We performed deconvolution of the bulk transcriptomic data to determine abundances of neuronal and nonneuronal cells in the brain. We assessed the relationships between gene-set expression levels, cell abundance, and standardized effect sizes representing regional changes in brain sizes in cases of ADHD. Our analysis yielded significant correlations between apoptosis, autophagy, and neurodevelopment genes with smaller brain sizes in ADHD, along with associations to regional abundances of astrocytes and oligodendrocytes. The lack of enrichment of common genetic risk variants for ADHD within implicated gene sets suggests an environmental etiology to these differences. This work provides novel mechanistic clues about SBRV in ADHD.Large-scale brain imaging studies by the ENIGMA Consortium identified structural changes associated with attention-deficit/hyperactivity disorder (ADHD). It is not clear why some brain regions are impaired and others spared by the etiological risks for ADHD. We hypothesized that spatial variation in brain cell organization and/or pathway expression levels contribute to selective brain region vulnerability (SBRV) in ADHD. In this study, we used the largest available collection of magnetic resonance imaging (MRI) results from the ADHD ENIGMA Consortium (subcortical MRI  Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder characterized by high levels of inattention, impulsivity, and hyperactivity, which frequently persist into adulthood . ADHD isMulti-site sMRI mega-analyses performed by the ENIGMA Consortium identified subcortical regions with significant smaller volumes in children diagnosed with ADHD compared to unaffected comparison (UC) participants, including the accumbens, amygdala, caudate, hippocampus, and putamen . StratifOur conceptualization about SBRV stemmed from the theory of pathoclisis introduced in 1922 . PathoclA diagram summarizing our analytic framework is provided in Fig. When identifying candidate pathways, we focused on studies that performed meta-analysis of experimental measurements among ADHD patients, or hypothesis-free pathway analysis of genome-wide signals associated with ADHD. Five biological pathways previously associated with ADHD  were selected for our current analysis –18. We hd) were obtained from published papers by the ENIGMA Consortium [We used data from two international multi-site neuroimaging meta-analyses conducted by the ENIGMA-ADHD working group that evaluated structural T1-weighted brain magnetic resonance imaging (MRI) data from individuals diagnosed according to DSM-IV, with ADHD and UC participants. Summary statistics denoting case-control differences in cortical thickness and subcortical volume as standardized effect sizes  [postmortem brain specimens harvested from six adult donors. Brain samples had been screened for trauma, toxicity, pathology, and history of drug or alcohol abuse, epilepsy, psychiatric or neurological disease, prion disease, infectious disease, cancer deaths, chronic renal failure, or on a ventilator for >1 h or, had a time of death > 30 h [2 expression level of probe clusters  within each donor and brain substructure. Mean log2 gene expression values were then calculated among brain substructures that mapped to one of the 22 brain regions of interest evaluated by ENIGMA-ADHD. Mean log2 expression levels were calculated among genes annotated to each of the five a priori selected gene sets to arrive at gene-set expression levels within 22 brain regions. A plot of normalized expression values across regions of interest from Allen Brain Atlas is provided in Supplementary Fig. We downloaded preprocessed microarray data from the Allen Brain Atlas website (ap.org/)  containih > 30 h . We onlypostmortem cortex of five male and three female healthy adult donors, ranging from 21 to 63 years of age (GEO accession ID: GSE67835) [n = 62), endothelial cells (n = 20), microglia (n = 16), neurons (n = 131), oligodendrocytes (n = 38), and oligodendrocyte progenitor cells (OPCs) (n = 18). We processed the scRNA-seq data by removing genes expressed at or near background levels (≤1 read counts in >90% of cells), transforming read counts for remaining genes to the log2 counts per million (CPM) scale with the software edgeR (v.3.24.3) [We used a statistical cell deconvolution method from the R package dtangle  to inferSE67835) , which w.3.24.3) , and qua.3.24.3) . Using dd values that reflect brain morphometric changes in ADHD cases, separated by the four case-control age groups evaluated by the ENIGMA Consortium and gene-set expression levels in “postmortem” subcortical and cortical brain regions obtained from the Allen Brain Atlas. Keeping in line with our previous study [p ≤ 0.0025 (5 gene sets × 4 age bins), we estimated having 80% power to detect a significant correlation for our primary analysis at a magnitude of Pearson’s r = 0.71. For our secondary analysis, we jointly analyzed all three brain imaging measurements  using a linear mixed model fit applying restricted maximum likelihood via the R package lmerTest (v.3.1-2). Gene-set expression was specified as a fixed-effect term, and a code for brain imaging measure was specified as a random-intercept term. Degrees of freedom and t-tests were computed using the Satterthwaite method. p values for our secondary analysis were corrected across all age groups using the Bonferroni procedure, but were treated separate from the association tests derived from our primary analysis.For our primary analysis, Pearson’s correlation tests were used to determine the association between Cohen’s us study , the Bonus study . Based od effect sizes and expression levels on a per-gene basis wherein the Bonferroni procedure was used in order to uncover genes with the strongest association with brain morphometric changes in the brains of ADHD cases among those gene sets already associated with ADHD-related brain changes . Pearson’s correlation test was used to examine the relationship between abundance of specific brain cell types across subcortical and cortical brain regions along with gene-set expression levels, wherein the effective number of independent tests was the dot product of the number of independent cell types and the number of gene sets evaluated. Lastly, Pearson’s correlation tests were performed on abundance of specific brain cell types and Cohen’s d effect sizes for brain structural changes in ADHD separately for each case-control age group. The Bonferroni procedure was used to adjust the significance threshold and account for multiple testing across the age bins. A post hoc multivariate regression model tested whether multiple significant gene sets had conditionally independent associations with Cohen’s d values.A post hoc correlation test was performed to examine the relationship between Cohen’s p values were supplied to MAGMA to perform quality control of summary statistics and compute z scores for gene-level associations with ADHD by averaging the observed significance values for intragenic SNPs. Gene-level associations were computed for a total of 20,274 genes, of which 1171 were included in our gene sets of interest. A genome-wide significance threshold was set at 2.47 × 10–6 for the gene-level tests  , 32. Ided values indexing differences between ADHD and UC participants in regional brain volumes and the gene-set expression levels for the five a priori selected gene sets. As the summary statistics for the correlation tests in Table r = −0.67, p = 7.0 × 10−4; all ages, Pearson’s r = −0.72, p = 2.0 × 10−4), apoptosis , and neurodevelopment . The associations between the autophagy and apoptosis gene sets with smaller brain sizes disappeared when the two gene sets were both specified as predictors in a post hoc multiple linear regression model that used Cohen’s d values from ENGIMA’s analysis of all ADHD cases and UC participants as the outcome variable , suggesting that these gene sets are not contributing conditionally independent effects on brain size differences in ADHD. This finding is consistent with the fact that the autophagy and apoptosis gene sets share a significantly greater number of genes than expected by chance  and all ages .Anatomical plots of the structural MRI signatures associated with ADHD alongside average expression levels of our five a priori selected gene sets are shown in Fig. p < 0.05 , a serine/threonine kinase gene, had the most significant association with smaller brain volumes in ADHD . A visualization of the developmental trajectory of TAOK2 gene expression in postmortem human brain tissue from 8 postconception weeks (pcw) to 40 years of age is provided in Supplementary Fig. TAOK2 expression levels are shown to be lowest at 8 pcw. Expression levels rise during prenatal development reaching a peak at 25 pcw, followed by a moderate decline until 4 years old and a slight increase through adulthood.Among 684 genes tested, 27 showed a significant association with brain size differences in ADHD at a Bonferroni p < 0.05). The cell types implicated by these associations were: astrocytes, neurons, oligodendrocytes, and OPCs. Brain size differences in ADHD were significantly associated with the abundance of astrocytes and OPCs . The other two genes are members of the neurotransmission gene set . Competitive gene-set enrichment did not uncover a significant association between autophagy, apoptosis, neurodevelopment, neurotransmission, or oxidative stress gene sets and ADHD  in the neurodevelopmental gene set showed evidence of regulating adult neurogenesis based on the Mammalian Adult Neurogenesis Gene Ontology database [−/− mice [ST3GAL3, a sialyltransferase gene linked with intellectual disability [Compared with our previous work, the association between SBRV in ADHD and the neurodevelopment gene set is a novel finding. Neurodevelopmental genes play a critical role in the proliferation and differentiation of progenitor cells and the formation of neural circuits during fetal life and infancy. In adulthood, neurodevelopmental genes also play an important role in synaptic and dendritic development, myelination and adult neurogenesis . Thirty database . The neudatabase  analysisdatabase  and astr−/− mice . Importa−/− mice . Oligode−/− mice . Myelina−/− mice . Numerou−/− mice . Furthersability , which hsability . AdditioTaken together, we hypothesize that the upregulation of neurodevelopment genes during adulthood might compensate for reduced brain volumes early in life. Although we need mechanistic studies to fully explain why the structural brain deficits found in ADHD in youth attenuate in adulthood , 3, 43 aTAOK2 had the strongest correlation with SBRV for adults with ADHD. Spatial expression profiles taken from the latest RNA-sequencing data from Genotype-Tissue Expression version 7 [TAOK2 is widely expressed in the central nervous system, especially the cerebellum. TAOK2 is part of the neurodevelopment gene set and has been shown to regulate brain size and neural connectivity, and is present in the 16p11.2 microdeletion region, a rare structural variant that has been associated with range of neurodevelopmental phenotypes including autism, ADHD, and intellectual disability [Taok2 in mice was found to lead to deficits in dendritic growth, synaptic formation, cortical layering, and autism-associated phenotypes [Expression of the gene encoding ersion 7  show thasability –51. DeleTAOK2 expression levels typically peak during prenatal brain development, but levels remain fairly consistent through development and maturation  to show that, although ADHD was genetically correlated with ICV , it was not genetically correlated with subcortical brain structure volumes corrected for ICV [rg = 0.22, SE = 0.069, p = 0.001), lingual cortex , and fusiform gyrus  based on LDSC, suggesting that common risk variants could play a small role in cortical SBRV in ADHD. The literature on rare variants in ADHD is too sparse [There is indirect support for the hypothesis that SBRV in ADHD may have an environmental etiology. We found that none of the biological pathways we identified as associated with SBRV were associated with ADHD in gene-set analyses of a very large GWAS of ADHD. The 27 genes that were significantly correlated with SBRV in ADHD were not significantly associated with ADHD as a gene set based on the most current GWAS meta-analysis GWAS of ADHD . It is p for ICV . Those f for ICV  found tho sparse , 55 to aGiven these weak genetic findings, we hypothesize that environmental risks and their interactions with genes may explain SBRV in ADHD, although this idea should be viewed with caution until additional supportive data are available. Given the observational nature of the ENIGMA studies, we cannot firmly conclude that structural MRI differences seen in ADHD are causal of the disorder, or occur as a consequence of ADHD. A possible consequential effect on brain size differences in medication use. Although medication use was accounted for in the ENIGMA studies, Hoogman et al.  found thpostmortem brain transcriptome data were based on a relatively small sample of normal adult brains; thus, our results might not generalize to all populations. Additionally, as the gene expression data and brain volume data are not from the same sample, it is difficult to infer whether pathways are upregulated or downregulated. The small sample size was prohibitive for assessing sex-specific profiles of brain gene expression; however, sex-related differences are unlikely to explain our findings since ENIGMA showed that brain volumetric changes associated with ADHD were not different between males and females. The use of summary statistics from ENIGMA limited the ability to assess for the familial effects; however, two subsets of ENIGMA-ADHD samples show no difference in cortical regions and level of ADHD symptoms in unaffected siblings compared to the controls. Interhemispheric differences could not be accounted for since Allen Brain Atlas assessed a single (left) hemisphere, and the ENIGMA-ADHD working group averaged volumetric differences in ADHD across right and left hemispheres. Different brain structure nomenclatures were used by the Allen Brain Atlas  and ENIGMA-ADHD , which could have potentially added unwanted variation to our analysis.There are limitations in our study that must be considered when interpreting our results. Our work is motivated by our theory of SBRV in ADHD, which is based on many assumptions as described above. If our results are not robust to violations of these assumptions our conclusion would be called into questions. We cannot quantify this robustness but can point out weaknesses as follows. We could only provide indirect evidence of pathways potentially relevant to SBRV in ADHD, as causal inferences could not be drawn from the cross-sectional data used in our study. Ideally, studies of gene expression in brain tissue from ADHD patients could be used to confirm our results but no such tissue resources exist. The use of neurotypical samples from the Allen Brain Atlas is informative because it allows us to infer biological pathways that might be affected by the DNA variants associated with ADHD. The Allen Brain Atlas Despite these limitations, our work is beginning to clarify potential causes of SBRV in ADHD and to provide hypotheses for testing in future research. By identifying specific cell types and biological pathways affected in ADHD, we provide guidance for future in vitro studies to test our theoretical model. Such studies may uncover genetic or environmental factors that influence SBRV and lead to cellular models that can be used to develop new medications for the disorder.Supplemental Materials


Analysing EEG complexity could provide insight into neural connectivity underlying attention-deficit/hyperactivity disorder symptoms. EEG complexity was calculated through multiscale entropy and compared between adults with attention-deficit/hyperactivity disorder and their peers during resting and go/nogo task states. Multiscale entropy change from the resting state to the task state was also examined as an index of the brain’s ability to change from a resting to an active state. Thirty unmedicated adults with attention-deficit/hyperactivity disorder were compared with 30 match-paired healthy peers on the multiscale entropy in the resting and task states as well as their multiscale entropy change. Results showed differences in multiscale entropy between individuals with attention-deficit/hyperactivity disorder and their peers during the resting state as well as the task state. The multiscale entropy measured from the comparison group was larger than that from the attention-deficit/hyperactivity disorder group in the resting state, whereas the reverse pattern was found during the task state. Our most robust finding showed that the multiscale entropy change from individuals with attention-deficit/hyperactivity disorder was smaller than that from their peers, specifically at frontal sites. Interestingly, individuals without attention-deficit/hyperactivity disorder performed better with decreasing multiscale entropy changes, demonstrating higher accuracy, faster reaction time and less variability in their reaction times. These data suggest that multiscale entropy could not only provide insight into neural connectivity differences between adults with attention-deficit/hyperactivity disorder and their peers but also into their behavioural performance. et al. found that the brains of individuals with attention-deficit/hyperactivity disorder are less variable when comparing passive to active mental states than the brains of their peers. Findings increase our understanding of different mental states and flexibly shift between them in attention-deficit/hyperactivity disorder.Gu   Under the theory of Bayesian optimality,19 increased brain signal variability relates to a broader probability distribution for networks to make optimal responses. Finally, in itinerant dynamic theory,20 variability dynamics of brain activity reflect a tendency not to settle to any particular state but to explore from one state to the next across moments, which is described as itinerancy. Increased variability in brain activity suggests larger itinerancy of the neural system which could translate into more flexible formation and utilization of functional modules. We refer to the review by Garrett et al.22 and Waschke et al.23 for a more detailed overview of these theories. In the current paper, we will mainly adopt the itinerancy theory as it relates to attention.A number of theories explain the functionality of EEG complexity, such as dynamic range,24 The tonic activity represents the brain’s default activity and provides the substrate for brain function,25 which is typically studied through spontaneous brain activity during resting states.26 The spontaneous brain activity measured on the scalp during resting states, noted as tonic activity, consists of electrical activity from distributed sub-networks.27 Each sub-network is representing a functional module and the configuration of these functional modules can be seen as a representation of the spontaneous brain network.28 Spontaneous brain activity has unique spatial–temporal patterns that instruct neural functioning.29Brain complexity can be measured during different mental states. Traditionally, brain activity has been divided into tonic and phasic activity.30 This is typically studied through brain activity during tasks.Phasic activity represents the stimulus-driven brain activity which is a relatively small portion of brain activity compared with the overall brain activity.22 The evoked brain activity is constructed by electrical activity from distributed functional sub-networks, and the overall activation has unique patterns that may instruct cognitive functioning and task requirements.31–33During active task states, the evoked brain activity measured on the scalp contains tonic activity and phasic activity, and the phasic activity is the stimulus-driven activity operating on existing tonic activity.EEG complexity in resting and task states may represent the itinerancy of the spontaneous and evoked brain activity, respectively. The itinerancy under such different mental states could relate to the degree of cognitive flexibility since cognitive flexibility can be understood as reflective of a set of neural properties that facilitate flexible switching between different mental states. The itinerancy of the brain, as a non-linear system spontaneously exploring the state space of possible thought and action tendencies, may then reflect its level of wandering across states. This can be true during task activity, when the itinerancy is perhaps more constrained due to stimuli, versus the more mind-wandering internal states experienced during rest.The present study constitutes the first comprehensive exploration of the difference in multiscale entropy (MSE), as an indicator of EEG complexity, between young adults with ADHD and their healthy peers. We intended to calculate MSE and evaluate the itinerancy of the brain across the resting and active task states and estimate their relationship with ADHD patients’ cognitive deficits.35 Sample entropy captures the unpredictability of the data and evaluates the appearance of repetitive patterns.37 MSE accesses the unpredictability in the data over multiple time scales, which assigns high values to random signals and low values to highly deterministic signals. MSE can evaluate both high-frequency temporal complexity over fine scales (1–6) and low-frequency temporal complexity over coarse scales (15–20), which could reveal local information processing and long-range interaction between different sites respectively.38–40MSE measures the temporal complexity by estimating the sample entropy of the data downsampled by different time scales.41–44 MSE in the resting and active task states increased with the maturation of the brain at an early age,46 which decreased with the shrinkage and cortical thinning of the brain in ageing.48 There’s concrete evidence suggesting that individuals with Mild Cognitive Impairment had lower MSE than their healthy peers and higher MSE than patients with Alzheimer’s disease.43 Bilingual individuals were found to have higher MSE than monolinguals in occipital sites during a task-switching experiment.37 Related to mental health, EEG complexity from individuals with Autism disorder was lower than that from healthy controls during resting and active task states.44MSE during resting and active task states has been related to several important features of the human condition, such as aspects of neural development, ageing, cognitive functioning and psychopathology.et al.,49 13 children diagnosed with ADHD and their healthy peers were recruited to conduct multi-source interference tasks. MSE in the delta and theta frequency bands was higher among children with ADHD compared with their peers, while MSE in the alpha frequency band was lower. These findings suggested MSE across different frequency bands could reveal abnormal EEG patterns associated with ADHD. In Boroujeni et al.,50 sample entropy was successfully used as one of the neural features to train classifiers for ADHD diagnosis on 50 children with ADHD and 26 healthy participants.To the best of our knowledge, few studies have applied MSE to examine ADHD. In a study by Chenxi 49 differences in MSE between adults with ADHD and their peers have not been explored. Moreover, to the best of our knowledge, within-subjects changes of MSE between the resting and active task states, which could reveal the transition between tonic activity and phasic activity,52 have not been investigated.Although group differences in MSE patterns between children with ADHD and control groups have been found,54 while EEG was recorded to investigate the brain signal complexity. By calculating and analysing MSE across different cognitive states, our study not only aims to determine if MSE could be a promising candidate as a neurocognitive indicator of adult ADHD but can also interpret executive function deficits in ADHD with MSE in different mental states.In the current study, college students participated in resting-state sessions and a go/nogo task,53 we hypothesized that in the resting state, the MSE from the ADHD group would be lower than that from the control group, which may indicate smaller itinerancy in the tonic activity from participants with ADHD and may be related to their difficulty in initializing and disengaging attention in a flexible manner. In the task state, we anticipated that the MSE from the ADHD group would be lower than that from the ADHD group, which may suggest lower itinerancy in the evoked activity from ADHD participants. This could explain their difficulty in switching between different trials of tasks. Finally, from the resting state to the task state, we expected larger differences in MSE transitions in the control group, which suggested that the brains of healthy individuals have a better ability to effectively switch between different mental states.Based on our previous finding that participants with ADHD showed lower inter-trial variability in EEG oscillation power,et al.55; Woltering et al.56). Data for this study were taken from pre-training visits only. For this study, 30 unmedicated college students with ADHD were pair-matched with 30 peers on gender  and age . We refer to the supplements for a more detailed breakdown of how the present sample was selected.Data were taken from a larger study investigating changes in neural and behavioural indices after a working memory training programme conducted at the University of Toronto from 2011 to 2013  were recruited through campus advertisements. Inclusion criteria for the ADHD group were (i) a previous diagnosis of ADHD and (ii) registration at the College Student Disability Services, which required supporting documentation proof.57) to assess their current symptoms of ADHD. Exclusion criteria (for control and ADHD group) were defined as (i) uncorrected sensory impairment; (ii) major neurological dysfunction; (iii) mood affecting medication rather than a prescription for ADHD. We also used the Cognitive Failures Questionnaire (CFQ) to support our classification of ADHD subjects. Participants in the ADHD group had evidence of a Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV diagnosis of ADHD as provided by the university student disability services.All participants were asked to finish the Adult ADHD Self Report Scale . After a break, the EEG assessments began. Participants were asked to sit in front of a screen and complete tasks while wearing a 129-electrode EEG net . After becoming familiar with the environment, instructions on a screen explained the task to participants. EEG tasks included, in order, a resting-state task,53 a change detection task,12 a delayed match-to-sample working memory task,60 a go/nogo task,61 and finally, another resting-state task. Participants typically took a 10–15 min break before the EEG tasks started and also took short breaks in between tasks. We also note that, with the exception of the go/nogo task, the other EEG tasks were self-paced and allowed participants to take short breaks even during the tasks.Participants came to the laboratory to complete a behavioural assessment and several computerized tasks with EEG. The behavioural assessment was conducted first and included a battery of neuropsychological tests and behavioural rating scales which lasted up to 3 h  and the original clinical trial was pre-registered (#NCT01657721). Participants received $20 for their participation in the pre-visit (and $150 if they completed the post-training visit). Informed written consent was obtained from all participants prior to beginning the assessments.53 we focused on intervals under eyes-closed conditions. Furthermore, data from the first resting state were used for this study to avoid conflation of potential fatigue effects in the second resting state after the tasks.The resting state consisted of six 40-s intervals . Before each interval, a sound signalled when they were to alternate closing or opening their eyes. To avoid potential artefacts from eye movement and based on prior research showing the eyes-closed condition to be most sensitive to group differences,et al.54 and Liu et al.,61 was presented by E-prime software . Participants were instructed to press a button as soon as a letter appeared on the screen during the go condition and hold their response when a letter was repeated for the second time during the nogo condition. Participants finished a practice block of 21 trials before the actual task started. In our study, we decided to focus on the correct go trials, which reflected a more action-oriented state and accounted for the majority of go/nogo trials.The go/nogo task, which is identical to the one reported in Woltering Several behaviour measurements of the go/nogo task were computed to evaluate participants’ performance, including reaction time (RT), accuracy (ACC) and standard deviation of reaction time (RTSD), each of which was extracted by E-prime software.All participants were asked to complete a series of standard questionnaires and tasks to evaluate their current levels of behavioural, cognitive and social emotional functioning.62 The ASRS Part A has been found to be most predictive of symptoms consistent with a diagnosis of ADHD. Scores for these questions were added up to calculate a final score in the current paper. For original inclusion purposes, we used a modified version of the six-item ASRS screener which was administered by telephone .The ASRS is a valid and reliable measurement for evaluating ADHD symptoms in adults, which has 18 questions based on the criteria used for ADHD diagnosis in the DSM-V.64 Its reliability and validity in quantifying the distractibility of individuals have been established in previous research.66The CFQ measures self-reported failures in perception, memory and motor function in everyday life. There are 25 questions, which ask participants to rank how often these mistakes occur.et al.33 and Ferree et al.67). The data were collected using a 0.1–1000 Hz bandpass hardware filter, a 500 Hz sampling rate, EGI Netstation stand-alone software and referenced to electrode Cz.Participants were seated in a comfortable chair. The EEG data were acquired using a 128-channel sensor net . Impedances for all channels were set below 50 kΩ which is commonplace for high-impedance systems and recommended for child and clinical populations due to fast application (see Brooker EGI’s Netstation software was used to filter (0.5–50 Hz) and segment the EEG data. For resting states, EEG data were segmented into 2-s epochs. For task states, EEG data were segmented into 1.4-s epochs . Average referencing was applied after the data segmentation. Then data were transferred into MATLAB for further analysis.68 Then one research assistant reviewed the results from SASICA and made adjustments for the definition of EEG components to remove EEG contributions from artefactual sources. On average, there were about 15% of ICA components removed for the resting-state task, while about 28% of ICA components were removed for the go/nogo task state.To remove artefacts from the EEG data, such as eyeblink or muscle movement, we utilized the Independent Component Analysis (ICA) procedure in EEGLAB. The EEG data were decomposed into independent components and automatically identified for brain activity, eye movement, muscle artefact and other artefacts by the SASICA package.For the resting-state task, there were about 7% trials rejected, while about 13% trials were rejected for the go/nogo task. On average, there are 56 resting artefact-free trials (SD = 4) and 149 correct go artefact-free trials (SD = 4) in the comparison group, while there are 55 resting (SD = 3) and 155 correct go artefact-free trials (SD = 24) in the ADHD group. There was no difference in trial count between the groups as tested using a Kruskal–Wallis test for the resting as well as go/nogo task state. We applied the same preprocessing protocol for data recorded in the resting and task states.www.physionet.org/physiotools/mse/. Parameters for m and r were chosen on the basis of the size of our segmentation, which were 700 time points16 and Costa et al.69 Parameter values m = 2, r = 0.5, were used to calculate the sample entropy of the data at different time scales.70 Scale refers to the length of the window used in the coarse-graining procedure and can provide an indication of temporal resolution of processing with distributed processes often reflected by activity in larger timescales versus local processing. We calculated MSE over Scales 1–20, which we grouped into three ranges: a fine scale , mid scale  and coarse scale . The calculation procedure includes two steps: (i) obtain coarse-grained EEG data by averaging the data points within non-overlapping windows of length t, which is corresponding to time scale; (ii) calculate sample entropy for each coarse-grained data series, which measures the regularity by evaluating the appearance of repetitive patterns.After the EEG was cleaned, MSE was calculated for each trial at each electrode over corresponding scales. The calculation of MSE was done by an open-source algorithm available at 71 while MSE in the task state was calculated for the fine and mid scales due to the shorter duration of the segment. To observe how the brain adapts to the task, the MSE transition (MSE-Δ) was obtained by subtracting MSE in the task state from MSE in the resting state over corresponding scales (Fine and Mid) and electrodes. In terms of the montage, the 10–10 system was utilized to group electrodes into five clusters at the left and right hemispheres correspondingly.72 Each cluster of electrodes represented one brain site as shown in MSE in the resting state was calculated over fine scale, mid scale and coarse scale,t-tests. To examine the relationship between behavioural performance and MSE measurements, Pearson’s r correlation coefficients were utilized.We averaged MSE measurements (MSE in the resting and task states and MSE transition MSE-Δ) across electrodes within different brain sites and scale ranges. Data points located outside two SDs from the mean for averaged MSE measurements within each group were identified as outliers and removed. After removing outliers, we paired participants between the healthy control and ADHD groups. No violations of normality were found using the Shapiro–Wilk test as applied for each MSE measurement in each group. Levene’s Test was used to check the homogeneity of variances for each MSE measurement. No violations were reported. A three-way mixed measure ANOVA was used to examine the effects of Scale  and Site  as within-subject factors and Group (COMP and ADHD) as a between-subjects factor on these MSE measurements. When justified, comparisons between factors were further explored using independent 73 partial η2 = 0.01 corresponds to a small effect, partial η2 = 0.10 standards for a medium effect and partial η2 = 0.25 represents a large effect.Partial eta-squared were computed to verify the effect size. According to Vacha-Haase and Thompson,The data that support the findings of this study are available from the corresponding author, upon reasonable request.The means and SDs of the questionnaire and behavioural measurements were compared between the ADHD group and their peers see . ResultsOur first hypotheses predicted that the MSE from the comparison group would be larger than that from the ADHD group.F  =  1979.945, P < 0.001, η2 = 0.972] and Site , which suggested MSE was different at different scales or sites as shown in F = 1.507, P = 0.225, η2 = 0.025].First, significant main effects were found for Scale , indicating that MSE between the two groups differed at different sites depending on Scale, as illustrated in However, the results of the repeated measure ANOVA also showed a significant three-way interaction among Group, Site and Scale . The results indicated that when all scales were combined, differences of MSE between the comparison and ADHD groups were found at frontal sites, while similar MSE distributions at other sites   =  96.422, P < 0.001, η2 = 0.624] was also found statistically significant, which suggested, MSE has different distribution over scales at different sites in both groups. The two-way interaction between Scale and Group  was not statistically significant.We also found a statistically significant two-way interaction between Site and Group  and Site , which suggested MSE was different at different scales or sites as listed in F  =  0.82, P =  0.369, η2 = 0.014].First, significant main effects were found for Scale , indicating that differences of MSE between the two groups during the go/nogo task differed at different electrode sites, which also depended on different scales   =  0.341, P = 0.561, η2 = 0.006] nor between Site and Group , which suggested similar distributions of MSE between the COMP and ADHD groups at any corresponding scale or sites. Interaction between Site and Scale  was statistically significant, which suggested, in both groups, MSE has different distribution over scales at different sites.There was no significant two-way interaction found between Scale and Group  and Site  which suggested different MSE-Δ at different scales or sites as shown in F =  2.022, P =  0.160, η2 = 0.034].First, significant main effects were found for Scale , indicating that differences of MSE-Δ between the two groups differed at different electrode sites, which also depended on different scales   =  5.006, P <  0.029, η2 = 0.079] and between Site and Group . Specifically, different MSE-Δ between the comparison and ADHD groups were found at the frontal, temporal and central sites, but not at the parietal and occipital site   =  11.595, P <  0.001, η2 = 0.167] was also statistically significant, which suggested, in both groups, MSE-Δ has different distribution over scales at different sites.There were significant two-way interactions found between Scale and Group , indicating group factors would influence MSE in different states, which is consistent with our finding of MSE-Δ, i.e. the change of MSE from the resting state to the active task state was different between the comparison and ADHD groups.When visualizing MSE from the frontal site within the mid scales in r = 0.37494, P < 0.05), larger RT  and a larger RTSD . Furthermore, correlation coefficients between MSE-Δ and task performance  were compared between the comparison and ADHD groups. The Fisher’s z-scores were 1.777, −2.03 and −2.128 , indicating these relations were significantly different between the comparison group and ADHD group.We also found a significantly negative correlation between MSE-Δ and behaviour measurements in the comparison group as shown in The present study aimed to investigate whether and how adults with ADHD differ from their peers in EEG complexity under different mental states. The ADHD group reported severe impairments in everyday functioning and showed relatively poorer task performance. Our most robust finding was that the transition of MSE, our brain complexity measure, from the resting state to the active task state at the frontal site was larger in the comparison group than in the ADHD group, and correlated with task performance in the comparison, but not the ADHD, group. Findings during the rest and task states were less robust, though the pattern indicated that MSE during the resting state was larger in the comparison group than the ADHD group across coarse scales—an effect that was driven by frontal sites. During the task state, counter to our expectations, the reverse pattern was present. In general, for task states, lower MSE at frontal sites was correlated with better task performance.38–40 The finding was consistent with the executive dysfunction theory, that symptoms of ADHD were associated with abnormalities in frontal–parietal and frontal–striatal circuits.75Our hypothesis concerning lower MSE in the ADHD group compared with the comparison group during the resting state was confirmed, albeit weakly, for coarse time scales at frontal sites. This could indicate weaker long-range interaction between the frontal site with other sites, respectively, in the ADHD group.40 Furthermore, smaller MSE during the resting state in the ADHD group could suggest a potential maturational delay of the cortex, which was in line with other behavioural and neuropsychological studies.77 Smaller MSE across coarse scales in the ADHD group was consistent with earlier findings that showed reduced neural oscillations around the alpha band for adults with ADHD during the resting state.53Previous studies on MSE during the resting state have revealed an increase of MSE along with the development of the brain into adulthood.78 and difficulty to initialize attention.20Smaller MSE within coarse scales at frontal sites in the ADHD group suggests lower itinerancy in distributed neural networks related to frontal sites, which could be associated with lower cognitive flexibility78–81 suggested that the complexity or neural oscillation of brain activity during the resting state could provide insight into cognitive performance, such as sensitivity to stimuli and cognitive flexibility. Regarding the relationships between behaviour measurements and MSE across different scale ranges at different brain sites, no significant correlation could be found. Although a significant relationship between brain entropy in resting state and task performance has been found in Saxe et al.,82 different entropy measurements and task requirements could explain the lack of significant correlation in the current study. The implementation of cognitive flexibility would require the coherent act of several executive function subdomains including inhibitory control,11 which is evaluated during go/nogo task. If the MSE in the resting state is more related to cognitive flexibility, it might not be sensitive to a go/nogo task.83Several studiesOur second hypothesis, predicting that the ADHD group would have lower MSE than the comparison group during an active state, was not confirmed. In fact, there was a three-way interaction showing an opposite effect suggesting, albeit not confirmed after correction for multiple comparisons, that the ADHD group has higher MSE during active states for frontal sites at coarse scales than the comparison group.When examining the overall pattern of results, a higher MSE during the task state for the ADHD group may indicate that increased brain complexity during highly constrained and predictable task conditions (such as a go trail in a go/nogo) may not be beneficial. Under such task conditions, a high convergence and predictability of brain system dynamics, as reflected by low itinerancy, may mediate a more focused performance. In other words, neural systems being in a less flexible, more rigid state, are more efficient to meet more constrained task demands.This interpretation is consistent with our task performance findings suggesting that lower MSE during task conditions was actually associated with better task performance. That this correlation was only there for the comparison group may suggest that this group was able to leverage their MSE to achieve better performance whereas this was not the case for individuals with ADHD.75 In other words, the neural activity from participants in the ADHD group is likely to be less flexible in adjusting to process tasks.Our third hypothesis was confirmed: smaller MSE transitions from the resting state to the active task state (MSE-Δ) within mid scales at frontal sites characterized individuals with ADHD. Smaller MSE-Δ in the ADHD group indicated their brain could not generate sufficient changes of long-range interaction between frontal sites with other regions for cognitive tasks, which was consistent with the executive dysfunction theory for ADHD.80 Furthermore, smaller MSE-Δ in the ADHD group could be indicative of a potential maturational delay of the brain,77 which was consistent with previous findings of smaller MSE in the ADHD group during the resting state.Previous studies on MSE-Δ revealed that there was an increase of MSE-Δ along with the maturation of the brain.This effect, since it was a difference measure, was not solely driven by the resting state. As previously discussed, the MSE from the comparison group was larger than that from the ADHD group  in the resting state but the MSE from the comparison group was smaller than that from the ADHD group in the active task state. MSE in both resting and task states contributed to the MSE difference measurement as shown in 85 When the relationships between MSE-Δ and task performance were investigated, MSE-Δ in the comparison group within mid scales at frontal sites were found positively correlated with ACC for go trials, negatively correlated with RT for correct go trials and negatively correlated with the RTSD for correct go trials. Such relationships were not found in the ADHD group, which could be related to abnormal functional connectivity associated with MSE across mid scales at frontal sites during the active task state.MSE-Δ could provide insight into task-related functional connectivity changes and evaluate dynamic itinerancy change within certain neural networks.86 The lack of group differences in RT could be explained by similar reasons.Our study also compared performance measures in the go/nogo task  between the comparison and ADHD groups. Consistent with previous literature, the ACC from individuals with ADHD was lower than that from their healthy peers. The relatively small effect size of group differences in ACC can be explained by the nature of the sample: college students with ADHD. These subjects are relatively high functioning adults with ADHD, especially in their performance on standardized neuropsychological tests.87 investigated the RTSD in individuals with ADHD during different tasks and linked RTSD to distractibility, which suggested RTSD could be a valid measure of inattention in ADHD.Compared with healthy subjects, the RTSD from adults with ADHD was larger than that from their peers with a medium effect size. In other words, at a behavioural level, the ADHD group represented increased intra-individual variability in RT for go/nogo tasks. A previous study88 and more discriminate measure53 of MSE than the eyes-open condition in our sample, the eyes-open condition may have had an advantage as it can function as a better control when comparing active and passive conditions.Several limitations of the current study are noteworthy. First, our sample size was relatively small, which could potentially reduce the power of this study. A correction for multiple comparisons suggested that the interpretation of the ANOVA effect for the rest and task states should be taken with caution, however, a larger sample size could have confirmed this finding and increased confidence in some of our findings pertaining to the rest and task states. This is something we hope future studies can provide more clarity on. Second, we did not employ the random sampling procedure for participant selection but used the convenience sampling method. Because our research was only done on college students, we will caution readers to readily extend our findings to the general population. Third, the experiment sessions were about 3 h, which might make participants tired and induce additional noise in the EEG recording. Although the authors believe the effects of fatigue on the data to be minimal on the basis of the experience of research assistants and procedures put in place combating fatigue, we cannot exclude its effects. Finally, we would like to make the reader aware that, although the eyes-closed condition was considered a cleanerTo the best of our knowledge, this is one of the few studies of EEG complexity for college students with ADHD. The study provides support for the idea that MSE can evaluate complex brain dynamics in subjects with ADHD, provide insight into neural connectivity and their relationships with ADHD symptoms. Findings suggest that the brains of individuals with ADHD have lower cognitive flexibility in the resting state than their healthy peers, as well as lower cognitive stability in the active task state. The brain’s ability to change from a resting state to an active state is represented by MSE transition and significantly different between individuals with ADHD and their peers. The interpretation of the MSE complexity over different mental states could provide insight into the neural mechanism of ADHD.fcac054_Supplementary_DataClick here for additional data file.


Symptoms of attention-deficit/hyperactivity disorder (ADHD) in childhood have been found to be predictive of compromised cognitive function, and possibly even dementia, in later adulthood. This study aimed to test vascular risk as a hypothesized moderator or mediator of this association, because individuals with elevated ADHD symptoms frequently have comorbid vascular disease or risk factors which are recognized to contribute to later-life cognitive decline. Data from 1,092 adults aged 18–85 were drawn from the Enhanced Nathan Kline Institute Rockland Sample. Childhood ADHD symptoms  were assessed as predictors of cognitive functioning in adulthood . Vascular risk factors  were tested as both a moderator and mediator of this relationship. Childhood ADHD symptoms and vascular risk factors were both independently associated with later-life cognition, but vascular risk was not a significant moderator or mediator of relationships between ADHD symptoms and cognition in statistical models. Results from this large community sample suggest that the relationship between ADHD symptoms and cognition is not accounted for by vascular risk. This question should also be investigated in clinical samples. Its cognitive features  tend to be associated with disruptions in executive functions 4. For example, increased ADHD symptoms in older adults, assessed as a continuous variable using the Conners Adult ADHD Rating Scale, have been linked to lower performance on tasks of working memory5 and retrieval6, as well as slowed reaction time6. This aligns with findings of compromised frontal lobe integrity in ADHD7 which, in older adults, may be compounded by the known deleterious effects of aging on frontal brain regions and processes9.Persistent symptoms of attention-deficit/hyperactivity disorder (ADHD) affect at least 3% of adults and seniors12. Similarly, individuals with Lewy body dementia retrospectively report more severe ADHD symptoms in childhood than do healthy controls13. The mechanistic processes underlying this proposed relationship are unknown. One possibility is that ADHD symptoms may increase risk for later-life cognitive impairment by promoting the accumulation of brain health-compromising factors and behaviors throughout adulthood14. Vascular risk factors, as well as health behaviors that jeopardize vascular health, have been associated with cognitive performance across numerous studies. Diabetes19, tobacco use20, obesity23, hypertension26, and hypercholesterolemia27 have all been linked to lower cognitive performance in older adults. These factors seem to have particular relevance for executive functions, as aggregate scores for vascular risk  relate negatively to global measures of executive function29 as well as specific measures of switching and working memory30.ADHD symptoms have recently been linked to accelerated cognitive decline later in life. Adults with a history of clinically-diagnosed ADHD may have higher likelihood of developing dementia than do adults without ADHD31. For instance, young people with ADHD are more likely than those without to develop diabetes later in life32, and those with high levels of ADHD symptoms are more prone to heavy daily cigarette smoking than those with low symptom levels33. ADHD symptoms have been robustly linked to later-life risk for overweight and obesity34, which may contribute to a secondary association between ADHD symptoms and hypertension in adults35. ADHD symptomatology has also been linked to higher blood concentrations of low-density lipoprotein (LDL) cholesterol36, which contributes to atherosclerosis and vascular disease with age37. In non-ADHD psychiatric samples, these comorbidities and health behaviors have been shown to account for a substantial proportion of variance in cognitive performance38.This relationship has not been tested in ADHD, but raises the possibility that impairments in executive functioning and potential accelerated cognitive decline and dementia risk in ADHD may be due in part to the disorder’s association with vascular risk factors. A corollary to this hypothesis is that active vascular risk management may alleviate some of the cognitive difficulties associated with ADHD symptoms.It is now well-recognized that ADHD symptoms are associated with increased risk of vascular medical comorbidities and engagement in harmful behaviors that may cumulatively compromise cerebrovascular and cognitive health throughout adulthood13 may be driven by vascular risk factors . We hypothesize that the relationship between ADHD symptoms and cognitive performance will be mediated or moderated by these factors, and we focus on frontal/executive aspects of cognitive performance  because these are the most associated with ADHD symptomatology in adults and have been robustly linked to physical health, as described above. We also focus on ADHD symptoms in childhood as they are more likely to have preceded, rather than followed, the presence of vascular risk factors.In light of the known adverse cognitive effects of vascular risk factors in epidemiological studies of adults and seniors, and considering their association with symptoms of ADHD, this study’s objective is to investigate whether previously-reported associations between ADHD symptoms and cognitive impairment39, a lifespan, cross-sectional community sample of individuals aged 6–85 years. Only data from participants 18 or older were retained for the present study . The NKI-RS has institutional ethical approval at the Nathan Kline Institute  and at Montclair State University (#000983A and #000983B), all participants within this sample provided written informed consent to participate, and all phenotypic protocols developed by the Child Mind Institute’s Scientific Research Council were followed39.This study used data from the Enhanced Nathan Kline Institute Rockland Sample (NKI-RS)39.All data described below were collected within the scope of a larger protocol administered over two days40.This clinician-administered interview retrospectively assesses childhood ADHD in adult participants, and the interview then follows with an expanded assessment of recent symptomology . Although childhood symptoms (averaged across items) were the main focus of the current study, a current symptoms score was also computed (averaged across items) to examine possible associations with outcomes in an exploratory fashion.ADHD symptoms were assessed using the Adult ADHD Clinical Diagnostic Scale (ACDS)41, the following risk factors were included in the present study. Diabetes was defined as either a self-reported diagnosis of diabetes on a medical history questionnaire, or a fasting glucose level of ≥ 140.0 mg/dL on bloodwork. Tobacco use in the past two years (yes/no) was ascertained using the Fagerstrom Test for Nicotine Dependence42. Obesity was quantified as body mass index (BMI) > 30 kg/m2. Participants with systolic blood pressure > 140.0 mm/Hg were considered hypertensive. Non-high-density lipoprotein (non-HDL) was isolated from total cholesterol volumes to define hypercholesterolemia, as recommended in prior literature43, by subtracting HDL from total cholesterol. Values > 158 mg/dL were considered to reflect hypercholesterolemia.Consistent with prior studies of vascular risk and cognition eg., 44, the Delis-Kaplan Executive Functioning System (D-KEFS)45, and the Wechsler Memory Scale-Revised (WMS-R)46. Domains assessed included switching 47, working memory 48, reaction time 49, and processing speed 50. A detailed description of the variables, along with any transformations applied to the data, appears as Supplementary Information.Cognitive performance was assessed using subtests of the University of Pennsylvania Computerized Neurocognitive Battery (CNB)51. Missing data in the predictors was dealt with by imputing 20 datasets52. In structural equation modeling, model fit can be assessed using various indexes54. The χ2 is a measure of exact fit and should ideally be small and non-significant. It is often significant and other fit indices are often used. The Comparative Fit Index (CFI) and the Tucker-Lewis Index (TLI) are measures of relative fit. Values above 0.90 and 0.95 indicate good excellent fit, respectively. The Root Mean Square Error of Approximation should be small, with values below 0.08 and 0.05 indicating good and excellent fit, respectively.Analyses were performed in Mplus 8.1. Since some variables were treated as categorical, we used the Weighted Least Square—Means and Variance adjusted (WLSMV) estimator. The WLSMV estimator includes participants with missing data and provides consistent estimates when the data is missing-at-random conditional on covariates55 we used a bifactor model to capture three components of executive function (EF). This model is a bifactor S-1 model56, which comprises a general factor as well as s-1 specific factors. Here, this implies that two out of the three components of EF have a specific factor. The component without a specific factor  can be interpreted as a reference component that accounts for variance in other components. Its variance is captured in the general factor (gEF), and the variance it shares with the other two EF components is absorbed by the general factor. As a result, the two specific factors can be interpreted as residual variance shared by indicators after accounting for the variance of the reference component . The model therefore has the following factors: (1) a general EF factor (gEF) capturing inhibition as well as its covariance with all other EF indicators; (2) a specific Switching factor (sSwitch) capturing variance shared by indicators of switching over and above what was accounted for by gEF; 3) a specific Working Memory factor (sWM) capturing variance shared by indicators of working memory, over and above what was accounted for by gEF.Consistent with recent models of executive function57. Although it is theoretically possible to include a method factor for each assessment battery, this strategy often results in statistical problems such as lack of convergence, as was the case in our data. A common solution consists of excluding the method factor for one assessment battery; this was the approach used here. More specifically, we created a latent factor to capture variance that was specific to the CNP battery. Thus, its variance was orthogonal  with all other latent factors. This CNP latent factor included loading on the six CNP tasks. All six loadings were statistically significant, indicating that these tasks indeed shared common variance that was not accounted for by the other factors.To ensure that the gEF, sSwitch and sWM factors captured independent sources of variance, they were not allowed to correlate by statistically constraining their covariance to zero . We also modeled a Reaction Time factor (RT) and a Processing Speed factor (PS), both of which were allowed to correlate with each other and with the three components of executive function. Finally, we included a method factor capturing variance shared by tasks measured in the CNB battery, because we expected a priori that tasks from within the same battery  would be more correlated with each other than with tasks from a different battery. Thus, tasks from a specific battery would share common variance that is not shared with tasks from another battery; this variance is assumed to be a methodological artifact. The inclusion of latent factors to account for this kind of artifact is common when using multi-trait, multi-method dataOur analyses were performed in four steps. First, we fitted a measurement model with only the outcomes  to ensure that the hypothesized factor structure adequately fit the data. Second, we documented the associations between childhood ADHD symptoms and vascular risk factors by running an analysis where these were entered simultaneously as predictors of cognitive outcomes using multiple linear regressions. Third, we tested whether cumulative vascular risk mediated the association between ADHD symptoms and cognitive outcomes Fig. . In othe58 and vascular risk59, we also considered the moderating effect of age on these factors by conducting analyses testing whether age predicted either the cumulative risk score or executive functioning, including an interaction between the predictors and age as a categorical factor.Because age contributes independently to declines in executive functioningSD = 18.0) and an average education of 15.5 years (SD = 2.3). Childhood ADHD symptom scores within the sample ranged from 0 to 30 , and 50 participants (4.6%) met diagnostic criteria for one of the ADHD subtypes  as determined by an NKI-RS clinician during assessment. Fifteen participants in the sample (1.4%) were taking stimulant medications at the time of testing . The two most frequent vascular risk factors in the sample were obesity and hypercholesterolemia, observed in 30.5% and 23.9% of cases respectively. Most people had no vascular risk factors (42.9%), fewer people had only one (35.3%) or two risk factors (17.6%), and a small proportion of people had three or more risk factors (4.2%).Participant characteristics are reported in Table 2 (85) = 312.037, p < 0.0001, CFI = 0.951, TLI = 0.931, RMSEA = 0.050 , sWM showed small associations with both RT and PS , and sSwitch showed large associations with RT and PS . RT and PS were also significantly associated .The fit of the model was good to very good, χr = -0.10, p = 0.018) and sWM , but there were no significant associations between current ADHD symptoms and any variable. Thus, there were differential associations despite the fact that childhood and current ADHD symptoms were moderately associated . Given this finding, the following analyses were performed using childhood ADHD symptoms unless otherwise noted.Inspection of associations between ADHD symptoms and EF revealed that childhood ADHD symptoms were correlated with gEF , we recoded the cumulative risk score into four categories: 0, 1, 2 and ≥ 3 risk factors.This cumulative score was used to predict cognitive functioning while also controlling for sex, age and childhood ADHD symptoms (see Table p = 0.002) and the magnitude of the effects of the number of risk factors was reduced by about half . Although vascular risk factors were indirectly associated to gEF via PS , accounting for about a third of this association (0.077/0.232 = 33.19%), ADHD symptoms were not associated with gEF via PS .Because vascular risk factors were associated with both gEF and PS, we tested whether the link between vascular risk factors and gEF was accounted for by their effects on processing speed. We ran the model reported in Table a in the model shown in Fig. a of the mediation model was not significant, we found no evidence that vascular risk factors mediated the link between ADHD symptoms in childhood and any of the cognitive outcomes.Contrary to our expectations, there was no association between ADHD symptoms in childhood and cumulative vascular risk .p = 0.791). In predicting the EF components, we looked at the interactions between age and either cumulative risk or ADHD symptoms. None of the interactions were significant in predicting gEF, sWM or sShifting.There was no significant interaction between age categories (above/below age 65) and ADHD symptoms in predicting cumulative risk score . To explore the possibility that meaningful effects of individual symptom subtypes may have been lost in the consolidation of subtypes into a global ADHD measure, we reconducted the above mediation and moderation analyses using inattentive and hyperactive/impulsive symptoms in separate models. All results remained non-significant (detailed results not reported).31 and are recognized to increase risk for cognitive impairment in adults62. Although we found that childhood ADHD symptoms and current vascular risk factors were independently associated with aspects of current cognitive performance, we found no evidence that vascular risk mediated or moderated the relationship between ADHD and cognition.Using a large community sample, this study aimed to investigate whether the relationship between ADHD symptoms and frontally mediated aspects of cognitive performance could be accounted for by vascular risk factors, which have been commonly reported in ADHDc in Fig. 63. These findings may also reflect community sampling within the NKI-RS, which resulted in relatively few (4.6%) cases who met diagnostic criteria for clinical ADHD. Surprisingly, we found small positive effects of childhood ADHD symptoms on the specific working memory factor, a finding which is particularly difficult to reconcile with prior reports of moderate-magnitude negative associations between ADHD symptoms and working memory performance in adults64. However, because we used a bifactor model, the specific WM and Switching factors are in fact residuals, as they exclude any variance shared with inhibition. Further, the sWM factor was also poorly captured in this sample, given that backwards digit span did not load significantly onto it. Given the factor loadings, the sWM factor captures shared variance by both n-back indicators, and it may thus be argued that this model is capturing task-specific variance rather than true working memory abilities.First, we found that childhood ADHD symptoms had only weak negative effects on current general executive functioning , we again found no evidence that vascular risk influenced the direction or strength of the relationship between ADHD symptoms and cognitive outcomes.The central question in this study was whether vascular risk factors explained a significant portion of the association between ADHD symptoms and cognitive outcomes. Contrary to our hypothesis, results from this sample demonstrated that the respective effects of ADHD symptoms and vascular risk factors on cognition are largely independent. After controlling for cumulative vascular risk, the direct effect of childhood ADHD symptoms on cognition remained significant, indicating that the association between ADHD symptoms and cognition is not accounted for by the presence or severity of vascular risk factors. At the same time, we found no evidence that childhood ADHD symptoms were linked to vascular health in adulthood in this sample  with even fewer of these in the older-adult range . It is possible that only individuals with very severe symptoms of ADHD experience significant vascular burden, potentially due to chronic stimulant medication useIn summary, results from this large community sample suggest that vascular risk does not play a significant role in explaining the relationship between milder, non-clinical childhood ADHD symptoms and cognition. This question should also be investigated in clinical samples of individuals with more severe ADHD symptomatology, for whom vascular health and cognition may be particularly compromised.Supplementary Information.


Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are associated with motor impairments, with some children holding a comorbid diagnosis of Developmental Coordination Disorder (DCD). However, DCD is underdiagnosed in these populations and the volume abnormalities that contribute to explaining these motor impairments are poorly understood. In this study, motor abilities as measured by the Developmental Coordination Disorder Questionnaire (DCDQ) were compared between children with ADHD, children with ASD and typically developing (TD) children, aged 8–12 years old. Additionally, the association between the DCDQ scores  and regional volume abnormalities were explored in 6 regions of interest , within each group and across all participants. Children with ASD and children with ADHD showed impaired motor abilities in all the DCDQ-derived scores compared to TD children. Additionally, most children with ASD or ADHD had an indication or suspicion of DCD. Within the ASD group, coordination abilities were associated with the volume of the right medial frontal gyrus, and within the ADHD group, the total DCDQ score was associated with the volume of the right superior frontal gyrus. This study underlines the importance of routinely checking motor abilities in populations with ASD or ADHD in clinical practise and contributes to the understanding of structural abnormalities subtending motor impairments in these disorders. Attention-Deficit/Hyperactivity Disorder (ADHD) is defined by impairing levels of inattention, and/or hyperactivity-impulsivity . Autism The Developmental Coordination Disorder Questionnaire (DCDQ) is considered a valid screening tool for identifying children with possible DCD , 14 and At the structural level, voxel-based morphometry (VBM) studies have evidenced volumetric abnormalities in populations with ADHD and/or ASD in regions typically associated with motor performance, including the pre-central and post-central gyri and the inferior parietal cortex –19. A reOverall, there is limited research on the association between motor performance measures and brain volume in populations with ASD and/or ADHD . MahajanThe final sample of this study consisted of 22 children with ADHD, 16 children with ASD and 17 TD children aged 8–12 years old. The comparison of regional and total brain volumes between these groups of children is published elsewhere . ChildreParticipants were excluded if they had a history of prematurity, a neurological disorder, a genetic disease, a disabling somatic pathology, suffered from complications during labour with neonatal care unit hospitalisation, were left-handed, presented any contraindications to MRI or had a General Ability Index lower than 70 on the Wechsler Intelligence Scale for Children−4th Edition , 28. SevThe DCDQ-FC , 14 is a2, matrix size = 256 × 256 × 128 and voxel size = 0.94 × 0.94 × 1.2 mm3.Anatomical image acquisition was conducted using a Discovery MR750w 3.0T scanner  at UZ Brussel . Anatomhttp://www.neuro.uni-jena.de/cat12/CAT12-Manual.pdf), implemented in SPM12 (http://www.fil.ion.ucl.ac.uk/spm/) running on MATLAB . Image pre-processing and quality control is described elsewhere  = 0.32, p = 0.72].Imaging data were analysed using the Computational Anatomy Toolbox  and across all participants. Global scaling was used with total intracranial volume (TIV) and age was entered as a covariate. All analyses were conducted without IQ as a covariate .Following pre-processing and data quality control, individual subject smoothed grey matter volumes were entered into a second level analysis to estimate the correlation between the different DCDQ-FC scores and grey matter volume in the ROIs using a one-sample ovariate , 29. Masovariate . Within-p = 0.011) or the ADHD (p = 0.004) groups. Groups differed significantly on all the ADHD RS-IV scores . Total, Inattention and Hyperactivity scores were significantly higher in both the ADHD and the ASD groups compared to the TD group. These scores were not significantly different between the ADHD and the ASD groups. Additional demographic data analyses fractionating the ASD group in terms of the presence or absence of comorbid ADHD are reported in Sex was not significantly related to diagnosis and groups did not differ on age see . IQ was ps < 0.001). “Control During Movement,” “Fine Motor/Handwriting,” “General Coordination” and Total scores were significantly lower in both the ADHD and the ASD groups compared to the TD group  was still a significant when entering ADHD severity as a covariate in the ASD group contrast. However, the correlation between the DCDQ-FC Total score and GMV in the right post-central gyrus did not survived significance threshold within the ASD group.Importantly, the negative correlation between the DCDQ-FC General Coordination score and GMV in the right medial frontal gyrus within the ASD group at This study contributes to the investigation of motor impairments in ASD and ADHD. Children with ASD and children with ADHD exhibited impaired motor abilities in all the DCDQ-derived scores compared to TD children. Additionally, most children with ASD or ADHD had an indication or suspicion of DCD. Within the ASD group, coordination abilities were negatively associated with the volume of the right medial frontal gyrus, and within the ADHD group, the total DCDQ score was negatively associated with the volume of the right superior frontal gyrus. Across all participants, control during movement abilities were associated with GMV in the right superior frontal gyrus and coordination abilities were associated with GMV in the left inferior parietal cortex.Our results showed that an indication or suspicion of DCD was present in the majority of children with ASD and ADHD. Children with ASD exhibited lower scores in all the domains assessed by DCDQ-FC  when compared to TD children, hence evidencing an extensive motor impairment in ASD. Additionally, 81.25% of the participants with ASD had an indication of, or suspicion for DCD, similarly to Bhat . In a saAt the cerebral level, increased impairments in motor coordination were associated with increased GMV in the right medial frontal gyrus within the ASD group. The medial frontal cortex encompasses cortical areas within the median wall of the frontal lobe and has been associated with a wide range of functions including performance monitoring, action anticipation, response inhibition, egocentric spatial processing, and motor coordination –33. MediOn the other hand, increased motor impairments were associated with increased GMV in the right superior frontal gyrus within the ADHD group. The superior frontal gyrus is implicated in a variety of tasks including motor movement –37. In aOur findings should be interpreted in light of some limitations. Poor quality scans were excluded from our sample, decreasing the presence of artefacts but also reducing the size of our sample. Additionally, there was a high percentage of children in the ASD group who presented an ADHD comorbidity, consistent with recent comorbidity reports , but potFuture work should continue exploring the association between volumetric abnormalities and motor impairments in a larger sample of children with non-comorbid ASD, non-comorbid ADHD and children with both ASD and ADHD. Additionally, although the DCDQ is considered a reliable screening tool for detecting children who are at risk for DCD, recent evidence suggests that combining different sources of information when exploring motor abilities in children may be more informative. In particular, using tools such as the Movement Assessment Battery for Children M-ABC , 41 may In conclusion, this study contributes to the understanding of motor anomalies in ASD and ADHD, as assessed by the DCDQ. Our results evidence frequent motor impairments and a high risk for DCD in populations with ASD and ADHD, underlying the importance of routinely checking for their presence in patients with ASD or ADHD, as well as considering appropriate motor interventions including physical therapy in their treatment. Additionally, the results of this study showed that motor impairments in these populations are associated with volumetric abnormalities in different regions of the brain and contributes to understanding the pathophysiology of motor impairments in these disorders.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee of the ULB-Erasme University Hospital in Belgium . Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.AS participated in the conceptualization, data acquisition and analysis, and the writing of the article. TV participated in the conceptualization, data analysis, and writing of the article. PV, SB, PD, VD, GP, HR, LV, EW, and PP participated in the conceptualization, data acquisition, and the writing of the article. MS participated in the conceptualization and the writing of the article. ND participated in the conceptualization, data acquisition, supervision, and the writing of the article. IM participated in the conceptualization, data acquisition and analysis, supervision, project administration, funding acquisition, and writing of the article.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.


No significant associations were seen in the lowest quintile of Vitamin B12 levels . This is the first study examining maternal sera Vitamin B12 levels during early pregnancy and offspring ADHD. The result suggests that Vitamin B12 deficiency during early pregnancy has specificity for some disorders but not with offspring ADHD.Maternal Vitamin B12 deficiency during pregnancy is associated with offspring neuropsychiatric disorders. Few previous studies examining this association with attention-deficit/hyperactivity disorder (ADHD) report inconsistent findings. The study examines the association between maternal serum Vitamin B12 levels and offsprings’ risk of ADHD. This study is based on the Finnish Prenatal Study of ADHD with a nested case–control design. All the singleton children born in Finland between January 1998 and December 1999 and diagnosed with ADHD were included in the study. A total of 1026 cases were matched with an equal number of controls on sex, date of birth and place of birth. Maternal Vitamin B12 levels were assessed using a chemiluminescence microparticle immunoassay and archived from maternal serum banks, collected during the first and early second trimester of pregnancy. Lower maternal Vitamin B12 levels when analyzed as a continuous variable was not associated with offspring ADHD (aOR 0.97, 95% CI 0.79–1.18,  Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by symptoms of hyperactivity, impulsivity, and inattention . The estVitamin B12  is an essential nutrient required for neural myelination, synaptogenesis and neurotransmitter synthesis . Vitaminn = 6246) [n = 420) [According to our systematic review on the association of offspring ADHD with maternal Vitamin B12 and folate, we found five relevant studies, summarized in Table  = 6246)  and fromn = 420)  did not n = 420) .Table 1LThe previous studies with inconsistent findings have several limitations. First, no studies have measured Vitamin B12 levels using maternal sera samples and ADHD outcomes in the offspring. Information in previous studies on Vitamin B12 was based on multivitamin supplementation or food intake and the composition of the multivitamins used during pregnancy was not clear. Second, in four of the studies, ADHD symptoms were assessed by parents’ , 31, 33 This is the first population-based study using maternal sera to analyze Vitamin B12 levels in early pregnancy and follow-up of the offsprings’ ADHD outcome. The aim of this study was to examine the association between maternal serum Vitamin B12 levels in early pregnancy and the risk of ADHD diagnosis in the offspring.The study is based on the Finnish Prenatal Study of ADHD (FIPS-ADHD) derived from a nested case–control design. The study includes all singleton live births in Finland from 1998 to 1999 and diagnosed with ADHD in the Care Register for Health Care (CRHC) by 2011. Ethical approval for the study was provided by the Ethics Committee of the Hospital District of Southwest Finland, by the data protection authorities at the National Institute for Health and Welfare, and by the Institutional Review Board of the New York State Psychiatric Institute.In this study, data were derived from three national registers: the CRHC, the Finnish Medical Birth Register (FMBR), and the Finnish Population Register Center (PRC). These registers are linked using personal identity codes (PIC).The CRHC includes computerized data on all public and private inpatient diagnoses since 1967 and all outpatient diagnoses since 1998. The diagnostic classification in Finland is based on the International Classification of Diseases (ICD), which is the international standard for reporting diseases and health conditions maintained by the World Health Organization . The ICDThe FMBR includes comprehensive and standardized data on all live births in Finland during the neonatal period up to 7 days of age, and stillbirths where the fetuses had reached at least 22 weeks of gestation or had a birth weight of at least 500 g. Data also include demographic characteristics, reproductive history, maternal health-related behaviors and perinatal events.The PRC is a computerized national archive that contains basic demographic information on Finnish citizens and permanent residents in Finland. The personal data recorded in the system include name, PIC, address, citizenship and native language, family relations, date of birth and death (if applicable).The Finnish public health care system covers both primary health care and specialized health services and the mental health care services are provided free of charge for both children and adults. Most patients are referred by primary health services, including child welfare clinics and school health care, to specialized services. ADHD is diagnosed by a specialist in psychiatry or neurology in public outpatient services.The ADHD cases were identified from the CRHC and included all singletons born in Finland between 1998 and 1999 and diagnosed with ADHD based on the ICD-10 codes of hyperkinetic disorders F90.0, F90.1, F90.8, and F90.9 or with the ICD-9 code 314X before 31.12.2011. Cases who had received the diagnosis of severe or profound mental retardation (F72–F73 in ICD-10 or 318 in ICD-9) and who were diagnosed before the age of 2 but not after that were excluded.The controls were singleton offspring born in Finland and without a diagnosis of ADHD or conduct disorder (F91–F92) or severe or profound mental retardation (F72–73). Each case is matched with one control on date of birth (± 30 days), sex, and place of birth. The controls were alive and residing in Finland at the time of the diagnosis of the matched cases. Among 1672 identified cases and controls, sufficient serum was available in the FMC for 1026 cases and 1026 matched controls.The Finnish Maternity Cohort (FMC) of the Northern Finland Biobank Borealis  was estaTo investigate Vitamin B12 levels in the prenatal serum samples, we measured Active B12  using a chemiluminescence microparticle immunoassay (CMIA) on the Architect i2000SR automatic immunoassay analyzer (Abbott Diagnostics) according to the manufacturer’s instructions . The coeA number of covariates that have shown to be associated with both Vitamin B12 and ADHD were examined as potential confounders , 43–49. χ2 and Student’s t tests for the association with log-transformed maternal Vitamin B12 levels among controls, and for the association with ADHD. The covariates were included in the adjusted regression models as potential confounders if they were associated with both the exposure and the outcome at p < 0.1. The point and interval estimate of odds ratios were obtained by fitting conditional logistic regression models for matched pairs. Statistical significance was based on p < 0.05. All the statistical analyses were performed with We examined the association between Vitamin B12 levels as a continuous variable and ADHD. Due to the skewed distribution, Vitamin B12 was log-transformed before analysis. In the secondary analysis, we examined maternal Vitamin B12 classified as quintiles. The cut-off of the quintiles for case and control groups was based on the distribution in the control group. The reference group was the highest quintile. The categorically defined covariates were tested with The study included 1026 ADHD case–control pairs. The mean age of diagnosis for cases was 7.3 years .Table p = 0.68) or the adjusted analyses . Maternal Vitamin B12 levels, measured in quintiles, were also not associated with offspring ADHD. There was no significant association between the lowest quintile of maternal Vitamin B12 and ADHD in either the unadjusted  or the adjusted analyses . When an additional analysis was conducted adjusting for all covariates for maternal Vitamin B12 levels measured as a continuous variable, and in quintiles, the findings did not change. As ADHD diagnoses may be considered unreliable before the age of 6 years, we conducted sensitivity analyses, including cases diagnosed at age 6 years or more (n = 948). These cases might have been diagnosed before age 6, but they had to have the diagnosis recorded at six years or more. The findings remained negative in all analyses (Supplementary Table 1).Table This is the first population-based study examining maternal Vitamin B12 levels in prenatal sera in relation to ADHD in offspring. There was no significant association observed between maternal Vitamin B12 and offspring ADHD. The findings of the study were in line with previous studies based on folate and multivitamin supplementation –33. In cn = 1026 per group) with alpha set at 5% for a two-sided t test, at 80% power the minimum detectable effect size would have been d = 0.12 (in units of SD), generally considered a “small” effect size.The major strengths of the present study are the large nationwide representative sample and availability of maternal Vitamin B12 levels from prospectively collected maternal serum samples during pregnancy. The study was well powered to detect even a relatively small difference between case and control levels of maternal Vitamin B12. Assuming two independent samples and given the number of cases and controls (We note some limitations to our study. First, ADHD is a multifactorial disorder involving a combination of genetic and environmental factors; yet, the present study concerns only Vitamin B12, a single nutrient. However, as in most research studies of complex outcomes, effects can still be observed for single risk factors including micronutrients as in studies of folic acid and neural tube defects , 51, andThe present study does not support the hypothesis that Vitamin B12 deficiency during pregnancy is an etiological factor for offspring ADHD. Some previous studies have suggested that maternal multivitamin or folate supplementation and high homocysteine levels might have an association with offspring neuropsychiatric disorders such as autism and schizophrenia –55. In a


Chronic Tic Disorder (CTD), Obsessive–Compulsive Disorder (OCD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are complex neuropsychiatric disorders that frequently co-occur. The aim of this study was to examine WISC-IV performance of a clinical cohort of children with CTD, OCD and/or ADHD.N = 185 children aged 6 to 17 years from Germany with CTD, OCD and/or ADHD were examined with the WISC-IV that comprises four index scores  and a Full Scale Intelligence Quotient (FSIQ). WISC-IV profiles of children with CTD-only, OCD-only, ADHD-only, CTD+ADHD, CTD+OCD and CTD+OCD+ADHD were compared with the WISC-IV norm  and among each other.Unpaired t-tests revealed that children with ADHD-only showed significant lower PSI scores, whereas children with CTD-only and OCD-only had significant higher VCI scores as compared to the German WISC-IV norm. One-way ANOVA revealed that children with ADHD-only showed significant lower WMI scores as compared to children with CTD+OCD.We were able to confirm previous evidence on WISC-IV profiles in ADHD in a German clinical sample and contribute new findings on cognitive performance in children with (non-)comorbid CTD and OCD that have to be seen in light of the study’s limitations. Chronic Tic Disorder (CTD) , Obsessive–Compulsive Disorder (OCD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are common neurodevelopmental disorders in childhood which tend to occur frequently as comorbid conditions –4.Tics are sudden, involuntary movements or vocalizations that wax and wane in severity, frequency and complexity . The meaOCD is characterized by recurrent, distressing thoughts and/or compulsive acts  with a pThe high rate of comorbidity suggests a shared neurobiological basis , 16, 17 The examination of strengths and difficulties in intellectual ability in children with CTD, OCD and/or ADHD is crucial, because this is important for academic development, social functioning and well-being –28. DiffPrevious studies on intelligence profiles in children with CTD and/or OCD are rare and studies on ADHD are characterized by large methodological differences with regard to the use of different WISC versions  –31, diffAs illustrated in Table With regard to CTD there is a considerable lower number of studies that revealed heterogeneous results and high variations in WISC profiles , 33. SevFinally, only a few studies investigated WISC profiles in children with OCD. For instance, in a study by Beers et al. children with OCD did not show cognitive impairment in WISC-III scores  whereas To summarize, there is ample evidence on WISC performance of children with ADHD, but WISC profiles in children with CTD and OCD were rarely examined. Comparison of studies is hampered by different study designs, substantial methodological differences  and different consideration of comorbidity (see Table The Department of Child and Adolescent Psychiatry (CAP) Dresden offers special consultation hours for these disorders and we were therefore able to analyze clinical data of a substantial number of affected children. Due to our comprehensive and standardized assessment process, we were furthermore able to identify comorbid and non-comorbid disorders. As we used field data, no healthy control group was assessed. Using the clinical data from the CAP Dresden electronic health records, this study was conducted in order to evaluate whether or not children with only one  differ from those with comorbid disorders  in intellectual functioning. For this, WISC-IV profiles of different groups will be compared to the German WISC-IV norm and among each other. The study was also undertaken as previous studies on intelligence profiles in CTD and OCD produced conflicting results and few have been published from Europe. Based on a large body of literature, we hypothesized that ADHD is associated with deficits in WISC-IV WMI and PSI scores , 41, 47.The Department of Child and Adolescent Psychiatry (CAP) of the University Hospital Carl Gustav Carus at the Technische Universität Dresden (Germany) offers special consultation hours for children with CTD, OCD and ADHD. Families mainly come from the area of Dresden, but also from all over Germany. All children pass an extensive diagnostic procedure including a physical examination, a comprehensive anamnesis, several clinical assessments , OCD (ICD-10: F42.-) and/or ADHD  )Finally, varying group sizes were predetermined by the clinical sample and the group OCD+ADHD (n = 4) had to be excluded from the statistical analyses for reason of statistical power. So it was not possible to apply a 3 × 2 factorial approach with the factors CTD (yes/no), OCD (yes/no) and ADHD (yes/no) that would allow drawing conclusions on the relevance of the non-comorbid and comorbid presentations of the considered disorders on intellectual ability. Although the size of the considered subgroups was sufficient in this study, analyses should be replicated within larger samples to verify our results. In addition, future studies could investigate the predictive or structural validity of the WISC-IV in more detail .We excluded other comorbidities  from this sample to disentangle the specifics of intellectual ability in the mentioned disorders. However, future studies need to examine the role of further comorbidities, accordingly.Overall, we were able to confirm studies from the US on the intellectual ability in children with ADHD in a German sample. In addition, this study contributes new evidence on intellectual ability in children with non-comorbid and comorbid CTD and/or OCD from a German clinical sample. Children with CTD-only and OCD-only showed strengths in verbal comprehension. In clinical care and future research, comorbidity and medication needs to be considered and professionals have to be aware of strengths and deficits in WISC performance in children who are affected by the examined disorders since intellectual ability is an important factor for academic development, social functioning and well-being. Thus, the WISC-IV profile can be an important piece of the puzzle within a comprehensive psychological assessment that includes also a clinical interview, rating scales, observations, and cognitive measures.