PON1) gene variants. We used participants from the MoBa Preschool ADHD Sub-study (n = 259 cases) and a random sample of MoBa sub-cohort participants (n = 547) with birth years from 2004 to 2008. Prenatal urinary dialkylphosphate (DAP) metabolites  were measured by an ultra-performance liquid chromatography-time-of-flight system and summed by molar concentration. Maternal DNA was genotyped for coding variants of PON1 (Q192R and L55M). We used a multivariable logistic regression to calculate the odds ratios (OR) and 95% confidence intervals, adjusted for maternal education, parity, income dependency, age, marital status, ADHD-like symptoms, pesticide use, produce consumption, and season. We found no associations between DAP metabolite concentrations and preschool ADHD. The adjusted ORs for exposure quartiles 2–4 relative to 1 were slightly inverse. No monotonic trends were observed, and the estimates lacked precision, likely due to the small sample size and variation in the population. We found no evidence of modification by PON1 SNP variation or child sex. Maternal urinary DAP concentrations were not associated with preschool ADHD. Prenatal organophosphorus pesticide (OPP) exposure has been associated with child attention-deficit/hyperactivity disorder (ADHD) in agricultural communities and those that are exposed to residentially applied insecticides. To examine this association in populations that are exposed primarily through diet, we estimate the associations between prenatal OPP exposure and preschool ADHD in the Norwegian Mother, Father and Child Cohort Study (MoBa), and describe modification by paraoxonase 1 ( Organophosphorus pesticides (OPPs) are widely used in commercial agriculture for pest control. They function through irreversibly inhibiting the acetylcholinesterase enzyme, disrupting neurotransmission . At highExposure in the general population is thought to occur to a large extent through diet, primarily due to the consumption of contaminated produce. Several studies have reported associations between measured urinary OPP metabolites and fruit and vegetable consumption ,9,10,11,Prior studies have found that prenatal OPP exposure results in cognitive and behavioral deficits in highly exposed agricultural communities, as well as in populations that are exposed to residential applications prior to the de-registration of chlorpyrifos and diazinon by the United States Environmental Protection Agency (US EPA) ,21,22,23PON1) gene, which has previously been shown to modify associations of OPPs with other neurodevelopmental endpoints [PON1 gene that affect the speed of metabolism and excretion [PON1 in the current investigation. Additionally, modification by child sex at birth was also of interest due to prior reports of differential effects of prenatal OPP exposure on child neurodevelopment and variation in prevalence of ADHD diagnoses by sex [Additionally, no prior study addressing ADHD symptoms has considered the potentially modifying influence of functional variants in the paraoxonase 1  to examine associations between prenatal exposure to OPPs and clinically evaluated preschool ADHD. Additionally, we aim to assess modification by genetic variation in The MoBa study is an ongoing prospective, population-based cohort of Norwegian-speaking women, conducted by the Norwegian Institute of Public Health (41% participation rate) . In MoBaThe Preschool ADHD sub-study is a sub-study within MoBa consisting of births that took place on 1 April 2004 through 31 December 2008 of mothers who completed the 3-year questionnaire and lived in close proximity to Oslo. Exclusions for this study included diagnosis with Cerebral Palsy or Down’s syndrome, or symptoms of autism spectrum disorder that were substantial enough to qualify the child for the concurrent MoBa Autism Birth Cohort study .n = 2798), along with children randomly selected from those that were eligible for the Preschool ADHD Sub-study (n = 654), were invited to participate in clinical assessments. In total, about 35% agreed to participate in a 1-day clinical assessment that included a diagnostic interview for preschool ADHD (further described below). The clinical assessments were completed between 2007 and 2011 . Of those who participated, 830 had available maternal prenatal urine samples stored in the MoBa biobank and were therefore eligible to participate in this current study.When the child was 3 years old, the mothers completed a MoBa questionnaire that included 11 items about ADHD, including six items from the Child Behavior Checklist/1.5–5 and five items from the DSM-IV-TR criteria for ADHD ,37,38. NClinical assessments were completed using the Preschool Age Psychiatric Assessment . Only syn = 16,577), we randomly sampled a reference population , which was frequency matched to the preschool ADHD cases based on year of birth (n = 556). Seven participants who were randomly sampled into the reference population were also Preschool ADHD cases. Given the small number of overlapping observations, we treated them only as cases in the current analysis, and therefore our reference population consists of 549 participants.Among the population eligible to participate in the Preschool ADHD Sub-study who also had prenatal maternal urine and blood available  metabolites in spot urines that were collected at approximately 17 weeks gestation: diethylphosphate (DEP); diethylthiophosphate (DETP); diethyldithiophosphate (DEDTP); dimethylphosphate (DMP); dimethylthiophosphate (DMTP); and dimethyldithiophosphate (DMDTP). Case and sub-cohort samples were randomly allocated across analytic batches. The DAP metabolites were measured using an ultra-performance liquid chromatography-time-of-flight system (UPLC-TOF) . In-hous® platform  [We collected maternal blood samples in EDTA tubes at participants’ 17th week of gestation and separated the plasma, before shipping overnight to the MoBa Biobank . The samstralia) .Covariate data were obtained through maternal self-report in MoBa questionnaires and from the Medical Birth Registry of Norway. The birth registry was used to obtain maternal age at delivery, in years, which was treated as a continuous measure. Birth year and child sex  were also obtained through this registry. Maternal education ; marital status during pregnancy ; parity (primiparous versus multiparous); maternal income dependency ; any self-reported contact with weed killers, insecticides, or fungicides either at work or in leisure time within six months prior to the 17th week of gestation (yes/no); and any maternal smoking and alcohol consumption during pregnancy up until the 17th week of gestation (yes/no) were all obtained using MoBa questionnaire 1, which was completed at approximately the 17th week of pregnancy. Total fruit consumption and raw vegetable consumption were measured using a food frequency questionnaire that was completed by MoBa women between the 17th and 22nd week of pregnancy, where mothers reported the number of fresh fruits they had eaten on average and how often they had eaten raw vegetables on average per day, week, and month since becoming pregnant. From this information, we created summary variables for servings of raw vegetables and of fresh fruit per day. We obtained maternal ADHD-like symptoms using the ADHD self-report scale, which was included in the 3-year questionnaire, and used a score of ≥4 as the threshold for ADHD-like symptoms (score of 4 or more vs. less than 4) . The seaWe assessed the distributions of covariates individually and according to case/subcohort status. We used multivariable logistic regression to obtain the odds ratios (ORs) and 95% confidence intervals (95% CI) of the association between measured DAP metabolite concentrations and preschool ADHD, adjusted for confounding variables that were determined by a directed acyclic graph (DAG) . Based oOur final model (model 1), consisting of all potential confounders, adjusted for maternal education, parity, maternal income dependency, maternal age, maternal ADHD-like symptoms, occupational/residential pesticide use, fruit and vegetable consumption, and season. We additionally compared models across three minimally sufficient adjustment sets: (2) maternal age, maternal education, and parity; (3) maternal income dependency, maternal education, and parity; (4) maternal age, maternal education, maternal ADHD-like symptoms, and parity. Lastly, we examined a 5th model, consisting of all potential confounders, excluding those that were eliminated using change in estimate selection (10% change in the effect size) . This moThe DAPs were modeled both continuously and in quartiles to assess functional form. Our primary tables present results from models including all the potential confounders that were identified on the DAG. However, parsimonious models with more limited adjustment sets are included in PON1 genotype, we included interaction terms for SNP genotype*DAP metabolite (∑DEP and ∑DMP) concentrations in our final model. Separately, to assess modification by child sex on the multiplicative scale, we included interaction terms for child sex*DAP metabolite (∑DEP and ∑DMP) concentrations in our final model. We considered the effect measure modification to be statistically significant at a p-value of <0.05, as determined a priori. The current analysis is based on version 9 of the MoBa quality-assured data files. We conducted all statistical analyses using SAS 9.4 software .To assess the effect measure modification by the Characteristics of the preschool ADHD case and sub-cohort populations are included in The raw DAP metabolite concentrations and the distributions of the measured maternal urinary molar DAP sums accounting for specific gravity are presented in PON1 SNP genotype or child’s sex at birth on the multiplicative scales  . In the PON1 genotype, prior studies have reported significant heterogeneity in associations of prenatal DAPs with psychomotor and mental development indices (PDI/MDI) by the PON1 genotype, and found stronger inverse associations among individuals who were slow metabolizers of OPPs [Although we found no evidence of effect measure modification by the  of OPPs ,17. Howe of OPPs ,57,58. S of OPPs ,57,58. A of OPPs . For thiPON1 genotype) of the association between prenatal OPP exposure and child neurodevelopment. Our study leveraged a high quality standardized clinical assessment with ADHD ascertainment that was based on a standardized diagnostic interview; well-measured confounders, including information on fruit and vegetable consumption during pregnancy and occupational and recreational use of pesticides; and adjustment for maternal symptoms of ADHD. Another major strength was our consideration of genetic determinants of effect measure modification by including mothers’ PON1 SNPs L55M and Q192R variants, since both are known to affect the speeds of metabolism and excretion, and Q192R has been shown to modify neurodevelopmental associations with DAPs in prior studies [Our study is distinct from the previously described studies because we assessed preschool ADHD using a validated diagnostic interview for preschool-aged children, and considered plausible modifiers (sex and  studies ,17. In aWhile our study leveraged a high quality standardized assessment of ADHD, ADHD that is diagnosed in the preschool period may have only moderate diagnostic stability , and theGiven their short growing season, most of the produce in Norway is imported from other countries, with Spain, the Netherlands, Italy, South Africa and Peru as key import partners . TherefoIn conclusion, prenatal urinary DAP metabolites were not associated with an increased risk of preschool ADHD in this nested case–cohort study of the MoBa cohort. A key determinant of pesticide exposure in the Norwegian population is consumption of fresh fruit and vegetables . Thus, o


Despite not being part of the core diagnostic criteria for attention-deficit/hyperactivity disorder (ADHD), emotion dysregulation is a highly prevalent and clinically important component of (adult) ADHD. Emotionally dysregulated behaviors such as reactive aggression have a significant impact on the functional outcome in ADHD. However, little is known about the mechanisms underlying reactive aggression in ADHD. In this study, we aimed to identify the neural correlates of reactive aggression as a measure of emotionally dysregulated behavior in adults with persistent ADHD during implicit emotion regulation processes. We analyzed associations of magnetic resonance imaging-based whole-brain activity during a dynamic facial expression task with levels of reactive aggression in 78 adults with and 78 adults without ADHD, and also investigated relationships of reactive aggression with symptoms and impairments. While participants with ADHD had higher reactive aggression scores than controls, the neural activation patterns of both groups to processing of emotional faces were similar. However, investigating the brain activities associated with reactive aggression in individuals with and without ADHD showed an interaction of diagnosis and reactive aggression scores. We found high levels of activity in the right insula, the hippocampus, and middle and superior frontal areas to be particularly associated with high reactive aggression scores within the ADHD group. Furthermore, the limbic activity was associated with more hyperactivity/impulsivity symptoms. These results suggest a partly differential mechanism associated with reactive aggression in ADHD as compared to controls. Emotional hyper-reactivity in the salience network as well as more effortful top–down regulation from the self-regulation network might contribute to emotionally dysregulated behavior as measured by reactive aggression. Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder , charactAn important expression of severe ED in ADHD is reactive aggression –9. Not oAltered structural or functional maturation of several brain areas might point toward a neurodevelopmental link of ADHD with reactive aggression. Among small morphological differences in several areas, the structural alterations implicated in ADHD involve reduced volumes within the limbic system, e.g., the amygdala and the hippocampus , as wellEmotional subprocesses are relevant for the emergence of reactive aggression and are frequently assessed using functional magnetic resonance imaging (fMRI) paradigms inducing an implicit or explicit emotional reaction to emotionally salient stimuli, e.g., the implicit processing of facial emotional expressions. FMRI studies in children and adolescents with ADHD have revealed patterns of elevated bottom-up emotional reactivity, reflected in altered activity in the amygdala, insula, ventral striatum, and the orbitofrontal cortex OFC; , 21–24. The above-mentioned brain regions altered in people with ADHD are overlapping broadly with the neural correlates of reactive aggression. While reactive aggressive behavior appears to be facilitated by activity of the limbic system and hypothalamus, prefrontal activity seems to indicate inhibition of such behaviors . Alia-KlHence, reactive aggression is associated with an imbalance of cognitive control  and hyper-reactivity to emotional stimuli of the limbic system and insula , 32, 33.Despite the high impact on the quality of life, our understanding of the co-occurrence of ADHD with and reactive aggression and of the underlying mechanisms is limited. Neural circuits engaged in reactive aggression -as a severe form of ED- overlap with structurally and functionally implicated brain regions in ADHD and are linked together in functional neuroimaging studies on children and adolescents. However, research on the neural circuits or alterations during emotion processing underlying reactive aggression in adults with persistent ADHD is clearly underrepresented, does not cover implicit facial emotion processing nor integrate behavioral impairments such as ratings of reactive aggression.post hoc correlations with the neural activity in areas implicated in reactive aggression in the fMRI analysis .This study aimed to identify the neural correlates of reactive aggression in adults with persistent ADHD during implicit emotion regulation processes. We acquired fMRI during a dynamic facial expression task , 35. We via newspaper advertisements, patient organizations, and local sports clubs in and around Nijmegen, Netherlands. All of the participants provided written informed consent before participating in the study and received monetary compensation for their participation. The study was approved by the local medical ethical committee.A total number of 83 adults with a confirmed diagnosis of ADHD and 79 healthy control subjects participated in this fMRI experiment. Participants were recruited Participants were included in the ADHD group if they had been diagnosed with ADHD by a clinician. To confirm the diagnosis and assess previous and current symptoms in all participants, we conducted the Diagnostic Interview for Adult ADHD . The DIvia an online platform, among others the Reactive-Proactive Aggression Questionnaire  were activated, but did not exceed the threshold of 11 voxels each. The activity in all clusters was positively associated with higher reactive aggression scores. There was no significant effect of low reactive aggression, see The analysis of reactive aggression within the ADHD group showed significantly elevated activation levels of clusters including the precentral gyrus, cortical frontal and temporal areas, as well as subcortical structures such as the hippocampus. Furthermore two clusters within the right Amygdala [No positive relationship of reactive aggression with brain activity was found in the control group. However, we additionally investigated a potential inverse effect, e.g., negative associations of reactive aggression scores with the brain activity of healthy controls. We found a negative relationship of reactive aggression with the activity in a cluster in the left MTG, right superior parietal lobule as well as the right precentral and lingual gyri .t = 4.50; p < 0.001, rstandardized = 0.34). Age was not associated with reactive aggression, but male gender was . The linear regression analysis of reactive aggression modeled by diagnostic group revealed a significant association of the factor diagnosis with reactive aggression scores  and the lingual gyrus . No correlations with neural activation in areas linked to reactive aggression were observed for inattention symptoms, see Additionally, the number of hyperactivity/impulsivity symptoms was correlated positively with the activity in two clusters associated with reactive aggression in the ADHD group; the more hyperactivity symptoms the adults with ADHD had, the more activation was seen in the right hippocampus , the fusiform gyrus (associated with face processing), broad temporoparietal and frontal networks. These findings are in line with the original paradigm , which upost hoc analyses of medication effects on reactive aggression, symptoms, impairments and the neural correlates of reactive aggression revealed no significant associations  and middle and superior frontal areas were implicated in emotion processing in the ADHD group with high reactive aggression only.The insula as well as the hippocampus and amygdala are associated with the reactivity and assignment of salience during emotion processing in ADHD , 48. EmoOur results on altered activity of limbic structures and the insula during emotion processing are in line with previous findings in medication-naive individuals with ADHD , 51. TheInterestingly, we observed higher activity in middle and superior frontal clusters in patients with high reactive aggression scores. This activation is associated with stronger cognitive control and might suggest more effortful top–down emotion regulation, a process frequently disturbed in ADHD , implyinThe activation of the left MTG as well as premotor areas was observed specifically in the ADHD group. The left MTG or temporo-parietal junction is often implicated in theory of mind abilities . This abThe healthy control group did not show any clusters associated with high reactive aggression, in coherence with the notably low levels of reactive aggression in this group. Interestingly, decreased activity in people in the control group with particularly low reactive aggression scores in some clusters that are implicated in the ADHD group as well suggest a protective function of these areas to develop reactive aggression .We found positive correlations between the expression of hyperactivity/impulsivity symptoms and the activity related to reactive aggression in the hippocampus as well as the lingual gyrus in individuals with ADHD. The more hyperactivity/impulsivity symptoms adults with ADHD showed, the more these areas were engaged during emotion processing in association with reactive aggression. This association of ADHD symptoms with brain activity related to reactive aggression could be an example of a deviant neurocognitive mechanism behind ED in ADHD where the brain mediates both, ED as well as ADHD symptoms. Inattentive symptoms or impairments were not significantly correlated with neural activities. These findings point toward a specific susceptibility of the hyperactive type of ADHD to exhibit ED such as reactive aggression, in line with findings on the close developmental coupling of hyperactive/impulsive symptoms with reactive aggression  and geneAll results need to be viewed in the context of the strengths and limitations of this study. Our sample sizes exceed most previous fMRI studies related to this topic in ADHD and is demographically well balanced.However, task activation maps across all subjects did not show some areas classically associated with emotion regulation, such as the anterior cingulate, OFC, dlPFC, and vmPFC, or the amygdala , the latIn conclusion, these findings convey evidence for a differential emotion processing mechanism in subjects with ADHD and reactive aggression, with a specific liability of individuals with hyperactivity/impulsivity symptoms to experience these alterations. The brain regions related to this mechanism suggest difficulties with both emotional hyper-reactivity , and more effortful regulation of emotional responses  in the ADHD group with higher reactive aggression. This differential mechanism appears to be related to an altered neurocognitive brain architecture of ADHD and supports the diagnostic and clinical view of emotionally dysregulated ADHD as a subgroup in the spectrum of the disorder, as discussed by Shaw P et al. . In the The raw data supporting the conclusions of this article will be made available by the authors upon motivated request with the intention to investigate ADHD.The studies involving human participants were reviewed and approved by CCMO. The patients/participants provided their written informed consent to participate in this study.MH, BF, and AA-V contributed to study conception and supervision, obtained the funding. PV and BJ provided the samples and data. BJ and DR conducted the analyses and data interpretation. BJ, DR, MH, and AA-V contributed to writing group. All authors contributed to manuscript revision, read and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopment disorder characteristically compromising of three persistent symptoms; Inattention, hyperactivity and impulsivity. Within the Tameside and Glossop CCG continuous performance tests from the company QbTech are used to aid diagnosis.The aim of this research is to evaluate the effectiveness of using both the QbCheck (triage tool) and QbTest (diagnostic tool) concordantly in the diagnostic pathway of ADHD in young people.20 Patients who had undergone both performance tests were identified and then five components evaluated in the QbCheck were then compared to the QbTest results.In the five areas identified by both the QbTest and QbCheck up to 80% had the same outcomes in the two tests. However, in one area (hyperactivity) only 60% of QbChecks outcomes were replicated by the QbTest. The symptom of inattention most commonly correlated between the two tests. The average wait between tests was 9.8 months. 100% of those who scored on QB Check, received diagnosis of ADHD, suggesting high referrer specificity.QbCheck diagnostic outcomes are comparable to patients who have undergone both the QbCheck and QbTest, only having one of these continuous performance tests making up the ADHD diagnostic pathway could be cost and time saving in the pathway to diagnosis. As QbCheck can be completed within the child’s school this reduces the number of clinic appointments that need to be attended by patients and their families.


Some features of attention-deficit/hyperactivity disorder (ADHD) may resemble those of mild cognitive impairment (MCI) in older adults, contributing to diagnostic uncertainty in individuals seeking assessment in memory clinics. We systematically compared cognition and brain structure in ADHD and MCI to clarify the extent of overlap and identify potential features unique to each.n = 80) underwent structural neuroimaging.Older adults from a Cognitive Neurology clinic  underwent neuropsychological assessment. A subsample  and an encoding deficit in ADHD . Both groups displayed normal executive functioning. Semantic retrieval was uniquely impaired in MCI.Although ADHD has been proposed as a dementia risk factor or prodrome, we propose it is rather a pathophysiologically-unique phenotypic mimic acting via overlap in memory and executive performance. Despite a 3% prevalence in later life , attentiDirect comparisons between ADHD and MCI are necessary to identify similarities  and potential features unique to each disorder . Some authors have additionally suggested that structural neuroimaging may help distinguish ADHD from MCI ; howeverOlder adults with ADHD presenting to memory clinics may be a particularly relevant group to study. Although they are unlikely to be representative of community cases of ADHD, they may be a subgroup at higher risk for accelerated decline because they are seeking assessment for cognitive concerns. Subjective cognitive change—which is often the impetus for referral to a memory clinic —has receA second rationale for studying ADHD in a memory clinic is that, as described above, these individuals arguably comprise a group whose clinical presentation is the most challenging to distinguish from prodromal dementia, because their complaints and clinical presentation may be highly similar to MCI. Therefore, they represent a higher “threat” to diagnostic accuracy when attempting to distinguish degenerative from non-degenerative conditions . Community cases of ADHD, on the other hand, may be less likely to struggle with subjective or objective impairments in cognition, and may instead tend to present to mental health professionals for help with interpersonal problems, for example . From thFor these reasons, this study aims to systematically compare the neuropsychological and neuroimaging characteristics of older adults with ADHD presenting to a cognitive neurology clinic, relative to clients with established MCI. We will first briefly summarize what is known about cognitive and structural brain features in each of these conditions.The extent and severity of neuropsychological impairments in later-life ADHD remain unclear. Only three studies have included samples aged >50, and results were inconsistent: one reported deficits in frontal-lobe functions , anothern = 25) and reported reduced gray matter volume within the right medial frontal orbital area relative to age-matched controls  also agreed to complete the ADHD module of the Structured Clinical Interview for DSM-5 (SCID-5). The remaining 22 were unavailable or refused to complete the SCID-5.Individuals were considered for inclusion if they were 50 to 85 years old. Fifty-nine participants suspected of ADHD were identified among patients presenting to a cognitive neurology clinic with cognitive concerns . All wern = 22). When SCID-5 had not been completed, ASRS scores were considered: 16 participants endorsed ≥4 items above a pre-specified threshold or obtained a total summed score of ≥14 and were considered to have significant ADHD symptoms  , were selected as a comparison group. The SDS is a well-characterized cohort with neurodegenerative or vascular cognitive impairment. SDS participants are thoroughly screened to exclude secondary causes of impairment or concomitant illness; thus, any suspected cases of ADHD  were not included, and participants were stroke-free. Diagnoses are determined by at least two experienced clinicians based on neurological and cognitive examination. Data from an additional six MCI participants enrolled in another clinical study ongoing at Sunnybrook were also used here, bringing the total MCI sample to 29. All were diagnosed using Data from an additional sample of 37 healthy controls, who also underwent testing in SDS, were used for comparison purposes. These participants reported no cognitive or behavioral complaints, performed within normal limits on all cognitive measures, and were stroke-free. The final sample consisted of 40 participants with ADHD, 29 with MCI and 37 controls.ADHD symptom measures were administered to all ADHD participants, and to a subset of 15 MCI participants who could be contacted following their SDS assessment. The remaining 15 MCI participants and 37 controls could not be reached to complete these measures at the time of this study.T scores using the technical manual  Self-Report Long Form. The ADHD Index, containing items that best distinguish ADHD from non-clinical cases , was thel manual .Childhood symptoms were quantified using the Barkley Adult ADHD Rating Scale-IV (BAARS-IV) Self-Report Childhood Scale. This tool asks participants to rate the frequency of their inattentive and hyperactive/impulsive behaviors from age 5 to 12, providing two subscores which can then be summed to produce a “total ADHD” score. Raw scores were standardized to age-adjusted percentiles using the technical manual .Participants with children were also queried about their children’s early-life ADHD symptoms using the BAARS-IV Other-Report Childhood Scale. This served as additional corroborative evidence of ADHD, as heritability studies estimate that ADHD occurs in 40% to 50% of parents who have a diagnosed child .n = 85), the Beck Depression Inventory II (BDI-II) (n = 14), or an informant completed the Cornell Scale for Depression in Dementia (n = 4). In four ADHD participants, the MMSE was completed >1 year after or prior to the rest of the neuropsychological assessment and was coded “missing.” One ADHD and two control participants did not complete any depression measure.The Mini-Mental State Examination (MMSE) estimated global cognitive function. To quantify depressive symptoms, participants completed either the 30-item Geriatric Depression Scale (GDS)  data collected withinSDS. Imaging data were acquired on 22 of the ADHD participants using the same protocol. Scans were acquired on a 1.5 Tesla GE Signa scanner  and included a T1-weighted axial three-dimensional spoiled gradient recalled echo  and interleaved PD and T2 sequences .http://surfer.nmr.mgh.harvard.edu/). Pre-processing of T1-weighted scans included motion correction, skull-stripping, transformation to Talairach space, intensity normalization, hemispheric separation, and tissue segmentation and parcellation. Two additional stages improved segmentation accuracy and quality assessment based on the PD/T2 sequence  , normal appearing WM (NAWM), sulcal and ventricular CSF . SABRE wdocs.io) .t-tests (ADHD symptom scores).Depressive symptoms were categorized as “none” , “mild/probable”  or “moderate/severe” . The groups were compared using Kruskal-Wallis H , chi-square , or Z-scores using published normative data, and NAGM volumes in inferior, middle, and superior frontal regions, superior and inferior parietal regions, and anterior and posterior temporal regions were corrected for head size . Transformed scores were entered into separate univariate ANOVAs adjusted for age, sex, MMSE and depressive symptoms. Non-significant predictors were removed except age, and pairwise comparisons were examined where main effects were significant.Raw cognitive scores were standardized to p(cwp)) of p < .05 (two-sided) was used to correct for multiple comparisons. Bonferroni correction was applied across the two hemispheres.For cortical thickness analyses, vertex-wise surface-based analysis was first performed within the frontal lobe only using the general linear model in FreeSurfer, based on cortical thickness alterations in young adults with ADHD. A second exploratory analysis was performed across the whole brain. Age, education, and MMSE scores were included as regressors of no interest. Monte Carlo simulation with 5000 iterations using a cluster-wise probability , all memory measures , language (BNT and semantic fluency), and one measure of executive functioning (Trails switching). CVLT free recall and ROCFT recall were also worse in MCI than in ADHD. ADHD participants were impaired on digit-symbol coding, Stroop color-naming, and immediate and delayed recall of the Short Story. Performance on all other tasks was comparable between groups .The groups’ mean NAGM volumes did not differ in any region of interest, except hippocampal volumes which were marginally lower in MCI participants than controls . In fronThis study aimed to quantify the shared and unique cognitive and imaging characteristics of older adults with ADHD or MCI presenting to a memory clinic. First, we found notable overlap in participants’ recall of a short story. However, ADHD participants’ performance was statistically comparable to controls’ in a context with added semantic structure (the CVLT), suggesting a frontally-mediated encoding deficit in ADHD and a temporally-mediated storage deficit in MCI. This interpretation is corroborated by compromised frontal and medial temporal lobe brain structures in our samples of ADHD and MCI, respectively, on neuroimaging.Only MCI participants showed robust semantic retrieval deficits (naming and semantic fluency) relative to controls. Semantic abilities have been normal in previous investigations of later-life ADHD  and in sBoth ADHD and MCI groups showed grossly normal executive performance relative to controls. Similarly, three previous studies of ADHD in individuals aged >50 found normal executive performance when considered on a standardized scale   and relaHippocampal volumes were relatively reduced in MCI, tentatively supporting an interpretation of a primary (storage) memory deficit. The ADHD group showed cortical thinning in the middle frontal gyrus which mediates working memory abilities  and sustPrevious studies have reported associations between ADHD symptoms and neurodegeneration in later life e.g., , leadingIt follows from this hypothesis that, in addition to unique cognitive and neuroimaging profiles, ADHD and MCI should show specific physiological and pathological markers of disease, and distinct longitudinal cognitive trajectories. Some important next tests of this hypothesis, then, may include quantification of various neurodegenerative biomarkers  and prospective follow-up of participants with ADHD to determine the extent to which their cognitive and structural trajectories resemble those of normal versus abnormal aging.In this study, the possibility cannot be ruled out that ADHD and MCI may indeed be different points along a single neurodegenerative continuum as others have proposed  and thatrecent onset of cognitive change, and healthy controls were determined to be free of any current or chronic cognitive or behavioral concerns. Because chronic inattention/impulsivity are core features of ADHD .


Attention-deficit/hyperactivity disorder (ADHD) is a childhood mental health disorder that often persists to adulthood and is characterized by inattentive, hyperactive, or impulsive behaviors. This study investigated structural and effective connectivity differences through voxel-based morphometry (VBM) and Granger causality analysis (GCA) across child, adolescent, and adult ADHD patients. Structural and functional MRI data consisting of 35 children (8.64 ± 0.81 years), 40 adolescents (14.11 ± 1.83 years), and 39 adults (31.59 ± 10.13 years) was obtained from New York University Child Study Center for the ADHD-200 and UCLA dataset. Structural differences in the bilateral pallidum, bilateral thalamus, bilateral insula, superior temporal cortex, and the right cerebellum were observed among the three ADHD groups. The right pallidum was positively correlated with disease severity. The right pallidum as a seed precedes and granger causes the right middle occipital cortex, bilateral fusiform, left postcentral gyrus, left paracentral lobule, left amygdala, and right cerebellum. Also, the anterior cingulate cortex, prefrontal cortex, left cerebellum, left putamen, left caudate, bilateral superior temporal pole, middle cingulate cortex, right precentral gyrus, and the left supplementary motor area demonstrated causal effects on the seed region. In general, this study showed the structural differences and the effective connectivity of the right pallidum amongst the three ADHD age groups. Our work also highlights the evidence of the frontal-striatal-cerebellar circuits in ADHD and provides new insights into the effective connectivity of the right pallidum and the pathophysiology of ADHD. Our results further demonstrated that GCA could effectively explore the interregional causal relationship between abnormal regions in ADHD. Attention-deficit/hyperactivity disorder (ADHD) is a mental health disorder characterized by inattentive, hyperactive, or impulsive behaviors  has been employed to study structural deficits of the brain in several neurodegenerative disorders on a voxel-by-voxel or seed-to-voxel scale. Structural magnetic resonance imaging (sMRI) studies using VBM and meta-analysis has shown that some brain regions in ADHD patients exhibit reduced volumes relative to age-matched healthy control subjects. Using cross-sectional mega-analysis, Hoogman and colleagues reported reduced volumes in the accumbens, caudate, putamen, amygdala, and hippocampus in ADHD patients compared to healthy controls  has been widely employed in neuroimaging studies  were obtained from the New York University Child Study Center for the ADHD-200 Global Competition and UCLA dataset  and the Staglin Center for Cognitive Neuroscience (Siemens version syngo MR B17) at UCLA. Functional MRI data were collected using a T2*-weighted echo-planar imaging (EPI) sequence with the following parameters: slice thickness = 4 mm, slices = 34, TR = 2 s, TE = 30 ms, flip angle = 90°, matrix 64 × 64, FOV = 192 mm. A T2-weighted matched-bandwidth high-resolution anatomical scan (with the same slice prescription as the fMRI scan) and MPRAGE were collected. The parameters for the high-resolution scan were: 4 mm slices, TR/TE = 5,000/34 ms, 4 averages, matrix = 128 × 128, 90-degree flip angle. The parameters for MPRAGE were the following: TR = 1.9 s, TE = 2.26 ms, FOV = 250 mm, matrix = 256 × 256, sagittal plane, slice thickness = 1 mm, 176 slices. For the child and adolescent datasets, MRI data were obtained using Siemens Magnetom Allegra Syngo Mr 2004a. FMRI data were collected using an echo-planar imaging sequence with the following parameters: slice thickness: 4 mm, Slices: 33, TR: 2,000 ms, TE: 15 ms, Rotation = 90°, FoV phase: 80.0%, FoV read = 240 mm. In addition, T1-weighted images were acquired using the following parameters: Slice thickness = 1.33 ms, TR = 2,530 ms, TE = 3.25 ms, rotation = 0 degrees, FoV phase = 100.0%, FoV read = 256 mm.3 voxels, nuisance covariates regression using Friston 24 , 40 adolescents (14.11 ± 1.83 years), and 39 adults (31.59 ± 10.13 years). All preprocessing steps were performed using the data processing assistant for resting-state fMRI, advanced edition (DPARSFA) in the DPABI toolbox  time points were removed, leaving final time points of 150. For the pediatric (adolescents and children) dataset, the first 26 time points were removed to ensure that all the data have equal time points since the time courses are used in the connectivity calculations. The same preprocessing steps were done for all subjects, including slice time correction, realignment, co-registration of  T1 images to corresponding functional images, segmentation, normalization by Diffeomorphic Anatomical Registration using Exponentiated Lie algebra  were used as covariates for statistical analyses. Furthermore, given that our data were obtained from different sites, and several studies have shown the effect of multi-site in different ADHD age groups  correction was employed for multiple comparison corrections. In addition, we examined the relationship between altered regions and symptomatic values; we performed correlation analysis between regions that show significant gray matter differences and disease severity. To this end, we defined a sphere ROI (radius = 7 mm) using MarsBaR toolboxP < 0.05) was used for multiple comparison corrections.We use the signed path coefficient-based GCA implemented in the REST toolkitP < 0.05, FWE corrected), as demonstrated in One-way Anova was used to determine the significant gray matter volume differences among the three ADHD age groups. Regions showing significant differences in gray matter volume include: the bilateral pallidum, bilateral thalamus, bilateral insula, Temporal_Sup_L, and the right cerebellum . The results are shown in P < 0.05, FWE corrected).Using the right pallidum as seed, significant causal effect from the seed region to the occipital_Mid_R, bilateral fusiform, postcentral_L, Paracentral_Lobule_Left, Amygdala_L, and the right cerebellum was observed  structural differences among the three ADHD groups were mainly in the bilateral thalamus, left pallidum, bilateral insula, superior temporal cortex, right cerebellum, and the right pallidum which showed a positive correlation with overall disease severity; (2) the seed region  showed causal effects on the Occipital_Mid_R, bilateral fusiform, Postcentral_L, Paracentral_Lobule_L, Amygdala_L, and the right cerebellum; (3) causal effect from the cerebellum_Crus2_L, Putamen_L, Caudate_L, bilateral superior temporal pole, prefrontal cortex, cingulate cortex, Precentral_R, and the Supp_Motor_Area_L to the seed region were observed; and (4) ROI-ROI GCA demonstrated directional causal relationships among ROIs. To the best of our knowledge, our study is the first to investigate structural differences among the three ADHD age groups, the causal relationship between the right pallidum and whole-brain, and the interregional causal relationships among these ROIs.The significant gray matter volume differences observed in ADHD patients in the current study were found in the bilateral pallidum, bilateral thalamus, bilateral insula, superior temporal cortex, and the right cerebellum. Reduced globus pallidus volume in ADHD patients has been reported in several previous ADHD studies  on the Occipital_Mid_R, bilateral fusiform, Postcentral_L, Paracentral_Lobule_L, Amygdala_L, and the right cerebellum indicating that the right pallidum precedes and allows prediction of these brain regions. Also, the anterior cingulate cortex, prefrontal cortex, cerebellum_Crus2_L, Putamen_L, Caudate_L, bilateral superior temporal pole, middle cingulate cortex, Precentral_R, and the Supp_Motor_Area_L demonstrated causal effects on the seed region (inflow), indicating that these brain regions precedes and allow prediction of the right pallidum. The ROI-ROI GCA showed a directional network for both outflow and inflow. The outflow ROI directional network demonstrated that the right pallidum was its pivot. Disruptions in the right pallidum might be related to disruptions in other brain areas in the outflow connectivity graph . The amyThe occipital cortex is responsible for visual processing and has been reported in previous ADHD studies (Wolf et al., Although we showed the structural differences in ADHD and the causal relationship between the right pallidum and whole brain, as well as among outflow and inflow ROIs in the three ADHD patient groups, the current study has some limitations. Albeit the control of sex variable in the statistical analysis, the male to female ratio in the current study was not the same, which may affect the results. Also, the sample size was a limitation in this study, as connectivity tends to be more stable with an increasing number of participants. We were not able to investigate whether our relatively small sample influenced the result presented, we suggest that future studies recruit larger sample sizes to include a balanced sex ratio.In conclusion, the current study investigated the structural differences among ADHD patients and the causal relationship between the right pallidum and the rest of the brain and among significant outflow and inflow regions, using VBM and coefficient-based GCA. The significant gray matter volume defects in the right pallidum were positively correlated with disease severity. Also, significant outflow and inflow regions obtained from the GC analysis revealed directional causal relationships among ROIs. Generally, the current study demonstrated structural differences among the three ADHD patient groups and the ability of GCA to capture the causal connectivity between brain regions. Furthermore, this work highlights the evidence of the frontal-striatal-cerebellar circuits in ADHD and provides new insights into the effective connectivity of the right pallidum and the pathophysiology of ADHD.http://fcon_1000.projects.nitrc.org/indi/adhd200/ and https://openneuro.org/datasets/ds000030/.Publicly available datasets were analyzed in this study. These data can be found here: HW, PH, and JJ: organized and preprocessed the data. EA and HW: formal analysis. EA and BK-B: statistical analysis and results. EA: writing the first draft. BK-B and BB: revision and editing. All authors contributed to the article and approved the submitted version.


Associations between attention-deficit/hyperactivity disorder (ADHD) and brain morphology have been reported, although with several inconsistencies. These may partly stem from confounding bias, which could distort associations and limit generalizability. We examined how associations between brain morphology and ADHD symptoms change with adjustments for potential confounders typically overlooked in the literature (aim 1), and for the intelligence quotient (IQ) and head motion, which are generally corrected for but play ambiguous roles (aim 2).N = 7722) and Generation R (N = 2531) Studies. Cortical area, volume, and thickness were measured with MRI and ADHD symptoms with the Child Behavior Checklist. Surface-based cross-sectional analyses were run.Participants were 10-year-old children from the Adolescent Brain Cognitive Development  generally attenuated associations, as cluster sizes halved and effect sizes substantially reduced. Cluster sizes further changed when including IQ and head motion (aim 2), however, we argue that adjustments might have introduced bias.Careful confounder selection and control can help identify more robust and specific regions of associations for ADHD symptoms, across two cohorts. We provided guidance to minimizing confounding bias in psychiatric neuroimaging.Authors are supported by an NWO-VICI grant (NWO-ZonMW: 016.VICI.170.200 to HT) for HT, LDA, SL, and the Sophia Foundation S18-20, and Erasmus University and Erasmus MC Fellowship for RLM. Large strides have been made in the identification of neuroanatomical correlates of psychiatric problems, with attention-deficit/hyperactivity disorder (ADHD) being a prominent example. ADHD is the most prevalent neurodevelopmental disorder in children worldwide and is characterized by atypical levels of inattention, hyperactivity, and/or impulsivity . StructuConsistently identifying the neuroanatomical substrate of ADHD, however, remains challenging. A recent meta-analysis did not find convergence across the literature on brain differences in children and adolescents with ADHD . One posConfounding bias arises when a third variable affects both the determinant (independent variable) and outcome (dependent variable) of interest  . ConfounWithin neuroimaging studies of ADHD, except for a few large investigations , studiesIn this study, we examined the association between brain structure and ADHD symptoms and how the selection and control for potential confounders may affect results (aim 1). Moreover, we discussed the complex role of IQ and head motion in brain structure–ADHD associations and the potential consequences of adjusting for them (aim 2). We leveraged two large, population-based cohorts: the Adolescent Brain Cognitive Development (ABCD) and the Generation R Studies. In line with most neuroimaging studies, we adopted a cross-sectional design.N = 7722, multisite) and Generation R  Studies  and no clusters in the Generation R Study, which suggests that cortical thickness does not relate or does not relate strongly to ADHD, in line with prior literature , a type of causal diagram . These gBased on the literature, lower SES is associated with a higher risk for ADHD  and withMoreover, several factors concerning maternal behavior, pre- and postnatally, have been associated with both ADHD and brain morphology. For instance, prenatal exposure to substances is known to increase the risk of developing ADHD symptoms and has been associated with variation in cerebral volume and surface area . Postnat2 in model 1 to 952.8 cm2 in model 2 (=−18%) in the ABCD Study, and from 446.1 to 229.6 cm2 (=−49%) in the Generation R Study. Similar reductions were observed for volume and thickness (Adjustments for SES (model 2) led to reductions in the spatial extent of the clusters for surface area and volume in both cohorts . For surhickness . After a2 in model 1 and reduced to 199.5 cm2 in model 2 (=−15%), and 55.5 cm2 in model 3 .To explore whether the results observed for associations between brain morphology and ADHD symptoms applied to children with an ADHD diagnosis, we repeated the primary analysis using the ADHD diagnostic data from the Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS) in the ABCD Study. In line with our primary results, ADHD diagnosis was associated with less bilateral surface area and volume. Compared to clusters for ADHD symptoms, those associated with ADHD diagnosis were smaller, but overlapping . We obseSurface-based studies generally focus on the spatial extent of cortical clusters associated with the phenotype, but, in this study, we also explored how confounding adjustments affected the regression coefficients for ADHD symptoms .At a vertex-wise level, adjusting for socioeconomic and maternal factors (model 3) led to reductions in the beta coefficients, across the brain, for both cohorts . Of noterM1 = 0.84, rM2 = 0.83, rM3 = 0.83) and volume   .We considered one additional scenario which included IQ, a factor that is often adjusted for in previous studies . HoweverFirst, it could be argued that IQ is partly innate and precedes brain development and ADHD, making it a confounder . Second,2 (=−20%) for the ABCD Study, and from 208.6 to 93.1 cm2 for the Generation R Study (=−55%). Clusters of association for surface area in model 4 were located in the fusiform, inferior parietal, insula, lateral occipital, middle temporal, pericalcarine, pre- and postcentral, precuneus, rostral middle, and superior frontal, superior parietal and temporal, and supramarginal cortices. Findings for volume and thickness are shown in After additionally adjusting for IQ, the spatial extent of the clusters associated with ADHD symptoms reduced further in both cohorts . For surA final scenario was also included, to reflect the commonly used adjustments for head motion during scanning . Motion The amount of measurement error in brain morphology may differ across children with versus without ADHD. In fact, children with impulsivity and inattention have been shown to move more during MRI scanning , determi2 (=+23.2%) for the ABCD Study and from 208.6 to 239.7 cm2 (=+14.9%) for the Generation R Study  associations between brain structure and ADHD symptoms, which were initially widespread, reduced when adjusting for socioeconomic and maternal behavioral confounders, and that (2) careful considerations are needed when including IQ and/or head motion due to their complex relation with ADHD and brain morphology.Widespread associations between surface area and ADHD symptoms were initially identified, with higher symptoms relating to smaller brain structures, in line with previous research . After aRegions that remained associated after adjustments and which were consistently identified across cohorts were the precuneus, isthmus of the cingulate, supramarginal, pre- and postcentral, and inferior parietal cortices for both area and volume. Most of these regions  have been previously implicated in ADHD in clinical samples . HoweverOf note, some inconsistencies between the ABCD and Generation R Studies, both in size of the clusters and the exact location, were observed. While we used the same processing pipelines and similar quality control procedures and measures across cohorts, potential reasons for discrepancies in results must be discussed. First, the larger sample size of the ABCD Study allows for greater power to detect smaller effects, which led to larger associated areas. Second, the multisite structure of the ABCD Study may have introduced noise in the results , and determined the identification of associations which are not replicable in the Generation R Study. Third, the two studies include children from different populations. While both are very diverse samples, the ABCD Study is comprised of a more heterogeneous sample from the US population, which, for instance, is characterized by a wider variety of ethnicities and cultures, potentially permitting the discovery of more associations. Nevertheless, there was considerable overlap in the findings from the ABCD and Generation R Studies, with consistencies across cohorts indicating the most robust and generalizable associations.Here, we discerned associated areas likely subject to confounding bias from areas robust to socioeconomic and maternal behavioral factors, and replicable across two large cohorts. Comparisons with prior findings should be made with caution due to differences in study design, samples , and analytical methods. Importantly, we highlighted the opportunity for future studies to include covariates that go beyond age and sex, can help refine associations, and can be readily collected. Future studies may want to consider other confounding factors, depending on their research question, design, and assumed causal relations.Avoiding bias from adjusting for variables that are not confounders is as important as identifying sources of confounding. Adjusting for mediators or colliders of the ADHD–brain structure relation would induce bias. Here, when adjusting for IQ, which plays an unclear role in brain structure–ADHD associations, cluster sizes reduced considerably in both the ABCD and Generation R Studies. This could indicate that IQ is a confounder, in which case adjustments would be necessary, or that IQ is a mediator or collider, in which case adjustments must be avoided.rABCD = −0.11, rGENR = −0.14), but this does not necessarily make it a weak confounder as the strength of confounding is due to a variable’s relation with the exposure and outcome. Second, if brain structure and ADHD symptoms both cause cognitive changes, adjusting for IQ could induce collider bias, although this is also dependent on when IQ is measured relative to the exposure and outcome . When adjusting for head motion during scanning, we observed increases in the spatial extent of the clusters. This might indicate a reduction or an increase in bias. First, bias might have been reduced by adjusting for the fact that children with ADHD will have more error in their cortical measures. Second, bias might have also been increased because we conditioned for head motion, which is a consequence of ADHD.Overall, the role of head motion in the relation between brain structure and ADHD is complex and warrants the utmost care. Adjusting or not would both lead to bias, meaning that considering which bias might be strongest is necessary. Moreover, methods aiming to reduce information bias should be leveraged ; howeverSimilar considerations should be applied to other neuroimaging parameters which often are indicators of information bias rather than confounders in brain structure–ADHD relations . For instance, time of the day has been shown to influence morphometric values , leadingOur considerations on confounding likely generalize to the psychiatric neuroimaging field, as several confounders considered here  also relate to brain function and other psychiatric disorders . SimilarConfounding control is paramount to studies examining determinants of a phenotype, like ADHD. However, even in these studies, one may be tempted to conduct correlational research with limited confounding adjustments, and then speculate about biological causal mechanisms . Rather,Despite leveraging two large samples with similar characteristics and assessments, this study presents several limitations. First, there is always potential for residual confounding through unmeasured confounders and misclassification of measured confounders. For example, given that genetic factors influence both ADHD and brain morphology and that there is a genetic correlation between ADHD risk and intracranial volume , certainSecond, due to our cross-sectional design, deliberately chosen to correspond to most neuroimaging studies, we must assume all confounders precede our determinant and outcome. This is a plausible assumption for the Generation R Study as, being a prospective birth cohort, we could ensure that the confounders here considered temporally preceded both ADHD and neuroanatomical assessments. However, this was not possible for the ABCD Study, which started sampling at child ages 9–10 years. Future research on the temporal relations between potential confounders, ADHD, and brain structure will aid the minimization of confounding bias when investigating the structural substrates of ADHD.Third, while we leveraged both symptom-level and diagnostic data for ADHD, this was done within population-based studies. Our results cannot, therefore, be generalized to a clinical population. Future research could examine the extent to which associations between brain structure and ADHD change after adjustments for likely confounders in clinical samples.In conclusion, leveraging an empirical example from two large studies on neuroanatomy and ADHD symptoms, we highlighted the opportunity for future studies to consider further key confounders. These can be identified based on prior literature and causal diagrams as well as be readily collected, offering a feasible venue for future research. Adjusting for these potential confounders helped identify more refined cortical associations with ADHD symptoms, robust to the influence of demographic and socioeconomic factors, pregnancy exposures, and maternal psychopathology. We also evaluated the potential role of IQ, which could be a mediator, collider, and/or confounder. While adjusting for IQ led to reductions in associations, these would, however, likely not be attributable to reduced confounding bias. Lastly, we explored how head motion reflects information, rather than confounding bias. We discussed the generalizability of these considerations on confounding bias to psychiatric neuroimaging, and suggest a workflow that can be followed to minimize confounding bias in future studies.We analyzed data from two independent population-based cohorts: the ABCD Study and the Generation R Study. The ABCD Study is conducted across 21 study sites in the US and recruited since 2015 children aged 9–10 at baseline . The Gen1-weighted MRI images. Participants were excluded if (1) they had dental braces, (2) incidental findings, (3) their brain scans failed processing or quality assurance procedures, or (4) they were twins or triplets. Of note, excluding children with dental braces is unlikely to determine selection bias by SES in either the ABCD or the Generation R Study as the former cohort covered the costs of dental braces removal for all children who enrolled, while dental care is insured for all children in the Netherlands. Within the Generation R Study, a small set of participants were additionally excluded because they had a different scan sequence. Finally, for each non-twin sibling set, one was randomly included to minimize shared method variance bias. Flowcharts for participant inclusion and exclusion are available in In this cross-sectional study, we leveraged data from the baseline assessment of the ABCD Study (release 2.0.1) and the 10-year assessment of the Generation R Study. Both waves included behavioral and neuroimaging measures. We included children with data on ADHD symptoms and TChildren’s ADHD symptoms, reported by the primary caregiver, were measured with the CBCL (school-age version) , an inve1-weighted data were obtained on multiple 3T scanners in the ABCD Study  750 and Philips) and one scanner in the Generation R Study (GE 750). Standard adult-sized coils were used for the ABCD Study and an eight-channel receive-only head coil for the Generation R Study. To acquire T1-weighted structural images, the ABCD Study used an inversion prepared RF-spoiled gradient echo scan with prospective motion correction while the Generation R Study used an inversion recovery fast spoiled gradient recalled sequence . More details can be found elsewhere  tissue segmentation, and (4) cortical surface-based reconstruction. Cortical surface maps were smoothed with a full width of a half-maximum Gaussian kernel of 10 mm. Within the ABCD Study, quality assessment was based on manual and automated quality control procedures and recommended inclusion criteria for structural data from the ABCD team (FreeSurfer (version 6.0.0) was used for both cohorts for image processing, which was run in-house to maximize comparability across cohorts. Processing involved (1) removal of non-brain tissue, (2) correction of voxel intensities for BBCD team . Within BCD team . Poor quAll data were collected at baseline (child age 9–10 years). Age and sex were recorded at intake. Child race/ethnicity was reported by the primary caregiver and was categorized as White, Black, Hispanic, Asian, Other by the ABCD team. Household combined net income  and highest parental education  were self-reported by the primary caregiver in the Parent Demographics Survey. Maternal age at childbirth was measured in the Developmental History Questionnaire. Tobacco and cannabis use during pregnancy were retrospectively reported by the mother  in the Developmental History Questionnaire. Caregiver psychopathology was obtained from the Total Problems Adult Self Report Syndrome Scale. The Wechsler Intelligence Scale for Children-6 Matrix Reasoning total scaled score was used as a proxy for IQ. We used the Euler number obtained from FreeSurfer as a proxy for head motion during the acquisition. The Euler number quantifies the topological complexity of the reconstructed cortical surface, with holes in the reconstructed surface leading to lower Euler numbers.Age and sex were measured based on medical records obtained at birth. Child ethnicity (western and non-western) was assessed based on the parents’ birth country, in line with the Statistics Netherlands bureau. Maternal age at childbirth was prospectively measured. Family income and highest maternal education were obtained through prospective self-reports by the mother and/or father at child age 5 years. Maternal education was coded into low (no/primary education), intermediate , and high (Bachelor’s degree/University). Household net monthly income was classified as low (<2000 euros), middle (2000–3200 euros), and high (>3200 euros). Maternal postnatal psychopathology, measured at child age 6 months, was prospectively reported by the mother based on the Brief Symptom Inventory questionnaire global severity index. Mothers prospectively reported smoking  and cannabis use during pregnancy (no use vs. use during pregnancy). Non-verbal child IQ was measured at child age 5 years, based on the Snijders-Oomen Niet-Verbale Intelligentie Test , a validSimilar covariates were grouped into confounding sets to minimize the number of tested models while including relevant confounders. Factors included in model 1 related to demographic and study characteristics . Age and sex were selected as these have been previously adjusted for in previous neuroimaging studies of ADHD . EthniciResults. In model 2, variables indicating socioeconomic factors were included . Household income and parental education are generally considered to measure childhood SES in health research  was used for all analyses. Missing data on covariates were imputed with chained equations using the  package . Linear  package , with su package . A Bonfe2.Our analyses for aim 1 involved three vertex-wise linear regression models, which progressively expanded to adjust for additional confounding factors. The first model focused on demographic covariates, the second on socioeconomic ones, and the third on maternal behavioral variables related to psychopathology and pregnancy exposures. These models were run for ADHD symptoms (in both the ABCD and Generation R Studies) and ADHD diagnosis . For the results, we primarily reported cluster sizes, that is, the area of the cerebral cortex that was statistically significantly associated with ADHD symptoms. The sizes of the clusters were reported in cmrABCD = −0.11, rGENR = −0.14), multicollinearity was not expected.Two additional models, building upon model 3, were run to address aim 2, to illustrate the consequences of adjusting for factors with a complex and unclear relation with brain structure and ADHD symptoms: IQ and head motion . Of note, given that IQ and ADHD were weakly correlated (https://github.com/LorenzaDA/ADHD_brainmorphology_confounding (Copy archived at swh:1:rev:95c01381fc7fad9bedb3b5918fb80b02b1dcbdfa) (Code used to conduct this project is publicly available at 1dcbdfa)  under CC This study provides important and useful information to researchers in brain morphology and ADHD. The strength of the evidence presented is convincing and solid. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Jonathan Roiser as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Francisco Xavier Catellanos (Reviewer #1).Thank you for submitting your article "Attention Deficit Hyperactivity Disorder Symptoms and Brain Morphology: Examining Confounding Bias" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:In this study, the authors examined the associations between brain morphology and ADHD symptoms and how the adjustments for confounders change these associations. The reviewers were generally supportive, but raised several points that should be addressed:1) The discrepancy between the two datasets needs to be validated/discussed further.2) Given the nature of ADHD, it is important to evaluate the influence of preprocessing pipeline , motion parameters, and even time of day. The authors should perform the analyses with comparable preprocessing and motion quality between the two datasets to validate the findings.3) The authors should include cortical thickness on top of the other two measures, and clarify the unit of outcome measures.Reviewer #1 (Recommendations for the authors):One major concern relates to the choice of outcome measures. The authors focus on vertex-wise surface area and volume and do not include cortical thickness. They note the latter was uninformative in a prior analysis. However, cortical volume is the product of area and thickness, so it's unclear what is gained by including it as if it were an independent measure. In fact, all the maps of differences in volume appear to be reduced versions of the surface area difference maps.It's also completely unclear why both surface area and volume measures are given in units of cm2. That unit is appropriate for area, but volume is a 3-dimensional measure, so it should be reported in cm3 (or mm3).These two issues made it difficult to truly address the value of the manuscript as currently submitted. The surface area measures appear to be informative – and they support the authors' contention that potentially confounding variables must be carefully attended to. I am confused and unconvinced regarding the value of the volume.Reviewer #2 (Recommendations for the authors):Two large datasets were used for replication to strengthen the confidence in the results. However, the significant clusters were different . Is this due to different preprocessing pipelines, motion parameters, or scanner acquisition parameters? Please discuss the possible reasons.Although the authors have discussed why this study did not consider head motion, it is interesting to see how the scanner acquisition protocol processing parameters and head motion confounds affect the brain morphology-ADHD associations for these two datasets. In addition, from prior literature, time of day also modulates tissue volume metrics ; therefore it would be interesting to consider time-related confounds as well. Essential revisions:In this study, the authors examined the associations between brain morphology and ADHD symptoms and how the adjustments for confounders change these associations. The reviewers were generally supportive, but raised several points that should be addressed:1) The discrepancy between the two datasets needs to be validated/discussed further.To address this comment, we have now expanded on the discrepancies across the datasets in the Discussion:Discussion (pages 11-12):Of note, some inconsistencies between the results from the ABCD and the Generation R studies, both in size of the clusters as well as the exact location, were observed. While we used the same processing pipelines and similar quality control procedures and measures across cohorts, potential reasons for discrepancies of results must be discussed. First, the larger sample size of the ABCD Study allows for greater power to detect smaller effects, which led to larger associated areas. Second, the multi-site structure of the ABCD Study may have introduced noise in the results , and determined the identification of associations which are not replicable in the Generation R Study. Third, the two studies include children from different populations. While both are very diverse samples, the ABCD Study sample is comprised of a more heterogeneous sample from the U.S. population, which, for instance, is characterized by a wider variety of ethnicities and cultures, potentially permitting the discovery of more associations. Nevertheless, there was considerable overlap in the findings from the ABCD and Generation R Studies, with consistencies across cohorts indicating the most robust and generalizable associations.2) Given the nature of ADHD, it is important to evaluate the influence of preprocessing pipeline , motion parameters, and even time of day. The authors should perform the analyses with comparable preprocessing and motion quality between the two datasets to validate the findings.Thank you for this comment. To address it, we further specified the similarities and differences in the pipelines across the two cohorts. First, we clarified the text to better emphasize that many of the processing steps, including the manual quality control, were performed similarly between the two cohorts, with the processing in FreeSurfer being identical. Second, we further aligned the steps in the procedures that we were able to, i.e., applying the inclusion/exclusion criteria in the same order and adding an additional step to exclude incidental findings consistently across the two studies. Third, we mentioned the potential influence of neuroimaging related information, such as head motion and time of the day, in the Discussion section .Changes are shown below:Methods (page 19):“FreeSurfer (version 6.0.0) was used for both cohorts for image processing, which was run in-house to maximize comparability across cohorts.”Methods (page 19):“Within the ABCD Study, quality assessment was based on manual and automated quality control procedures and recommended inclusion criteria for structural data from the ABCD team (1). Within the Generation R Study, quality assurance was manually performed by visually inspecting all images by trained raters, as previously described in the literature (2).”3) The authors should include cortical thickness on top of the other two measures, and clarify the unit of outcome measures.2 . Manuscript changes are shown below, in-text and in the Supplement. Overall, few clusters for cortical thickness were related to ADHD symptoms but had similar patterns of reductions with covariate adjustments to clusters for surface area and volume.We have now included analyses of cortical thickness. We have also clarified why all findings are reported as cmMethods (page 21):“Linear vertex-wise analyses were performed with the QDECR R package (3), with surface area/volume/thickness and ADHD symptoms as variables of interest.”Methods (page 21):2.”“For the results we primarily reported cluster sizes, i.e., the area of the cerebral cortex that was statistically significantly associated with ADHD symptoms. The sizes of the clusters were reported in cmResults (page 5):“We ran vertex-wise linear regression models for ADHD with cortical surface area, volume, and thickness. Results for surface area, which constituted the main findings here, are presented in-text, while findings for volume and thickness in the Supplementary Figures.”Results (page 5):2) and no clusters in the Generation R Study, which suggests that cortical thickness does not relate or does not relate strongly to ADHD, in line with prior literature (4) .”“For cortical thickness, we found two small frontal clusters in the ABCD Study (16.1 cmResults (page 6):“Similar reductions were observed for volume and thickness .”Results (page 7):Compared to clusters for ADHD symptoms, those associated with ADHD diagnosis were smaller, but overlapping . We observed similar patterns of reduction in the spatial extent of the clusters after adjusting for each set of confounders .”Results (page 7):“At a vertex-wise level, adjusting for socioeconomic and maternal factors (model 3) led to reductions in the β coefficients, across the brain, for both cohorts . Of note, some β coefficients also showed increases. As confounding bias may lead to under- or over-estimation, it is not surprising to observe both decreases and increases in the average β coefficients after adjustments.”Results (page 9):“Findings for volume and thickness are shown in Figure 6 – Supplement 1.”Results (page 10):“Results for cortical volume and thickness are shown in Figure 8 – Supplement 1.”Reviewer #1 (Recommendations for the authors):One major concern relates to the choice of outcome measures. The authors focus on vertex-wise surface area and volume and do not include cortical thickness. They note the latter was uninformative in a prior analysis. However, cortical volume is the product of area and thickness, so it's unclear what is gained by including it as if it were an independent measure. In fact, all the maps of differences in volume appear to be reduced versions of the surface area difference maps.Essential revisions, comment 3) are also described, partly in-text and in the Supplementary Figures.We thank the Reviewer for this comment. To address it, we have focused on surface area, which, as the Reviewer rightly pointed out, provides the main finding of this project. More specifically, the main text is now built around the results of the analyses of surface area. At the same time, results for volume and thickness .2. At the level of the vertex we indeed consider different characteristics, such as thickness (mm/cm), area (mm2/cm2), and volume (mm3/cm3). The vertices that reach statistical significance cover a certain area, and it is this area that we report in the results. We have clarified the text of the Methods section and the Results section (to explicitly state it at first occurrence):We agree with the Reviewer that this comes across as incorrect. For completeness: The clusters relate to the surface of the cerebral cortex, and the clusters reflect the surface that associates with ADHD symptoms. That surface is expressed in cmMethods (page 21):2.”“For the results we primarily reported cluster sizes, i.e., the area of the cerebral cortex that was statistically significantly associated with ADHD symptoms. The sizes of the clusters were reported in cmResults (page 5):2 of the cerebral cortex in the ABCD Study, and 446.1 cm2 in the Generation R Study.”“As shown in Figure 1, associations were widespread, as the clusters of association covered 1,165.7 cmThese two issues made it difficult to truly address the value of the manuscript as currently submitted. The surface area measures appear to be informative – and they support the authors' contention that potentially confounding variables must be carefully attended to. I am confused and unconvinced regarding the value of the volume.We hope that we have now sufficiently addressed the Reviewer’s comments and rendered the manuscript more informative.Reviewer #2 (Recommendations for the authors):Two large datasets were used for replication to strengthen the confidence in the results. However, the significant clusters were different . Is this due to different preprocessing pipelines, motion parameters, or scanner acquisition parameters? Please discuss the possible reasons.Discussion section highlighting potential reasons for inconsistencies across cohorts. Such changes have been aforementioned in Essential Revisions, comment 1. Furthermore, the differences in widespread versus localized findings could reflect greater power in the ABCD Study (due to large sample size) to detect smaller effects. On top of the aforementioned changes , we added a sentence about power as a possible explanation for the differences in the Discussion section, as shown below.We addressed this important comment by specifying the use of the same processing pipelines and similar motion quality control as well as including a paragraph in the Discussion (pages 21-22):“First, the larger sample size of the ABCD Study allows for greater power to detect smaller effects, which led to larger associated areas.”Although the authors have discussed why this study did not consider head motion, it is interesting to see how the scanner acquisition protocol processing parameters and head motion confounds affect the brain morphology-ADHD associations for these two datasets. In addition, from prior literature, time of day also modulates tissue volume metrics ; therefore it would be interesting to consider time-related confounds as well.We agree with the Reviewer that neuroimaging confounds needed further discussion in our manuscript. We have therefore examined the potential role of head motion in the relations between brain structure and ADHD by using causal diagrams and have additionally adjusted for it in another model. Briefly, head motion, rather than inducing confounding bias, induces measurement error (information bias) in the brain morphology values. Head motion during scanning often results from ADHD symptoms, determining a non-causal path from ADHD to brain structure through motion. In this case, adjustments for head motion can cause both increases or decreases in bias. Increases in bias can occur because head motion is a consequence of ADHD, and consequences of outcomes should not be adjusted for. Decreases in bias can occur because the non-causal path through head motion would be closed with adjustments. Further information is shown in-text and reported below:Introduction (page 4):“Of the 19 studies included in a systematic review of neuroimaging studies on ADHD (5), 17 adjusted or matched for age in their analyses, 14 for sex, 9 for neuroimaging-related variables like head motion during scanning, and 8 for the intelligence quotient (IQ)(Supplementary File 1a).[…]Conversely, previous studies have adjusted for IQ and head motion, which may not be confounders in the association between ADHD symptoms and the brain, and may thus have led to further bias in the results (6).In this study, we examined the association between brain structure and ADHD symptoms and how the selection and control for potential confounders may affect results (aim 1). Moreover, we discussed the complex role of IQ and head motion in brain structure – ADHD associations and the potential consequences of adjusting for them (aim 2).”Results (pages 9-10):Head motion does not induce confounding bias, but information biasA final scenario was also included, to reflect the commonly used adjustments for head motion during scanning (Supplementary File 1a). Motion can be a large source of bias in neuroimaging studies which is important to address. While it does not meet the criteria for confounding as it is not a common cause of ADHD problems and brain morphology (7), head motion can induce measurement error of brain morphology (8) . This is also referred to as information bias and can distort estimates from their true value.The amount of measurement error in brain morphology may differ across children with vs without ADHD. In fact, children with impulsivity and inattention have been shown to move more during MRI scanning , determining different levels of error in the brain morphology assessments . In this scenario, adjusting for motion might lead to two situations. On one hand, since motion is a consequence of the outcome (ADHD), adjustments would lead to bias (11). On the other hand, not adjusting for motion would also lead to bias because part of the observed relation between ADHD symptoms and brain structure would be due to the higher head motion  of children with ADHD. In this study, we explored the effect of adjusting for motion during scanning in the relation between brain morphology and ADHD (model 5).Adjustments for motion led to increases in clusters2 to 936.4 cm2 ( = +23.2%) for the ABCD Study and from 208.6 cm2 to 239.7 cm2 ( = +14.9%) for the Generation R Study . Clusters of associations consistently found across cohorts were highly similar to the ones identified in model 3. Results for cortical volume and thickness are shown in Figure 8 – Supplement 1.After additional adjustments for head motion, the spatial extent of the clusters generally increased. For surface area, compared to model 3, clusters increased from 760.2 cmDiscussion (pages 13-14):There is no easy fix for dealing with head motion in brain morphology – ADHD associationsAdjustments for neuroimaging covariates, such as head motion, are often run to reduce confounding bias. However, head motion, rather than inducing confounding bias, creates measurement error (information bias). When adjusting for head motion during scanning, we observed increases in the spatial extent of the clusters. This might indicate a reduction or an increase in bias. First, bias might have been reduced by adjusting for the fact that children with ADHD will have more error in their cortical measures. Second, bias might have also been increased because we conditioned for head motion, which is a consequence of ADHD.Overall, the role of head motion in the relation between brain structure and ADHD is complex and warrants the utmost care. Adjusting or not could both lead to bias, meaning that considering which bias might be strongest is necessary. Moreover, methods aiming to reduce information bias should be leveraged (12); however, further developments are needed for their application to the neuroimaging field.Similar considerations should be applied to other neuroimaging parameters which often are indicators of information bias rather than confounders in brain structure – ADHD relations . For instance, time of the day has been shown to influence morphometric values , leading to information bias. If we expect for ADHD symptoms to influence the time of the day in which children with ADHD vs. without come to the scanner , the same considerations as for head motion would apply. If instead, children with ADHD and without will come to the MRI at similar times of the day , then adjustments for time of the day would be appropriate and reduce the measurement error.”Discussion (page 14):“Our considerations on confounding likely generalize to the psychiatric neuroimaging field, as several confounders considered here  also relate to brain function and other psychiatric disorders (15–17). Similarly, other psychiatric disorders are also characterized by complex relations with IQ (18). Moreover, mental health problems featuring state anxiety, like internalizing and externalizing symptoms, have also been related to increased head motion during scanning (19).”Methods (pages 19-20):For The ABCD Study:“We used the Euler number obtained from FreeSurfer as a proxy for head motion during the acquisition. 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Methylphenidate is frequently used to treat attention-deficit/hyperactivity disorder (ADHD). It has been reported to correct (aberrant) connectivity between brain regions. However, previous neuroimaging research has not provided a coherent picture. One possible reason is that methylphenidate may not necessarily result in a satisfactory clinical treatment response of every patient. This suggests that neuroimaging research about methylphenidate’s mode of action could benefit from subdividing patients into groups of treatment “Responders” and “Non-Responders”. However, previous research has not applied this idea so far. Here, we used machine learning techniques to discern Responders from Non-Responders based on clinical symptoms. Fifty-three stimulant-naïve adult ADHD patients underwent functional magnetic resonance imaging to determine functional connectivity at rest. Measurements were repeated after 6 weeks’ treatment with methylphenidate. We found that, prior to treatment, a specific part of the so-called salience network was hypo-connected with the bilateral putamen in the patient sample as a whole, compared with 50 healthy controls. After methylphenidate treatment, this aberrant connectivity was restored to almost “normal” levels in Responders, but not in Non-Responders. Involvement of the putamen is reminiscent of early positron emission tomography findings, already suggesting modulation of dopaminergic brain regions for the successful treatment of ADHD with methylphenidate.Positron emission tomography (PET) studies have shown involvement of the striatum when treating adult attention-deficit/hyperactivity disorder (ADHD) with methylphenidate (MPH). Results from resting-state functional magnetic resonance imaging (rs-fMRI) for the same issue were less unequivocal. Here, a new analytical framework was set up to investigate medication effects using seed-based rs-fMRI analysis to infer brain regions with alterations in intrinsic functional connectivity (IFC) corresponding with ADHD symptom reduction. In a within-subjects study design, 53 stimulant-naïve adult ADHD patients were investigated before and after 6 weeks of MPH treatment, using two major clinical symptom scales and rs-fMRI. The same data were acquired in a sample of 50 age- and sex-matched healthy controls at baseline. A consensual atlas provided seeds for five predefined major resting-state networks. In order to avoid biasing of medication effects due to putative treatment failure, the entire ADHD sample was first categorized into treatment Responders (N = 36) and Non-Responders (N = 17) using machine learning-based classification with the clinical scales as primary data. Imaging data revealed medication effects only in Responders. In that group, IFC of bilateral putamen changed significantly with medication and approached almost normal levels of IFC. Present results align well with results from previous PET studies, with seed-based rs-fMRI as an entirely different neuroimaging method. Attention-deficit/hyperactivity disorder (ADHD) is the most frequently diagnosed neurodevelopmental disorder in childhood, which can persist into adulthood , with a Among the various medications, stimulants (amphetamines and methylphenidate (MPH)) have generally been recommended as first-line pharmacologic treatment . In 195511C]raclopride; for review, see )The trained classifiers were then fed with the patients’ M2 data after adjusting them with the settings acquired during normalizing the patients’/controls’ M1 data. If, for a given patient, the majority of classifiers predicted the label “healthy control”, the patient was classified as “Responder”, otherwise as “Non-Responder”.www.nitrc.org/projects/conn, last accessed: 25 March 2020) in combination with Statistical Parametric Mapping (SPM) 12 , operating under MATLAB 2019b. As the data sets from ADHD patients and control subjects differed in the number of functional runs , both groups were analyzed separately using two CONN projects with otherwise identical parameters. For more details on the following processing steps, please refer to Nieto-Castanon raclopride PET imaging as a different imaging modality. Quantified changes in [11C]raclopride binding were interpreted that methylphenidate binds to the dopamine transporters, thereby enhancing extracellular dopamine in the basal ganglia, that is, caudate nucleus and particularly adjacent putamen, very similar to the results reported here  would have been part of preregistering the study protocol including imaging and group analysis, it must be noted that this study was not preregistered. Present results are therefore exploratory and await empirical substantiation to support clinical relevance. This limitation relates not only to the inference of treatment effects but also to the inference of initial group differences between healthy controls and the entire ADHD group.The prerequisite for inference of medication effects for only those brain regions where IFC was initially different between patients and controls can obscure observation of other brain regions bearing medication effects that may also mediate ADHD symptom reductions. Insofar, the strictness applied here can be acknowledged as a limitation. The inverted analytical sequence, with a focus first on medication effects in ADHD patients and then followed by post-hoc comparisons to infer how these effects relate to normal data, can be informative . This, hDespite these limitations, we conclude that differentiation between Responders and Non-Responders to MPH treatment appears as a helpful and fruitful approach to further investigate neuroimaging markers of medication effects in (adult) ADHD.