Decreased impulsiveness and MEG normalization after AI- digital therapy in ADHD children: a RCT

Decreased impulsiveness and MEG normalization after AI- digital therapy in ADHD children: a RCT | 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 Article Decreased impulsiveness and MEG normalization after AI- digital therapy in ADHD children: a RCT Danylyna Shpakivska Bilan, Irene Alice Chicchi Giglioli, Pablo Cuesta, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4329802/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Attention-deficit/hyperactivity disorder (ADHD) presents with symptoms like impulsiveness, inattention, and hyperactivity, often affecting children’s academic and social functioning. Non-pharmacological interventions, such as digital cognitive therapy, are emerging as complementary treatments for ADHD. The randomized controlled trial explored the impact of an AI-driven digital cognitive program on impulsiveness, inattentiveness, and neurophysiological markers in 41 children aged 8–12 with ADHD. Participants received either 12 weeks of AI-driven therapy or a placebo intervention. Assessments were conducted pre- and post-intervention and magnetoencephalography (MEG) analyzed brain activity. Results showed significant reductions in impulsiveness and inattentiveness scores in the treatment group, associated with normalized MEG spectral profiles, indicating neuromaturation. Notably, improvements in inhibitory control correlated with spectral profile normalization in the parieto-temporal cortex. Improvements in inhibitory control, linked to normalized spectral profiles, suggest AI-driven digital cognitive therapy can reduce impulsiveness in ADHD children by enhancing neurophysiological efficiency. This emphasizes personalized, technology-driven ADHD treatment, using neurophysiological markers for assessing efficacy. Neurophysiological Changes Digital Intervention ADHD management Brain Activity Modulation Controlled Clinical Trial Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders that affects 2–7% worldwide of children, mostly boys, and often lasts into adulthood 1–3 . It is characterized by persistent, pervasive, and impairing symptoms of inattention and/or hyperactivity/impulsivity that affects functioning in daily life 4 . Indeed, children with ADHD show moderate impairments in multiple cognitive domains including attention, executive functions and memory 1,4 . These cognitive impairments have also been investigated in the brain correlates of ADHD through neurophysiological techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG) 5,6 . Increasing evidence in spectral analysis are showing that ADHD patients present a pattern of significantly higher theta and alpha relative power and lower beta relative power, along with higher theta/alpha and theta/beta ratios 7–9 . The “Maturational Lag” hypothesis suggests that electrophysiological correlates of ADHD support a model of maturational delay on the central nervous system, rather than a different neurological dysfunction 10,11 . When compared to controls, ADHD groups’ slow frequency activity decreased later in age, revealing a delay in maturation 10 . However, among ADHD types, the hyperactive/impulsive showed a faster reduction in slow frequency bands in comparison to the inattentive type 8 . Recently, this maturational hypothesis has also been supported from graph theory analysis 12 , as ADHD children seem to have differential functional network development (decreased integration and segregation) in the regions overlapping with default mode network (DMN), salience network (SAL), dorsal attention network (DAN) and visual network (VN). Treatment options can be divided into three main categories: a) pharmacological; b) nonpharmacological; and c) combined treatments 13 . Pharmacological treatments use stimulant or non-stimulant medications according to the specific ADHD symptoms. Non pharmacological treatments include several psychosocial therapies such as behavioral training of parents, classroom and peer interventions, cognitive behavior therapies including skill training, cognitive training and neurofeedback), and mindfulness 14,15 . Pharmacological treatments showed to be effective on the persistent ADHD symptoms and among the psychosocial treatments, behavioral parent, classroom and peer training, and skills training, have shown moderate improvement in ADHD symptoms 16–18 while the other psychosocial interventions, such as the mindfulness therapies, need more research in order to establish the efficacy. Despite the scientific evidence on ADHD treatments, several barriers of access to treatments and long waiting lists, and other factors related to costs, stigma, and low treatment adherence constitute some of the limitations in treatment access 19–21 . In order to overcome such barriers, technological developments such as mobile applications, have increased to assess and treat several disorders, including ADHD 23,24 . These applications are familiar to patients and can provide more engagement and motivation than traditional cognitive treatments and patients can test their abilities and skills without any danger, as well as they can improve the treatment effectiveness providing a personalization of the different levels of cognitive task according to the severity of the symptoms and overcoming the limited resources and facilitation of conventional rehabilitation methods 22 . Neudecker et al . 23 found significant improvements in executive functions and inhibition, parent ratings of psychological difficulty, and motor skills, after a home-based exergaming intervention in a sample of 51 ADHD children (ages 8–12 years). Preliminary positive evidence indicates that such interventions are associated with reduced inattentive symptoms 24 and with mixed findings on impulsivity 25–27 . Recently, several scoping and systematic reviews aimed at synthesizing the evidence around the use of technological cognitive intervention systems in children and youth with ADHD 25,28,29 . Despite these preliminary positive results and the interest shown by the healthcare professionals, these methods need more evidence to be practically and widely implemented. Regarding the neural mechanisms that underlie these cognitive changes, stimulant pharmacological treatment appears to normalize electroencephalogram (EEG) abnormalities post-administration, reducing theta [4–7 Hz] band power spectra in 44% of the studies (8 out of 18) 30–36 . Non-stimulant pharmacological treatment has been found to normalize the EEG spectral profile in 40% of the studies (2 out of 5) 30,37,38 . Several studies have also focused on non-pharmacological treatments 39 . While physical exercise 40 have shown to normalize EEG effects, findings have been inconclusive for neurofeedback 31,41 . Finally, digital cognitive treatments (DCT) have demonstrated brain enhancements and a normalization of the EEG spectral profile 42 . For example, it has been found that alpha is suppressed during oddball tasks after training, suggesting that alpha may be related to attention switching and workload 43 . Computer-based inhibitory control training showed decreased relative theta power in resting EEG and trending improvements in parent ratings in inattentive behaviors 44 . According to this, the latest technological cognitive intervention systems are designed and developed to focus on brain neuroplasticity. Usually, it consists of training one or more cognitive functions through personalized and adaptive methods based on artificial intelligence algorithms that automatically adjust task difficulty or game modality according to the patient’s performance and needs. Starting from these premises, the main objective of this study was to assess the efficacy of the digital neuropsychological intervention tool (KAD_SCL_01) on inhibitory control in pediatric ADHD combined type. By comparing two intervention conditions of random allocation (KAD_SCL_01 condition vs. control condition), we assessed pre-post interaction effects of KAD_SCL_01 on inhibitory control. Main outcome measure for this objective has been the Commission score from Conners Continuous Performance Test (CPT-3) 45 . Secondly, the study tested the efficacy of intervention with KAD_SCL_01 on other cognitive processes and clinical measures (see “Treatment assessment” in Materials and Methods). Finally, the study aimed to show the relationship and changes between neuropsychological and clinical measures and power spectral activity in the ADHD brain networks. Materials and method Participants An initial sample of 56 children diagnosed with combined-type ADHD (ADHD-C) were enrolled from health facilities, schools, and associations in the community of Madrid (Spain), with prior authorization by the latter to researchers to contact with legal guardians. The enrollment consisted of emails, phone, and video calls with the legal guardians in which were provided all the clinical trial information. The legal guardians of the participants who agreed to participate, were subsequently contacted to verify the eligibility criteria to be included in the clinical trial. To be included in the clinical trial, the following criteria had to be met: a) children age between 8 and 11 years old; b) children diagnosed with ADHD-C according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV-TR) 46 criteria by an authorized professional; c) stopping ADHD medication three days before visit days (according to the methylphenidate specifications, it has a half-life of 3.5 hours and the 90% is excreted in urine and the rest in feces in 48–96 hours); d) maintaining of the same pharmacological doses during the clinical trial; e) non-use of other psychoactive drugs; f) no other psychiatric comorbidities and g) the compliance with the clinical protocol. The exclusion criteria were the following: a) beginning or discontinuing behavioral therapies or psychoactive drugs during the clinical trial; b) use of psychoactive drugs and presence of suspicion of substance abuse in the last six months; c) any other psychological diagnosis and comorbidity; d) children with hand motor difficulties enabled to use the mobile devices (tablet or smartphone); and e) children with blindness or visual acuity difficulties. From the initial sample of 56 participants, n = 6 declined to participate and n = 1 was excluded for not meeting all the inclusion criteria. Those who met the inclusion criteria were randomly assigned to the experimental or control condition. From the 49 participants, n = 3 subjects did not perform the post-assessment with magnetoencephalography (MEG) because they did not complete the treatment protocol and n = 5 were discarded from the analysis because of the quality of the MEG register. The final sample of n = 41 subjects underwent the final pre-and-post intervention assessment including MEG recordings, neuropsychological batteries, and clinical questionnaires: 20 in experimental condition (male = 16, female = 4; M age = 9.41 years, SD = 1.22) and 21 in control condition (male = 19, female = 2; M age = 9,38 years, SD = 1,21). Prior to inclusion in the clinical trial, the legal guardians of all participants received and signed an informed consent form explaining the objectives of the research and the characteristics of the experimental procedure. The clinical trial obtained the approval of the Ethics Committee at the San Carlos Hospital (Madrid, Spain) and the entire procedure was designed following the guidelines of the Declaration of Helsinki regarding the ethical standards to be followed in any procedure that includes human beings. This clinical trial is registered in the ISRCTN registry (ISRCTN71041318). Reporting Trials (CONSORT) 2010 flow diagram is presented in Figure. 1. Outcomes Neuropsychological Outcomes The assessment protocol pre-and-post digital intervention consisted of the following neuropsychological batteries: Main neuropsychological outcome measure: Commission score (CPT_C) from the Conners Continuous Performance Test (CPT-3) 45 Secondary neuropsychological outcomes measures: Conners Continuous Performance Test (CPT-3): for each dimension (inattentiveness, impulsivity, sustained attention, and vigilance) the following scores had been considered as secondary outcomes: Inattentiveness: detectability (CPT_d), omissions (CPT_O), hit reaction time (CPT_HRT), standard deviation of HRT (CPT_HRTSD), response variability (CPT_Var); Impulsivity: HRT and preservations (CPT_P) Sustained Attention: HRT block change, omissions by block (CPT_HRT and CPT_O); Vigilance: HRT inter-stimulus (CPT_HRTISI), Interval (ISI) change and omissions by ISI Developmental Neuropsychological Assessment-II (NEPSY-II) 47 the following subtests related to attention domain were administered to participants pre-and-post the digital intervention: Auditory attention and response set. From this test, number of correct answers (NAtAu_Ac) and commissions (NAtAu_EC), omissions (NAtAu_ EO) and inhibition errors (NAtAu_ EI) scores have been computed. Inhibition. From this subtest, response time (Ninh_1T), number of errors (Ninh_1E) and number of self-corrected errors (Ninh_1EAc) have been computed. Card Classification. From this subtest, number of correct answers (Clas_C), repeated errors (Clas_R), inaccurate answers (Clas_O) and total errors (Clas_TE) have been computed. Wechsler Intelligence Scales for Children-IV (WISC-IV) 48 : the following subtests from the Working Memory Index and Processing Speed Index were administered to participants pre-and-post digital intervention: Digit Span. From this subtest, total number of correct responses (DIG_D and DIG_I) and the length of the last sequence (DIG_D + and DIG_I+) successfully repeated for each condition have been computed. Coding. From this subtest, correct (CN_Ac) and incorrect responses (CN_E) and the total number of processed integer numbers (CN_T) have been computed. Symbol search. From this subtest, number of correct (BS_Ac) and incorrect items (BS_E) and total processed items (BS_T) have been computed. From Weschler Non-Verbal Scales (WNV), the Corsi Block Tapping Test 49 has been administered and total number of correct answers (LE_D and LE_I) and the length of the last sequence (LE_D + and LE_I+) in each condition have been computed. Clinical outcomes Scale for the Evaluation of Attention Deficit Hyperactivity Disorder (EDAH) 50 : it consists of 20-items and aims to assess the main ADHD features and any coexisting behavioral disorders. Inattention (EDAH_DA), hyperactivity (EDAH_H), hyperactivity and inattention (EDAH_DAH) behavioral disorders (EDAH_TC) and the global indexes have been computed. Behaviour Rating Inventory of Executive Function, Parent Version (BRIEF) 51 : it consists of two forms (parent and teacher form) of 86-items each and aims to assess executive functioning in daily life activities. The parent form has been administered in the clinical trial and inhibition (BrPa_Ih), flexibility (BrPa_Flx), working memory (BrPa_MO), emotional control (BrPa_CE), planning (BrPa_Pla), initiate (BrPa_Ini), organization (BrPa_Org), and monitoring (BrPa_Mon) scores have been computed. Neurophysiological outcomes Neurophysiological pre-and-post digital intervention data have been recorded using the Elekta-Neuromag MEG system composed of 306 channels (Elekta AB) at the Center for Biomedical Technology (Madrid, Spain). MEG data have been recorded at a sampling frequency of 1000 Hz and have been online filtered with a band-pass between 0.1 Hz and 330 Hz. Magnetoencephalography data acquisition Participants were placed inside the magnetically shielded room in which the MEG was located. The shape of each subject’s head was defined with respect to three anatomical points (nasion and bilateral preauricular points) using a 3D digitizer (Fastrak, Polhemus, VT, USA) and head movement was tracked through four HPI (Head Position Indicator) coils attached to the scalp. These HPI coils continuously monitored the subjects’ head movements, while eye movements were monitored by a vertical electrooculogram (EOG) unit consisting of a pair of bipolar electrodes. For the MEG recording, the participants were given instructions that included to relax, not to move and not to move their heads outside the MEG helmet, as well as to remain silent. For the pre-and-post MEG data recording was asked to participants to close their eyes for 5-minutes to stay in a resting state. Closing the eyes was facilitated by reducing the room lighting. Pre-processing and power calculations Data preprocessing was carried out in several steps: 1) The temporal extension of the Space Signal Separation (tSSS) 52 method was applied to remove external noise from raw data. A window length of 10 seconds and a correlation threshold of 0.90 were used as input parameters for the Maxfilter (v 2.2 Elekta AB, Stockholm, Sweden) software; 2) Ocular, cardiac and muscle artifacts were automatically detected with FieldTrip package 53 and manually validated by a MEG expert. 3) Eye-blinks and cardiac activity were removed using an independent component analysis based on SOBI 54 ; 4) The data were segmented in 4-second trials and trials marked as containing artifacts were discarded from subsequent analysis. Power calculation . First, clean MEG time series were filtered with a band-pass filter between 2–30 Hz with 0.5 s padding. For each node of the grid, the power spectrum was computed using discrete prolate spheroidal (Slepian) sequences (dpss) with 1 Hz smoothing. The power spectrum was normalized by the total power over the 2–30 Hz range. Then, the source template with 2459 nodes in a 10 mm spacing grid was segmented into 78 regions of the Automated Anatomical Labeling (AAL48) atlas66, excluding the cerebellum, basal ganglia, thalamus, and olfactory cortices. These 78 regions of interest included 1202 of the original 2459 nodes. After averaging trials across subjects, the result ended up with a source-reconstructed power matrix of 1202 nodes × 41 participants. Finally, the power ratio (post-condition/pre-condition) was calculated to assess the change between the two conditions of the follow-up. Source reconstruction . A template head model was used for source reconstruction due to the absence of individual anatomy data. The head model consisted of a single layer representing the inner skull interface, generated from the union of grey matter, white matter, and cerebrospinal fluid in the Montreal Neurological Institute (MNI) brain. As a result, a regular grid of sources with 10mm spacing defined in MNI space was obtained. From these, the 1202 source positions falling under cortical areas of the AAL atlas were extracted. The scalp of the MNI template was linearly transformed to match the individual head shape using an affine transformation generated with an iterative algorithm, and the same transformation was applied to both head and source models. The lead field was calculated using a single shell model. Finally, a Linearly Constrained Minimum Variance beamformer was applied to reconstruct the source's time series using the trial-average covariance matrix and a regularization factor of 5% of the average sensor power. Digital cognitive intervention Experimental condition (KAD_SCL_01) Experimental condition consisted of a digital cognitive intervention delivered through a serious game via a mobile device (mobile and/or tablet). The intervention included 14 cognitive tasks-games that have been designed and developed based on scientific-supported neuropsychological tasks (such as go/no-go task, n-back task, etc.). The scheduled intervention consisted of 12-weeks for three sessions per week of 15-minutes each session. The first intervention session consisted of a selection from among the 14 cognitive tasks-games computed according to the age and the cognitive profile, which will change over the course of the intervention to address the different cognitive functions. The results obtained in each treatment session have been transmitted to an AI that through algorithms automatically adjusted the selection of the cognitive tasks-games and levels of difficulty to continue the intervention. Control condition Control condition consisted of three entertainment games (Knightmare Tower, Bloons Super Monkey and Super Staker 2) including in the Kongregate open-access platform (Kongragate Inc). Knightmare Tower is a runner-like video game in which the player must ascend to the top of a tower while avoiding enemies and traps. Bloons Super Monkey is a video game, like the classic Space Invaders, in which the player must defeat enemies and obstacles by moving left or right. Last, Super Stacker 2 is a puzzle-like video game in which the player must locate a certain number of geometrical pieces to keep them balanced. The participants played the three games according to the same protocol of the experimental condition. Experimental design and procedure A single-center, parallel, single-blind, randomized controlled trial has been conducted. The study procedure included four visits: 1. Recruitment and screening according to inclusion and exclusion criteria; 2. Pre-intervention assessment included MEG recordings, neuropsychological batteries and clinical questionnaires; 3. At-home digital intervention and 4. Post-intervention assessment included MEG recordings, neuropsychological batteries and clinical questionnaires. The order of neuropsychological batteries and MEG recordings was counterbalanced in the pre-and-post-assessments. Participants who met with the eligibility criteria, have successively been randomized with a ratio of 1:1 and an allocation probability of 0.50 to be included in the experimental or control group. Pre-and-post MEG and neuropsychological assessments have been performed at the Center for Biomedical Technology, at the Technical University of Madrid by a Sincrolab researcher. Clinical pre-and-post questionnaires have been performed by the children’s legal guardians. The at-home digital intervention consisted of 3 sessions per week of 15 minutes for 12 weeks for both groups. The whole intervention period of compliance, as well as the possible adverse events have been monitored by the Sincrolab researcher. After the 12 weeks of intervention protocol, participants who completed at least 80% of the intervention sessions (28 out of 36) have been appointed for the post-intervention assessment. Statistical Analysis Power analyses determined that a sample size of 56 participants would be sufficient to detect a mean difference of 0.64 SD in the commission score from the CPT-3, with a significance level of α = .05 and a power of 0.8 (1-β = .8). The calculation procedure followed the sample size estimation for a 2-tailed, 2-samples mean difference with a correction factor for repeated measures. Analysis I. Group differences in Cognitive and Clinical Outcomes: linear mixed-effects models The cognitive and clinical outcome measures were adjusted to linear mixed-effects models with a random intercept and fixed slope. For the random effect factor, an unstructured covariance matrix (Sigma) using the robust restricted maximum likelihood method has been estimated. Using a stepwise method, each model added age as a co-variable. To control p-values for multiple comparisons, False Discovery Rate (FDR) correction was applied 55 . As the commission score from CPT-3 was set as the main outcome measure, no correction for multiplicity was applied. Regarding the rest of the outcome measures, FDR adjustments were applied considering different cognitive processes (i.e. inhibition) as independent statistical families. Analysis II. Power ratio values correlation with CPT-C and other cognitive and clinical outcomes The goal of this methodology was to extract any neurophysiological markers whose dynamics could be associated with the evolution of the inhibition-control performance. Such analysis relied on network-based statistics 56 . First, clusters were formed based on a criterion of spatial and frequency adjacency. Each cluster comprised several adjacent nodes, which systematically exhibited a significant partial correlation (with age as a covariate) at a minimum of three 3 consecutive frequency steps (a 1-Hz interval) between their corresponding power ratio values and CPT ratio (Spearman correlation coefficient P < .05). All nodes within a cluster needed to display the same sign for the correlation coefficient for the cluster to be considered a functional unit. Only clusters involving at least 0.5% of the nodes (i.e., a minimum of 6 nodes) at each frequency step were considered. Cluster-mass statistics were assessed by summing the Spearman ρ values across all nodes and significant frequency steps. Second, to control for multiple comparisons, the entire analysis pipeline was then repeated 5000 times, with random assignments between power ratio estimates and the neuropsychological scores. At each iteration, the maximum statistic of the surrogate clusters (in absolute value) was recorded, creating a maximal null distribution that would ensure control of the familywise error rate at the cluster level. The cluster-mass statistics for each cluster in the original dataset were compared with the same measure in the randomized data. The network-based statistics P value represents the proportion of the permutation distribution with cluster-mass statistic values greater or equal to the cluster-mass statistic value of the original data. Power ratio values were averaged across all nodes and frequencies that belonged to the cluster. This average was the representative MEG marker value for that cluster and was used in subsequent correlation analyses. Therefore, the statistics presented in the results section were derived from the correlation between the averaged power ratio value of each significant cluster and the corresponding CPT ratio for each participant. As mentioned previously, correlations were first performed within the entire sample. In a second step, correlations between the average power ratio and the CPT commission ratio scores were performed independently for both intervention conditions within the sample (experimental and control). Analysis III. Responder analysis and minimal clinical important difference (MCID) Anchor-based responder analysis 57 to experimental and control groups, following a Fisher’s test to analyze statistical differences between groups for each CPT-3 outcome. The proportions of responders at the end of treatment phase for primary and secondary outcomes were pre-specified on the basis of previous work 45,58 and clinical meaningfulness for these analyses was defined as: CPT-3 (commissions, perseveration, omissions, response variability) pre-treatment score of > 54 and post-treatment score [ 45 , 54 ] (reduction to normative range); EDAH-H and EDAH-DA pre-treatment score of > 10 and post-treatment score 18 and post-treatment score < 10 (below clinically meaningful cut-off). Furthermore, for the purpose of summarizing findings across various outcomes, we calculated odds ratios using Fisher’s test and determined confidence intervals (CIs) to assess the efficacy of the experimental group compared to the control. Odds ratios for CPT-C and CPT-P were not calculated using Fisher’s test but were estimated straight from the contingency matrix due to the small sample size. No subjects in the control group reached the MCID, leading to an infinite estimation of odds ratio by Fisher’s test. We estimated a downward odds ratio in CPT-C and CPT-P by considering one subject in the control group that reached MCID in our calculations. In the responder analysis based on effect size (distribution-based method) 57 , the effect size is a standardized measure of change obtained by dividing the difference in scores from pre-treatment to post-treatment by the standard deviation of pretreatment scores. We evaluated the proportion of patients in each group that reached a MCID with small (0.3), medium (0.5) and large (0.7) effect sizes. Statistical analyses were carried out using MATLAB R2020b (Mathworks Inc) and Rstudio software. Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Results Analysis I. Group differences in Cognitive and Clinical Outcomes: linear mixed-effects models The main outcome measure showed no deviation from normality in any of the study periods. Table 1 and Fig. 2 describe skewness and kurtosis statistics for the main outcome measure CPT-C. The Shapiro-Wilk test of normality indicates that the distribution of CPT-C in any study period is not significantly different from a normal distribution ( P > 0.05). Mixed-effects models for CPT-C measure were estimated using robust constrained maximum likelihood method, introducing condition-period interaction effect with stepwise procedure to assess improvements in model fitting. Table 1 Descriptive statistics for main outcome measure commission score on Conners continuous performance test (CPT-3). Group Stage Mean SD Asymmetry Kurtosis Shapiro-Wilk test of normality p-value Control PRE 47,5217 1,5866 0,2257 -0,5393 0,904 POST 47,4783 1,6101 0,1188 -1,4289 0,2061 Experimental PRE 51,64 1,6622 -0,0234 -0,9191 0,8793 POST 47,24 1,3544 -0,0635 -0,0124 0,9505 The linear mixed-effects model for main cognitive measure (CPT-C) with a condition-period interaction effect (see Model 2 in Table 2 ) did not show a statistically significant improvement in adjustment (xi2 = 3,320; P = 0,068) compared to Model 1 (without condition-period interaction effect). However, Table 3 shows that β estimator for the condition-period interaction effect in Model 2 was statistically different from 0 (β = .56; t46 = 2,03; P = .0473). In Model 2, the inclusion of the interaction effect explains a greater proportion of the variance in CPT-C scores (R2 total = 0.56; R2 fixed = 0.06) when compared to Model 1 (R2 total = 0.52; R2 fixed = 0.04). Table 2 Standardized mean differences (β estimators) for model comparison: Model 1 (with no interaction effect); Model 2 (with interaction effect); Model 3 (with interaction effect and age as covariable). Model 1 Model 2 Model 3 (Intercept) 0,27 0.40 * 0,89 [-0.10, 0.64] [0.02, 0.79] [-1.11, 2.90] Groupcn -0,25 -0,53 -0,51 [-0.74, 0.24] [-1.09, 0.03] [-1.08, 0.06] MomentPOST -0.30 * -0.57 ** -0.57 ** [-0.58, -0.02] [-0.94, -0.19] [-0.94, -0.19] Groupcn:MomentPOST 0.56 * 0.56 * [0.02, 1.11] [0.02, 1.11] Age -0,05 [-0.26, 0.16] N 96 96 96 N (ID) 48 48 48 AIC 269,42 268,10 272,49 BIC 282,24 283,48 290,44 R2 (fixed) 0,04 0,06 0,06 R2 (total) 0,52 0,56 0,56 * P < 0.05; ** P < 0.01; *** P < 0.001 Table 3 Coefficients estimation for Model 2 (with interaction effect). Est. 2.5% 97.5% t val. d.f. p (Intercept) 0,4049 0,0184 0,7914 2,0532 71,8842 0,0437 Group-cn -0,5328 -1,0911 0,0256 -1,8702 71,8842 0,0655 Moment-POST -0,5692 -0,9444 -0,1941 -2,9742 46,0000 0,0047 Group-cn:MomentPOST 0,5636 0,0217 1,1055 2,0384 46,0000 0,0473 Additionally, it is noteworthy that Model 3, which encompasses both the interaction effect and age as a covariate, exhibits a similar performance to that of Model 2. However, it is important to acknowledge that Model 3 faces a penalty for its increased complexity when assessed through the criteria of AIC and BIC, as elucidated by Vrieze in 2012. Notably, Model 2 presents the most favorable AIC index among all models, with an AIC value of 268. Conversely, as BIC penalizes complexity, it is Model 1 that achieves the most favorable fit according to this metric (BIC = 282). It is worth noting that Model 1's BIC value is closely aligned with that of the more complex Model 2 (BIC = 283). Thus, Model 2 is accepted as the final model as it shows a statistically significant condition–moment effect, the best combination of R2 explained variance, AIC and BIC adjustment. Finally, the other Cognitive and Clinical outcome measures (a total of 53 sub-indices) were adjusted to mixed-effects models. Table 4 shows the sub-indices that had a statistically significant condition–moment interaction effect and which of them remain significant after multiple comparisons correction with FDR (applied by cognitive domain). The measures that survived family-wise FDR multiple comparisons were spatial processing inverse (β=-1,10; P = 0.0012); inhibition time (β=-0.23; P = 0.0253) and spatial processing inverse (β= -0.61; P = 0.036) (Fig. 3 ). Interestingly, most effect sizes are medium Cohen’s d > 0.5 and effect size for LE_I+, Ninh_1T and LE_I are large (Cohen’s d > 0.8). Table 4 Standardized mean differences for interaction effects in secondary outcome measures. Cognitive and Clinical Outcomes Family coeff beta (2.5% 97.5%) t stat p-value Family Adjusted FDR d d 95% CI Continuous Performance Test Commissions (%) (CPT-C) CPT-3 0,56 (0,02 1,11) 2,04 0,0473** 0,1589 0.60 [0.01, 1.19] Continuous Performance Test Detectability (CPT-d') CPT-3 0,52 (0,05 0,99) 2,16 0,0363** 0,1589 0.64 [0.04, 1.22] Continuous Performance Test Perseveration (%) (CPT-P) CPT-3 0,61 (0,01 1,21) 1,99 0,0530* 0,1589 0.59 [-0.01, 1.17] Spatial location Inverse Max items (LE_I+) LE -1,10 (-1,73 − 0,48) -3,4592 0,0012** 0,0048** -1.03 [-1.64, -0.40] Inhibition Time (Ninh_1T) Ninh -0,23 (-0,43 − 0,04) -2,39 0,0253** 0,0759* -1.00 [-1.86, -0.12] Spatial location Inverse (LE_I) LE -0,61 (-1,17 − 0,05) -2,1667 0,0360** 0,0720* -0.67 [-1.29, -0.04] NEPSY Classification Correct (Clas-C) NEPSY- Clas -0,66 (-1,28 − 0,04) -2,09 0,0418** 0,1674 -0.63 [-1.22, -0.02] ** P < 0.05; * statistical tendency P < 0.1 Analysis II. Power ratio values correlation with main outcome and other cognitive and clinical outcomes Two main dimensions were tested for correlations with power: Impulsivity Domain (CPT-Commissions, CPT-Perseverations) and Inattentiveness Domain (CPT-Omissions, CPT-Variance in response). The main outcome measure of CPT-Commissions was included in impulsivity domain as in our sample high commission error rates are combined with fast reaction times (CPT-HRT; r=-0.26; P < 0.0009; Supplementary Fig. 1 and Table 1 ). Statistically significant clusters of correlation between power ratio values and measures of impulsive and inattentive domains are presented in Table 5 . For each cognitive outcome, the clusters p-value in alpha [7–13 Hz] and beta [12–30 Hz] frequency bands are shown. Impulsivity CPT - Commissions (%) - Main outcome measure The power ratio was found to be positively correlated with the CPT-C ratio in the beta frequency band. For the whole sample analysis, the power ratio at all frequencies of this interval is higher in the treatment group compared to the control group, and it shows a positive correlation with the CPT ratio (ρ = 0.53; P = .00036) (Table 5 ; Supplementary Fig. 2). In the experimental group analysis, the power ratio shows a positive correlation with the CPT-C ratio (ρ = 0.41; P = .0078) (Fig. 4 A.2). The nodes with a statistically significant correlation to the CPT-C ratio are grouped in a cluster that is close to significance ( P = .06; Fig. 4 A.1) within the beta frequency interval (25.5–30 Hz). This cluster is primarily located in the right temporal gyrus (32%), right precuneus (12%) and right angular gyrus (12%). CPT - Perseverations (%) The power ratio was found to be positively correlated with the CPT-P ratio in the alpha frequency band. In the whole sample analysis, the power ratio at all frequencies of this interval is lower in the treatment group compared to the control group, and it shows a positive correlation with the CPT-P ratio (ρ = 0.63; P = .00001) (Table 5 ; Supplementary Fig. 3). For the experimental group analysis, the power ratio shows a positive correlation with the CPT-P ratio (ρ = 0.43; P = .0044; Fig. 4 B.2). The nodes with a statistically significant correlation to the CPT-P ratio are grouped in a statistically significant cluster ( P = .01; Fig. 4 B.1) within the frequency interval (8.25–10.5 Hz). This cluster is mainly located in the bilateral postcentral gyrus (18%), right precentral gyrus (10%) and right right middle frontal gyrus (7%). Table 5 Cluster p-values of correlations performed within the whole sample (Nall) and within the experimental sample (N ex ). Alpha [7–13 Hz] Beta [13–30 Hz] N all N ex N all N ex Impulsive Domain CPT-C - - 0.047* 0.06* CPT-P 0.03 0.01* - - Inattentiveness Domain CPT-O 0.02* 0.051 - - CPT-Var 0.047* 0.052 - - Clinical outcome EDAH-H - 0.009 - 0.006 Inattentiveness CPT - Omissions (%) The power ratio was found to be positively correlated with CPT-O ratio in the alpha frequency band. For whole sample analysis, the power ratio in all frequencies of this interval is lower in the treatment group compared to control and it shows a positive correlation with the CPT-O ratio (ρ = 0.525; P = .000043; Fig. 4 C.2). The nodes with a statistically significant correlation to CPT-O ratio are grouped in a statistically significant cluster ( P = .02) in the frequency interval (7-10Hz; Fig. 4 C.1). This cluster is mainly located in the bilateral pre-cuneus (18%), the right angular gyrus (8.5%) and the right precentral gyrus (7.5%). When the experimental group was evaluated, the power ratio shows a positive correlation with the CPT-O ratio within a cluster close to statistical significance ( P = .051) (Supplementary Fig. 4). CPT - Variability (%) The power ratio was found to be positively correlated with CPT-Var ratio in the alpha frequency band. For whole sample analysis, the power ratio in all frequencies of this interval is lower in the treatment group compared to control and it shows a positive correlation with the CPT-Var ratio (ρ = 0.58; P = .00022; Fig. 4 D.2). The nodes with a statistically significant correlation to CPT-Var ratio are grouped in a statistically significant cluster ( P = .047) in the frequency interval (8–10 Hz; Fig. 4 D.1). This cluster is mainly located in the bilateral pre-cuneus (21%), the right angular gyrus (7.8%) and the right postcentral gyrus (9.2%) When the experimental group was evaluated, the power ratio showed a positive correlation with the CPT-O ratio within a cluster close to statistical significance ( P = .052) (Supplementary Fig. 5). In essence, the enhancements observed in inattentive domains following cognitive training are linked to reductions in relative power spectra, specifically within the alpha frequency band. Moreover, it is noteworthy that the experimental condition involving personalized digital cognitive intervention (KAD_SCL_01) does not exhibit superior performance compared to the control condition. In both conditions of cognitive training, we discern a very similar correlation pattern between the improvement in inattention and the reduction in alpha power. Nevertheless, these associations seem to be stronger in the experimental group, indicating that there are alterations in brain activity that lack the strength to distinguish between the two cognitive training conditions. Clinical outcome EDAH – Hyperactivity (EDAH-H) The correlation analysis of power ratio with clinical outcome measure EDAH-H ratio in the treatment group reveals a positive correlation in alpha frequency band [7–14 Hz] (ρ = 0.846; P = .00001; Fig. 5 A.2 top) and beta frequency band [14–23 Hz] (ρ = 0.787; P = .00006) (Fig. 5 A.2 bottom). The nodes with a statistically significant correlation to EDAH-H ratio are grouped in a statistically significant cluster in the alpha frequency interval (10.5-15Hz; P = 0.004) (Fig. 5 A.1, blue) and beta frequency interval (14-23Hz; P = 0.006) (Fig. 5 A.1, red). It can be observed how this alpha cluster (blue) is mainly located in the fronto-temporo-parietal regions and almost overlapped the beta cluster (blue). Overlapping areas between alpha and beta clusters are represented in pink in Fig. 5 A.2. Interestingly, alpha involves frontal regions while beta extends throughout the parieto-temporal lobes. Analysis III. Responder analysis and minimal clinical important difference (MCID) Responder analyses demonstrated that the experimental group exhibited statistically significant reductions in CPT-C scores, bringing them within the normative range [ 45 , 54 ], with 28% (7 out of 25) of patients achieving this outcome, compared to none (0%) in the control group ( P = 0.0098; Table 6 ). Furthermore, the experimental group showed a notable shift of more patients into normative ranges across various objective measures of attention and impulsivity in CPT-3. Specifically, CPT-P scores reached the [ 45 , 54 ] range in 20% (5 out of 25) of experimental group patients versus 0% in the control group, exhibiting a statistical tendency ( P = 0.057; Table 6 ). Similarly, CPT-O showed a movement into the [ 45 , 54 ] range in 24% of the experimental group compared to 4.35% in the control group, also suggesting a statistical tendency ( P = 0.099; Table 6 ). Responder analyses for other comparisons, including all EDAH, did not indicate significant differences between the groups (Table 7 ). Nevertheless, it is noteworthy that most of the odds ratios are positive (7/8, 87,5%; Fig. 6 ), suggesting a trend wherein the experimental group exhibits a higher likelihood of having subjects with MCID after treatment. Table 6 CPT-3 minimal clinical important difference based on Anchor methods and distribution methods (effect size). Anchor-Based Fisher p-value Distribution-Based CPT-3 Group 0.3 0.5 0.7 CPT-C Experimental (7/25) 28% 0.0098 ** (15/25) 60% (14/25) 56% (13/25) 52% Control (0/23) 0% (8/23) 26.09% (6/23) 26.09% (3/23) 13.04% CPT-P Experimental (5/25) 20% 0.0507 ** (10/25) 40% (10/25) 40% (9/25) 36% Control (0/23) 0% (4/23) 17.39% (1/23) 4.35% (0/23) 0% CPT-O Experimental (6/25) 24% 0.0995* (10/25) 40% (8/25) 32% (8/25) 32% Control (1/23) 4.35% (7/23) 30.43% (5/23) 21.74% (3/23) 13.04% CPT-Var Experimental (4/23) 17.39% 0.3508 (10/23) 43.48% (9/23) 39.13% (9/23) 39.13% Control (1/20) 5% (6/20) 30% (3/20) 15% (3/20) 15% ** P < 0.05; * statistical tendency P < 0.1 Table 7 EDAH minimal clinical important difference based on Anchor methods and distribution methods (effect size). Anchor-Based Fisher p-value Distribution-Based EDAH Group 0.3 0.5 0.7 EDAH-H Experimental (5/25) 20% 0.7299 (5/25) 20% (10/25) 40% (8/25) 32% Control (6/21) 28.57% (6/21) 28.57% (11/23) 47.83% (7/23) 30.43% EDAH-DA Experimental (11/25) 44% 0.551 (11/25) 44% (11/25) 44% (9/25) 36% Control (7/21) 33.33% (7/21) 33.33% (10/21) 47.62% (8/21) 38.1% EDAH-DAH Experimental (10/25) 40% 0.7624 (10/25) 40% (11/25) 44% (9/25) 36% Control (7/21) 33.33% (7/21) 33.33% (10/21) 47.62% (8/21) 38.1% EDAH-GLOBAL Experimental (11/25) 44% 0.3635 (18/25) 72% (17/25) 68% (16/25) 64% Control (6/21) 28.57% (16/21) 76.19% (15/21) 71.43% (15/21) 71.43% ** P < 0.05; * statistical tendency P < 0.1 Discussion The randomized controlled clinical trial showed that the KAD_SCL_01 significantly improved performance on the primary outcome measure, an objective measure of inattention and impulsivity (CPT-C), in pediatric patients with ADHD compared with the control group. Across the range of secondary outcomes, additional inattentive and impulsivity scores in perseverations (CPT-P) and detectability (CPT-d’) in the CPT-C showed significant greater improvements in the experimental group from pre-intervention to post-intervention. Furthermore, other cognitive secondary measures, including the ability to inhibit automatic responses in favor of novel responses and the ability to switch between response types, cognitive flexibility and spatial working memory showed significant improvements in the experimental group compared to the controls in the post intervention assessment. In the other cognitive and clinical measures, the effect of KAD_SCL_01 from pre intervention to post intervention were not different from the control group. The current study findings of improved attention and impulsivity (via CPT-3) following treatment with KAD_SCL_01 are consistent with positive results reported in previous studies (He et al., 2023). As a digital cognitive treatment, KAD_SCL_01 could address several challenges faced by traditional interventions. First, its risk–benefit profile is favorable, none of the patients assigned to the experimental group had AEs, compared with rates of 40–60% in trials of commonly used stimulant medications 59 . Second, the digital nature of this intervention could reduce barriers to access that are inherent in other forms of behavioral or nonpharmacological interventions 60 . Digital interventions have been cited as possible ways to improve otherwise poor access to mental health services, reducing waiting lists and providing an earlier neuropsychological recovery 21 . The primary outcome measure for this trial, the CPT-3, differs from most pharmacological efficacy trials for ADHD, which typically use parent-rated or clinician-rated symptom measures. The selection of the CPT-3 was based on several factors. First, because the digital tool was designed specifically to target inattention, sustained attention, impulsivity, and vigilance, we sought an outcome that would most precisely and validly index these processes. The CPT-3 is a tool for the objective assessment of attention and inhibitory control as part of an ADHD diagnosis or for monitoring intervention outcomes and has been widely used in both clinical practice and research studies. Second, the CPT-C measures cognitive functions that are relevant to the clinical presentation of ADHD, and attention performance metrics such as commissions, omission, perseverations, and reaction times metrics are well characterized indicators of ADHD-relevant cognitive processes and are associated with clinically relevant outcomes including academic behavior and inattention and social problems 61 . In the current study, there were no differences between the experimental and the control condition on the other secondary measures, and several factors might explain these findings. First, it is possible that parent or clinician reported outcomes (i.e., EDAH and BRIEF) are not sensitive to the effects of the KAD_SCL_01. In other words, the shown effects of the intervention on cognitive processes may not be as readily observable by parents and clinicians. The clinical implications of this possibility will be important to explore in future studies. Second, expectations of efficacy have been shown to moderate intervention effects in general, and for digital interventions 62 . In our study, parents of patients in both groups believed that their child received a novel intervention for ADHD; thus, the expectation of intervention effect can be assumed for both interventions and may partially explain improvements in both groups. This design feature is different from most pharmacological studies in which patients and their caregivers are aware of a non-active, placebo condition. Finally, specific mechanisms common to KAD_SCL_01 and the control condition may have resulted in improvements in both groups. Both interventions required continued perseverance, sometimes in the face of failure, and may have trained coping and reappraisal skills or even increased the sense of self-efficacy and mastery 63 . Thus, any intervention that requires the patient to engage in a regular, structured setting that may include repeated failure or repetitiveness can be seen as a potential intervention for ADHD. In the magnetoencephalography analysis, we investigated the neurophysiological basis of improvements in ADHD performance after a cognitive training intervention. The findings revealed a positive association between changes in neuropsychological functions and electrophysiological patterns, providing biological evidence of neuromaturation 10 . Specifically, improvements in attention and inhibitory control were associated with a reduction in power within the alpha and beta frequency bands for both, KAD_SCL_01 and control treatments. Regarding impulsivity domain, the neurophysiological correlates were predominantly observed in parietal and temporal cortex, related to voluntary sensorimotor control and visuospatial processing. On the other hand, in the case of inattentiveness domain, the correlated brain regions were associated with visuospatial imagery, episodic memory retrieval, and self-processing operations. An important finding from this study was that digital cognitive stimulation driven by artificial intelligence (AI) appears to enhance neurocognitive maturation, as indicated by the stronger associations between brain activation and cognitive improvements observed in the experimental group. Particularly noteworthy is the impact on impulsivity: ADHD patients who underwent KAD_SCL_01 treatment demonstrated that greater improvements in inhibitory control were linked to a more substantial reduction in electrophysiological activity within the alpha and beta frequencies. Interestingly, it has been reported that these findings are further supported by clinical outcomes in EDAH-H, which exhibited a reduction in parents’ reported hyperactivity symptomatology associated with decreases in power across all cortex areas. These results are in line with several previous studies on the electrophysiological mechanisms of cognitive training in ADHD 43,44,64,65 . In consequence, reductions in relative power induced by the digital cognitive treatment might be reflecting an increase in the efficiency of neural networks involved in inhibitory control, involving a process of neuroplasticity through long term potentiation 66 . Finally, responder analyses showed that KAD_SCL_01 intervention exhibited significant reductions in the main outcome of commission scores CPT-3, suggesting that the digital cognitive stimulation program may have a positive behaviorally effect on the disorder. Limitations and future studies The current study has several important limitations. First, the sample size was small because the first inclusion criteria required that patients had an ADHD-C diagnosis. Second, children could not be taking medication for ADHD during the trial and could not have significant psychiatric comorbidity. Therefore, it is unclear if these findings will generalize to the broader population of patients with ADHD who have comorbid conditions or patients taking medication. Third, the study evaluated a 12-weeks intervention period with approximately 15-min per 3-days sessions. It is unclear if the benefits in attentional functioning might have been observed with a different dosing schedule. Additional studies with different intervention periods are needed, also including durability of effects 1 month after the intervention. In addition, studies investigating whether the intervention has effects in children currently treated with stimulant medication, which will help address questions of generalizability. Given these limitations, the transfer of benefit of the KAD_SCL_01 intervention to real-world settings and the full clinical meaningfulness of the findings, as well as the mechanisms underlying these effects, should be explored in further studies. Conclusions The study was a randomized controlled trial evaluating a digital neuropsychological intervention to improve attention and impulsivity in children with ADHD and its results strengthen the scientific evidence on the use of digital interventions. Indeed, KAD_SCL_01 showed significant improvements in the measurement of attention and impulsivity using the CPT-3. Furthermore, we found that these improvements in cognitive and clinical outcomes have an underlying neurophysiological basis. Improvement in the impulsivity domain has been found to be related to a MEG relative power spectral normalization, suggesting an increase in the efficiency of inhibitory control networks after KAD_SCL_0 training by long term potentiation (LTP) neuroplasticity. Finally, the risk-benefit ratio suggests that KAD_SCL_01 could be considered as a new intervention option for ADHD alongside traditional treatments, accelerating access and treatments for earlier recovery of cognitive function for activities of daily living. Abbreviations ADHD = Attention-Deficit/Hyperactivity Disorder; ADHD-C = Attention-Deficit/Hyperactivity Disorder Combined Type; AI = Artificial Intelligence; BRIEF = Behaviour Rating Inventory of Executive Function, Parent Version; BrPa_CE = Emotional Control from the BRIEF; BrPa_Flx = Flexibility from the BRIEF; BrPa_Ih = Inhibition from the BRIEF; BrPa_Ini = Initiate from the BRIEF; BrPa_MO = Working Memory from the BRIEF; BrPa_Mon = Monitoring from the BRIEF; BrPa_Org = Organization from the BRIEF; BrPa_Pla = Planning from the BRIEF; BS_Ac = Correct Items from the Symbol Search; BS_E = Incorrect Items from the Symbol Search; BS_T = Total Processed Items from the Symbol Search; Clas_C = Correct Answers from Card Classification; Clas_O = Inaccurate Answers from Card Classification; Clas_R = Repeated Errors from Card Classification; Clas_TE = Total Errors from Card Classification; CN_Ac = Correct Responses from the Coding; CN_E = Incorrect Responses from the Coding; CN_T = Processed Integer Numbers from the Coding; CPT-3 = Conners Continuous Performance Test; CPT_C = Commissions from CPC-3; CPT_d = Detectability from CPC-3; CPT_HRT = Hit Reaction Time from CPC-3; CPT_HRTISI = Hit Reaction Time Inter-Stimulus from CPC-3; CPT_HRTSD = Standard Deviation of Hit Reaction Time from CPC-3; CPT_O = Omissions from CPC-3; CPT_P = Perseverations from CPC-3; CPT_Var = Variability from CPC-3; DAN = Dorsal Attention Network; DCT = Digital Cognitive Treatment; DIG_D = Direct Correct Answers from Digit Span; DIG_D+ = Length of the Direct Last Sequences from Digit Span; DIG_I = Inverse Correct Answers from Digit Span; DIG_I+ = Length of the Inverse Last Sequences from Digit Span; DMN = Default Mode Network; DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition; EDAH = Evaluation of Attention Deficit Hyperactivity Disorder; EDAH_DA = Attention Disorder from the EDAH; EDAH_DAH = Attention Disorder and Hyperactivity from the EDAH; EDAH_H = Hyperactivity from the EDAH; EDAH_TC = Behavioral Disorder from the EDAH; EEG = electroencephalogram; FDR = False Discovery Rate; LE_D = Direct Correct Answers from WNV; LE_D+ = Length of the Direct Last Sequences from WNV; LE_I = Inverse Correct Answers from WNV; LE_I+ = Length of the Inverse Last Sequences from WNV; MEG = magnetoencephalography; NAtAu_Ac = number of correct answers from NEPSY-II; NAtAu_EC = Commissions from NEPSY-II; NAtAu_ EI = Inhibition errors from NEPSY-II; NAtAu_ EO = Omissions from NEPSY-II; Ninh_1EAc = Self-Corrected errors from NEPSY-II; Ninh_1T = Response Time from NEPSY-II; NEPSY-II = Developmental Neuropsychological Assessment-II; RCT = Randomized Controlled Trail; SAL = Salience Network; tSSS = temporal extension of the Space Signal Separation; VN = Visual Network; WISC-IV = Wechsler Intelligence Scales for Children-IV; WNV = Weschler Non-Verbal Scales; Declarations Author contributions IR, FM, JQ, JA, JARQ, JH, AM, EC, and IACG conceived and planned the research protocol. IR, DSB, IACG and PB carried out the research and analyzed the data. DSB and PC carried out the processing and analysis of the MEG. DSB, IACG, and EC wrote the manuscript and all authors provided critical feedback and helped shape the research, analysis and manuscript. Competing interests Sincrolab participated in the study design, data analysis, decision to publish, and preparation of the manuscript. IR is the cofounder of Sincrolab. JQ, JARQ, JA, AA, and JH are members of the Scientific Board of Sincrolab. JQ is also a shareholder of Instituto Neuroconductual de Madrid Ltd and a speaker on the advisory board for Takeda & Jansen. He also receives investigation funding from the Carlos III Health Institute. J.A.R.Q was on the speakers’ bureau and/or acted as consultant for Janssen-Cilag, Novartis, Shire, Takeda, Bial, Shionogi, Sincrolab, Neuraxpharm, Novartis, BMS, Medice, Rubió, Uriach, Technofarma and Raffo in the last 3 years. He also received travel awards (air tickets + hotel) for taking part in psychiatric meetings from Janssen-Cilag, Rubió, Shire, Takeda, Shionogi, Bial and Medice. The Department of Psychiatry chaired by him received unrestricted educational and research support from the following companies in the last 3 years: Janssen- Cilag, Shire, Oryzon, Roche, Psious, and Rubió. Funding This study was funded by Sincrolab and partly funded by the Centre for the Development of Industrial Technology of the Spanish Ministry of Economy, Industry, and Competitiveness. Sincrolab provided financial support in the form of salaries used for partial salary support for the authors. DS and PC received punctual financial support for carrying out the magnetoencephalography and statistical analysis and redaction. Supplementary information Supplementary information is available online. References Faraone, S. V. et al. The world federation of ADHD international consensus statement: 208 evidence-based conclusions about the disorder. Neurosci. Biobehav. Rev. 128 , 789–818 (2021). Sayal, K., Prasad, V., Daley, D., Ford, T. & Coghill, D. ADHD in children and young people: prevalence, care pathways, and service provision. Lancet Psychiatry 5 , 175–186 (2018). Sibley, M. H., Mitchell, J. T. & Becker, S. P. Method of adult diagnosis influences estimated persistence of childhood ADHD: a systematic review of longitudinal studies. Lancet Psychiatry 3 , 1157–1165 (2016). Banaschewski, T., Coghill, D. & Zuddas, A. Oxford Textbook of Attention Deficit Hyperactivity Disorder . (Oxford University Press, 2018). Sharon, D., Hämäläinen, M. S., Tootell, R. B., Halgren, E. & Belliveau, J. W. The advantage of combining MEG and EEG: comparison to fMRI in focally stimulated visual cortex. Neuroimage 36 , 1225–1235 (2007). Cortese, S. et al. Candidate diagnostic biomarkers for neurodevelopmental disorders in children and adolescents: a systematic review. World Psychiatry 22 , 129–149 (2023). Arns, M., Conners, C. K. & Kraemer, H. C. A Decade of EEG Theta/Beta Ratio Research in ADHD: A Meta-Analysis. J. Atten. Disord. 17 , 374–383 (2013). Barry, R. J., Clarke, A. R. & Johnstone, S. J. A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clin. Neurophysiol. 114 , 171–183 (2003). Satterfield, J. H., Schell, A. M., Backs, R. W. & Hidaka, K. C. A cross-sectional and longitudinal study of age effects of electrophysiological measures in hyperactive and normal children. Biol Psychiatry 19 , 973–990 (1984). Clarke, A. R., Barry, R. J., Johnstone, S. J., McCarthy, R. & Selikowitz, M. EEG development in Attention Deficit Hyperactivity Disorder: From child to adult. Clin. Neurophysiol. 130 , 1256–1262 (2019). El‐Sayed, E., Larsson, J., Persson, H., Santosh, P. & Rydelius, P. “Maturational lag” hypothesis of attention deficit hyperactivity disorder: an update. Acta Paediatr. 92 , 776–784 (2003). Soman, S. M., Vijayakumar, N., Ball, G., Hyde, C. & Silk, T. J. Longitudinal changes of resting-state networks in children with attention-deficit/hyperactivity disorder and typically developing children. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 8 , 514–521 (2023). Dobrean, A., Pasarelu, C. R. & Dopfner, M. Varieties of Psychotherapy for Attention-Deficit Hyperactivity Disorder. Evid.-Based Psychother. State Sci. Pract. (2018). Canu, W. H. & Hilton, D. C. Attention deficit hyperactivity disorder: second wave conceptualization and intervention. in Behavior Therapy: First, Second, and Third Waves 629–648 (Springer, 2022). Kretschmer, C. R., Göz Tebrizcik, B. & Dommett, E. J. Mindfulness Interventions for Attention Deficit Hyperactivity Disorder: A Systematic Review and Meta-Analysis. Psychiatry Int. 3 , 363–399 (2022). Bikic, A., Reichow, B., McCauley, S. A., Ibrahim, K. & Sukhodolsky, D. G. Meta-analysis of organizational skills interventions for children and adolescents with Attention-Deficit/Hyperactivity Disorder. Clin. Psychol. Rev. 52 , 108–123 (2017). Evans, S. W., Owens, J. S. & Bunford, N. Evidence-Based Psychosocial Treatments for Children and Adolescents with Attention-Deficit/Hyperactivity Disorder. J. Clin. Child Adolesc. Psychol. 43 , 527–551 (2014). Groenman, A. P. et al. An individual participant data meta-analysis: behavioral treatments for children and adolescents with attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 61 , 144–158 (2022). Wright, N. et al. Practitioner review: pathways to care for ADHD–a systematic review of barriers and facilitators. J. Child Psychol. Psychiatry 56 , 598–617 (2015). Kappi, A. & Martel, M. Parental barriers in seeking mental health services for attention deficit hyperactivity disorder in children: systematic review. J. Atten. Disord. 26 , 408–425 (2022). Hollis, C. et al. Annual Research Review: Digital health interventions for children and young people with mental health problems–a systematic and meta-review. J. Child Psychol. Psychiatry 58 , 474–503 (2017). Elaklouk, A. M. & Zin, N. A. M. Design and usability evaluation of rehabilitation gaming system for cognitive deficiencies. in 2017 6th International Conference on Electrical Engineering and Informatics (ICEEI) 1–6 (IEEE, 2017). Neudecker, C., Mewes, N., Reimers, A. K. & Woll, A. Exercise interventions in children and adolescents with ADHD: a systematic review. J. Atten. Disord. 23 , 307–324 (2019). Kollins, S. H. et al. A novel digital intervention for actively reducing severity of paediatric ADHD (STARS-ADHD): a randomised controlled trial. Lancet Digit. Health 2 , e168–e178 (2020). Adabla, S., Nabors, L. & Hamblin, K. A scoping review of virtual reality interventions for youth with attention-deficit/hyperactivity disorder. Adv. Neurodev. Disord. 5 , 304–315 (2021). de Ramón, I. et al. Alpha-band power increases in posterior brain regions in attention deficit hyperactivity disorder after digital cognitive stimulation treatment: randomized controlled study. Brain Commun. 4 , fcac038 (2022). Medina, R. et al. Electrophysiological brain changes associated with cognitive improvement in a pediatric attention deficit hyperactivity disorder digital artificial intelligence-driven intervention: randomized controlled trial. J. Med. Internet Res. 23 , e25466 (2021). Penuelas-Calvo, I. et al. Video games for the assessment and treatment of attention-deficit/hyperactivity disorder: A systematic review. Eur. Child Adolesc. Psychiatry 1–16 (2020). Rodrigo-Yanguas, M., González-Tardón, C., Bella-Fernández, M. & Blasco-Fontecilla, H. Serious video games: angels or demons in patients with attention-deficit hyperactivity disorder? A quasi-systematic review. Front. Psychiatry 13 , 798480 (2022). Michelini, G. et al. Methylphenidate, Guanfacine, and Combined Treatment Effects on Electroencephalography Correlates of Spatial Working Memory in Attention-Deficit/Hyperactivity Disorder. J. Am. Acad. Child Adolesc. Psychiatry 62 , 37–47 (2023). Janssen, T. W. P. et al. A Randomized Controlled Trial Investigating the Effects of Neurofeedback, Methylphenidate, and Physical Activity on Event-Related Potentials in Children with Attention-Deficit/Hyperactivity Disorder. J. Child Adolesc. Psychopharmacol. 26 , 344–353 (2016). Loo, S. K. & Arns, M. Should the EEG–Based Theta to Beta Ratio Be Used to Diagnose ADHD? ADHD Rep. 23 , 8–13 (2015). Clarke, A. R., Barry, R. J., Bond, D., McCarthy, R. & Selikowitz, M. Effects of stimulant medications on the EEG of children with attention-deficit/hyperactivity disorder. Psychopharmacology (Berl.) 164 , 277–284 (2002). Hermens, D. F. et al. Responses to methylphenidate in adolescent AD/HD: evidence from concurrently recorded autonomic (EDA) and central (EEG and ERP) measures. Int. J. Psychophysiol. Off. J. Int. Organ. Psychophysiol. 58 , 21–33 (2005). Bresnahan, S. M., Barry, R. J., Clarke, A. R. & Johnstone, S. J. Quantitative EEG analysis in dexamphetamine-responsive adults with attention-deficit/hyperactivity disorder. Psychiatry Res. 141 , 151–159 (2006). Clarke, A. R., Barry, R. J., McCarthy, R., Selikowitz, M. & Johnstone, S. J. Effects of stimulant medications on the EEG of girls with attention-deficit/hyperactivity disorder. Clin. Neurophysiol. 118 , 2700–2708 (2007). Barry, R. J. et al. Acute atomoxetine effects on the EEG of children with attention-deficit/hyperactivity disorder. Neuropharmacology 57 , 702–707 (2009). Chiarenza, G. A. et al. The quantified EEG characteristics of responders and non-responders to long-term treatment with atomoxetine in children with attention deficit hyperactivity disorders. Int. J. Psychophysiol. Off. J. Int. Organ. Psychophysiol. 104 , 44–52 (2016). Michelini, G., Norman, L. J., Shaw, P. & Loo, S. K. Treatment biomarkers for ADHD: Taking stock and moving forward. Transl. Psychiatry 12 , 444 (2022). Choi, H., Park, S., Kim, K. K., Lee, K. & Rhyu, H.-S. Acute effects of aerobic stretching, health and happiness improving movement exercise on cortical activity of children. J. Exerc. Rehabil. 12 , 320–327 (2016). Luo, X. et al. A randomized controlled study of remote computerized cognitive, neurofeedback, and combined training in the treatment of children with attention-deficit/hyperactivity disorder. Eur. Child Adolesc. Psychiatry 32 , 1475–1486 (2023). Taya, F., Sun, Y., Babiloni, F., Thakor, N. & Bezerianos, A. Brain enhancement through cognitive training: a new insight from brain connectome. Front. Syst. Neurosci. 9 , (2015). Maclin, E. L. et al. Learning to multitask: effects of video game practice on electrophysiological indices of attention and resource allocation. Psychophysiology 48 , 1173–1183 (2011). Meyer, K. N. et al. Computer-based inhibitory control training in children with Attention-Deficit/Hyperactivity Disorder (ADHD): Evidence for behavioral and neural impact. PloS One 15 , e0241352 (2020). Conners, C. K. Conners continuous performance test 3rd edition (Conners CPT 3) & connors continuous auditory test of attention (Conners CATA): Technical manual. in (MHS, 2014). Segal, D. L. Diagnostic and statistical manual of mental disorders (DSM-IV-TR). Corsini Encycl. Psychol. 1–3 (2010). Korkman, M., Kirk, U., & Kemp, S. L. (2007). NEPSY II. Administrative Manual. San Antonio, Texas: Psychological Corporation. Wechsler, D. (2007). WISC-IV: Escala de Inteligencia de Wechsler Para Niños-IV (2a Ed.). Madrid, Spain: Pearson Clinical. Wechsler, D. & Naglieri, J. A. Escala No Verbal de Aptitud Intelectual de Wechsler (WNV) . (Pearson, 2011). i Riba, A. F. & Narbona, J. Escalas Para La Evaluación Del Trastorno Por Déficit de Atención Con Hiperactividad (EDAH) . (Tea, 1998). Gioia, G. A., Isquith, P. K., Guy, S. C. & Kenworthy, L. Behavior Rating Inventory of Executive Function: BRIEF . (Psychological Assessment Resources Odessa, FL, 2000). Taulu, S. & Hari, R. Removal of magnetoencephalographic artifacts with temporal signal-space separation: Demonstration with single-trial auditory-evoked responses. Hum. Brain Mapp. 30 , 1524–1534 (2009). Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J.-M. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011 , 156869 (2011). Belouchrani, A., Abed-Meraim, K., Cardoso, J.-F. & Moulines, E. A blind source separation technique using second-order statistics. IEEE Trans. Signal Process. 45 , 434–444 (1997). Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B Methodol. 57 , 289–300 (1995). Zalesky, A., Fornito, A. & Bullmore, E. T. Network-based statistic: identifying differences in brain networks. Neuroimage 53 , 1197–1207 (2010). Franceschini, M. et al. The Minimal Clinically Important Difference Changes Greatly Based on the Different Calculation Methods. Am. J. Sports Med. 51 , 1067–1073 (2023). Farré, A. & Narbona, J. EDAH. evaluación del trastorno por déficit de atención con hiperactividad. Madr. Spain TEA (2003). Wolraich, M. L. et al. Randomized, controlled trial of OROS methylphenidate once a day in children with attention-deficit/hyperactivity disorder. Pediatrics 108 , 883–892 (2001). Koerting, J. et al. Barriers to, and facilitators of, parenting programmes for childhood behaviour problems: a qualitative synthesis of studies of parents’ and professionals’ perceptions. Eur. Child Adolesc. Psychiatry 22 , 653–670 (2013). Antonini, T. N., Narad, M. E., Langberg, J. M. & Epstein, J. N. Behavioral correlates of reaction time variability in children with and without ADHD. Neuropsychology 27 , 201 (2013). Boot, W. R., Simons, D. J., Stothart, C. & Stutts, C. The pervasive problem with placebos in psychology: Why active control groups are not sufficient to rule out placebo effects. Perspect. Psychol. Sci. 8 , 445–454 (2013). Granic, I., Lobel, A. & Engels, R. C. The benefits of playing video games. Am. Psychol. 69 , 66 (2014). Borghini, G. et al. Quantitative Assessment of the Training Improvement in a Motor-Cognitive Task by Using EEG, ECG and EOG Signals. Brain Topogr. 29 , 149–161 (2016). Jaušovec, N. & Jaušovec, K. Working memory training: improving intelligence--changing brain activity. Brain Cogn. 79 , 96–106 (2012). Constantinidis, C. & Klingberg, T. The neuroscience of working memory capacity and training. Nat. Rev. Neurosci. 17 , 438–449 (2016). Additional Declarations No competing interests reported. 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The figure illustrates a positive correlation cluster of power ratio with the CPT ratios in the experimental subgroup, with the control group represented. 4.B) Measuring impulsivity based on Perseverations (A) in CPT-3 test. Positive correlation cluster of the power ratio with the CPT ratios in the experimental subgroup, with the control group represented. 4C) and 4D) Measuring inattentiveness based on Omissions (A) and Variance (B) in CPT-3 test. Positive correlation clusters of power ratio with CPT ratios in the whole sample.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4329802/v1/25fedf547d3bc7feb53f94cd.png"},{"id":55770555,"identity":"0861fadf-c773-4792-9788-39ec78d5091a","added_by":"auto","created_at":"2024-05-02 21:00:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1170666,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation clusters between power ratio and clinical outcome EDAH – Hyperactivity (EDAH-H). A.1) Blue: positive correlation cluster of power ratio within alpha band in treatment group. Red: positive correlation cluster of power ratio with EDAH – Hyperactivity (EDAH-H) in beta band in treatment group. Pink: Overlapping areas between alpha and beta frequency clusters. A.2) Positive correlation cluster of power ratio with the EDAH-H ratios in the experimental subgroup, with the control group represented.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4329802/v1/4044252dd629b40a61ff0b06.png"},{"id":55770554,"identity":"06e5e6bc-4f2c-4eec-9609-631965bb2388","added_by":"auto","created_at":"2024-05-02 21:00:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":16501,"visible":true,"origin":"","legend":"\u003cp\u003eOdds ratio Forest plots for CPT-3 and EDAH outcomes based on Fisher’s test.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4329802/v1/fc7e0fe8d6ba0d520f677fb7.png"},{"id":55771345,"identity":"2838d137-51e0-4b88-931e-7668242f1687","added_by":"auto","created_at":"2024-05-02 21:08:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2758904,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4329802/v1/29b5dc38-6529-42de-a66b-0833fcfaad49.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decreased impulsiveness and MEG normalization after AI- digital therapy in ADHD children: a RCT","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAttention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders that affects 2\u0026ndash;7% worldwide of children, mostly boys, and often lasts into adulthood\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. It is characterized by persistent, pervasive, and impairing symptoms of inattention and/or hyperactivity/impulsivity that affects functioning in daily life\u003csup\u003e4\u003c/sup\u003e. Indeed, children with ADHD show moderate impairments in multiple cognitive domains including attention, executive functions and memory\u003csup\u003e1,4\u003c/sup\u003e. These cognitive impairments have also been investigated in the brain correlates of ADHD through neurophysiological techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG)\u003csup\u003e5,6\u003c/sup\u003e. Increasing evidence in spectral analysis are showing that ADHD patients present a pattern of significantly higher theta and alpha relative power and lower beta relative power, along with higher theta/alpha and theta/beta ratios\u003csup\u003e7\u0026ndash;9\u003c/sup\u003e. The \u0026ldquo;Maturational Lag\u0026rdquo; hypothesis suggests that electrophysiological correlates of ADHD support a model of maturational delay on the central nervous system, rather than a different neurological dysfunction\u003csup\u003e10,11\u003c/sup\u003e. When compared to controls, ADHD groups\u0026rsquo; slow frequency activity decreased later in age, revealing a delay in maturation\u003csup\u003e10\u003c/sup\u003e. However, among ADHD types, the hyperactive/impulsive showed a faster reduction in slow frequency bands in comparison to the inattentive type\u003csup\u003e8\u003c/sup\u003e. Recently, this maturational hypothesis has also been supported from graph theory analysis\u003csup\u003e12\u003c/sup\u003e, as ADHD children seem to have differential functional network development (decreased integration and segregation) in the regions overlapping with default mode network (DMN), salience network (SAL), dorsal attention network (DAN) and visual network (VN).\u003c/p\u003e \u003cp\u003eTreatment options can be divided into three main categories: a) pharmacological; b) nonpharmacological; and c) combined treatments\u003csup\u003e13\u003c/sup\u003e. Pharmacological treatments use stimulant or non-stimulant medications according to the specific ADHD symptoms. Non pharmacological treatments include several psychosocial therapies such as behavioral training of parents, classroom and peer interventions, cognitive behavior therapies including skill training, cognitive training and neurofeedback), and mindfulness\u003csup\u003e14,15\u003c/sup\u003e. Pharmacological treatments showed to be effective on the persistent ADHD symptoms and among the psychosocial treatments, behavioral parent, classroom and peer training, and skills training, have shown moderate improvement in ADHD symptoms\u003csup\u003e16\u0026ndash;18\u003c/sup\u003e while the other psychosocial interventions, such as the mindfulness therapies, need more research in order to establish the efficacy. Despite the scientific evidence on ADHD treatments, several barriers of access to treatments and long waiting lists, and other factors related to costs, stigma, and low treatment adherence constitute some of the limitations in treatment access\u003csup\u003e19\u0026ndash;21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn order to overcome such barriers, technological developments such as mobile applications, have increased to assess and treat several disorders, including ADHD\u003csup\u003e23,24\u003c/sup\u003e. These applications are familiar to patients and can provide more engagement and motivation than traditional cognitive treatments and patients can test their abilities and skills without any danger, as well as they can improve the treatment effectiveness providing a personalization of the different levels of cognitive task according to the severity of the symptoms and overcoming the limited resources and facilitation of conventional rehabilitation methods\u003csup\u003e22\u003c/sup\u003e. Neudecker \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e23\u003c/sup\u003e found significant improvements in executive functions and inhibition, parent ratings of psychological difficulty, and motor skills, after a home-based exergaming intervention in a sample of 51 ADHD children (ages 8\u0026ndash;12 years). Preliminary positive evidence indicates that such interventions are associated with reduced inattentive symptoms\u003csup\u003e24\u003c/sup\u003e and with mixed findings on impulsivity\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e. Recently, several scoping and systematic reviews aimed at synthesizing the evidence around the use of technological cognitive intervention systems in children and youth with ADHD\u003csup\u003e25,28,29\u003c/sup\u003e. Despite these preliminary positive results and the interest shown by the healthcare professionals, these methods need more evidence to be practically and widely implemented.\u003c/p\u003e \u003cp\u003eRegarding the neural mechanisms that underlie these cognitive changes, stimulant pharmacological treatment appears to normalize electroencephalogram (EEG) abnormalities post-administration, reducing theta [4\u0026ndash;7 Hz] band power spectra in 44% of the studies (8 out of 18)\u003csup\u003e30\u0026ndash;36\u003c/sup\u003e. Non-stimulant pharmacological treatment has been found to normalize the EEG spectral profile in 40% of the studies (2 out of 5)\u003csup\u003e30,37,38\u003c/sup\u003e. Several studies have also focused on non-pharmacological treatments\u003csup\u003e39\u003c/sup\u003e. While physical exercise\u003csup\u003e40\u003c/sup\u003e have shown to normalize EEG effects, findings have been inconclusive for neurofeedback\u003csup\u003e31,41\u003c/sup\u003e. Finally, digital cognitive treatments (DCT) have demonstrated brain enhancements and a normalization of the EEG spectral profile\u003csup\u003e42\u003c/sup\u003e. For example, it has been found that alpha is suppressed during oddball tasks after training, suggesting that alpha may be related to attention switching and workload\u003csup\u003e43\u003c/sup\u003e. Computer-based inhibitory control training showed decreased relative theta power in resting EEG and trending improvements in parent ratings in inattentive behaviors\u003csup\u003e44\u003c/sup\u003e. According to this, the latest technological cognitive intervention systems are designed and developed to focus on brain neuroplasticity. Usually, it consists of training one or more cognitive functions through personalized and adaptive methods based on artificial intelligence algorithms that automatically adjust task difficulty or game modality according to the patient\u0026rsquo;s performance and needs.\u003c/p\u003e \u003cp\u003eStarting from these premises, the main objective of this study was to assess the efficacy of the digital neuropsychological intervention tool (KAD_SCL_01) on inhibitory control in pediatric ADHD combined type. By comparing two intervention conditions of random allocation (KAD_SCL_01 condition vs. control condition), we assessed pre-post interaction effects of KAD_SCL_01 on inhibitory control. Main outcome measure for this objective has been the Commission score from Conners Continuous Performance Test (CPT-3)\u003csup\u003e45\u003c/sup\u003e. Secondly, the study tested the efficacy of intervention with KAD_SCL_01 on other cognitive processes and clinical measures (see \u0026ldquo;Treatment assessment\u0026rdquo; in Materials and Methods). Finally, the study aimed to show the relationship and changes between neuropsychological and clinical measures and power spectral activity in the ADHD brain networks.\u003c/p\u003e"},{"header":"Materials and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eAn initial sample of 56 children diagnosed with combined-type ADHD (ADHD-C) were enrolled from health facilities, schools, and associations in the community of Madrid (Spain), with prior authorization by the latter to researchers to contact with legal guardians. The enrollment consisted of emails, phone, and video calls with the legal guardians in which were provided all the clinical trial information. The legal guardians of the participants who agreed to participate, were subsequently contacted to verify the eligibility criteria to be included in the clinical trial. To be included in the clinical trial, the following criteria had to be met: a) children age between 8 and 11 years old; b) children diagnosed with ADHD-C according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV-TR)\u003csup\u003e46\u003c/sup\u003e criteria by an authorized professional; c) stopping ADHD medication three days before visit days (according to the methylphenidate specifications, it has a half-life of 3.5 hours and the 90% is excreted in urine and the rest in feces in 48\u0026ndash;96 hours); d) maintaining of the same pharmacological doses during the clinical trial; e) non-use of other psychoactive drugs; f) no other psychiatric comorbidities and g) the compliance with the clinical protocol. The exclusion criteria were the following: a) beginning or discontinuing behavioral therapies or psychoactive drugs during the clinical trial; b) use of psychoactive drugs and presence of suspicion of substance abuse in the last six months; c) any other psychological diagnosis and comorbidity; d) children with hand motor difficulties enabled to use the mobile devices (tablet or smartphone); and e) children with blindness or visual acuity difficulties. From the initial sample of 56 participants, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6 declined to participate and \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 was excluded for not meeting all the inclusion criteria. Those who met the inclusion criteria were randomly assigned to the experimental or control condition. From the 49 participants, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 subjects did not perform the post-assessment with magnetoencephalography (MEG) because they did not complete the treatment protocol and \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5 were discarded from the analysis because of the quality of the MEG register. The final sample of \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;41 subjects underwent the final pre-and-post intervention assessment including MEG recordings, neuropsychological batteries, and clinical questionnaires: 20 in experimental condition (male\u0026thinsp;=\u0026thinsp;16, female\u0026thinsp;=\u0026thinsp;4; M\u003csub\u003eage\u003c/sub\u003e = 9.41 years, SD\u0026thinsp;=\u0026thinsp;1.22) and 21 in control condition (male\u0026thinsp;=\u0026thinsp;19, female\u0026thinsp;=\u0026thinsp;2; M\u003csub\u003eage\u003c/sub\u003e = 9,38 years, SD\u0026thinsp;=\u0026thinsp;1,21). Prior to inclusion in the clinical trial, the legal guardians of all participants received and signed an informed consent form explaining the objectives of the research and the characteristics of the experimental procedure. The clinical trial obtained the approval of the Ethics Committee at the San Carlos Hospital (Madrid, Spain) and the entire procedure was designed following the guidelines of the Declaration of Helsinki regarding the ethical standards to be followed in any procedure that includes human beings. This clinical trial is registered in the ISRCTN registry (ISRCTN71041318). Reporting Trials (CONSORT) 2010 flow diagram is presented in Figure. 1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eNeuropsychological Outcomes\u003c/h2\u003e \u003cp\u003eThe assessment protocol pre-and-post digital intervention consisted of the following neuropsychological batteries:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMain neuropsychological outcome measure:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCommission score (CPT_C) from the Conners Continuous Performance Test (CPT-3)\u003csup\u003e45\u003c/sup\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSecondary neuropsychological outcomes measures:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eConners Continuous Performance Test (CPT-3): for each dimension (inattentiveness, impulsivity, sustained attention, and vigilance) the following scores had been considered as secondary outcomes:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInattentiveness: detectability (CPT_d), omissions (CPT_O), hit reaction time (CPT_HRT), standard deviation of HRT (CPT_HRTSD), response variability (CPT_Var);\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eImpulsivity: HRT and preservations (CPT_P)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSustained Attention: HRT block change, omissions by block (CPT_HRT and CPT_O);\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVigilance: HRT inter-stimulus (CPT_HRTISI), Interval (ISI) change and omissions by ISI\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDevelopmental Neuropsychological Assessment-II (NEPSY-II)\u003csup\u003e47\u003c/sup\u003e the following subtests related to attention domain were administered to participants pre-and-post the digital intervention:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAuditory attention and response set. From this test, number of correct answers (NAtAu_Ac) and commissions (NAtAu_EC), omissions (NAtAu_ EO) and inhibition errors (NAtAu_ EI) scores have been computed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInhibition. From this subtest, response time (Ninh_1T), number of errors (Ninh_1E) and number of self-corrected errors (Ninh_1EAc) have been computed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCard Classification. From this subtest, number of correct answers (Clas_C), repeated errors (Clas_R), inaccurate answers (Clas_O) and total errors (Clas_TE) have been computed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWechsler Intelligence Scales for Children-IV (WISC-IV)\u003csup\u003e48\u003c/sup\u003e: the following subtests from the Working Memory Index and Processing Speed Index were administered to participants pre-and-post digital intervention:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDigit Span. From this subtest, total number of correct responses (DIG_D and DIG_I) and the length of the last sequence (DIG_D\u0026thinsp;+\u0026thinsp;and DIG_I+) successfully repeated for each condition have been computed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCoding. From this subtest, correct (CN_Ac) and incorrect responses (CN_E) and the total number of processed integer numbers (CN_T) have been computed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSymbol search. From this subtest, number of correct (BS_Ac) and incorrect items (BS_E) and total processed items (BS_T) have been computed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFrom Weschler Non-Verbal Scales (WNV), the Corsi Block Tapping Test\u003csup\u003e49\u003c/sup\u003e has been administered and total number of correct answers (LE_D and LE_I) and the length of the last sequence (LE_D\u0026thinsp;+\u0026thinsp;and LE_I+) in each condition have been computed.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eClinical outcomes\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eScale for the Evaluation of Attention Deficit Hyperactivity Disorder (EDAH)\u003csup\u003e50\u003c/sup\u003e: it consists of 20-items and aims to assess the main ADHD features and any coexisting behavioral disorders. Inattention (EDAH_DA), hyperactivity (EDAH_H), hyperactivity and inattention (EDAH_DAH) behavioral disorders (EDAH_TC) and the global indexes have been computed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBehaviour Rating Inventory of Executive Function, Parent Version (BRIEF)\u003csup\u003e51\u003c/sup\u003e: it consists of two forms (parent and teacher form) of 86-items each and aims to assess executive functioning in daily life activities. The parent form has been administered in the clinical trial and inhibition (BrPa_Ih), flexibility (BrPa_Flx), working memory (BrPa_MO), emotional control (BrPa_CE), planning (BrPa_Pla), initiate (BrPa_Ini), organization (BrPa_Org), and monitoring (BrPa_Mon) scores have been computed.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNeurophysiological outcomes\u003c/h2\u003e \u003cp\u003eNeurophysiological pre-and-post digital intervention data have been recorded using the Elekta-Neuromag MEG system composed of 306 channels (Elekta AB) at the Center for Biomedical Technology (Madrid, Spain). MEG data have been recorded at a sampling frequency of 1000 Hz and have been online filtered with a band-pass between 0.1 Hz and 330 Hz.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMagnetoencephalography data acquisition\u003c/h2\u003e \u003cp\u003eParticipants were placed inside the magnetically shielded room in which the MEG was located. The shape of each subject\u0026rsquo;s head was defined with respect to three anatomical points (nasion and bilateral preauricular points) using a 3D digitizer (Fastrak, Polhemus, VT, USA) and head movement was tracked through four HPI (Head Position Indicator) coils attached to the scalp. These HPI coils continuously monitored the subjects\u0026rsquo; head movements, while eye movements were monitored by a vertical electrooculogram (EOG) unit consisting of a pair of bipolar electrodes. For the MEG recording, the participants were given instructions that included to relax, not to move and not to move their heads outside the MEG helmet, as well as to remain silent. For the pre-and-post MEG data recording was asked to participants to close their eyes for 5-minutes to stay in a resting state. Closing the eyes was facilitated by reducing the room lighting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePre-processing and power calculations\u003c/h2\u003e \u003cp\u003eData preprocessing was carried out in several steps: 1) The temporal extension of the Space Signal Separation (tSSS)\u003csup\u003e52\u003c/sup\u003e method was applied to remove external noise from raw data. A window length of 10 seconds and a correlation threshold of 0.90 were used as input parameters for the Maxfilter (v 2.2 Elekta AB, Stockholm, Sweden) software; 2) Ocular, cardiac and muscle artifacts were automatically detected with FieldTrip package\u003csup\u003e53\u003c/sup\u003e and manually validated by a MEG expert. 3) Eye-blinks and cardiac activity were removed using an independent component analysis based on SOBI\u003csup\u003e54\u003c/sup\u003e; 4) The data were segmented in 4-second trials and trials marked as containing artifacts were discarded from subsequent analysis.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePower calculation\u003c/span\u003e. First, clean MEG time series were filtered with a band-pass filter between 2\u0026ndash;30 Hz with 0.5 s padding. For each node of the grid, the power spectrum was computed using discrete prolate spheroidal (Slepian) sequences (dpss) with 1 Hz smoothing. The power spectrum was normalized by the total power over the 2\u0026ndash;30 Hz range. Then, the source template with 2459 nodes in a 10 mm spacing grid was segmented into 78 regions of the Automated Anatomical Labeling (AAL48) atlas66, excluding the cerebellum, basal ganglia, thalamus, and olfactory cortices. These 78 regions of interest included 1202 of the original 2459 nodes. After averaging trials across subjects, the result ended up with a source-reconstructed power matrix of 1202 nodes \u0026times; 41 participants. Finally, the power ratio (post-condition/pre-condition) was calculated to assess the change between the two conditions of the follow-up.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSource reconstruction\u003c/span\u003e. A template head model was used for source reconstruction due to the absence of individual anatomy data. The head model consisted of a single layer representing the inner skull interface, generated from the union of grey matter, white matter, and cerebrospinal fluid in the Montreal Neurological Institute (MNI) brain. As a result, a regular grid of sources with 10mm spacing defined in MNI space was obtained. From these, the 1202 source positions falling under cortical areas of the AAL atlas were extracted. The scalp of the MNI template was linearly transformed to match the individual head shape using an affine transformation generated with an iterative algorithm, and the same transformation was applied to both head and source models. The lead field was calculated using a single shell model. Finally, a Linearly Constrained Minimum Variance beamformer was applied to reconstruct the source's time series using the trial-average covariance matrix and a regularization factor of 5% of the average sensor power.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDigital cognitive intervention\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eExperimental condition (KAD_SCL_01)\u003c/h2\u003e \u003cp\u003eExperimental condition consisted of a digital cognitive intervention delivered through a serious game via a mobile device (mobile and/or tablet). The intervention included 14 cognitive tasks-games that have been designed and developed based on scientific-supported neuropsychological tasks (such as go/no-go task, n-back task, etc.). The scheduled intervention consisted of 12-weeks for three sessions per week of 15-minutes each session. The first intervention session consisted of a selection from among the 14 cognitive tasks-games computed according to the age and the cognitive profile, which will change over the course of the intervention to address the different cognitive functions. The results obtained in each treatment session have been transmitted to an AI that through algorithms automatically adjusted the selection of the cognitive tasks-games and levels of difficulty to continue the intervention.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eControl condition\u003c/h2\u003e \u003cp\u003eControl condition consisted of three entertainment games (Knightmare Tower, Bloons Super Monkey and Super Staker 2) including in the Kongregate open-access platform (Kongragate Inc). Knightmare Tower is a runner-like video game in which the player must ascend to the top of a tower while avoiding enemies and traps. Bloons Super Monkey is a video game, like the classic Space Invaders, in which the player must defeat enemies and obstacles by moving left or right. Last, Super Stacker 2 is a puzzle-like video game in which the player must locate a certain number of geometrical pieces to keep them balanced. The participants played the three games according to the same protocol of the experimental condition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExperimental design and procedure\u003c/h2\u003e \u003cp\u003eA single-center, parallel, single-blind, randomized controlled trial has been conducted. The study procedure included four visits: 1. Recruitment and screening according to inclusion and exclusion criteria; 2. Pre-intervention assessment included MEG recordings, neuropsychological batteries and clinical questionnaires; 3. At-home digital intervention and 4. Post-intervention assessment included MEG recordings, neuropsychological batteries and clinical questionnaires. The order of neuropsychological batteries and MEG recordings was counterbalanced in the pre-and-post-assessments.\u003c/p\u003e \u003cp\u003eParticipants who met with the eligibility criteria, have successively been randomized with a ratio of 1:1 and an allocation probability of 0.50 to be included in the experimental or control group. Pre-and-post MEG and neuropsychological assessments have been performed at the Center for Biomedical Technology, at the Technical University of Madrid by a Sincrolab researcher. Clinical pre-and-post questionnaires have been performed by the children\u0026rsquo;s legal guardians.\u003c/p\u003e \u003cp\u003eThe at-home digital intervention consisted of 3 sessions per week of 15 minutes for 12 weeks for both groups. The whole intervention period of compliance, as well as the possible adverse events have been monitored by the Sincrolab researcher. After the 12 weeks of intervention protocol, participants who completed at least 80% of the intervention sessions (28 out of 36) have been appointed for the post-intervention assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003ePower analyses determined that a sample size of 56 participants would be sufficient to detect a mean difference of 0.64 SD in the commission score from the CPT-3, with a significance level of α\u0026thinsp;=\u0026thinsp;.05 and a power of 0.8 (1-β\u0026thinsp;=\u0026thinsp;.8). The calculation procedure followed the sample size estimation for a 2-tailed, 2-samples mean difference with a correction factor for repeated measures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis I. Group differences in Cognitive and Clinical Outcomes: linear mixed-effects models\u003c/h2\u003e \u003cp\u003eThe cognitive and clinical outcome measures were adjusted to linear mixed-effects models with a random intercept and fixed slope. For the random effect factor, an unstructured covariance matrix (Sigma) using the robust restricted maximum likelihood method has been estimated. Using a stepwise method, each model added age as a co-variable. To control p-values for multiple comparisons, False Discovery Rate (FDR) correction was applied\u003csup\u003e55\u003c/sup\u003e. As the commission score from CPT-3 was set as the main outcome measure, no correction for multiplicity was applied. Regarding the rest of the outcome measures, FDR adjustments were applied considering different cognitive processes (i.e. inhibition) as independent statistical families.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis II. Power ratio values correlation with CPT-C and other cognitive and clinical outcomes\u003c/h2\u003e \u003cp\u003eThe goal of this methodology was to extract any neurophysiological markers whose dynamics could be associated with the evolution of the inhibition-control performance. Such analysis relied on network-based statistics\u003csup\u003e56\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFirst, clusters were formed based on a criterion of spatial and frequency adjacency. Each cluster comprised several adjacent nodes, which systematically exhibited a significant partial correlation (with age as a covariate) at a minimum of three 3 consecutive frequency steps (a 1-Hz interval) between their corresponding power ratio values and CPT ratio (Spearman correlation coefficient \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). All nodes within a cluster needed to display the same sign for the correlation coefficient for the cluster to be considered a functional unit. Only clusters involving at least 0.5% of the nodes (i.e., a minimum of 6 nodes) at each frequency step were considered. Cluster-mass statistics were assessed by summing the Spearman ρ values across all nodes and significant frequency steps.\u003c/p\u003e \u003cp\u003eSecond, to control for multiple comparisons, the entire analysis pipeline was then repeated 5000 times, with random assignments between power ratio estimates and the neuropsychological scores. At each iteration, the maximum statistic of the surrogate clusters (in absolute value) was recorded, creating a maximal null distribution that would ensure control of the familywise error rate at the cluster level. The cluster-mass statistics for each cluster in the original dataset were compared with the same measure in the randomized data. The network-based statistics P value represents the proportion of the permutation distribution with cluster-mass statistic values greater or equal to the cluster-mass statistic value of the original data.\u003c/p\u003e \u003cp\u003ePower ratio values were averaged across all nodes and frequencies that belonged to the cluster. This average was the representative MEG marker value for that cluster and was used in subsequent correlation analyses. Therefore, the statistics presented in the \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003eresults\u003c/span\u003e section were derived from the correlation between the averaged power ratio value of each significant cluster and the corresponding CPT ratio for each participant. As mentioned previously, correlations were first performed within the entire sample. In a second step, correlations between the average power ratio and the CPT commission ratio scores were performed independently for both intervention conditions within the sample (experimental and control).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis III. Responder analysis and minimal clinical important difference (MCID)\u003c/h2\u003e \u003cp\u003eAnchor-based responder analysis\u003csup\u003e57\u003c/sup\u003e to experimental and control groups, following a Fisher\u0026rsquo;s test to analyze statistical differences between groups for each CPT-3 outcome. The proportions of responders at the end of treatment phase for primary and secondary outcomes were pre-specified on the basis of previous work\u003csup\u003e45,58\u003c/sup\u003e and clinical meaningfulness for these analyses was defined as: CPT-3 (commissions, perseveration, omissions, response variability) pre-treatment score of \u0026gt;\u0026thinsp;54 and post-treatment score [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] (reduction to normative range); EDAH-H and EDAH-DA pre-treatment score of \u0026gt;\u0026thinsp;10 and post-treatment score\u0026thinsp;\u0026lt;\u0026thinsp;10 (below clinically meaningful cut-off); EDAH-DAH pre-treatment score of \u0026gt;\u0026thinsp;18 and post-treatment score\u0026thinsp;\u0026lt;\u0026thinsp;10 (below clinically meaningful cut-off).\u003c/p\u003e \u003cp\u003eFurthermore, for the purpose of summarizing findings across various outcomes, we calculated odds ratios using Fisher\u0026rsquo;s test and determined confidence intervals (CIs) to assess the efficacy of the experimental group compared to the control. Odds ratios for CPT-C and CPT-P were not calculated using Fisher\u0026rsquo;s test but were estimated straight from the contingency matrix due to the small sample size. No subjects in the control group reached the MCID, leading to an infinite estimation of odds ratio by Fisher\u0026rsquo;s test. We estimated a downward odds ratio in CPT-C and CPT-P by considering one subject in the control group that reached MCID in our calculations.\u003c/p\u003e \u003cp\u003eIn the responder analysis based on effect size (distribution-based method)\u003csup\u003e57\u003c/sup\u003e, the effect size is a standardized measure of change obtained by dividing the difference in scores from pre-treatment to post-treatment by the standard deviation of pretreatment scores. We evaluated the proportion of patients in each group that reached a MCID with small (0.3), medium (0.5) and large (0.7) effect sizes.\u003c/p\u003e \u003cp\u003eStatistical analyses were carried out using MATLAB R2020b (Mathworks Inc) and Rstudio software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis I. Group differences in Cognitive and Clinical Outcomes: linear mixed-effects models\u003c/h2\u003e \u003cp\u003eThe main outcome measure showed no deviation from normality in any of the study periods. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e describe skewness and kurtosis statistics for the main outcome measure CPT-C. The Shapiro-Wilk test of normality indicates that the distribution of CPT-C in any study period is not significantly different from a normal distribution (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eMixed-effects models for CPT-C measure were estimated using robust constrained maximum likelihood method, introducing condition-period interaction effect with stepwise procedure to assess improvements in model fitting.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for main outcome measure commission score on Conners continuous performance test (CPT-3).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsymmetry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eShapiro-Wilk test of normality p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47,5217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,5866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,2257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,5393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePOST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47,4783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,6101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,1188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1,4289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,2061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51,64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,6622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,0234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,9191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,8793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePOST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,3544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,0635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,0124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,9505\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 \u003c/p\u003e \u003cp\u003eThe linear mixed-effects model for main cognitive measure (CPT-C) with a condition-period interaction effect (see Model 2 in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) did not show a statistically significant improvement in adjustment (xi2\u0026thinsp;=\u0026thinsp;3,320; \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0,068) compared to Model 1 (without condition-period interaction effect). However, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that β estimator for the condition-period interaction effect in Model 2 was statistically different from 0 (β\u0026thinsp;=\u0026thinsp;.56; t46\u0026thinsp;=\u0026thinsp;2,03; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.0473). In Model 2, the inclusion of the interaction effect explains a greater proportion of the variance in CPT-C scores (R2 total\u0026thinsp;=\u0026thinsp;0.56; R2 fixed\u0026thinsp;=\u0026thinsp;0.06) when compared to Model 1 (R2 total\u0026thinsp;=\u0026thinsp;0.52; R2 fixed\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized mean differences (β estimators) for model comparison: Model 1 (with no interaction effect); Model 2 (with interaction effect); Model 3 (with interaction effect and age as covariable).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e(Intercept)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.10, 0.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.02, 0.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-1.11, 2.90]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGroupcn\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.74, 0.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-1.09, 0.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-1.08, 0.06]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMomentPOST\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.30 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.57 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.57 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.58, -0.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.94, -0.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-0.94, -0.19]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGroupcn:MomentPOST\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.02, 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.02, 1.11]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\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 \u003cp\u003e-0,05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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 \u003cp\u003e[-0.26, 0.16]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN (ID)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAIC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268,10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e272,49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBIC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e290,44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR2 (fixed)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR2 (total)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e* P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003cem\u003e** P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients estimation for Model 2 (with interaction effect).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEst.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et val.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ed.f.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e(Intercept)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,4049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,0184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,7914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,0532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71,8842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,0437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGroup-cn\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,5328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1,0911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,0256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1,8702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71,8842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,0655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMoment-POST\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,5692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,9444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,1941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2,9742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46,0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,0047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGroup-cn:MomentPOST\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,5636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,0217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,1055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,0384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46,0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,0473\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\u003eAdditionally, it is noteworthy that Model 3, which encompasses both the interaction effect and age as a covariate, exhibits a similar performance to that of Model 2. However, it is important to acknowledge that Model 3 faces a penalty for its increased complexity when assessed through the criteria of AIC and BIC, as elucidated by Vrieze in 2012. Notably, Model 2 presents the most favorable AIC index among all models, with an AIC value of 268. Conversely, as BIC penalizes complexity, it is Model 1 that achieves the most favorable fit according to this metric (BIC\u0026thinsp;=\u0026thinsp;282). It is worth noting that Model 1's BIC value is closely aligned with that of the more complex Model 2 (BIC\u0026thinsp;=\u0026thinsp;283).\u003c/p\u003e \u003cp\u003eThus, Model 2 is accepted as the final model as it shows a statistically significant condition\u0026ndash;moment effect, the best combination of R2 explained variance, AIC and BIC adjustment.\u003c/p\u003e \u003cp\u003eFinally, the other Cognitive and Clinical outcome measures (a total of 53 sub-indices) were adjusted to mixed-effects models. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the sub-indices that had a statistically significant condition\u0026ndash;moment interaction effect and which of them remain significant after multiple comparisons correction with FDR (applied by cognitive domain). The measures that survived family-wise FDR multiple comparisons were spatial processing inverse (β=-1,10; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0012); inhibition time (β=-0.23; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0253) and spatial processing inverse (β= -0.61; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Interestingly, most effect sizes are medium Cohen\u0026rsquo;s d\u0026thinsp;\u0026gt;\u0026thinsp;0.5 and effect size for LE_I+, Ninh_1T and LE_I are large (Cohen\u0026rsquo;s d\u0026thinsp;\u0026gt;\u0026thinsp;0.8).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized mean differences for interaction effects in secondary outcome measures.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive and Clinical Outcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecoeff beta (2.5% 97.5%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et stat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFamily Adjusted\u003c/p\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ed 95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eContinuous Performance Test Commissions (%)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(CPT-C)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCPT-3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,56\u003c/p\u003e \u003cp\u003e(0,02 1,11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0473**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,1589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.01, 1.19]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eContinuous Performance Test Detectability (CPT-d')\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCPT-3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,52\u003c/p\u003e \u003cp\u003e(0,05 0,99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0363**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,1589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.04, 1.22]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eContinuous Performance Test Perseveration (%)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(CPT-P)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCPT-3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,61\u003c/p\u003e \u003cp\u003e(0,01 1,21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0530*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,1589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[-0.01, 1.17]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpatial location\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eInverse Max items (LE_I+)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1,10\u003c/p\u003e \u003cp\u003e(-1,73\u0026thinsp;\u0026minus;\u0026thinsp;0,48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3,4592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0012**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,0048**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[-1.64, -0.40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInhibition Time (Ninh_1T)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNinh\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,23\u003c/p\u003e \u003cp\u003e(-0,43\u0026thinsp;\u0026minus;\u0026thinsp;0,04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2,39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0253**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,0759*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[-1.86, -0.12]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpatial location\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eInverse (LE_I)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,61\u003c/p\u003e \u003cp\u003e(-1,17\u0026thinsp;\u0026minus;\u0026thinsp;0,05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2,1667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0360**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,0720*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[-1.29, -0.04]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNEPSY Classification\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCorrect (Clas-C)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNEPSY-\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eClas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,66\u003c/p\u003e \u003cp\u003e(-1,28\u0026thinsp;\u0026minus;\u0026thinsp;0,04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0418**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,1674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[-1.22, -0.02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; * statistical tendency \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis II. Power ratio values correlation with main outcome and other cognitive and clinical outcomes\u003c/h2\u003e \u003cp\u003eTwo main dimensions were tested for correlations with power: Impulsivity Domain (CPT-Commissions, CPT-Perseverations) and Inattentiveness Domain (CPT-Omissions, CPT-Variance in response). The main outcome measure of CPT-Commissions was included in impulsivity domain as in our sample high commission error rates are combined with fast reaction times (CPT-HRT; r=-0.26; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0009; Supplementary Fig.\u0026nbsp;1 and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStatistically significant clusters of correlation between power ratio values and measures of impulsive and inattentive domains are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. For each cognitive outcome, the clusters p-value in alpha [7\u0026ndash;13 Hz] and beta [12\u0026ndash;30 Hz] frequency bands are shown.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eImpulsivity\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eCPT - Commissions (%) - Main outcome measure\u003c/h2\u003e \u003cp\u003eThe power ratio was found to be positively correlated with the CPT-C ratio in the beta frequency band. For the whole sample analysis, the power ratio at all frequencies of this interval is higher in the treatment group compared to the control group, and it shows a positive correlation with the CPT ratio (ρ\u0026thinsp;=\u0026thinsp;0.53; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.00036) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eIn the experimental group analysis, the power ratio shows a positive correlation with the CPT-C ratio (ρ\u0026thinsp;=\u0026thinsp;0.41; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.0078) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA.2). The nodes with a statistically significant correlation to the CPT-C ratio are grouped in a cluster that is close to significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.06; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA.1) within the beta frequency interval (25.5\u0026ndash;30 Hz). This cluster is primarily located in the right temporal gyrus (32%), right precuneus (12%) and right angular gyrus (12%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eCPT - Perseverations (%)\u003c/h2\u003e \u003cp\u003eThe power ratio was found to be positively correlated with the CPT-P ratio in the alpha frequency band. In the whole sample analysis, the power ratio at all frequencies of this interval is lower in the treatment group compared to the control group, and it shows a positive correlation with the CPT-P ratio (ρ\u0026thinsp;=\u0026thinsp;0.63; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.00001) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eFor the experimental group analysis, the power ratio shows a positive correlation with the CPT-P ratio (ρ\u0026thinsp;=\u0026thinsp;0.43; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.0044; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB.2). The nodes with a statistically significant correlation to the CPT-P ratio are grouped in a statistically significant cluster (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB.1) within the frequency interval (8.25\u0026ndash;10.5 Hz). This cluster is mainly located in the bilateral postcentral gyrus (18%), right precentral gyrus (10%) and right right middle frontal gyrus (7%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCluster p-values of correlations performed within the whole sample (Nall) and within the experimental sample (N\u003csub\u003eex\u003c/sub\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAlpha [7\u0026ndash;13 Hz]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eBeta [13\u0026ndash;30 Hz]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003csub\u003eall\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003csub\u003eex\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003csub\u003eall\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003csub\u003eex\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eImpulsive Domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCPT-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.047*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCPT-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInattentiveness Domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCPT-O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCPT-Var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEDAH-H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eInattentiveness\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eCPT - Omissions (%)\u003c/h2\u003e \u003cp\u003eThe power ratio was found to be positively correlated with CPT-O ratio in the alpha frequency band. For whole sample analysis, the power ratio in all frequencies of this interval is lower in the treatment group compared to control and it shows a positive correlation with the CPT-O ratio (ρ\u0026thinsp;=\u0026thinsp;0.525; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.000043; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC.2). The nodes with a statistically significant correlation to CPT-O ratio are grouped in a statistically significant cluster (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02) in the frequency interval (7-10Hz; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC.1). This cluster is mainly located in the bilateral pre-cuneus (18%), the right angular gyrus (8.5%) and the right precentral gyrus (7.5%).\u003c/p\u003e \u003cp\u003eWhen the experimental group was evaluated, the power ratio shows a positive correlation with the CPT-O ratio within a cluster close to statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.051) (Supplementary Fig.\u0026nbsp;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eCPT - Variability (%)\u003c/h2\u003e \u003cp\u003eThe power ratio was found to be positively correlated with CPT-Var ratio in the alpha frequency band. For whole sample analysis, the power ratio in all frequencies of this interval is lower in the treatment group compared to control and it shows a positive correlation with the CPT-Var ratio (ρ\u0026thinsp;=\u0026thinsp;0.58; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.00022; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD.2). The nodes with a statistically significant correlation to CPT-Var ratio are grouped in a statistically significant cluster (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.047) in the frequency interval (8\u0026ndash;10 Hz; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD.1). This cluster is mainly located in the bilateral pre-cuneus (21%), the right angular gyrus (7.8%) and the right postcentral gyrus (9.2%)\u003c/p\u003e \u003cp\u003eWhen the experimental group was evaluated, the power ratio showed a positive correlation with the CPT-O ratio within a cluster close to statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.052) (Supplementary Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eIn essence, the enhancements observed in inattentive domains following cognitive training are linked to reductions in relative power spectra, specifically within the alpha frequency band. Moreover, it is noteworthy that the experimental condition involving personalized digital cognitive intervention (KAD_SCL_01) does not exhibit superior performance compared to the control condition. In both conditions of cognitive training, we discern a very similar correlation pattern between the improvement in inattention and the reduction in alpha power. Nevertheless, these associations seem to be stronger in the experimental group, indicating that there are alterations in brain activity that lack the strength to distinguish between the two cognitive training conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eClinical outcome\u003c/h2\u003e \u003cdiv id=\"Sec28\" class=\"Section4\"\u003e \u003ch2\u003eEDAH \u0026ndash; Hyperactivity (EDAH-H)\u003c/h2\u003e \u003cp\u003eThe correlation analysis of power ratio with clinical outcome measure EDAH-H ratio in the treatment group reveals a positive correlation in alpha frequency band [7\u0026ndash;14 Hz] (ρ\u0026thinsp;=\u0026thinsp;0.846; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.00001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA.2 top) and beta frequency band [14\u0026ndash;23 Hz] (ρ\u0026thinsp;=\u0026thinsp;0.787; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.00006) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA.2 bottom). The nodes with a statistically significant correlation to EDAH-H ratio are grouped in a statistically significant cluster in the alpha frequency interval (10.5-15Hz; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA.1, blue) and beta frequency interval (14-23Hz; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA.1, red). It can be observed how this alpha cluster (blue) is mainly located in the fronto-temporo-parietal regions and almost overlapped the beta cluster (blue). Overlapping areas between alpha and beta clusters are represented in pink in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA.2. Interestingly, alpha involves frontal regions while beta extends throughout the parieto-temporal lobes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis III. Responder analysis and minimal clinical important difference (MCID)\u003c/h2\u003e \u003cp\u003eResponder analyses demonstrated that the experimental group exhibited statistically significant reductions in CPT-C scores, bringing them within the normative range [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], with 28% (7 out of 25) of patients achieving this outcome, compared to none (0%) in the control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0098; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Furthermore, the experimental group showed a notable shift of more patients into normative ranges across various objective measures of attention and impulsivity in CPT-3. Specifically, CPT-P scores reached the [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] range in 20% (5 out of 25) of experimental group patients versus 0% in the control group, exhibiting a statistical tendency (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.057; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Similarly, CPT-O showed a movement into the [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] range in 24% of the experimental group compared to 4.35% in the control group, also suggesting a statistical tendency (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.099; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResponder analyses for other comparisons, including all EDAH, did not indicate significant differences between the groups (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Nevertheless, it is noteworthy that most of the odds ratios are positive (7/8, 87,5%; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), suggesting a trend wherein the experimental group exhibits a higher likelihood of having subjects with MCID after treatment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCPT-3 minimal clinical important difference based on Anchor methods and distribution methods (effect size).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnchor-Based\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFisher p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eDistribution-Based\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPT-3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCPT-C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(7/25) 28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0098\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(15/25) 60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(14/25) 56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(13/25) 52%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0/23) 0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(8/23) 26.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(6/23) 26.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3/23) 13.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCPT-P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5/25) 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0507\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(10/25) 40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(10/25) 40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(9/25) 36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0/23) 0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4/23) 17.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1/23) 4.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0/23) 0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCPT-O\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6/25) 24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0995*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(10/25) 40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(8/25) 32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(8/25) 32%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1/23) 4.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(7/23) 30.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5/23) 21.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3/23) 13.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCPT-Var\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4/23) 17.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(10/23) 43.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(9/23) 39.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(9/23) 39.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1/20) 5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6/20) 30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3/20) 15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3/20) 15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; * statistical tendency \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEDAH minimal clinical important difference based on Anchor methods and distribution methods (effect size).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnchor-Based\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFisher p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eDistribution-Based\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDAH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEDAH-H\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5/25) 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5/25) 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(10/25) 40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(8/25) 32%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6/21) 28.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6/21) 28.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(11/23) 47.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(7/23) 30.43%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEDAH-DA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(11/25) 44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(11/25) 44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(11/25) 44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(9/25) 36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(7/21) 33.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(7/21) 33.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(10/21) 47.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(8/21) 38.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEDAH-DAH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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\u003cp\u003e(10/21) 47.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(8/21) 38.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEDAH-GLOBAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(11/25) 44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(18/25) 72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(17/25) 68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(16/25) 64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6/21) 28.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(16/21) 76.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(15/21) 71.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(15/21) 71.43%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; * statistical tendency \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe randomized controlled clinical trial showed that the KAD_SCL_01 significantly improved performance on the primary outcome measure, an objective measure of inattention and impulsivity (CPT-C), in pediatric patients with ADHD compared with the control group. Across the range of secondary outcomes, additional inattentive and impulsivity scores in perseverations (CPT-P) and detectability (CPT-d\u0026rsquo;) in the CPT-C showed significant greater improvements in the experimental group from pre-intervention to post-intervention. Furthermore, other cognitive secondary measures, including the ability to inhibit automatic responses in favor of novel responses and the ability to switch between response types, cognitive flexibility and spatial working memory showed significant improvements in the experimental group compared to the controls in the post intervention assessment. In the other cognitive and clinical measures, the effect of KAD_SCL_01 from pre intervention to post intervention were not different from the control group.\u003c/p\u003e \u003cp\u003eThe current study findings of improved attention and impulsivity (via CPT-3) following treatment with KAD_SCL_01 are consistent with positive results reported in previous studies (He et al., 2023). As a digital cognitive treatment, KAD_SCL_01 could address several challenges faced by traditional interventions. First, its risk\u0026ndash;benefit profile is favorable, none of the patients assigned to the experimental group had AEs, compared with rates of 40\u0026ndash;60% in trials of commonly used stimulant medications\u003csup\u003e59\u003c/sup\u003e. Second, the digital nature of this intervention could reduce barriers to access that are inherent in other forms of behavioral or nonpharmacological interventions\u003csup\u003e60\u003c/sup\u003e. Digital interventions have been cited as possible ways to improve otherwise poor access to mental health services, reducing waiting lists and providing an earlier neuropsychological recovery\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe primary outcome measure for this trial, the CPT-3, differs from most pharmacological efficacy trials for ADHD, which typically use parent-rated or clinician-rated symptom measures. The selection of the CPT-3 was based on several factors. First, because the digital tool was designed specifically to target inattention, sustained attention, impulsivity, and vigilance, we sought an outcome that would most precisely and validly index these processes. The CPT-3 is a tool for the objective assessment of attention and inhibitory control as part of an ADHD diagnosis or for monitoring intervention outcomes and has been widely used in both clinical practice and research studies. Second, the CPT-C measures cognitive functions that are relevant to the clinical presentation of ADHD, and attention performance metrics such as commissions, omission, perseverations, and reaction times metrics are well characterized indicators of ADHD-relevant cognitive processes and are associated with clinically relevant outcomes including academic behavior and inattention and social problems\u003csup\u003e61\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the current study, there were no differences between the experimental and the control condition on the other secondary measures, and several factors might explain these findings. First, it is possible that parent or clinician reported outcomes (i.e., EDAH and BRIEF) are not sensitive to the effects of the KAD_SCL_01. In other words, the shown effects of the intervention on cognitive processes may not be as readily observable by parents and clinicians. The clinical implications of this possibility will be important to explore in future studies. Second, expectations of efficacy have been shown to moderate intervention effects in general, and for digital interventions\u003csup\u003e62\u003c/sup\u003e. In our study, parents of patients in both groups believed that their child received a novel intervention for ADHD; thus, the expectation of intervention effect can be assumed for both interventions and may partially explain improvements in both groups. This design feature is different from most pharmacological studies in which patients and their caregivers are aware of a non-active, placebo condition. Finally, specific mechanisms common to KAD_SCL_01 and the control condition may have resulted in improvements in both groups. Both interventions required continued perseverance, sometimes in the face of failure, and may have trained coping and reappraisal skills or even increased the sense of self-efficacy and mastery\u003csup\u003e63\u003c/sup\u003e. Thus, any intervention that requires the patient to engage in a regular, structured setting that may include repeated failure or repetitiveness can be seen as a potential intervention for ADHD.\u003c/p\u003e \u003cp\u003eIn the magnetoencephalography analysis, we investigated the neurophysiological basis of improvements in ADHD performance after a cognitive training intervention. The findings revealed a positive association between changes in neuropsychological functions and electrophysiological patterns, providing biological evidence of neuromaturation\u003csup\u003e10\u003c/sup\u003e. Specifically, improvements in attention and inhibitory control were associated with a reduction in power within the alpha and beta frequency bands for both, KAD_SCL_01 and control treatments.\u003c/p\u003e \u003cp\u003eRegarding impulsivity domain, the neurophysiological correlates were predominantly observed in parietal and temporal cortex, related to voluntary sensorimotor control and visuospatial processing. On the other hand, in the case of inattentiveness domain, the correlated brain regions were associated with visuospatial imagery, episodic memory retrieval, and self-processing operations.\u003c/p\u003e \u003cp\u003eAn important finding from this study was that digital cognitive stimulation driven by artificial intelligence (AI) appears to enhance neurocognitive maturation, as indicated by the stronger associations between brain activation and cognitive improvements observed in the experimental group. Particularly noteworthy is the impact on impulsivity: ADHD patients who underwent KAD_SCL_01 treatment demonstrated that greater improvements in inhibitory control were linked to a more substantial reduction in electrophysiological activity within the alpha and beta frequencies. Interestingly, it has been reported that these findings are further supported by clinical outcomes in EDAH-H, which exhibited a reduction in parents\u0026rsquo; reported hyperactivity symptomatology associated with decreases in power across all cortex areas.\u003c/p\u003e \u003cp\u003eThese results are in line with several previous studies on the electrophysiological mechanisms of cognitive training in ADHD\u003csup\u003e43,44,64,65\u003c/sup\u003e. In consequence, reductions in relative power induced by the digital cognitive treatment might be reflecting an increase in the efficiency of neural networks involved in inhibitory control, involving a process of neuroplasticity through long term potentiation\u003csup\u003e66\u003c/sup\u003e. Finally, responder analyses showed that KAD_SCL_01 intervention exhibited significant reductions in the main outcome of commission scores CPT-3, suggesting that the digital cognitive stimulation program may have a positive behaviorally effect on the disorder.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future studies\u003c/h2\u003e \u003cp\u003eThe current study has several important limitations. First, the sample size was small because the first inclusion criteria required that patients had an ADHD-C diagnosis. Second, children could not be taking medication for ADHD during the trial and could not have significant psychiatric comorbidity. Therefore, it is unclear if these findings will generalize to the broader population of patients with ADHD who have comorbid conditions or patients taking medication. Third, the study evaluated a 12-weeks intervention period with approximately 15-min per 3-days sessions. It is unclear if the benefits in attentional functioning might have been observed with a different dosing schedule. Additional studies with different intervention periods are needed, also including durability of effects 1 month after the intervention. In addition, studies investigating whether the intervention has effects in children currently treated with stimulant medication, which will help address questions of generalizability.\u003c/p\u003e \u003cp\u003eGiven these limitations, the transfer of benefit of the KAD_SCL_01 intervention to real-world settings and the full clinical meaningfulness of the findings, as well as the mechanisms underlying these effects, should be explored in further studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe study was a randomized controlled trial evaluating a digital neuropsychological intervention to improve attention and impulsivity in children with ADHD and its results strengthen the scientific evidence on the use of digital interventions. Indeed, KAD_SCL_01 showed significant improvements in the measurement of attention and impulsivity using the CPT-3. Furthermore, we found that these improvements in cognitive and clinical outcomes have an underlying neurophysiological basis. Improvement in the impulsivity domain has been found to be related to a MEG relative power spectral normalization, suggesting an increase in the efficiency of inhibitory control networks after KAD_SCL_0 training by long term potentiation (LTP) neuroplasticity. Finally, the risk-benefit ratio suggests that KAD_SCL_01 could be considered as a new intervention option for ADHD alongside traditional treatments, accelerating access and treatments for earlier recovery of cognitive function for activities of daily living.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADHD = Attention-Deficit/Hyperactivity Disorder; ADHD-C = Attention-Deficit/Hyperactivity Disorder Combined Type; AI = Artificial Intelligence; BRIEF = Behaviour Rating Inventory of Executive Function, Parent Version; BrPa_CE = Emotional Control from the BRIEF; BrPa_Flx = Flexibility from the BRIEF; BrPa_Ih = Inhibition from the BRIEF; BrPa_Ini = Initiate from the BRIEF; BrPa_MO = Working Memory from the BRIEF; BrPa_Mon = Monitoring from the BRIEF; BrPa_Org = Organization from the BRIEF; BrPa_Pla = Planning from the BRIEF; BS_Ac = Correct Items from the Symbol Search; BS_E = Incorrect Items from the Symbol Search; BS_T = Total Processed Items from the Symbol Search; Clas_C = Correct Answers from Card Classification; Clas_O = Inaccurate Answers from Card Classification; Clas_R = Repeated Errors from Card Classification; Clas_TE = Total Errors from Card Classification; CN_Ac = Correct Responses from the Coding; CN_E = Incorrect Responses from the Coding; CN_T = Processed Integer Numbers from the Coding; CPT-3 = Conners Continuous Performance Test; CPT_C = Commissions from CPC-3; CPT_d = Detectability from CPC-3; CPT_HRT = Hit Reaction Time from CPC-3; CPT_HRTISI = Hit Reaction Time Inter-Stimulus from CPC-3; CPT_HRTSD = Standard Deviation of Hit Reaction Time from CPC-3; CPT_O = Omissions from CPC-3; CPT_P = Perseverations from CPC-3; CPT_Var = Variability from CPC-3; DAN = Dorsal Attention Network; DCT = Digital Cognitive Treatment; DIG_D = Direct Correct Answers from Digit Span; DIG_D+ = Length of the Direct Last Sequences from Digit Span; DIG_I = Inverse Correct Answers from Digit Span; DIG_I+ = Length of the Inverse Last Sequences from Digit Span; DMN = Default Mode Network; DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition; EDAH = Evaluation of Attention Deficit Hyperactivity Disorder; EDAH_DA = Attention Disorder from the EDAH; EDAH_DAH = Attention Disorder and Hyperactivity from the EDAH; EDAH_H = Hyperactivity from the EDAH; EDAH_TC = Behavioral Disorder from the EDAH; EEG = electroencephalogram; FDR = False Discovery Rate; LE_D = Direct Correct Answers from WNV; LE_D+ = Length of the Direct Last Sequences from WNV; LE_I = Inverse Correct Answers from WNV; LE_I+ = Length of the Inverse Last Sequences from WNV; MEG = magnetoencephalography; NAtAu_Ac = number of correct answers from NEPSY-II; NAtAu_EC = Commissions from NEPSY-II; NAtAu_ EI = Inhibition errors from NEPSY-II; NAtAu_ EO = Omissions from NEPSY-II; Ninh_1EAc = Self-Corrected errors from NEPSY-II; Ninh_1T = Response Time from NEPSY-II; NEPSY-II = Developmental Neuropsychological Assessment-II; RCT = Randomized Controlled Trail; SAL = Salience Network; tSSS = temporal extension of the Space Signal Separation; VN = Visual Network; WISC-IV = Wechsler Intelligence Scales for Children-IV; WNV = Weschler Non-Verbal Scales;\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e Author contributions\u003c/p\u003e\n\u003cp\u003eIR, FM, JQ, JA, JARQ, JH, AM, EC, and IACG conceived and planned the research protocol. IR, DSB, IACG and PB carried out the research and analyzed the data. DSB and PC carried out the processing and analysis of the MEG. DSB, IACG, and EC wrote the manuscript and all authors provided critical feedback and helped shape the research, analysis and manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSincrolab participated in the study design, data analysis, decision to publish, and preparation of the manuscript. IR is the cofounder of Sincrolab. JQ, JARQ, JA, AA, and JH are members of the Scientific Board of Sincrolab. JQ is also a shareholder of Instituto Neuroconductual de Madrid Ltd and a speaker on the advisory board for Takeda \u0026amp; Jansen. He also receives investigation funding from the Carlos III Health Institute. J.A.R.Q was on the speakers\u0026rsquo; bureau and/or acted as consultant for Janssen-Cilag, Novartis, Shire, Takeda, Bial, Shionogi, Sincrolab, Neuraxpharm, Novartis, BMS, Medice, Rubi\u0026oacute;, Uriach, Technofarma and Raffo in the last 3 years. He also received travel awards (air tickets + hotel) for taking part in psychiatric meetings from Janssen-Cilag, Rubi\u0026oacute;, Shire, Takeda, Shionogi, Bial and Medice. The Department of Psychiatry chaired by him received unrestricted educational and research support from the following companies in the last 3 years: Janssen- Cilag, Shire, Oryzon, Roche, Psious, and Rubi\u0026oacute;.\u003c/p\u003e\n\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was funded by Sincrolab and partly funded by the Centre for the Development of Industrial Technology of the Spanish Ministry of Economy, Industry, and Competitiveness. Sincrolab provided financial support in the form of salaries used for partial salary support for the authors. DS and PC received punctual financial support for carrying out the magnetoencephalography and statistical analysis and redaction.\u003c/p\u003e\n\u003cp\u003eSupplementary information\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSupplementary information is available online.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFaraone, S. V. \u003cem\u003eet al.\u003c/em\u003e The world federation of ADHD international consensus statement: 208 evidence-based conclusions about the disorder. \u003cem\u003eNeurosci. Biobehav. Rev.\u003c/em\u003e \u003cstrong\u003e128\u003c/strong\u003e, 789\u0026ndash;818 (2021).\u003c/li\u003e\n\u003cli\u003eSayal, K., Prasad, V., Daley, D., Ford, T. \u0026amp; Coghill, D. ADHD in children and young people: prevalence, care pathways, and service provision. \u003cem\u003eLancet Psychiatry\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 175\u0026ndash;186 (2018).\u003c/li\u003e\n\u003cli\u003eSibley, M. H., Mitchell, J. T. \u0026amp; Becker, S. P. Method of adult diagnosis influences estimated persistence of childhood ADHD: a systematic review of longitudinal studies. \u003cem\u003eLancet Psychiatry\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 1157\u0026ndash;1165 (2016).\u003c/li\u003e\n\u003cli\u003eBanaschewski, T., Coghill, D. \u0026amp; Zuddas, A. \u003cem\u003eOxford Textbook of Attention Deficit Hyperactivity Disorder\u003c/em\u003e. (Oxford University Press, 2018).\u003c/li\u003e\n\u003cli\u003eSharon, D., H\u0026auml;m\u0026auml;l\u0026auml;inen, M. S., Tootell, R. B., Halgren, E. \u0026amp; Belliveau, J. W. The advantage of combining MEG and EEG: comparison to fMRI in focally stimulated visual cortex. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 1225\u0026ndash;1235 (2007).\u003c/li\u003e\n\u003cli\u003eCortese, S. \u003cem\u003eet al.\u003c/em\u003e Candidate diagnostic biomarkers for neurodevelopmental disorders in children and adolescents: a systematic review. \u003cem\u003eWorld Psychiatry\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 129\u0026ndash;149 (2023).\u003c/li\u003e\n\u003cli\u003eArns, M., Conners, C. K. \u0026amp; Kraemer, H. C. A Decade of EEG Theta/Beta Ratio Research in ADHD: A Meta-Analysis. \u003cem\u003eJ. Atten. Disord.\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 374\u0026ndash;383 (2013).\u003c/li\u003e\n\u003cli\u003eBarry, R. J., Clarke, A. R. \u0026amp; Johnstone, S. J. A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. \u003cem\u003eClin. Neurophysiol.\u003c/em\u003e \u003cstrong\u003e114\u003c/strong\u003e, 171\u0026ndash;183 (2003).\u003c/li\u003e\n\u003cli\u003eSatterfield, J. H., Schell, A. M., Backs, R. W. \u0026amp; Hidaka, K. C. A cross-sectional and longitudinal study of age effects of electrophysiological measures in hyperactive and normal children. \u003cem\u003eBiol Psychiatry\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 973\u0026ndash;990 (1984).\u003c/li\u003e\n\u003cli\u003eClarke, A. R., Barry, R. J., Johnstone, S. J., McCarthy, R. \u0026amp; Selikowitz, M. EEG development in Attention Deficit Hyperactivity Disorder: From child to adult. \u003cem\u003eClin. Neurophysiol.\u003c/em\u003e \u003cstrong\u003e130\u003c/strong\u003e, 1256\u0026ndash;1262 (2019).\u003c/li\u003e\n\u003cli\u003eEl‐Sayed, E., Larsson, J., Persson, H., Santosh, P. \u0026amp; Rydelius, P. \u0026ldquo;Maturational lag\u0026rdquo; hypothesis of attention deficit hyperactivity disorder: an update. \u003cem\u003eActa Paediatr.\u003c/em\u003e \u003cstrong\u003e92\u003c/strong\u003e, 776\u0026ndash;784 (2003).\u003c/li\u003e\n\u003cli\u003eSoman, S. M., Vijayakumar, N., Ball, G., Hyde, C. \u0026amp; Silk, T. J. Longitudinal changes of resting-state networks in children with attention-deficit/hyperactivity disorder and typically developing children. \u003cem\u003eBiol. Psychiatry Cogn. Neurosci. Neuroimaging\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 514\u0026ndash;521 (2023).\u003c/li\u003e\n\u003cli\u003eDobrean, A., Pasarelu, C. R. \u0026amp; Dopfner, M. Varieties of Psychotherapy for Attention-Deficit Hyperactivity Disorder. \u003cem\u003eEvid.-Based Psychother. State Sci. Pract.\u003c/em\u003e (2018).\u003c/li\u003e\n\u003cli\u003eCanu, W. H. \u0026amp; Hilton, D. C. Attention deficit hyperactivity disorder: second wave conceptualization and intervention. in \u003cem\u003eBehavior Therapy: First, Second, and Third Waves\u003c/em\u003e 629\u0026ndash;648 (Springer, 2022).\u003c/li\u003e\n\u003cli\u003eKretschmer, C. R., G\u0026ouml;z Tebrizcik, B. \u0026amp; Dommett, E. J. Mindfulness Interventions for Attention Deficit Hyperactivity Disorder: A Systematic Review and Meta-Analysis. \u003cem\u003ePsychiatry Int.\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 363\u0026ndash;399 (2022).\u003c/li\u003e\n\u003cli\u003eBikic, A., Reichow, B., McCauley, S. A., Ibrahim, K. \u0026amp; Sukhodolsky, D. G. Meta-analysis of organizational skills interventions for children and adolescents with Attention-Deficit/Hyperactivity Disorder. \u003cem\u003eClin. Psychol. Rev.\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 108\u0026ndash;123 (2017).\u003c/li\u003e\n\u003cli\u003eEvans, S. W., Owens, J. S. \u0026amp; Bunford, N. Evidence-Based Psychosocial Treatments for Children and Adolescents with Attention-Deficit/Hyperactivity Disorder. \u003cem\u003eJ. Clin. Child Adolesc. Psychol.\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 527\u0026ndash;551 (2014).\u003c/li\u003e\n\u003cli\u003eGroenman, A. P. \u003cem\u003eet al.\u003c/em\u003e An individual participant data meta-analysis: behavioral treatments for children and adolescents with attention-deficit/hyperactivity disorder. \u003cem\u003eJ. Am. Acad. Child Adolesc. Psychiatry\u003c/em\u003e \u003cstrong\u003e61\u003c/strong\u003e, 144\u0026ndash;158 (2022).\u003c/li\u003e\n\u003cli\u003eWright, N. \u003cem\u003eet al.\u003c/em\u003e Practitioner review: pathways to care for ADHD\u0026ndash;a systematic review of barriers and facilitators. \u003cem\u003eJ. Child Psychol. Psychiatry\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 598\u0026ndash;617 (2015).\u003c/li\u003e\n\u003cli\u003eKappi, A. \u0026amp; Martel, M. Parental barriers in seeking mental health services for attention deficit hyperactivity disorder in children: systematic review. \u003cem\u003eJ. Atten. Disord.\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 408\u0026ndash;425 (2022).\u003c/li\u003e\n\u003cli\u003eHollis, C. \u003cem\u003eet al.\u003c/em\u003e Annual Research Review: Digital health interventions for children and young people with mental health problems\u0026ndash;a systematic and meta-review. \u003cem\u003eJ. Child Psychol. Psychiatry\u003c/em\u003e \u003cstrong\u003e58\u003c/strong\u003e, 474\u0026ndash;503 (2017).\u003c/li\u003e\n\u003cli\u003eElaklouk, A. M. \u0026amp; Zin, N. A. M. Design and usability evaluation of rehabilitation gaming system for cognitive deficiencies. in \u003cem\u003e2017 6th International Conference on Electrical Engineering and Informatics (ICEEI)\u003c/em\u003e 1\u0026ndash;6 (IEEE, 2017).\u003c/li\u003e\n\u003cli\u003eNeudecker, C., Mewes, N., Reimers, A. K. \u0026amp; Woll, A. Exercise interventions in children and adolescents with ADHD: a systematic review. \u003cem\u003eJ. Atten. Disord.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 307\u0026ndash;324 (2019).\u003c/li\u003e\n\u003cli\u003eKollins, S. H. \u003cem\u003eet al.\u003c/em\u003e A novel digital intervention for actively reducing severity of paediatric ADHD (STARS-ADHD): a randomised controlled trial. \u003cem\u003eLancet Digit. Health\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, e168\u0026ndash;e178 (2020).\u003c/li\u003e\n\u003cli\u003eAdabla, S., Nabors, L. \u0026amp; Hamblin, K. A scoping review of virtual reality interventions for youth with attention-deficit/hyperactivity disorder. \u003cem\u003eAdv. Neurodev. Disord.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 304\u0026ndash;315 (2021).\u003c/li\u003e\n\u003cli\u003ede Ram\u0026oacute;n, I. \u003cem\u003eet al.\u003c/em\u003e Alpha-band power increases in posterior brain regions in attention deficit hyperactivity disorder after digital cognitive stimulation treatment: randomized controlled study. \u003cem\u003eBrain Commun.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, fcac038 (2022).\u003c/li\u003e\n\u003cli\u003eMedina, R. \u003cem\u003eet al.\u003c/em\u003e Electrophysiological brain changes associated with cognitive improvement in a pediatric attention deficit hyperactivity disorder digital artificial intelligence-driven intervention: randomized controlled trial. \u003cem\u003eJ. Med. Internet Res.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, e25466 (2021).\u003c/li\u003e\n\u003cli\u003ePenuelas-Calvo, I. \u003cem\u003eet al.\u003c/em\u003e Video games for the assessment and treatment of attention-deficit/hyperactivity disorder: A systematic review. \u003cem\u003eEur. Child Adolesc. Psychiatry\u003c/em\u003e 1\u0026ndash;16 (2020).\u003c/li\u003e\n\u003cli\u003eRodrigo-Yanguas, M., Gonz\u0026aacute;lez-Tard\u0026oacute;n, C., Bella-Fern\u0026aacute;ndez, M. \u0026amp; Blasco-Fontecilla, H. Serious video games: angels or demons in patients with attention-deficit hyperactivity disorder? A quasi-systematic review. \u003cem\u003eFront. Psychiatry\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 798480 (2022).\u003c/li\u003e\n\u003cli\u003eMichelini, G. \u003cem\u003eet al.\u003c/em\u003e Methylphenidate, Guanfacine, and Combined Treatment Effects on Electroencephalography Correlates of Spatial Working Memory in Attention-Deficit/Hyperactivity Disorder. \u003cem\u003eJ. Am. Acad. Child Adolesc. Psychiatry\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 37\u0026ndash;47 (2023).\u003c/li\u003e\n\u003cli\u003eJanssen, T. W. P. \u003cem\u003eet al.\u003c/em\u003e A Randomized Controlled Trial Investigating the Effects of Neurofeedback, Methylphenidate, and Physical Activity on Event-Related Potentials in Children with Attention-Deficit/Hyperactivity Disorder. \u003cem\u003eJ. Child Adolesc. Psychopharmacol.\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 344\u0026ndash;353 (2016).\u003c/li\u003e\n\u003cli\u003eLoo, S. K. \u0026amp; Arns, M. Should the EEG\u0026ndash;Based Theta to Beta Ratio Be Used to Diagnose ADHD? \u003cem\u003eADHD Rep.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 8\u0026ndash;13 (2015).\u003c/li\u003e\n\u003cli\u003eClarke, A. R., Barry, R. J., Bond, D., McCarthy, R. \u0026amp; Selikowitz, M. Effects of stimulant medications on the EEG of children with attention-deficit/hyperactivity disorder. \u003cem\u003ePsychopharmacology (Berl.)\u003c/em\u003e \u003cstrong\u003e164\u003c/strong\u003e, 277\u0026ndash;284 (2002).\u003c/li\u003e\n\u003cli\u003eHermens, D. F. \u003cem\u003eet al.\u003c/em\u003e Responses to methylphenidate in adolescent AD/HD: evidence from concurrently recorded autonomic (EDA) and central (EEG and ERP) measures. \u003cem\u003eInt. J. Psychophysiol. Off. J. Int. Organ. Psychophysiol.\u003c/em\u003e \u003cstrong\u003e58\u003c/strong\u003e, 21\u0026ndash;33 (2005).\u003c/li\u003e\n\u003cli\u003eBresnahan, S. M., Barry, R. J., Clarke, A. R. \u0026amp; Johnstone, S. J. Quantitative EEG analysis in dexamphetamine-responsive adults with attention-deficit/hyperactivity disorder. \u003cem\u003ePsychiatry Res.\u003c/em\u003e \u003cstrong\u003e141\u003c/strong\u003e, 151\u0026ndash;159 (2006).\u003c/li\u003e\n\u003cli\u003eClarke, A. R., Barry, R. J., McCarthy, R., Selikowitz, M. \u0026amp; Johnstone, S. J. Effects of stimulant medications on the EEG of girls with attention-deficit/hyperactivity disorder. \u003cem\u003eClin. Neurophysiol.\u003c/em\u003e \u003cstrong\u003e118\u003c/strong\u003e, 2700\u0026ndash;2708 (2007).\u003c/li\u003e\n\u003cli\u003eBarry, R. J. \u003cem\u003eet al.\u003c/em\u003e Acute atomoxetine effects on the EEG of children with attention-deficit/hyperactivity disorder. \u003cem\u003eNeuropharmacology\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 702\u0026ndash;707 (2009).\u003c/li\u003e\n\u003cli\u003eChiarenza, G. A. \u003cem\u003eet al.\u003c/em\u003e The quantified EEG characteristics of responders and non-responders to long-term treatment with atomoxetine in children with attention deficit hyperactivity disorders. \u003cem\u003eInt. J. Psychophysiol. Off. J. Int. Organ. Psychophysiol.\u003c/em\u003e \u003cstrong\u003e104\u003c/strong\u003e, 44\u0026ndash;52 (2016).\u003c/li\u003e\n\u003cli\u003eMichelini, G., Norman, L. J., Shaw, P. \u0026amp; Loo, S. K. Treatment biomarkers for ADHD: Taking stock and moving forward. \u003cem\u003eTransl. Psychiatry\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 444 (2022).\u003c/li\u003e\n\u003cli\u003eChoi, H., Park, S., Kim, K. K., Lee, K. \u0026amp; Rhyu, H.-S. Acute effects of aerobic stretching, health and happiness improving movement exercise on cortical activity of children. \u003cem\u003eJ. Exerc. Rehabil.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 320\u0026ndash;327 (2016).\u003c/li\u003e\n\u003cli\u003eLuo, X. \u003cem\u003eet al.\u003c/em\u003e A randomized controlled study of remote computerized cognitive, neurofeedback, and combined training in the treatment of children with attention-deficit/hyperactivity disorder. \u003cem\u003eEur. Child Adolesc. Psychiatry\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 1475\u0026ndash;1486 (2023).\u003c/li\u003e\n\u003cli\u003eTaya, F., Sun, Y., Babiloni, F., Thakor, N. \u0026amp; Bezerianos, A. Brain enhancement through cognitive training: a new insight from brain connectome. \u003cem\u003eFront. Syst. Neurosci.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, (2015).\u003c/li\u003e\n\u003cli\u003eMaclin, E. L. \u003cem\u003eet al.\u003c/em\u003e Learning to multitask: effects of video game practice on electrophysiological indices of attention and resource allocation. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 1173\u0026ndash;1183 (2011).\u003c/li\u003e\n\u003cli\u003eMeyer, K. N. \u003cem\u003eet al.\u003c/em\u003e Computer-based inhibitory control training in children with Attention-Deficit/Hyperactivity Disorder (ADHD): Evidence for behavioral and neural impact. \u003cem\u003ePloS One\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, e0241352 (2020).\u003c/li\u003e\n\u003cli\u003eConners, C. K. Conners continuous performance test 3rd edition (Conners CPT 3) \u0026amp; connors continuous auditory test of attention (Conners CATA): Technical manual. in (MHS, 2014).\u003c/li\u003e\n\u003cli\u003eSegal, D. L. Diagnostic and statistical manual of mental disorders (DSM-IV-TR). \u003cem\u003eCorsini Encycl. Psychol.\u003c/em\u003e 1\u0026ndash;3 (2010).\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eKorkman, M., Kirk, U., \u0026amp; Kemp, S. L. (2007). \u003c/em\u003e\u003cem\u003eNEPSY II. Administrative Manual. San Antonio, Texas: Psychological Corporation.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eWechsler, D. (2007). \u003c/em\u003e\u003cem\u003eWISC-IV: Escala de Inteligencia de Wechsler Para Ni\u0026ntilde;os-IV (2a Ed.). Madrid, Spain: Pearson Clinical.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eWechsler, D. \u0026amp; Naglieri, J. A. \u003cem\u003eEscala No Verbal de Aptitud Intelectual de Wechsler (WNV)\u003c/em\u003e. (Pearson, 2011).\u003c/li\u003e\n\u003cli\u003ei Riba, A. F. \u0026amp; Narbona, J. \u003cem\u003eEscalas Para La Evaluaci\u0026oacute;n Del Trastorno Por D\u0026eacute;ficit de Atenci\u0026oacute;n Con Hiperactividad (EDAH)\u003c/em\u003e. (Tea, 1998).\u003c/li\u003e\n\u003cli\u003eGioia, G. A., Isquith, P. K., Guy, S. C. \u0026amp; Kenworthy, L. \u003cem\u003eBehavior Rating Inventory of Executive Function: BRIEF\u003c/em\u003e. (Psychological Assessment Resources Odessa, FL, 2000).\u003c/li\u003e\n\u003cli\u003eTaulu, S. \u0026amp; Hari, R. Removal of magnetoencephalographic artifacts with temporal signal-space separation: Demonstration with single-trial auditory-evoked responses. \u003cem\u003eHum. Brain Mapp.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 1524\u0026ndash;1534 (2009).\u003c/li\u003e\n\u003cli\u003eOostenveld, R., Fries, P., Maris, E. \u0026amp; Schoffelen, J.-M. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. \u003cem\u003eComput. Intell. Neurosci.\u003c/em\u003e \u003cstrong\u003e2011\u003c/strong\u003e, 156869 (2011).\u003c/li\u003e\n\u003cli\u003eBelouchrani, A., Abed-Meraim, K., Cardoso, J.-F. \u0026amp; Moulines, E. A blind source separation technique using second-order statistics. \u003cem\u003eIEEE Trans. Signal Process.\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 434\u0026ndash;444 (1997).\u003c/li\u003e\n\u003cli\u003eBenjamini, Y. \u0026amp; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. \u003cem\u003eJ. R. Stat. Soc. Ser. B Methodol.\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 289\u0026ndash;300 (1995).\u003c/li\u003e\n\u003cli\u003eZalesky, A., Fornito, A. \u0026amp; Bullmore, E. T. Network-based statistic: identifying differences in brain networks. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 1197\u0026ndash;1207 (2010).\u003c/li\u003e\n\u003cli\u003eFranceschini, M. \u003cem\u003eet al.\u003c/em\u003e The Minimal Clinically Important Difference Changes Greatly Based on the Different Calculation Methods. \u003cem\u003eAm. J. Sports Med.\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 1067\u0026ndash;1073 (2023).\u003c/li\u003e\n\u003cli\u003eFarr\u0026eacute;, A. \u0026amp; Narbona, J. EDAH. evaluaci\u0026oacute;n del trastorno por d\u0026eacute;ficit de atenci\u0026oacute;n con hiperactividad. \u003cem\u003eMadr. Spain TEA\u003c/em\u003e (2003).\u003c/li\u003e\n\u003cli\u003eWolraich, M. L. \u003cem\u003eet al.\u003c/em\u003e Randomized, controlled trial of OROS methylphenidate once a day in children with attention-deficit/hyperactivity disorder. \u003cem\u003ePediatrics\u003c/em\u003e \u003cstrong\u003e108\u003c/strong\u003e, 883\u0026ndash;892 (2001).\u003c/li\u003e\n\u003cli\u003eKoerting, J. \u003cem\u003eet al.\u003c/em\u003e Barriers to, and facilitators of, parenting programmes for childhood behaviour problems: a qualitative synthesis of studies of parents\u0026rsquo; and professionals\u0026rsquo; perceptions. \u003cem\u003eEur. Child Adolesc. Psychiatry\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 653\u0026ndash;670 (2013).\u003c/li\u003e\n\u003cli\u003eAntonini, T. N., Narad, M. E., Langberg, J. M. \u0026amp; Epstein, J. N. Behavioral correlates of reaction time variability in children with and without ADHD. \u003cem\u003eNeuropsychology\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 201 (2013).\u003c/li\u003e\n\u003cli\u003eBoot, W. R., Simons, D. J., Stothart, C. \u0026amp; Stutts, C. The pervasive problem with placebos in psychology: Why active control groups are not sufficient to rule out placebo effects. \u003cem\u003ePerspect. Psychol. Sci.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 445\u0026ndash;454 (2013).\u003c/li\u003e\n\u003cli\u003eGranic, I., Lobel, A. \u0026amp; Engels, R. C. The benefits of playing video games. \u003cem\u003eAm. Psychol.\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e, 66 (2014).\u003c/li\u003e\n\u003cli\u003eBorghini, G. \u003cem\u003eet al.\u003c/em\u003e Quantitative Assessment of the Training Improvement in a Motor-Cognitive Task by Using EEG, ECG and EOG Signals. \u003cem\u003eBrain Topogr.\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 149\u0026ndash;161 (2016).\u003c/li\u003e\n\u003cli\u003eJau\u0026scaron;ovec, N. \u0026amp; Jau\u0026scaron;ovec, K. Working memory training: improving intelligence--changing brain activity. \u003cem\u003eBrain Cogn.\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e, 96\u0026ndash;106 (2012).\u003c/li\u003e\n\u003cli\u003eConstantinidis, C. \u0026amp; Klingberg, T. The neuroscience of working memory capacity and training. \u003cem\u003eNat. Rev. Neurosci.\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 438\u0026ndash;449 (2016).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-mental-health-research","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"npjmentalhealth","sideBox":"Learn more about [npj Mental Health Research](https://www.nature.com/npjmentalhealth/)","snPcode":"44184","submissionUrl":"https://mts-npjmentalhealth.nature.com/cgi-bin/main.p...","title":"npj Mental Health Research","twitterHandle":"@npjmentalhealth\n","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"npj","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Neurophysiological Changes, Digital Intervention, ADHD management, Brain Activity Modulation, Controlled Clinical Trial","lastPublishedDoi":"10.21203/rs.3.rs-4329802/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4329802/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAttention-deficit/hyperactivity disorder (ADHD) presents with symptoms like impulsiveness, inattention, and hyperactivity, often affecting children\u0026rsquo;s academic and social functioning. Non-pharmacological interventions, such as digital cognitive therapy, are emerging as complementary treatments for ADHD. The randomized controlled trial explored the impact of an AI-driven digital cognitive program on impulsiveness, inattentiveness, and neurophysiological markers in 41 children aged 8\u0026ndash;12 with ADHD. Participants received either 12 weeks of AI-driven therapy or a placebo intervention. Assessments were conducted pre- and post-intervention and magnetoencephalography (MEG) analyzed brain activity. Results showed significant reductions in impulsiveness and inattentiveness scores in the treatment group, associated with normalized MEG spectral profiles, indicating neuromaturation. Notably, improvements in inhibitory control correlated with spectral profile normalization in the parieto-temporal cortex. Improvements in inhibitory control, linked to normalized spectral profiles, suggest AI-driven digital cognitive therapy can reduce impulsiveness in ADHD children by enhancing neurophysiological efficiency. This emphasizes personalized, technology-driven ADHD treatment, using neurophysiological markers for assessing efficacy.\u003c/p\u003e","manuscriptTitle":"Decreased impulsiveness and MEG normalization after AI- digital therapy in ADHD children: a RCT","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 21:00:10","doi":"10.21203/rs.3.rs-4329802/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-21T15:51:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-20T16:33:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-17T16:26:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-16T12:29:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3429716699389005391145992459138164223","date":"2024-08-29T15:35:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129984784013074985536145043775218035947","date":"2024-08-28T08:04:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153309651640818351334074275271814211793","date":"2024-08-26T08:30:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89930062203846006278223835004312401013","date":"2024-08-26T07:50:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-25T14:52:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-30T14:11:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-26T13:45:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Mental Health Research","date":"2024-04-26T11:50:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-mental-health-research","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"npjmentalhealth","sideBox":"Learn more about [npj Mental Health Research](https://www.nature.com/npjmentalhealth/)","snPcode":"44184","submissionUrl":"https://mts-npjmentalhealth.nature.com/cgi-bin/main.p...","title":"npj Mental Health Research","twitterHandle":"@npjmentalhealth\n","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"npj","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ddfe175-2041-4774-a0c7-3a442a775a60","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-12-13T17:23:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 21:00:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4329802","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4329802","identity":"rs-4329802","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}