VBM analysis revealed no difference in local distribution of gray matter between the 20 ADHD patients  and the 20 age-matched and sex-matched control participants . In mixed-model repeated-measures analysis with NET availability as dependent and ROI as repeated measure we found a significant main effect group in fronto-parietal-thalamic-cerebellar regions  indicating a reduced NET availability in ADHD patients. None of the other investigated brain regions yielded significant differences in NET availability between groups after applying a Benjamini-Hochberg correction at a significance level of 0.05. Overall our findings demonstrate the pathophysiological involvement of NET availability in adult ADHD.The norepinephrine transporter (NET) has been suggested to play a critical role in attention-deficit/hyperactivity disorder (ADHD). In this prospective controlled study we tested the a-priori-hypothesis that central NET availability is altered in adult ADHD patients compared to healthy controls. Study participants underwent single positron emission tomography-magnetic resonance imaging (PET-MRI). MRI sequences included high resolution T1-MPRAGE data for regions of interest (ROI) delineation and voxel-based morphometry (VBM) and T2-weighted fluid-attenuated inversion-recovery for detection and exclusion of pathological abnormalities. NET availability was assessed by NET-selective -O-[ Moreover, a mortality rate of 5.8% has been found in ADHD, highest in individuals diagnosed in adulthood and mainly driven by deaths from unnatural causes, e.g., accidents3. ADHD is often accompanied by other comorbidities, such as depression, anxiety and conduct disorder4. ADHD symptoms have a detrimental impact on social, financial and professional functioning5—the risk of losing one’s job for example is three times higher in ADHD. Not surprisingly, the economic impact of adult ADHD places a significant burden on society. The gold standard treatment is pharmacotherapy, but 30% of adult patients with ADHD do not respond to medication7.Adult attention-deficit/hyperactivity disorder (ADHD) is an underrecognized chronic disorder with childhood-onset characterized by attention deficit, impulsivity and hyperactivity. The worldwide prevalence of ADHD is estimated to be 2.8%9 and norepinephrine transporter (NET) expression. Animal models of ADHD demonstrate the involvement of the central locus-coeruleus norepinephrine (LC-NE) system in behavioral control and the attentive process11. Drugs modulating NE transmission and targeting NET, such as atomoxetine or methylphenidate, are effective in ADHD patients6 and have been shown to significantly occupy NET in vivo at clinically relevant doses13. There is ample and clear evidence that the LC-NE system is involved in regulating wakefulness and arousal15 shown to be reduced in patients with adult ADHD18.Patients with ADHD show executive functioning deficits, impaired attention and behavioral control, relating to brain areas modulated by noradrenergic transmission19. Moreover, studies show functional abnormalities in two distinct domain-dissociated networks: (1) the fronto-parietal-thalamic-cerebellar network for attention and (2) the fronto-basal-ganglia network for inhibition22. The normalizing effect of methylphenidate and atomoxetine on the right fronto-parietal-thalamic activation during sustained attention23, known to be challenging for patients with ADHD24, corroborates the validity of the first model, further supported by the benefits of stimulation of the right dorsolateral frontal cortex on symptoms of inattention in patients with adult ADHD25. Concerning the second model, evidence from lesion and imaging studies suggest a direct pathway from the right inferior frontal cortex to the basal ganglia27 in stopping tasks. Importantly, dysfunctions of the prefrontal-striatal systems are implied in ADHD31 and basal ganglia are areas of NET expression, albeit less dense than in the LC33.While NE is released throughout the entire mammalian brain, evidence from structural and functional neuroimaging studies suggests that there are right hemispheric deficits in ADHD, specifically reduced overall cerebral volume including fronto-parietal areas, basal nuclei, globus pallidus and cerebellum18F]FMeNER-D233 in selected regions of interest (ROI) did not yield any significant differences between adults ADHD patients and healthy controls. However, using NET-selective -O-[11C]methylreboxetine, a previous study found associations between central NET availability and trait impulsivity in twenty healthy individuals34.So far, changes in NET availability have not been described in adults with ADHD. One study exploring NET binding potential using radioligand -[11C]methylreboxetine (MRB) was applied in unmedicated adult patients with ADHD and age-matched and sex-matched healthy controls. We applied a ROI approach, focusing on areas known to be functionally relevant for ADHD that are linked to the LC-NE system.In the present study we tested the hypothesis that central NET availability is altered in adult patients with ADHD compared to healthy control subjects. We further explored whether NET availability is associated with ADHD symptom severity, neuropsychological and neurophysiological measures. To evaluate differences in NET availability, PET and NET-selective -O-MRB for 90 min . For attenuation correction we applied the MR-based μ-map estimation by Catana et al.39. Using PMOD , PET data motion correction was performed, as well as the generation of the regional DVR maps. Accordingly, we applied the multi-linear reference tissue model with 2 constraints (MRTM240) using a fixed clearance rate of k2’ = 0.0238 min−1 and the occipital cortex serving as the reference region. We used PMOD’s Neuro module for automatic individual ROI delineation of the merged Automated Anatomical Labeling (AAL41) atlas. For the Harvard Ascending Arousal Network (AAN42) probability atlas we calculated the inverse transformation matrix of the spatial normalization to the MNI-space based on the T1-MPRAGE in SPM12 (Wellcome Centre for Human Neuroimaging University College London https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) and applied the inverse matrix to the AAN atlas to transfer the atlas to the participant’s individual space.PET-MRI data were acquired on a hybrid PET-MRI device  and processed as previously describedhttp://www.neuro.uni-jena.de/cat/) running in SPM12. In short, individual T1 data were spatially normalized, segmented in gray matter, white matter and cerebrospinal fluid and smoothed using a Gaussian filter with 8 mm full-width half-maximum filter size.For VBM analysis we used the CAT12 (44) were applied at the screening visit to assess ADHD symptom severity. The Structured Clinical Interview for DSM-IV Axis I and Axis II disorders46 was performed to exclude comorbid psychiatric disorders. IQ was determined with vocabulary-based IQ screening . Substance use was evaluated with Alcohol Use Disorders Identification Test48 and Drug Use Disorders Identification Test49. The Beck Depression Inventory-II50 (BDI) was applied to assess depressive symptom severity.The DSM-IV subscales of the Conners’ Adult ADHD Rating Scale Self-Report (CAARS-SR–long version51) was used to assess attention and behavioral control at the screening visit. In this 15-min test (3 × 5-min blocks), a sequence of stimuli ranging in color, shape, size and filling, is presented on the monitor. A target stimulus occurs whenever it corresponds in shape with the preceding stimulus . Participants were instructed to press a button as quickly as possible when the target stimulus appeared. The probability for the appearance of the target-stimuli was 12 percent . Errors of omission were defined as number of targets that were presented but not responded to and were used as measure of sustained attention. Errors of commissions were defined as number of non-targets that were responded to and used as measure of behavioral control (inhibition/impulsivity).The sustained attention subtest of the computerized test of attentional performance , the current source values for each voxel. The current density values in the right and left dorsolateral prefrontal cortices  were averaged over all voxels within this ROI.We utilized standardized low-resolution brain electromagnetic tomography (sLORETA) to estimate the prefrontal current density of the theta band (6.5–8.0 Hz). To calculate the current source density of prefrontal EEG theta we recorded EEG signals at the screening visit with Ag/AgCl electrodes using a QuickAmp amplifier  from 31 electrode positions according to 10–20 system under a 15-min resting condition. Impedance of each electrode was kept below 10kΩ. The raw EEG was firstly down sampled to 256 Hz and then a band pass filtering between 0.1 and 100 Hz was performed. For all participants, the first 5 minutes of entire EEG was segmented and imported into sLORETA, to analyze the cortical three-dimensional distribution of scalp current densityU tests  were performed to identify relevant covariates. To determine whether the NET availability is different between ADHD patients and healthy controls mixed-model repeated-measures (MMRM) analyses were applied with NET availability as outcome and ROI as repeated factor, and an unstructured covariance matrix. Models were implemented with restricted maximum likelihood algorithm. Fixed class effects of group (patients versus controls), ROIs, the interaction of group and ROI, BDI score as covariate, as well as random effects for subjects, matched participants and intercept were included. Separate models for attention— and behavioral control-related  ROIs based on the AAL probability atlas were calculated for the right and left side, as well as a model for LC and Raphe Nuclei based on the AAN probability atlas. The statistical significance of the main effect group or the interaction group x ROI was utilized to determine whether the NET availabilities in selected ROIs are different across the groups. In cases of significant main effects or interactions, Spearman correlation analyses between the mean DVR of respective ROIs and clinical , neuropsychological (T-scores of numbers of omission and commission errors during the 15-min sustained attention test) and neurophysiological measures (EEG theta current source density of BA9) were conducted; in case of significant findings, group was included as covariate to estimate the effect of group. All statistical analyses were computed using SPSS, version 24.0 for Windows . The two-tailed significance level was set at p = .05. The corrected significance level for multiple tests according to Benjamini and Hochberg53 was p = .014.To investigate whether there are significant differences between ADHD patients and healthy controls regarding demographic variables and clinical variables Chi-square tests, t-tests for independent sample comparison, or Mann–Whitney t-test with total intracranial volume as a confound to correct for different brain sizes. All clusters with a combined threshold of p < .001 uncorrected at peak level and p < .05 family-wise error corrected at cluster level were considered as significantly different.We used VBM to investigate differences in local distribution of gray matter between ADHD patients and age-matched and sex-matched healthy control participants. The modulated and spatially normalized gray matter images were fed into SPM12, where we applied a two-sample p > .10), however, a significant between-group difference in BDI sum scores was found (p < .0001). Thus, we decided to integrate BDI sum score in multivariate analysis.The final study sample consisted of 20 adult patients with ADHD  and 20 age- and sex-matched healthy controls . Sample characteristics are presented in Table Examples of parametric images of the DVRs in axial and sagittal view, together with their corresponding T1-MPRAGE images, are presented in Fig. We found no difference in local distribution of gray matter between ADHD patients and control participants.F3,38 = 10.72; p < .001) and a significant interaction of group x ROI  which did not survive adjustment for multiple tests. No main effects were obtained for group  or BDI .MMRM analysis using AAN atlas-based ROIs of the ascending reticular activating system  indicated a significant main effect of ROI , a main effect of ROI , but no main effect of BDI  or group × ROI interaction . When analyzing an analogous model for the left side, a main effect of ROI  and a main effect group emerged , but no main effect of BDI  or group x ROI interaction .In the right fronto-parietal-thalamic-cerebellar ROIs, MMRM analysis revealed a main effect group  and group × ROI interaction , a main effect of ROI , but no main effect of BDI . The analogous model for the left side yielded a main effect of ROI  but no significant main effect group, main effect BDI or group × ROI interaction (.559 ≤ p ≤ .706).In the right fronto-basal-ganglia ROIs, MMRM analysis revealed a statistical trend for main effect group ; participants with lower NET availability scored higher on the CAARS DSM-IV scores, indicating higher symptom severity. The correlation coefficients decreased to −0.01, and 0.10, respectively, when group was included as covariate, which indicated a medium to large size effect of group. Analogous analysis for the left side did not yield any significant findings  after adjustment for multiple tests.T-scores of CAARS DSM-IV subscales inattention and hyperactivity/impulsivity are presented in Table T-scores of omission and commission errors during the 15-min sustained attention test were calculated ; participants with lower NET availability had worse performance (=more omission errors) in the sustained attention test.p = .732). Analogous analysis for the left side did not survive the correction for multiple tests  and was not significant regarding commission errors .The correlation coefficient decreased to 0.28 when group was included as covariate, indicating that the effect of group was small. No significant correlations were obtained for mean DVR and commission errors  indicating that participants with lower NET availability had greater theta current density in the right BA9 .The current source density values of resting EEG theta in the right and left dorsolateral prefrontal cortices (indexed by current source density of right/left BA9) were calculated. Correlation analysis revealed a significant negative association between mean DVR in right fronto-parietal-thalamic-cerebellar ROIs and theta current density  errors in a sustained attention test and greater EEG theta current density in the right dorsolateral prefrontal cortex.19. The authors analyzed 17 reviews of whom none reported structural changes in adults with ADHD, while changes were reported in ADHD children. Thus, the obtained differences in NET availability between groups cannot be attributed to structural volume changes in our sample of adult ADHD patients, but rather suggest the involvement of the central LC-NE system corroborating the hypothesis of ADHD as a noradrenergic disorder. This is further supported by the fact that the LC–NE system mediates arousal15, which is impaired in adult ADHD, as shown in many EEG-studies of brain arousal54. Moreover, drugs, such as methylphenidate and atomoxetine, that modulate the LC-NE system12, are effective in ADHD, as demonstrated in randomized controlled clinical trials56. It is of note that a recent placebo-controlled double-blind fMRI study, testing the comparative neurofunctional effects of methylphenidate and atomoxetine during sustained attention, showed normalization in right fronto-parietal-thalamic areas under methylphenidate, but not under placebo23. In line with these findings, in the current study the associations between NET availability in right fronto-parietal-thalamic-cerebellar regions and neuropsychological and neurophysiological parameters, suggest a pathophysiological role of NET availability in adult ADHD and support the model of right hemisphere deficits57 in ADHD. While brain arousal has been understood as a generalized state of the brain58, it is a consistent finding that the ability to maintain an alert state relies heavily on the right cerebral hemisphere60. Our findings of greater right-frontal theta current density in individuals with lower NET availability in attention-related right-hemispheric regions point to a NET-related origin of this deficit. Interestingly, early lesion studies in rats revealed a right hemisphere bias in the LC-NE system62 indicating a top-down regulation of the NE activation by the right frontal cortex since lesions in this area resulted in lower NE levels in both hemispheres and the LC.Importantly, we found no differences in local gray matter distribution, which is in line with the systematic review by de Melo et al.31, which we could not confirm in the current study of adult ADHD patients. It is conceivable that the fronto-striatal deficits in ADHD children are due to the overall maturation delay31 and no longer present in adult ADHD patients. Our findings also do not replicate the findings of Vanicek et al. who found no significant differences in NET binding potential between ADHD and HC using -[18F]FMeNER-D2 between 22 ADHD patients und 22 HC33. Similar to this study, we did not find any differences in NET availability in key regions of the ascending arousal system . One reason may be that the spatial resolution of the applied method reaches its limitation when quantifying small brain regions such as the LC. As seen in Fig. 20 and their analyses did not consider the effect of hemisphere. Therefore, the question of consistency cannot be conclusively clarified given the differences in study design.However, functional changes in fronto-basal-ganglia regions on the right side have equally been suggested in ADHD, more often in children11C]MRB in the current study is consistent with the known distribution of NET in the brain and in line with previous studies reporting highest distribution in midbrain raphe, the thalamus and the LC65. Despite these limitations, our results warrant further studies to explore how NE/NET modulates resting-state activity (simultaneously measured by means of echo planar MR sequences) and appropriate behavior as assessed by thorough neuropsychological testing.Some limitations have to be mentioned. The relatively low spatial resolution of PET may have led to a DVR underestimation in the LC. The choice of specific templates  may have influenced the results, other parcellation schemes may generate different results, reducing the generalizability of our findings. However, the regional distribution of [For the first time, the current study demonstrates decreased NET availability in adult ADHD patients in specific brain areas. Our findings indicate the pathophysiological involvement of NET availability in adult ADHD and warrant further clinical intervention studies targeting these specific areas.


A brain-computer-interface (BCI)-based attention training game system has shown promise for treating attention deficit/hyperactivity disorder (ADHD) children with inattentive symptoms. However, little is known about brain network organizational changes underlying behavior improvement following BCI-based training. To cover this gap, we aimed to examine the topological alterations of large-scale brain functional networks induced by the 8-week BCI-based attention intervention in ADHD boys using resting-state functional magnetic resonance imaging method. Compared to the non-intervention (ADHD-NI) group, the intervention group (ADHD-I) showed greater reduction of inattention symptoms accompanied with differential brain network reorganizations after training. Specifically, the ADHD-NI group had increased functional connectivity (FC) within the salience/ventral attention network (SVN) and increased FC between task-positive networks , somatomotor, and executive control network) and subcortical regions; in contrast ADHD-I group did not have this pattern. In parallel, ADHD-I group had reduced degree centrality and clustering coefficient as well as increased closeness in task-positive and the default mode networks  after the training. More importantly, these reduced local functional processing mainly in the SVN were associated with less inattentive/internalizing problems after 8-week BCI-based intervention across ADHD patients. Our findings suggest that the BCI-based attention training facilitates behavioral improvement in ADHD children by reorganizing brain functional network from more regular to more random configurations, particularly renormalizing salience network processing. Future long-term longitudinal neuroimaging studies are needed to develop the BCI-based intervention approach to promote brain maturation in ADHD. Inattention is the common presentation of ADHD, representing approximately 38–57% of all ADHD cases in the community2. Children with inattention symptoms usually present with passive, lethargic attention problems or a deficit of sustained attention, such as procrastination, hesitation, and forgetfulness2. Clinically significant inattention and other functional impairment greatly affect their academic performance and social interaction, resulting in increased pressure and burden on their families and society3. Nevertheless, the etiological bases and neural substrates of ADHD are far from being fully understood and ADHD can be difficult to treat5.Attention deficit/hyperactivity disorder (ADHD) is one of the most commonly diagnosed neuropsychiatric disorders of childhood affecting 3–10% of children4. Therefore, despite the potential benefit of drug therapy for ADHD in children, medication must be dispensed with caution. Other standard therapy includes psychosocial or behavioral treatment, which may improve the social interaction problems, but have unknown efficacy for inattention problems6. Recently, electroencephalography (EEG)-based neurofeedback systems have been developed as an alternative modality for attention training and shown the effectiveness of recovery of function8. A few studies hypothesized that brain-computer-interface (BCI)-based neurofeedback system using specific EEG signals could induce neuroplastic changes in nervous systems10. Following this hypothesis, a BCI-based attention training game system was designed for treating ADHD children with significant inattentive symptoms11. The proposed system requires children to modulate their brain activity in attention training games, in which feedback represents the measured concentration level. It entails the value of maintaining player interest and utilizing the virtual situation to maximize the transferability to real-life contexts. Following the BCI-based training, parent-rated inattentive and hyperactive-impulsive symptoms on the ADHD Rating Scale (ADHD-RS) showed significant improvement in children with ADHD, exhibiting perspectives to be a potential new treatment for ADHD. The treatment effects of such neurofeedback-based training in children with ADHD were thought to relate to the successful regulation of brain activity and the ability of the brain to change and adapt, known as brain plasticity9.The most common treatment for ADHD is pharmacotherapy. Medications used to treat ADHD such as methylphenidate, amphetamine, and atomoxetine indicate a dopamine/norepinephrine deficit as the neurochemical basis of ADHD, but the etiology is more complex. Moreover, these agents have poor adverse effect profiles and a multitude of drug interactions12. Several intrinsic connectivity networks (ICNs) playing distinct functional roles have been consistently identified in healthy individuals using resting-state functional magnetic resonance imaging (RS-fMRI), which measures correlated low-frequency blood-oxygenation-level-dependent (BOLD) signal fluctuations between brain regions under resting or task-free condition16. This discovery has also opened new avenues for investigating the developing brain. Among these ICNs, large-scale cognitive networks undergo significant developmental reconfigurations and maturations to support more flexible cognitive control processes in adulthood. For example, the salience network is responsible for orienting attention to salient stimuli and internal events and the default mode network (DMN) is associated with self-referential mental activity. Deficits in these cognitive networks play a significant role in many psychiatric and neurodevelopmental disorders, including ADHD18.Accumulating evidence suggests that non-invasive neuroimaging methods can provide in vivo insights on brain plasticity at the macro level of large-scale functional networks, uncovering the mechanisms underlying both loss and recovery of function19, which displayed hyper-connectivity within the DMN and ventral attention network and between ventral attention and dorsal attention networks (DAN)21. Furthermore, graph theory has been recently applied in functional connectomics analysis to elucidate the complex network organization at the regional and system level22. Graph theoretical analysis found that children with ADHD showed abnormal small-world architecture characterized by higher local clustering  combined with a tendency of lower global efficiency as compared to healthy controls, exhibiting a shift toward the configuration of regular networks23. A regular network has a high local efficiency  and low global efficiency (associated with distributed or integrated processing) while a random network has a low local efficiency and high global efficiency24. This abnormality in children with ADHD pointed to a developmental lag of whole-brain functional networks in them. Previous studies have demonstrated that the maturation of the healthy developmental human brain follows a “local to distributed” principle, specifically a reduction in local efficiency and increase in global efficiency, suggesting a shift of topological organization toward more random configurations26.Recent evidence suggest altered intrinsic organization of brain networks is implicated in the dysfunction of the ADHD disorderAlthough the BCI-based training has shown promise in behavioral improvement in children with ADHD, whether and how these behavioral improvements are supported by functional network changes facilitating brain maturation following the BCI-based treatment in children with ADHD remain largely unknown. To address this gap, we aimed to examine whether and how the large-scale brain networks reorganize after the 8-week BCI-based attention treatment in children with ADHD using RS-fMRI imaging. Given previous findings on more regular configurations and higher intra- and inter-network connectivity in ADHD, we hypothesized that after the intervention, the brain functional network of childhood ADHD would have lower intra- and inter-network connectivity especially in task-positive networks and move from more regular configurations toward more random configurations accompanying their behavioral improvement. We also sought to test whether changes in functional connectivity and topological measures in ADHD were associated with changes in clinical symptoms.27. Parents were also interviewed using the Diagnostic Interview Schedule for Children based on DSM-IV. ADHD participants on medicine were only allowed to participate after at least 1 month of washout. Exclusion criteria included history of epileptic seizures, mental retardation, and an intelligence quotient of <70, which was measured using Kaufman Brief Intelligence Test, Second Edition. Written informed consent from parents and assent consent from children were obtained and ethical approval was governed by National Healthcare Group IRB, Singapore. The ADHD participants were randomly divided into two groups as part of another larger behavioral clinical trial and their involvement in this subcohort of neuroimaging study was on a purely voluntary basis. As a result, we studied 8-week BCI-based intervention group  and non-intervention group . As there was no prior neuroimaging data using this novel BCI-based intervention, based on the effect size reported in one prior fMRI study of neurofeedback therapy showed significant changes in the brain with a sample size of 15 in treatment group, we estimate 30 per group would be adequate after taking into account the 20% losses to follow-up28. MRI scans and clinical assessments at baseline and follow-up were obtained for all participants. The researchers were blinded to the group allocation during the MRI scans and clinical assessments. Of the 66 participants, 15 subjects had incomplete MRI data due to various reasons, including dropping out and scan tolerance. Therefore, 51 participants  had both T1 and RS-fMRI imaging data for both scan sessions. After careful quality control, 18 subjects from ADHD-I group and 11 subjects from ADHD-NI group have good structural and functional MRI data at both time points. The two groups were matched in age, handedness, ethnicity, and motion parameters   for ADHD11). Briefly, the headband was worn around the forehead, with a grounding reference electrode clipped to the earlobe. Two dry EEG electrode sensors were positioned at the frontal sites FP1 and FP2. The advanced signal processing techniques based on machine learning algorithm pick up useful information about attentional activities from the recorded frontal EEG signals and then send the feedback using the computerized three-dimensional (3D) graphic game (CogoLand) presented on the screen11. In the game, each participant controlled an avatar to complete a task, for example, making the avatar run around an island in the shortest time possible. The avatar ran faster if participants were more attentive. A short break was allowed between attempts.The ADHD-I participants underwent three BCI-based training sessions per week for 8 weeks Fig. . For eac29 and the Child Behavior Checklist (CBCL)30 were administered on children with ADHD at baseline and after BCI-based training. ADHD-RS is an essential part of the full assessment process for ADHD while the CBCL is a parent-rated questionnaire designed to obtain descriptions of a child’s competencies and behavioral/emotional problems, which provides both empirical-based symptoms and dimensional constructs for psychopathology. We expected the attention would be significantly improved in the participants with ADHD through the BCI-based treatment, hence the ADHD-RS clinician inattentive score were used as the primary outcome. As ADHD often co-occurs with internalizing disorders as previously reported31, the CBCL internalizing problems scale, which comprises problems that are mainly within the self, reflecting anxiety disorder, and social phobia, was also used as secondary outcomes. The ADHD-NI and ADHD-I groups  had comparable ADHD-RS clinician inattentive scores and CBCL internalizing problem scores at baseline  were collected while the subjects were asked to relax and stare at a cross centered on a screen. The RS-fMRI data collection  was broken up into two consecutive short runs to minimize motion artifacts; the duration for each of the two runs were 4 min 6 s each. We concatenated the two runs of RS-fMRI data for further processing. An eye tracker was used to ensure that the children stayed awake for the entire RS-fMRI scan. The high-resolution structural T1-weighted magnetization prepared rapid gradient echo images  were collected for atlas registration of the RS-fMRI images. To minimize the influence of scanner difference, we included the scanner type as a covariate in all statistical analysis.All functional and structural MRI images were collected at the Center for Cognitive Neuroscience, Duke-National University of Singapore Medical School using a 12-channel head coil on a 3-T Tim Trio or a 20-channel head coil on a 3-T Prisma scanner  due to unavoidable system upgrade. The same imaging parameters were used for both scanners for maximum consistency. Moreover, during the scanner upgrade, we conducted a test-retest study to ensure comparability of T1 and fMRI data between the old and new scanners (see Supplementary Methods and Results). The RS-fMRI data using T2*-weighted echo planar images 32 and the Analysis of Functional NeuroImages software program33 following our previous work35. The structural image preprocessing included: (1) image noise reduction; (2) skull stripping; (3) linear and nonlinear registration to the Montreal Neurological Institute (MNI) 152 standard space; and (4) segmentation of the brain into gray matter, white matter, and cerebrospinal fluid (CSF) compartments. Preprocessing steps for the RS-fMRI data included (1) discarding the first five volumes and interleaved slice-timing correction, (2) motion correction using first functional image with skull, (3) skull stripping, (4) despiking and grandmean scaling, (5) spatial smoothing using a 6 mm full-width half-maximum Gaussian kernel to improve signal-to-noise ratio and to reduce inter-subject variability, (6) temporal band-pass filtering (0.009–0.1 Hz) and detrending (first and second order), (7) structural MRI co-registration using Boundary-based Registration, and nonlinear registration (FNIRT) to the MNI 152 stereotactic standard space of 2 mm isotropic resolution, and (8) nuisance signals reduction by regressing out signals estimated from CSF, white matter, and six motion parameters. We did not regress out the global signal because the global signal may contain neural information36. Registration and normalization quality was visually inspected. Subsequently, we performed motion scrubbing to minimize spurious functional connectivity in brain networks. Frame displacement (FD) and the rate of change of BOLD signal across the entire brain (DVARS) at each frame were calculated37 and frames with FD larger than 0.8 and DVARS larger than 0.05 were removed.RS-fMRI images and structural MRI images were both preprocessed using a standard pipeline based on the FMRIB’s Software Library , the DAN, the DMN, the executive control network (ECN), the somatomotor network (SMN), the visual network, and the limbic network) and 30 subcortical regions. Due to the lack of coverage in certain brain scans, 141 ROIs were used for network construction. At the individual level, we calculated the Pearson’s correlation between the time series of each pair of ROIs and then Fisher’s r-to-z transformed it into the FC z-score matrix. We then summarized the intra-network FC and inter-network FC between seven major ICNs and subcortical regions for statistical analyses.We derived the individual whole-brain functional connectivity matrix based on mean time series extracted from a set of 144 regions of interest (ROIs) defined by a previous data-driven functional parcellation scheme22 following our previous work34, to evaluate the network integration and segregation features (see Supplementary Methods). Small-world topology is one of the fundamental characteristics of brain networks40, i.e., the mean shortest path between nodes increases sufficiently slowly as a function of the number of nodes in the network. It is defined as the ratio of the normalized clustering coefficient to the normalized shortest path length41. The global measures investigated were the global efficiency and clustering coefficient, to quantify the overall extent to which the networks were integrated and segregated respectively. The nodal measures assessed, namely the nodal degree, closeness, and clustering coefficient, were computed for each of the 141 nodes separately. The degree is the most commonly used metric for node centrality, which provides an evaluation of how the node is connected to the other nodes in the network. The nodal closeness and clustering coefficient quantified the extent to which each particular region was integrated within the network and segregated among its immediate neighbors, respectively22.To characterize the brain network topology, we derived graph theoretical measures from individual-level weighted FC matrices for both time points. In graph theory, each ROI stands for a node, and the FC of each pair of the nodes is defined as an edge. We focused on degree centrality, clustering coefficient, and closeness at the nodal level and efficiency, clustering coefficient, and small-worldness at global level using in-house Matlab scripts based on Brain Connectivity ToolboxOnly positive FC values were considered while negative values were set to zero. To make sure that results were not contingent upon the choice of a specific network density threshold, we derived the three topological measures from FC matrices across a variety of network density thresholds  and then took the integration across all thresholds for statistical analyses.To test whether ADHD-I group had behavioral improvement compared to ADHD-NI after the BCI-based training, two-way repeated analysis of variance (ANOVA) was performed on internalizing problems and inattention scores.To examine the possible group (ADHD-I vs. ADHD-NI) and time (pre- and post-BCI training) interaction effect on brain functional connectivity, we performed two-way repeated ANOVA on intra-network and inter-network FC values using permutation testing . The individual effects of age and scanner type were included as covariates for all the tests. Moreover, we repeated the same two-way repeated ANOVA on the global and nodal graph theoretical measures to test the possible BCI-related network topological changes.We then sought to test if the identified brain functional connectivity changes were associated with changes in symptom severity across ADHD patients using Pearson’s correlation analysis. Consistently, the individual effects of age and scanner type were regressed out as covariates from the neuropsychological assessments.Requests for code can be addressed to the corresponding author.p = 0.038, Fig. p = 0.44).After 8-week BCI-based intervention, the ADHD-I group had significantly greater reduction in the ADHD-RS clinician inattention scores compared to the ADHD-NI group (p = 0.019) and between the SVN and DAN (p = 0.035), SVN and SMN (p = 0.014), SVN and subcortical network (p = 0.050), and SMN and ECN . In contrast, the small-worldness measure showed a significant time and group interaction (p = 0.045). After the BCI-based training, the small-worldness of the ADHD-I group remained almost the same while the small-worldness of the ADHD-NI group decreased significantly. Moreover, reduction of small-worldness was correlated with less behavioral improvement  over time across all ADHD patients .Global efficiency and clustering coefficient did not show any significant effect of the BCI-based training over time  after BCI-based training.For nodal measures, the nodal degree and clustering coefficient were reduced and the nodal closeness was increased after the BCI-based intervention, mainly in the SVN, ECN, and DMN  in the two ADHD groups in terms of possible BCI-intervention effects. The in-scanner head motion parameters were comparable between the two groups and across the two time points, which did not reflect BCI-based intervention effect (see Supplementary Methods and Results).We presented evidence for brain network topology reconfigurations by the BCI-based intervention in children with ADHD. Following the BCI-based training, FC was decreased within the SVN, and between task-positive networks and subcortical regions. In parallel, the nodal degree and clustering coefficient were reduced and the nodal closeness was increased in nodes mainly from task-positive networks, DMN, and visual network, suggesting reduced local functional processing after BCI-based training. Importantly, these functional network re-organization were correlated with the improvement of inattention symptom and internalizing problems in ADHD children. Our findings highlight the value of network-sensitive neuroimaging methods to uncover brain plasticity mechanism related to intervention efficacy in neurodevelopmental disorders such as ADHD.This study utilized a BCI-based attention training program in the treatment of combined and inattentive subtypes of ADHD. The results showed that an 8-week intervention significantly improved inattentive symptoms of ADHD children based on ADHD-RS inattention subscale. There are several advantages the BCI-based attention training program can offer: it is non-pharmacological, much safer, and easy to learn and it can be done at home, the system represents a novel treatment modality for ADHD childhood, which not only has the potential for being used in combination with present evidence-based treatment but also uniquely in a nonclinical setting for its convenience.43. Recent evidence suggests that dysregulation of salience-processing systems can occur in many brain conditions, including neurodevelopmental disorders45. Emerging imaging evidence points to the dysfunctional coordination of the attention networks and DMN, controlled by salience network, contributing to attentional engagement, and disengagement in ADHD46. The attention systems, ventral attention network and DAN, have specialized roles as well as interaction: the DAN is involved in top-down voluntary allocation of goal-driven attention, whereas the ventral attention network is involved in the stimulus-driven attention20. Our data suggest that the BCI-based treatment had the SVN connectivity decreased, and the SVN and DAN more functionally segregated in ADHD children, and importantly, these two changes were significantly correlated with the behavior improvement, potentially indicating that the attention training games re-normalized the salience-processing system and the effective coordination between the two attention systems.The changes of ADHD brain network by the BCI-based intervention we revealed majorly involved the SVN, which might bring some interesting aspects to note. The salience network has a central role in the detection of behaviorally relevant stimuli, integrating information, the coordination of neural resources, and the mediation of information flow between other networks involved in higher order cognition, such as default mode and ECN47, and there was some evidence for differential abnormalities in the basal ganglia48. The BCI-based intervention induced reduced connectivity between SVN and subcortical network might reflect the renormalization of subcortical network FC.We also found reduced connectivity between SVN and subcortical and SMN following the BCI-based intervention in ADHD, indicating that the BCI-based intervention also altered the interaction between the salience network and these two important networks. From the pathophysiological basis, the general distractibility in ADHD has been attributed to a deficit in dopaminergic signaling in subcortical-cortical networks that regulate goal-directed behavior49. Also, across development, the topological organization of multiple functional networks shifts from a local anatomical emphasis to a more “distributed” architecture, specifically a reduction in local efficiency and increase in global efficiency, suggesting a shift of topological organization toward more random configurations25. It has been proposed that ADHD involves a delayed or altered maturation of brain’s developing functional architecture as well as its structural features such as gray matter volume or cortical thickness50. The developmental delay in functional connectivity patterns in children with ADHD lead to a more regular configuration of networks, characterized by higher local efficiencies and a tendency of lower global efficiency23. In this study, we found decreased connectivity in inter-networks between the task-positive networks by the BCI-based intervention, indicating increased segregated processing in children of ADHD-I group. In parallel, we found the BCI-based intervention led to decreased nodal clustering coefficient indicating decreased local efficiency in children of ADHD-I group, which suggest a trend toward a more random topology configuration. In contrast, the ADHD-NI group did not have such pattern over time or had the rather opposite trajectory (Supplementary Tables The human brain is functionally organized into hierarchical, modular structure, and the network modules become more segregated with age in neurotypical development, allowing for specialized processing to occur within densely interconnected groups of brain regions, which reduce interference among systems and facilitate cognitive performance51. Furthermore, our study did not distinguish between the ADHD subtypes due to the limited sample size. Although both the inattentive and combined subtypes have predominant inattention problems, each subtype may have unique response of brain networks following the BCI-based treatment. It is however noted that the subtyping of ADHD has been removed in DSM-5. Further studies may need to take the clinical heterogeneity of ADHD into account. Further studies were needed to study ADHD patients with longer duration of BCI-based treatment and long-term follow-ups.The primary limitation of our study was the relatively small sample size after the removal of poor-quality data due to excessive motion. Second, physiological noise in the fMRI dataset could be further corrected by implementing advanced fMRI preprocessing techniquesIn conclusion, our study revealed the neural mechanism underlying behavioral improvement following a BCI-based training on children with ADHD. The BCI-based intervention can help re-normalize brain functional network topology among cognitive networks, which is associated with behavioral improvement and facilitate brain maturation in ADHD children. These findings underscore the potential value of BCI-based attention training game as an attractive treatment strategy for ADHD.supplemental material


Blood erythrocytes PUFAs were measured before and after treatment, to examine the effects of baseline endogenous EPA levels on treatment response and the effects of EPA treatment on PUFAs levels. Secondary measures included other ADHD symptoms, emotional symptoms, and levels of plasma high-sensitivity c-reactive protein (hs-CRP) and brain-derived neurotrophic factor (BDNF). Overall, EPA group improved more than placebo group on focused attention  = 0.38, p = 0.041); moreover, within youth with the lowest baseline endogenous EPA levels, EPA group improved more than placebo group in another measure of focused attention  and in vigilance . Interestingly, EPA group improved less than placebo group in impulsivity (commission errors), both overall and in youth with the highest baseline EPA levels, who also showed less improvement in other ADHD and emotional symptoms. EPA increased blood erythrocytes EPA by 1.6-fold but not DHA levels, and did not affect hs-CRP and BDNF plasma levels. In conclusion, EPA treatment improves cognitive symptoms in ADHD youth, especially if they have a low baseline endogenous EPA level, while youth with high EPA levels may be negatively affected by this treatment.No studies have examined the relationship between endogenous polyunsaturated fatty acids (PUFAs) levels and treatment response to PUFAs. We conducted a 12-week, double-blind, placebo-controlled trial comparing the effects of high-dose eicosapentaenoic acid  and placebo on cognitive function (continuous performance test) in  N-3 PUFAs, including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are essential fatty acids (EFA) for our brain and body2, and have been closely associated with cognitive function3 and academic performance4. Our recent meta-analysis has shown that youth with ADHD have lower blood levels of DHA, EPA and total n-3 PUFAs, when compared with typically developing youth1. Moreover, EFA deficiency  has been described in children with ADHD5, and the severity of EFA deficiency has been associated with ADHD symptom severity6.A deficiency of omega-3 polyunsaturated fatty acids (n-3 PUFAs) may play a role in the pathogenesis of attention deficit hyperactivity disorder (ADHD)10. N-3 PUFAs have a very good tolerability and safety profile, and thus may be a preferable treatment option for youth with ADHD. Indeed, our recent meta-analysis has shown that n-3 PUFAs treatment improves clinical symptoms in youth with ADHD1, although individual studies have conflicting findings, possibly because of different dosages of EPA and DHA used across studies. Of note, a previous meta-analysis examining clinical trials in children with ADHD showed that a high dose of omega-3 (1–2 g) supplementation was required to show significant improvement in clinical symptoms11. Moreover, the therapeutic effects of n-3 PUFAs in ADHD seems to be particularly evident when measuring cognitive function  rather than generic ADHD symptoms1. Interestingly, most of the n-3 PUFAs studies in ADHD used either a high-dose DHA12, or a relative low-doses of DHA and EPA combination treatment13, despite the evidence that EPA is the more effective therapeutic component, at least in depression16. In fact, our meta-analysis1 shows that only studies with EPA supplementation greater or equal to 500 mg per day find an improvement in the clinical hyperactivity-impulsivity symptoms.Although meta-analysis report that stimulants are tolerated and effective treatments for youth with ADHD, these drugs have side effects that need monitoring, such as insomnia, risk of abuse, and changes in blood pressure, heart rate and body weight18. Although there have been no clinical trials using PUFAs’ endogenous levels to stratify patients in PUFAs clinical trials, we have previously shown a direct link between lower endogenous PUFAs levels and increased risk of developing interferon-alpha (IFN-α)-induced depression, suggesting that endogenous PUFAs levels can offer clinically-relevant information on course and outcome 19.Of note, there is some evidence that psychiatric treatments may be more effective when subjects are stratified based on biomarkers that are mechanistically relevant to the intervention. For example, two studies have shown that depressed patients with higher inflammation, as measured by C-reactive protein (CRP) or high interleukin (IL)-6, have a better response to the anti-inflammatory, infliximab, or to n-3 PUFAs supplementation, respectively21, and two studies have found increased interleukin IL-6 in children with ADHD23. In addition, neurotrophins, such as brain-derived neurotrophic factor (BDNF), may also play a role in ADHD, although the results have been inconsistent24. PUFAs are generally considered to modulate neuroplasticity25 and to be anti-inflammatory26; in fact we have previously shown that pre-treatment with EPA reduces the onset of depression induced by the pro-inflammatory cytokine, IFN-α16. Thus, both these biological systems might be relevant to the mechanisms of action of PUFAs in ADHD.Finally, in term of the possible mechanism underpinning the therapeutic effects of PUFAs in ADHD, both inflammation and neuroplasticity have been proposed as relevant. There is some evidence suggesting that ADHD is associated with increased inflammation: for example, children with ADHD are more likely to suffer from asthma and atopic dermatitisBased on the gaps in knowledge described above, we have conducted a 12-week double-blind, randomised, placebo-controlled trial (RCT) in youth (6–18 years) with ADHD, which: (1) compares a high dose of EPA (1.2 g/day) with placebo; (2) measures cognitive function as the primary outcome; (3) examines endogenous PUFAs levels to stratify patients and to examine the effects of treatments on PUFAs levels; and (4) investigate the inflammatory biomarker, high-sensitivity CRP (hs-CRP), and the neurotrophin, BDNF, as potential mechanisms.The Institutional Review Board of China Medical University Hospital  approved this study (CMUH 104-REC-058) and written informed consent was obtained from the participants and their parents. We recruited youth aged 6–18 years, with the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnosis of ADHD with either inattention , hyperactivity, or combined presentation; we also assessed the presence of oppositional defiant disorder (ODD), characterised by symptoms such as often losing temper, arguing with adults, defying rules and blaming others. All diagnoses were confirmed by a child and adolescent psychiatrist, and all participants were referred to the Department of Psychiatry, CMUH, from July 2016 to December 2017. The participants were either drug naïve or had no medication for the past 6 months. The ClinicalTrials.gov identifier for this study is: NCT03542643.The exclusion criteria were: (1) Intelligence quotient <70, based on a documented history of mental retardation; (2) for those ages 6–12 years old, a Peabody Picture Vocabulary Test-Revised (PPVT-R) percentile scores less than 5% ; (3) other comorbid psychiatric disorders, such as autism spectrum disorder, anxiety disorder, conduct disorder, and other major psychiatric disorders; (4) comorbid physical disorders, such as thyroid dysfunction and cerebral palsy; (5) currently using n-3 PUFAs supplements; and (6) allergy to n-3 PUFAs.1. The online sample size calculator is used to calculate the sample size, https://www.ai-therapy.com/psychology-statistics/sample-size-calculator. Significance level: 0.05, power: 0.8, ES: 0.38 to 0.81, and the estimated sample sizes are between n = 246 to n = 50. One hundred and three youth were recruited and randomised to n-3 PUFAs (1.2 g/day EPA) or placebo (1.2 g/day soybean oil) for 12 weeks, and 92 subjects  completed the 12-week trial .We were to find the effect size of high dose EPA treatment is around 0.38 to 0.81n = 86) and levels of plasma hs-CRP (n = 80) and BDNF (n = 84), using enzyme-linked immunosorbent assay (ELISA). Additional details of the clinical, cognitive and laboratory measurements are provided in the Our main outcome measures were the four items of the Continuous Performance Test (CPT), measuring focused attention, impulsivity, sustained attention and vigilance, assessed at baseline and week 12 . Additional measures assessed at baseline and week 12 were the Strength and Difficulties Questionnaire (SDQ), for the emotional problems of the child, and the Digit Span Subset of Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) for working memory and short-term memory; moreover, we used the Swanson, Nolan, and Pelham IV (SNAP-IV) to measure ADHD clinical symptoms rated by parents, teachers and youth (for those ages 12 years or older), at baseline and week 1, 2, 4, 8, and 12. Blood samples were collected at baseline and week 12  for evaluation of erythrocytes levels of PUFAs .All the main analyses were conducted on the 92 subjects who completed the trial, as the primary outcome measure (the CPT) was only collected at baseline and at the end of the trial. Reassuringly, the 11 children who dropped out from the trial were not different in ADHD severity from the 92 who completed, although they tended to be younger  and levels of biomarkers, between baseline and 12-weeks; that is, we compared the deltas, using t-test for deltas that were normally distributed (most of the variables) and Mann–Whitney test for the deltas that were not normally distributed , SDQ emotion problems (SDQE), SDQ conduct problems (SDQC), SDQ peer problems (SDQP), WISC-IV digit span forward (WISCDSF) and longest digit span forward (WISCLDSF)). This same approach was used after stratification of subjects based on endogenous EPA levels. In addition, effect size (ES) and 95% confidence interval (CI)s were calculated, when appropriate. A p-value of <0.05 indicates statistical significance.All statistical analysis was carried out with Statistical Package for the Social Science (SPSS), version 25.0 for Windowsn = 92), we found that youth on EPA improved more than youth on placebo in focused attention  but less than those on placebo in impulsivity . There were no other differences between the groups in other CPT measures of focused attention , p = 0.573; detection p = 0.094), impulsivity , sustained attention  and vigilance , p = 0.492) .As 51 out of 92 participants had comorbid ODD, and children with ADHD and ODD tend to be generally less responsive to pharmacological treatmentn = 86), we found that, in the lowest EPA group, youth on EPA improved more than youth on placebo in another measure of attention , oppositional symptoms (SNpO), total ADHD symptoms (SNpH) and internalising problems (SDQIn), as well as in teachers’ report of inattention (SNtaA)  and total n-3 PUFAs levels (p = 0.012), and a greater decrease of n-6/n-3 ratio (p = 0.008), compared with placebo (see Table p = 0.342). Changes in hs-CRP and BNDF values are also reported in Table p = 0.747 and p = 0.070, respectively).As expected, EPA group showed a greater increase in EPA  focused attention at the CPT test, one of our main outcome measures. Moreover, youth with low endogenous EPA levels show even stronger effects of EPA in a further measure of attention (HRT) and in a measure of vigilance (HRTISIC), both improving more than in the placebo group. To our knowledge, this is the first study to report the effects of EPA monotherapy in ADHD, and the first study ever to use endogenous baseline PUFAs levels to stratify subjects in a PUFAs clinical trial. Further analyses indicate that EPA treatment in those with 1 has shown that PUFAs improves attention and impulsivity in children with ADHD, but the results of the individual studies have been inconsistent. For example, Sinn et al.29 found an improvement in attention, but Voigt et al.30 did not; similarly, Vaisman et al.31 found an improvement in impulsivity, but Voigt et al.30 found that, much like this present study, placebo did better than PUFAs for impulsivity. While it is difficult to draw firm conclusions on the reasons behind these discrepant findings, it is worth mentioning that both our study and Voigt et al.30 have around half of the sample with comorbid ODD. Interestingly, subgroup analyses in our data show that the improvement in attention does occur in the children with comorbid ODD, thus suggesting that specific classes of symptoms  may respond differently to EPA also based on the presence of comorbid psychiatric conditions.Our main findings are consistent with (at least some) previous studies investigating n-3 PUFAs  in ADHD. Our recently-published meta-analysis17 and Rapaport et al.18 : they both found antidepressant effects by an anti-TNF-alpha medication and by n-3 PUFAs, but only in patients with high baseline inflammation, i.e., those with biological evidence of abnormalities in the mechanism targeted by the interventions . Consistent with this framework, we find that youth with the lowest levels of baseline endogenous EPA show the largest improvement in cognitive function following EPA. Although there are no other similar studies, previous research, in healthy children, as well as children and adults with ADHD and other neurodevelopmental disorders, has shown that PUFAs improve cognitive function more in the presence of low “n-3 PUFAs status”, i.e., in those with evidence of low dietary intake of fish, or if they present symptoms of the aforementioned EFA deficiency32.To test whether low endogenous PUFAs levels predict response to PUFAs, we use a similar approach to that used by two previous ‘personalised psychiatry’ studies, i.e., Raison et al.17 and Rapaport et al.18 find not only that high inflammation predicts a better response to the anti-inflammatory intervention, but also that low inflammation predicts a better response to placebo, i.e., lowering inflammation in people who already have low inflammation actually has adverse effects. We also find that children with the highest baseline EPA levels perform better on placebo than on EPA, in impulsivity and in parental and teachers’ reports of ADHD and emotional symptoms. A previous meta-analysis in depression has also shown a “J-shaped curve” in the protective effects of PUFAs, increasing for doses of up to 1.8 g/day of n-3 PUFAs (or 0.6 g/day of EPA + DHA intake), and then decreasing for higher doses33; this suggests that very high doses of PUFAs , may have adverse effects. However, it is important to emphasise that this J-shaped curve has been described for the intake of PUFAs from the diet, rather than supplementation33, and hence some potential mechanisms to explain these negative effects  are not present in our study. Reassuringly, most studies of children with ADHD (conducted largely in Western countries) have shown average endogenous EPA levels that are lower than those in our study1, probably because of the low-fish and high-omega-6 dietary intake in non-Asian countries34; thus, it is unlikely that non-Asian children would normally reach endogenous PUFAs levels that are similar to those in the highest tertile of our sample. Nevertheless, with fish intake and other natural sources of omega-3 being constantly advocated as part of a healthy diet, it is important to be aware that supplementing those who already have high levels of EPA may be detrimental.Interestingly, and much like our study, Raison et al.35. Similarly, studies in patients with dyslipidemia and healthy subjects found that 3-4 g/day EPA increases plasma EPA but not DHA levels37. In humans, there is a poor enzymatic conversion of EPA to DHA37, and genetic variants in fatty acid desaturase 2 gene (FADS2), the key enzyme responsible for the conversion from EPA to DHA, further reduce this conversion38. Interestingly, and of specific interest for our study, a single nucleotide polymorphism (SNP) in FADS2 has been significantly associated with ADHD39, suggesting that the genetic profile of ADHD may have further effects on PUFAs metabolism. While we wanted specifically to test whether EPA alone was enough (following from the evidence in depression16), most of the other studies included in our previous meta-analysis1 have combined EPA + DHA interventions, and, indeed, in a previous cross-over clinical trial, an increase in erythrocyte EPA and DHA after a combined EPA + DHA intervention was associated with improved attention and behavioural symptoms in children with ADHD40. Thus, taken together, our data do seem to suggest that, for ADHD clinical symptoms, combined EPA and DHA may have a broader impact than EPA alone on clinical symptoms, possibly because affecting both EPA and DHA levels.The youth receiving EPA in our study show an increase in blood erythrocytes EPA and total n-3 PUFAs levels, and a decrease in the n-6/n-3 ratio, at the end of the 12 weeks. This is an expected finding, and it confirms the compliance of the youth with treatment. However, we also found that DHA levels do not increase in youth who received EPA, which is surprising since EPA is physiologically converted into DHA. However, this finding is consistent with a previous study showing that 1 g/day EPA in patients with depression only increases plasma EPA levels but not DHA levels41. However, EPA did not affect hs-CRP levels in our study. Of course, the average hs-CRP levels of youth with ADHD in our study (just above 2 mg/L) were below the threshold for even low-grade inflammation (3 mg/L); thus, the presence of only very mild levels of inflammation might have made impossible to detect an anti-inflammatory action of EPA. Moreover, it is also important to highlight that a previous study found a significant decrease in plasma CRP levels in children with ADHD treated with combined 900 mg PUFAs per day (DHA 165 + EPA 635 mg)42; thus, it is also possible that EPA monotherapy is ineffective to induce an anti-inflammatory action. In a previous study we found that EPA pre-treatment is able to prevent inflammation-induced depression following IFN-alpha treatment16; however, we did not measure CRP or indeed other immune biomarkers in that study, and thus we cannot exclude that the beneficial effects of EPA were due to changes in other mechanisms. Thus, the present study does support the possibility that both EPA and DHA are needed to exert a clear anti-inflammatory action.We investigated inflammation as one mechanism that might explain the actions of EPA on cognitive function in ADHD. EPA has been shown to have anti-inflammatory action via antagonising membrane arachidonic acid (AA) formation and inhibition of the synthesis of pro-inflammatory mediators43 and atomoxetine44, modulate levels of BDNF, although BDNF levels were increased after 6 weeks treatment of methylphenidate43 and decreased after a 3-month treatment of atomoxetine44. Studies of the associations between n-3 PUFAs intake and BDNF levels have also been inconsistent: for example, a positive association has been reported between n-3 PUFAs consumption and serum BDNF levels in adolescents45, but clinical trials do not find an effect of n-3 PUFAs on BDNF levels in adults with distress following trauma46 or in adults with diabetes mellitus and depression47. Again, preclinical evidence indicates that neuroplasticity effects of PUFAs may require both EPA and DHA48, suggesting an explanation for our negative findings.The levels of neurotrophins, BDNF, were also not affected by EPA in this study. Previous studies have demonstrated that treatments for ADHD, such as methylphenidate1 have longer durations (mean of 15.6 weeks). Perhaps a longer duration of the trial would have been more successful in eliciting effects of EPA on clinical symptoms in ADHD. However, longer trials risk high drop-out rates, and we wanted to test an intervention that was easily deliverable in non-specialist settings; our drop-out rate of around 10% testifies to the success of this approach. It is also interesting to note that those who discontinued the study had a younger age than those who completed the study; this may be due to the fact that parents of young children with ADHD might feel less distressed by the symptoms and thus less keen in pursuing treatment, as suggested by previous studies49. Finally, we did not perform multiple comparison corrections in our analysis; we felt that this would have been too stringent, and might have generated false-negative findings. Instead, we have relied on the presentation of effect size differences throughout the papers, so that clinical significance, rather than statistical, is used to convey the strengths of the results; improvement in the overall group was at least moderate in its effect size (.38), and the improvements in the youth with the lowest EPA levels were big in their effect sizes (>0.8), thus we trust that these are true positive findings.Our study, although with several strengths, is not without possible limitations. First the ADHD population is heterogeneous, with age ranging 6–18 years and comorbidity with ODD; however, these characteristics make our sample more similar to the ADHD population in clinical settings, which reinforces the generalisability of our study. A second limitation is the duration of our study, 12 weeks, while most of the studies included in our previous meta-analysis50. However, we additionally recommend that this strategy should be even more strongly advocated for children with evidence of low endogenous PUFAs levels, as indicated by direct measurement, dietary habits or symptoms of EFA deficiency. Conversely, in the cases where high endogenous levels of PUFAs might already be present because of a dedicated diet or previous supplements, PUFAs levels should be investigated before trialling this strategy, to limit any potential negative effects. In this way, we can start bringing the benefits of ‘personalised treatment’ to children with ADHD.In conclusion, our study shows some benefits of EPA monotherapy on cognitive symptoms of ADHD. As amply discussed, it is possible that a combined EPA + DHA strategy might have been more beneficial, and as such we support the recent recommendation by a panel of ADHD experts that patients who prefer omega-3 supplements over stimulants should take a combination of DHA and EPA at doses ≥750 mg per day for at least 12 weeksSupplementary Material


Background: Gaming disorder (GD) has been shown to co-occur with attention deficit hyperactivity disorder (ADHD), yet few studies to date have investigated their longitudinal associations.Method: The sample included 5,067 young Swiss men (mean age was 20 years at wave 1 and 25 years at wave 3). Measures were the Game Addiction Scale and the Adult ADHD Self-Report Scale (6-item screener). Longitudinal associations were tested using autoregressive cross-lagged models for binary measures of GD and ADHD, as well as continuous measures for GD score and ADHD subscales of inattention and hyperactivity.Results: ADHD at age 20 increased the risk for GD at age 25 . GD at age 20 also increased the risk for ADHD at wave 3 . Only the ADHD inattention subscale showed a bidirectional longitudinal relationship with the GD score , whereas associations between the hyperactivity subscale and GD were not significant.Discussion: GD had bidirectional longitudinal associations with ADHD, in that ADHD increased the risk for GD and GD increased the risk for ADHD, and they may reinforce each other. These associations may be linked more to the inattention ADHD component than to the hyperactivity ADHD component. Individuals with ADHD or GD should be screened for the other disorder, and preventive measures for GD should be evaluated in individuals with ADHD. Video gaming is a widespread activity among young men. Although gaming is an unproblematic leisure activity like many others for most people , it doesADHD is classified as a neurodevelopmental disorder. It is characterised by two components: inattention  and hyperactivity  . PrevaleThere has been relatively little research about the link between GD and ADHD. This is partly because before the DSM-5 included internet GD as a condition for further study, in 2013, internet GD was often studied together with internet addiction, and only afterwards as an independent condition . In a reRegarding associations with ADHD's inattention and hyperactivity subscales, another recent study reported that attention problems  in adolescents predicted internet GD 1 year later . A crossSeveral theories have been proposed for the link between ADHD and GD. For example, the “optimal stimulation model” proposes that individuals with ADHD have a higher threshold for reaching an agreeable level of arousal, and the rapid visual and acoustical stimulations in computer games requiring fast motor responses may be one way to reach this level . AnotherMost explanations and research have focussed on how ADHD leads to GD, although some explanations for a relationship in the other direction have also been proposed. Notably, ADHD symptoms may make gaming more attractive, whereas increased gaming may, in turn, exacerbate ADHD symptoms “by providing an activity that continuously reinforces the exact disinhibition, quick responsiveness, need for immediate reward, and inattention that are areas of concern” . A studyThis study aimed to re-examine the association between GD and ADHD in a longitudinal sample of young Swiss men. We first investigated whether our data confirmed cross-sectional associations between GD and ADHD and the ADHD subscales of inattention and hyperactivity. In a second step, we tested the longitudinal associations between GD and ADHD using an autoregressive cross-lagged (ARCL) model. The model examined whether ADHD at age 20 was associated with GD at age 25, whether GD at age 20 was associated with ADHD at 25, or whether there were bidirectional associations between GD and ADHD. We also tested GD for longitudinal associations with ADHD's inattention and hyperactivity subscales. In a third step, we tested whether participants with ADHD and GD at wave 1 (at about 20 years old) had worse outcomes with both of those disorders at wave 3 (about 25 years old) than participants with GD only or ADHD only, as well as several other outcomes potentially associated with ADHD or GD, namely major depression, mental health, life satisfaction, and poor performance at work or school.www.c-surf.ch). This study follows a large sample of young Swiss men recruited in their late adolescence to their adulthood, with measurement points at the age of about 20, 21, and 25 years, with more measurement waves being in planning. The main aim of the study is to evaluate patterns, trajectories, and associated risk or protective factors of substance use and non-substance related behaviours in these young men , during the recruitment procedure for military service. These procedures are mandatory for all young Swiss men at about the age of 20, therefore the sampling at this occasion has the advantage of covering most of young men of that cohort. Responses to questionnaires were independent of army procedures as individuals responded privately at home and confidentiality from the army was ensured. Participants could choose between paper questionnaires per mail or online questionnaires which were accessible by a link sent per e-mail. A total of 13,237 of young men have been asked to participate in the study, and 7,556 finally gave their written consent to participate in the study, of which 5,987 returned the baseline questionnaire (wave 1) and 5,516 returned the second follow-up questionnaire (wave 3) between April 2016 and March 2018. To increase response rates, participants who did not answer the questionnaire after standard reminders were encouraged by trained interviewers via phone calls to participate .The present study includes all 5,125 (85.6% retention rate) participants who responded to the baseline and second follow-up questionnaires. Of those, 58 (1.1%) participants with missing values for GD or ADHD in waves 1 or 3 were excluded, leaving 5,067 participants included in our present analysis. Participants received vouchers (50 CHF per questionnaire) as compensation for their efforts. Data from wave 2 were not used  because the measure for ADHD was only included in waves 1 and 3. The research protocol was approved by the Human Research Ethics Committee of the Canton Vaud (Protocol No. 15/07).never) to 4 (very often), and participants who responded to at least three items with a score of at least 2 (sometimes) were defined as presenting GD, as suggested by Lemmens and Valkenburg . This was done, because at the time when the questionnaire for wave 1 was developed, it was thought that lots of games involve internet activities, and that GD may be impossible without spending time in the internet (online games). After the DSM-5  was measured using the Game Addiction Scale (GAS) , which wlkenburg . Additiohe DSM-5 , releaseTable 2). Response options were on a five-point Likert-type scale ranging from 0 (never) to 4 (very often). For building a binary measure of ADHD, items were dichotomised—at least 2 (sometimes) for the first three items and at least 3 (often) for the last three items—and ADHD was defined as the presence of at least 4 symptoms as suggested by the authors of the scale  was measured using the six-item screener version of the Adult ADHD Self-Report Scale (ASRS-v1.1)  developehe scale . For anaAlcohol use disorder  was measured using 12 items for the 11 DSM-5 criteria , 36, 37 never) to 4 , a measure of the frequency of cannabis use ranging from 1 (monthly or less often) to 4 (four or more times per week), and one item with two response options, 0 (smoking cannabis for fun) or 4 (smoking cannabis out of habit). A cut-off of 8 out of 40 possible points was used to define cannabis use disorder. Cronbach's Alpha for the cannabis use disorder scale was 0.894 in wave 1 and 0.906 in wave 3.Cannabis use disorder (last 12 months) was measured using the revised version of the Cannabis Use Disorder Identification Test . Prevalence of ADHD increased from 5.7% in wave 1 to 7.6% in wave 3 [McNemar test p < 0.001]. Cross-sectionally, ADHD was more frequent in participants with GD than without GD, in both waves, with an Odds Ratio (OR) of 3.21  for wave 1 and 2.56  for wave 3. SUDs were not significantly associated with GD in wave 1, yet SUDs were significantly more frequent in participants with GD than without GD in wave 3. Accordingly, adjusting for SUDs only marginally changed the association between ADHD and GD in wave 1, but reduced this association in wave 3 (from OR = 2.56 to OR = 2.08). The mean scores of each of the six ADHD items were higher in participants with GD at waves 1 and 3, although this was not significantly higher for the second item of the ADHD hyperactivity subscale  to the coefficient for GD at wave 1 on ADHD at wave 3 . Adjustments for SUD had only a minor impact on the cross-lagged paths .Participants with GD at wave 1 were more likely to show ADHD at wave 3, and participants with ADHD at wave 1 were more likely to show GD at wave 3 . The ADHD hyperactivity subscale showed no significant longitudinal associations with GD score .As regards longitudinal associations between ADHD subscale scores and GD score, the ARCL including the GD score and the ADHD inattention and hyperactivity subscales only showed significant  and attention deficit hyperactivity disorder (ADHD) in a longitudinal sample of young Swiss men. At both measurement points, GD was considerably more frequent  among participants with ADHD than among those without ADHD. Similarly, ADHD was more frequent among participants with GD than those without GD. These findings are well in line with existing studies showing cross-sectional associations between GD and ADHD  Several theories have been proposed concerning the mechanisms underlying associations between ADHD and gaming. Notably, gaming may optimally stimulate individuals with ADHD by providing an exciting activity with immediate rewards: it may therefore be a way to cope with the symptoms of ADHD. However, because gaming provides exactly what individuals with ADHD may prefer, frequent exposure to such a potent stimulus may in turn reinforce ADHD symptoms  and leadA further finding was that the inattention and hyperactivity subscales of ADHD also showed significant cross-sectional associations with GD. However, if jointly entered in a regression model, only inattention remained significant, indicating that the link between ADHD and GD may be mainly accounted for by this variable. Similarly, the ARCL model using both the continuous ADHD subscales and the GD score showed that the link between ADHD and GD (in both directions) was dominated by the inattention subscale, with longitudinal associations for the hyperactivity subscale being non-significant (and even slightly negative). This finding is consistent with those from an earlier cross-sectionall study  of 205 aThe present study tested whether individuals with GD and ADHD at age 20 had worse outcomes at age 25 than individuals with only GD or only ADHD. Our results indicate that GD might have been more persistent  among individuals who also had ADHD at age 20 than among those with only GD at age 20, however, the coefficient in our study was just below significance after adjustment for SUD, indicating that other factors besides ADHD may also influence the persistence of GD. This is in line with similar evidence from the field of SUDs showing that ADHD may have a negative impact on the courses of those disorders, i.e., individuals with ADHD may become addicted more easily and have lower remission rates . The preAt age 25, participants with both ADHD and GD at age 20 had the worst outcomes on all the other scales measured—SF-12 mental health scale scores, major depression scores, life satisfaction, and poor performance at work or school. Participants who only had ADHD at age 20 had the second-worst outcomes. Participants who only had GD at age 20 had somewhat better outcomes at age 25 than those with only ADHD at age 20. Participants who had neither ADHD nor GD at age 20 had the best other outcomes. However, the differences in other outcomes between participants with GD and ADHD at age 20 and those with only ADHD were relatively small and only significant for major depression scores. However, there were relatively few cases with both GD and ADHD at wave 1.Nevertheless, our results provide evidence that individuals with GD and ADHD may have worse outcomes than individuals who only have GD or who only have ADHD. They also suggest that GD is more than just a symptom or correlate of ADHD, as it is associated with worse outcomes even in individuals with ADHD. GD should therefore be considered as a potentially serious condition, and individuals with comorbid ADHD and GD may require special consideration.Our sample consisted only of young Swiss men of a restricted age range. Thus, our results may not be generalizable to other populations. Overall, although coefficients for longitudinal associations between GD and ADHD were significant, they were relatively small. However, they remained relatively unchanged, even when adjusted for potentially confounding variables like SUDs. The instrument used for measuring GD differed somewhat between waves 1 and 3, as the Game Addiction Scale was extended in waves 1 and 2 to assess internet addiction too. This was partly corrected by setting the instrument's score to 0 for participants who played video games less than weekly. Overall, small differences in prevalence rates were in the expected direction , and consistent results indicated that the impact of the differences in wording between the instruments was small. For space reasons, we used the short, six-item screener version of the Adult ADHD Self-Report Scale, consisting of only four items for inattention and two for hyperactivity. Further research using longer ADHD scales, allowing for better differentiation of subtypes, is certainly required.The present study adds to existing evidence that GD may be associated with serious negative mental health outcomes by providing evidence that GD and adult ADHD have bidirectional longitudinal associations, i.e., each increases the risk of the other. This also suggests the possibility that the two disorders may reinforce each other, i.e., cause a vicious circle : early ASM analysed the data and wrote the paper. GG and JS designed the study. GG, JS, and VG assisted data analysis and commented on earlier versions of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Little is known about sociodemographic and clinical factors that predict and act as barriers to ADHD medication independently of symptom severity. We examined the proportion of children using medication for ADHD, age of initiation of medication, and predictors of medication use in a population-representative cohort.Data from the Millennium Cohort Study on child ADHD, medication use for ADHD at age 14 (in 2014–2015) and child, parent and sociodemographic variables were collated. Logistic regression models were used to identify factors that predict medication use for ADHD (the main outcome measure), adjusting for symptom severity at age seven.N = 11,708). 45.57% of children with ADHD (N = 305) were taking medication. The median age at initiation was 9 years (range 3–14). Male gender  and conduct problems at age seven  and 14 predicted medication use at age 14 after adjusting for symptom severity.The weighted prevalence of ADHD was 3.97% (Our study is the first to assess predictors of medication whist adjusting for ADHD symptom severity. Girls with ADHD were less likely to be prescribed medication, even when they displayed similar ADHD symptom levels to boys. Conduct problems also predicted medication independently of ADHD symptoms. ADHD may be more often medicated in boys because clinicians may think a prototypical ADHD child is male, and perhaps conduct problems make boys more disruptive in the classroom, leading to boys being more often treated. Barriers to accessing care for attention deficit/hyperactivity disorder (ADHD) have been studied at the levels of identification and referral to specialist services  where anExisting evidence as to whether socio-cultural factors also operate as barriers and predictors to accessing pharmacological treatment for ADHD is mixed: some studies find no differences by gender –7, anothThe current study explores child, parent, and sociodemographic predictors of medication use among children with ADHD in a UK population-representative cohort. Our aims were to estimate the proportion of children with ADHD in the UK who are treated with medication, and to examine barriers to and predictors of medication use for ADHD at age 14, controlling for ADHD symptom severity at age seven.The Millennium Cohort Study (MCS) is a longitudinal UK population-representative cohort of British children born between the 1st September 2000 and the 11th January 2002. Families were eligible if they received child benefit . Detail on MCS design and sampling is reported extensively elsewhere –15. The Six waves of data collection have been conducted: when children were aged 9 months, 3, 5, 7, 11, and 14 years. At each wave information on a range of social, economic, and health topics was collected through structured interviews at the family home, and on occasion via teachers in schools. Cognitive assessments and other data were also collected.All participants of the MCS provided informed consent and the main study had full ethical approval in place. Ethical approval for the current analysis was given by the University of Exeter College of Social Sciences and International Studies Ethics Committee (ref 201718-093).Child ADHD (yes/no). Parents were asked “Has a doctor or health professional ever told you that [child’s name] has had any of the following problems: attention deficit/hyperactivity disorder (ADHD)?” The child was classed as having ADHD if the parent responded in the affirmative at when the child was aged seven, 11 or 14. This question was based on the wording used by US National Health Interview Survey (NHIS) and National Survey of Children’s Health, which has been widely used to estimate prevalence of ADHD  currently taking any medicines on a regular basis that were prescribed by a doctor or hospital for their ADHD?” A list of licensed ADHD medications was also provided, that included methylphenidate, dexamphetamine and atomoxetine.Age at medication initiation: Those who indicated that their child was taking medication were asked “How old was [child’s name] when [he/she] was first prescribed these medicines?” Three responses of two years old and younger, and ‘don’t know’ were classified as missing as no ADHD medication is licensed for children under the age of three in the UK.ADHD symptom severity: the Strengths and Difficulties Questionnaire (SDQ) [re (SDQ) ; a widelre (SDQ) , and hasre (SDQ) . The hypre (SDQ) . An algore (SDQ)  was utilre (SDQ)  and has re (SDQ) .Predictors comprised child gender, cognitive ability at age three , and coMother report of ever being diagnosed with depression or anxiety at the child age seven wave (yes/no), and mother and father report of having a mental health or social/behavioural condition including autism or ADHD (yes/no) were included as predictors. Maternal age at child’s birth (years), and smoking during pregnancy (number of cigarettes per day) were also included.Characteristics measured at the 9 month wave were ethnicity (white British or other) and maternal education . When the child was seven the remaining predictors were captured: family size , single parent family (vs two parents), family weekly income , whether living in poverty (income ≤ 60% of the median household income for the UK population), and housing tenure in three categories . We also provide descriptive statistics based on whether children were living in an urban or rural area at age 14, however, as this was calculated differently across countries in the UK we have not analysed this further.The prevalence of ADHD by age 14 (parent-reported), overall and by gender was calculated relative to those who had responded in the negative to the ADHD diagnosis question. The proportion of children reported as having ADHD who used medication were described by gender and age of medication initiation. Descriptive statistics on predictors were generated showing the characteristics of children with medicated ADHD, non-medicated ADHD, and, for comparison, the wider MCS cohort.p value of ≤ 0.10 were carried forth into a multivariable regression model that adjusted for ADHD symptom severity. Continuous predictors (other than SDQ scores) were rescaled by dividing by two standard deviations, so that the strength of association was comparable across continuous and binary predictors [svy command and weights to adjust for sampling and attrition.Logistic regression models were used to calculate the association between child, socioeconomic and parent predictors and ADHD medication use. Our comparison was between non-medicated children with ADHD and those taking medication. Unadjusted bivariate associations, expressed as odds ratios (OR) and 95% confidence intervals (95% CI) were reported. Following this, variables that were associated with ADHD medication with a edictors . ORs, thAt age 14, only 0.09% of ADHD diagnosis data were missing, 1.3% of those who were asked the medication question did not provide a valid response. MCS study design accounted for attrition by over-sampling socioeconomically disadvantaged communities, then assessing drop-out at each time point. The MCS provides weights at each age point which account for drop-out in each sweep, keeping the sample representative of the UK population. Weights were generated using a logit model where the dependent variable was response/non-response, and included 12 variables predicting missingness. Weights were constructed and calculated based on the contribution of different sociodemographic predictors to this regression model . As suchAs the SDQ algorithm data were not complete  we conducted a sensitivity analysis adjusting for parent-reported SDQ-H/I score at age seven. This measure has been used in other studies as a measure of ADHD symptoms .N = 11,708) of 3.97% . Boys were five times more likely than girls to have ADHD ; Table Four hundred and seventy-one children were reported to have ADHD, which equated to a weighted prevalence of ADHD N = 1,708 of N = 139); of these 11.5% were female and 88.5% male. The median age that medication was reported to have been first taken was 9 years old (range 3–14). Most reported beginning to take medication during their primary school years  of children with ADHD were taking medication at this age . There were insufficient responses from fathers about these conditions to allow analysis beyond a descriptive level. Children with ADHD displayed higher SDQ-CP and SDQ-H/I scores than children in the wider cohort. Further, children with ADHD who received medication were more likely to have higher parent-report SDQ-H/I scores at age seven than their ADHD peers who did not, although these symptoms were not rated as significantly more impactful. This resulted in a higher proportion of children with ADHD who took medication being classified as ‘probable ADHD’ by the ADHD symptom algorithm than those who did not.Table p = 0.002), have higher levels of conduct problems at age seven  and age 14  and have mothers who reported smoking more cigarettes when pregnant  in unadjusted regression models. White British ethnicity was associated with ADHD medication at p < 0.10, so this was taken forward into the adjusted model. After adjustment for symptom severity at age seven, male gender and parent-reported conduct problems at age seven and 14 remained significantly associated with medication use at age 14 . A larger proportion received medication at age five. Previous UK reports have been limited to children age six and above . Early mMany studies have found that more severe ADHD is associated with medication use , 10, 27.Taking symptom severity into account, boys were over three times more likely to be taking medication for their ADHD than girls: a finding not seen in some other studies –7. TreatThe second significant predictor of whether children received ADHD medication was conduct problems. The SDQ-CP subscale asks about temper, obedience, fighting, lying and stealing. Our finding is in line with other studies that report co-occurring conduct symptoms to be more frequent in those who receive medication for ADHD , 8, 10. Although children with ADHD were of lower SES than the MCS cohort, no measure of SES was associated with ADHD medication use. This is encouraging as it suggests that the current system is free from bias in either direction: it has been argued that low SES may preclude individuals from medication access because of the affordability of medication, or stigma around the perceived cause of the child’s symptoms. As the UK healthcare system is free at the point of use, and prescriptions are free for those on low incomes, our results may reflect societal organisational structures that allow healthcare access equally to those from different socioeconomic backgrounds.Previous studies , 43, fouThe use of longitudinal cohort data is necessarily constrained by the range of data collected. Cohorts are not best suited for estimating prevalence due to the inevitable attrition, although to an extent this is statistically accounted for by the sampling frame and weighting. Our case definition of ADHD was parent-report of whether they have been told that their child has ADHD, and omits precise terminology about diagnosis. Parent-report of ADHD diagnosis using terminology almost identical to that used in the MCS is considered appropriate for national estimates of ADHD prevalence in the USA . The valWe are unable to ascertain whether children who received a diagnosis of ADHD had this rescinded prior to age 14: our estimates are therefore of ever receiving a diagnosis, rather than current diagnosis at 14. Medication use and age of medication initiation was restricted to those who were currently taking medication at the age of 14. Other children with ADHD may have used medication but discontinued this by the age of 14, or received a diagnosis of ADHD that did not persist to age 14. Despite these limitations, the proportion of children reported to have ADHD in MCS falls in line with current prevalence estimates . A furthThe study also has strengths: a population-representative sample that has over 300 children with ADHD with pertinent longitudinal data, collected relatively recently. There were some missing data: parent mental health questions tended to be the least frequently answered (perhaps due to their sensitive nature), and teacher SDQs were not completed for all children. However, we considered it important to have a measure of symptom severity that did not solely rely on a parent informant, as ADHD must be present across settings, and conducted a sensitivity analysis using parent SDQ-H/I scores. Due to small sample sizes and occurrence of missing data, we did not have adequate power to explore whether some characteristics  were predictive of child ADHD medication use.More work is required into whether children with conduct disorders are appropriately receiving medication for comorbid ADHD. Further exploration of predictors of medication use by medication type, and whether treatment response is associated with any of these predictors is needed. Finally, further research should explore the other treatment options that children are being offered to manage their ADHD  as we a