r\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;=\u0026thinsp;0.027), left NAc-shell (r\u0026thinsp;=\u0026thinsp;0.34, p\u0026thinsp;=\u0026thinsp;0.037), and right posterior caudate (r\u0026thinsp;=\u0026thinsp;0.33, p\u0026thinsp;=\u0026thinsp;0.05). Although not significant, the correlations in the opposite hemisphere showed the same trend with left posterior putamen (r\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;=\u0026thinsp;0.06), right NAc-shell (r\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;=\u0026thinsp;0.11), and left posterior caudate (r\u0026thinsp;=\u0026thinsp;0.23, p\u0026thinsp;=\u0026thinsp;0.16). Additionally, the FPN\u0026thinsp;+\u0026thinsp;fractional occupancy change showed significant correlation with MP D2R availability in the left NAc-shell (r\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eEstablishing the links between DA signaling, brain functional dynamics, and cognitive performance allowed us to reveal the effects of MP on brain at multiple levels. In this research, we employed a comprehensive set of measurements including PET, fMRI, and cognitive performance. Dynamic functional analysis revealed a significant enhancement of FPN\u0026thinsp;+\u0026thinsp;and VIS- following oral administration of MP. The change of dynamic features after MP showed significant correlations with striatal DA receptor availability. Furthermore, the change in FPN\u0026thinsp;+\u0026thinsp;dwell time showed a significant positive correlation with response speed in the 2-ball-track task, though the correlation was not significant with response speed in the 3-ball-track task, consistent with the limited benefits of MP in cognition in healthy individuals (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In summary, the use of dynamic functional methodologies allowed us to establish the connection of striatal D1R availability to task performances after MP.\u003c/p\u003e \u003cp\u003eDifferent from traditional functional connectivity methods that primarily emphasize the synchronicity between brain regions, dynamic functional connectivity reveals changes in characteristics such as dwell time, appearance rate, and fractional occupancy in different functional network activities before and after drug administration. From a dynamical perspective, the most salient feature observed was an increase of fractional occupancy and dwell time in FPN\u0026thinsp;+\u0026thinsp;and VIS-, and a decrease in fractional occupancy of SOM+. These findings align with our prior findings revealing increased activity in association networks but decreased activity in sensorimotor networks after MP (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Regarding the specifics of dynamic functional states, the DMN consistently appeared in an opposing direction to the SOM and VIS networks. Specifically, when the amplitude of the DMN network was high, the corresponding amplitude of the SOM or VIS was low, and vice versa. These brain states might be influenced by our use of resting-state data for analysis and different brain states might emerge while engaged in various tasks.\u003c/p\u003e \u003cp\u003eThe FPN\u0026thinsp;+\u0026thinsp;brain state primarily comprises high-amplitude FPN and DAT networks in juxtaposition with the VIS network. Since the FPN and DAT are associated with cognitive processing, an increase of dwell time and fractional occupancy in FPN\u0026thinsp;+\u0026thinsp;suggests a brain state more likely to be conducive to cognitive efforts and one that is less distracted by visual stimuli. Dixon et al investigated the FPN\u0026rsquo;s functional connectivity under varying cognitive demands and identified two distinct FPN subsystems. One subsystem showed stronger connectivity with the DMN, indicating greater involvement in introspective processes. The other subsystem had stronger connections with the DAT, suggesting a key role in visuospatial perceptual attention (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Our study used the n-ball-track tasks to differentiate task complexity. Following MP administration, participants showed improved performance in the 2-ball-track task (significant for accuracy and trend for response time), which correlated positively with increased FPN\u0026thinsp;+\u0026thinsp;dwell time. However, this correlation wasn't significant in the more complex 3-ball-track task, suggesting that while MP-induced brain changes can boost cognitive effort (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), their effectiveness in highly demanding cognitive tasks is limited. Notably, the placebo accuracy with the 3-ball-track task was almost 100% which could have limited our ability to detect any improvement whereas the placebo accuracy for the 2-ball-track evinced lower performance for some participants which might have reflected their disinterest in this very simple task. This is consistent with Bowman et al.'s findings, where 'smart drugs' increased motivation but not the quality of effort in complex problem-solving (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), and with studies showing that stimulants are particularly effective in improving performance by enhancing motivation to perform the task (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMP\u0026rsquo;s therapeutic effects are in part due to amplification of DA signals and the inter-subject variability reported in the response to MP is due in part to differences in DA tone and receptor availability between individuals (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Thus, measuring baseline dopamine receptor availability could theoretically predict changes in brain activity caused by MP, a hypothesis that our experiment confirmed. Our correlation analysis showed that the baseline level of striatal D1R availability positively correlated with the increase in MP-induced FPN\u0026thinsp;+\u0026thinsp;dwell time.\u003c/p\u003e \u003cp\u003eInterestingly, the increase in FPN\u0026thinsp;+\u0026thinsp;was also associated with striatal D2R availability (left NAc-shell, r\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;=\u0026thinsp;0.05) but only for the measure after MP (not after placebo). Because MP reduces striatal D2R availability by increasing binding competition with [\u003csup\u003e11\u003c/sup\u003eC]raclopride, as synaptic dopamine is increased (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), the correlation with D2R after MP was significantly correlated with MP-induced increase in FPN\u0026thinsp;+\u0026thinsp;fractional occupancy but not for placebo suggests an opposing effect for D2R with DA stimulation. These results pinpoint to a counteracting role of striatal D1R and D2R in MP-induced increase in the predominance of the FPN\u0026thinsp;+\u0026thinsp;brain state. Similarly opposing patterns for striatal D1R and D2R have been noted for other behaviors (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The association of MP effects in brain state dynamics with dopamine receptor availability could explain why MP is beneficial for ADHD, which is characterized by deficits in dopaminergic singling (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Our results also align with previous findings that participants with higher striatal DA synthesis capacity showed greater willingness to exert effort, while MP increased cognitive motivation more in participants with lower synthesis capacity (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Our experiment established a significant relationship between D1R receptor availability after placebo and D2R receptor availability after MP and the predominance of FPN\u0026thinsp;+\u0026thinsp;brain state, providing valuable guidance for selecting individuals who would be more likely to benefit from MP to advance personalized treatment approaches.\u003c/p\u003e \u003cp\u003eThe study has limitations and potential directions for future research. First, our multimodal study was conducted on healthy adults, and considering MP is a frontline medication for ADHD, it's crucial to collect and compare multimodal data in ADHD participants. Second, due to equipment limitations, our data collection for PET and fMRI post-MP or placebo administration was conducted separately. This led to the fMRI data being collected approximately 180\u0026ndash;300 minutes after medication, potentially when drug effects were reduced. Further studies using simultaneous PET/MR imaging might address this issue. Third, our study predominantly focuses on the D1R and D2R in the striatum whereas cortical dopamine receptors are engaged in the attention process and involved with MP\u0026rsquo;s effects. This will require studies with PET radioligands with stronger signals in cortical regions.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study investigated the relationship between dopamine D1R and D2R availability, the effects of MP on performance in a visual attention task, and brain state dynamics. The experiment established a correlation between baseline D1R and measures of D2R after MP and the changes induced by MP in brain functional dynamics that favor FPN+, a brain state characterized by dominance of FPN and DAT and VIS suppression, as well as a correlation between MP-induced changes in FPN\u0026thinsp;+\u0026thinsp;and performance on an attention task. This study also presents a multi-level neuroimaging approach to investigate the effect of drugs, which has the potential for personalizing medication interventions by predicting individual variations in drug responses such as MP.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e \u003cp\u003eThis work was accomplished with the support of the National Institute of Alcohol Abuse and Alcoholism (ZIAAA000550).\u003c/p\u003e\u003ch2\u003eAUTHOR CONTRIBUTIONS\u003c/h2\u003e \u003cp\u003eN.D.V., G.W., D.T. designed and implemented the original methylphenidate experiments and data collection. W.Y., S.B.D., R.Z., and P.M. preprocessed the fMRI and PET data. W.Y. and N.D.V. conceptualized the present study, performed the data analysis, and drafted the manuscript. W.Y. produced all the figures. W.Y., N.D.V, G.W., S.B.D., R.Z., D.T., P.M., contributed to the interpretation and discussion of the results. All authors reviewed, edited, and approved the final version.\u003c/p\u003e\u003ch2\u003eCODE AVAILABILITY\u003c/h2\u003e \u003cp\u003eAll codes related to the dynamic analysis and regression analysis are shared on GitHub \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/WizardYan/MP_PET_fMRI\u003c/span\u003e\u003cspan address=\"https://github.com/WizardYan/MP_PET_fMRI\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eButcher S, Liptrot J, Aburthnott G (1991) Characterisation of methylphenidate and nomifensine induced dopamine release in rat striatum using in vivo brain microdialysis. Neurosci Lett 122:245\u0026ndash;248\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoods SK, Meyer JS (1991) Exogenous tyrosine potentiates the methylphenidate-induced increase in extracellular dopamine in the nucleus accumbens: a microdialysis study. Brain Res 560:97\u0026ndash;105\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuring MJ, Bean AJ, Roth RH (1992) Effects of CNS stimulants on the in vivo release of the colocalized transmitters, dopamine and neurotensin, from rat prefrontal cortex. Neurosci Lett 140:129\u0026ndash;133\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuczenski R, Segal DS (1997) Effects of methylphenidate on extracellular dopamine, serotonin, and norepinephrine: comparison with amphetamine. J Neurochem 68:2032\u0026ndash;2037\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolkow ND, Wang G-J, Fowler JS, Logan J, Gerasimov M, Maynard L et al (2001) Therapeutic Doses of Oral Methylphenidate Significantly Increase Extracellular Dopamine in the Human Brain. J Neurosci 21:RC121\u0026ndash;RC121\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolkow ND, Wang G-J, Tomasi D, Kollins SH, Wigal TL, Newcorn JH et al (2012) Methylphenidate-Elicited Dopamine Increases in Ventral Striatum Are Associated with Long-Term Symptom Improvement in Adults with Attention Deficit Hyperactivity Disorder. J Neurosci 32:841\u0026ndash;849\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeter CJ, McCabe SE, LaGrange K, Cranford JA, Boyd CJ (2006) Illicit Use of Specific Prescription Stimulants Among College Students: Prevalence, Motives, and Routes of Administration. Pharmacotherapy: J Hum Pharmacol Drug Therapy 26:1501\u0026ndash;1510\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWestbrook A, van den Bosch R, M\u0026auml;\u0026auml;tt\u0026auml; JI, Hofmans L, Papadopetraki D, Cools R et al (2020) Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work. Science 367:1362\u0026ndash;1366\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowman E, Coghill D, Murawski C, Bossaerts P (2023) Not so smart? Smart drugs increase the level but decrease the quality of cognitive effort. Sci Adv 9:eadd4165\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKortekaas-Rijlaarsdam AF, Luman M, Sonuga-Barke E, Oosterlaan J (2019) Does methylphenidate improve academic performance? A systematic review and meta-analysis. Eur Child Adolesc Psychiatry 28:155\u0026ndash;164\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolkow ND, Wang GJ, Newcorn JH, Kollins SH, Wigal TL, Telang F et al (2011) Motivation deficit in ADHD is associated with dysfunction of the dopamine reward pathway. Mol Psychiatry 16:1147\u0026ndash;1154\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNora DV, Gene-Jack W, Dardo T, Scott HK, Tim LW, Jeffrey HN et al (2012) Methylphenidate-Elicited Dopamine Increases in Ventral Striatum Are Associated with Long-Term Symptom Improvement in Adults with Attention Deficit Hyperactivity Disorder. J Neurosci 32:841\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManza P, Shokri-Kojori E, Demiral SB, Wiers CE, Zhang R, Giddens N et al (2022) Cortical D1 and D2 dopamine receptor availability modulate methylphenidate-induced changes in brain activity and functional connectivity. Commun Biol 5:514\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidaurre D, Smith SM, Woolrich MW (2017) : Brain network dynamics are hierarchically organized in time. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e. 114:12827\u0026ndash;12832\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBreakspear M (2017) Dynamic models of large-scale brain activity. Nat Neurosci 20:340\u0026ndash;352\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCornblath EJ, Ashourvan A, Kim JZ, Betzel RF, Ciric R, Adebimpe A et al (2020) Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands. Commun Biol 3:261\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai W, Mizuno Y, Tomoda A, Menon V (2023) : Bayesian dynamical system analysis of the effects of methylphenidate in children with attention-deficit/hyperactivity disorder: a randomized trial. \u003cem\u003eNeuropsychopharmacology\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMizuno Y, Cai W, Supekar K, Makita K, Takiguchi S, Tomoda A et al (2022) Methylphenidate remediates aberrant brain network dynamics in children with attention-deficit/hyperactivity disorder: A randomized controlled trial. NeuroImage 257:119332\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang R, Manza P, Tomasi D, Kim SW, Shokri-Kojori E, Demiral SB et al (2021) : Dopamine D1 and D2 receptors are distinctly associated with rest-activity rhythms and drug reward. J Clin Investig. 131\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarjalainen T, Tuisku J, Santavirta S, Kantonen T, Bucci M, Tuominen L et al (2020) Magia: Robust Automated Image Processing and Kinetic Modeling Toolbox for PET Neuroinformatics. Front Neuroinform 14:3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA et al (2018) Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167:104\u0026ndash;120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian Y, Margulies DS, Breakspear M, Zalesky A (2020) Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat Neurosci 23:1421\u0026ndash;1432\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsteban O, Markiewicz CJ, Blair RW, Moodie CA, Isik AI, Erramuzpe A et al (2019) fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 16:111\u0026ndash;116\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuPre E, Salo T, Ahmed Z, Bandettini PA, Bottenhorn KL, Caballero-Gaudes C et al (2021) TE-dependent analysis of multi-echo fMRI with* tedana. J Open Source Softw 6:3669\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchaefer A, Kong R, Gordon EM, Laumann TO, Zuo X-N, Holmes AJ et al (2018) Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex 28:3095\u0026ndash;3114\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingleton SP, Luppi AI, Carhart-Harris RL, Cruzat J, Roseman L, Nutt DJ et al (2022) Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain's control energy landscape. Nat Commun 13:5812\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRustichini A, Tomasi D, Volkow ND, Wang R, Telang F, Wang G-J et al (2009) : Dopamine Transporters in Striatum Correlate with Deactivation in the Default Mode Network during Visuospatial Attention. PLoS ONE. 4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M et al (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106:1125\u0026ndash;1165\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRepantis D, Bovy L, Ohla K, K\u0026uuml;hn S, Dresler M (2021) Cognitive enhancement effects of stimulants: a randomized controlled trial testing methylphenidate, modafinil, and caffeine. Psychopharmacology 238:441\u0026ndash;451\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDixon ML, De La Vega A, Mills C, Andrews-Hanna J, Spreng RN, Cole MW et al (2018) : Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e. 115:E1598-E1607\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZametkin AJ (2010) Dopamine Reward Pathway in Adult ADHD. JAMA 303:232\u0026ndash;234\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIlieva IP, Farah MJ (2013) Enhancement stimulants: perceived motivational and cognitive advantages. Front Neurosci 7:198\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolkow ND, Fowler JS, Wang G-J, Ding Y-S, Gatley SJ (2002) Role of dopamine in the therapeutic and reinforcing effects of methylphenidate in humans: results from imaging studies. Eur Neuropsychopharmacol 12:557\u0026ndash;566\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolkow ND, Wang G-J, Fowler JS, Logan J, Schlyer D, Hitzemann R et al (1994) Imaging endogenous dopamine competition with [11C]raclopride in the human brain. Synapse 16:255\u0026ndash;262\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen W, Flajolet M, Greengard P, Surmeier DJ (2008) Dichotomous Dopaminergic Control of Striatal Synaptic Plasticity. Science 321:848\u0026ndash;851\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e The demographic characteristics of the participants\u003c/p\u003e \n\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eScanner 1: HRRT\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eScanner 2: PET/CT\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003et(dt), P\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAge\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMin-Max\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e33\u0026ndash;64\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e22\u0026ndash;61\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.43(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), 0.021\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e48.4(9.6)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e37.6(12.5)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSex\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003en, Female (%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e7(41%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e6(30%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.5037(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), 0.48\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eBMI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMin-Max\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e21\u0026ndash;39\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e21\u0026ndash;33\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.49(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), 0.63\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e27.2(5.1)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e27.9(3.5)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eIQ\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMin-Max\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e79\u0026ndash;139\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e97\u0026ndash;129\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.85(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e),0.007\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e122.4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e109.6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eRace\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003en, White (%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e11 (65%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e5 (25%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003en, Black (%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e6 (35%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e10 (50%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003en, Asian(%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0 (0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3 (15%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003en, Other(%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0 (0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2 (10%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBMI: body-mass index; IQ: intelligence quotient; SD: standard deviation.\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"ab842376-951b-4c91-a478-38c1b5bd3409","identifier":"10.13039/100000027","name":"National Institute on Alcohol Abuse and Alcoholism","awardNumber":"ZIAAA000550","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National Institute On Alcohol Abuse and Alcoholism","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Methlyphenidate, dynamic functional analysis, attention, fMRI, PET, dopamine","lastPublishedDoi":"10.21203/rs.3.rs-4096379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4096379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMethylphenidate (MP) is a widely used stimulant medication for the treatment of attention deficit hyperactivity disorder (ADHD) that enhances brain dopamine signaling and improves attention. However, how dopamine stimulation alters brain state dynamics to support improved attention during task performance is still unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe employed a multimodal neuroimaging approach combining positron emission tomography, functional magnetic resonance imaging, and behavioral tests, to discover associations between dopamine signaling, brain activity, and cognition. Multimodal images were collected from 37 healthy adults under a single-blind, counterbalanced, placebo-controlled crossover study. Dynamic functional analysis was used to compare the alterations in dynamic features of brain states before and after MP. Subsequently, we analyzed the correlation between these brain state changes and baseline striatal D1 and D2 dopamine receptor (D1R, D2R) availability. We then examined alterations in dynamic brain states and their effects on attention performances.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results showed that MP primarily affected frontoparietal-dominant activated (FPN+), somatomotor-dominant activated (SOM+), and visual-dominant suppressed (VIS-) brain states. Specifically, the dwell time and fractional occupancy exhibited significant increases within the FPN\u0026thinsp;+\u0026thinsp;and VIS- while an opposite trend within the SOM+. Furthermore, the increase of dwell time in FPN+, which was positively correlated with baseline striatal D1R availability, was also associated with quicker response in the 2-ball-track task, but not significant for the 3-ball-track task.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings suggest that MP\u0026rsquo;s enhancement of brain states with FPN\u0026thinsp;+\u0026thinsp;and VIS- while decreasing SOM+, in part through D1R signaling might underlie the MP\u0026rsquo;s improvement of attention for low cognitive effort tasks in healthy populations.\u003c/p\u003e","manuscriptTitle":"Methylphenidate enhances a frontoparietal-dominant brain state improving cognitive performance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-15 18:39:30","doi":"10.21203/rs.3.rs-4096379/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0649da1f-de9c-45d9-9c11-a9406f440cb0","owner":[],"postedDate":"March 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29422757,"name":"Psychiatry"},{"id":29422758,"name":"Nuclear Medicine \u0026 Medical Imaging"},{"id":29422759,"name":"Personalized Medicine"},{"id":29422760,"name":"Computational Neuroscience"}],"tags":[],"updatedAt":"2024-03-15T18:39:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-15 18:39:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4096379","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4096379","identity":"rs-4096379","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}