Neuropsychiatric imaging remains a pioneering frontier in modern medicine. Recent decades have witnessed marked advances in identifying biological correlates for a broad array of illnesses  provided a model through which large-scale datasets can be obtained , which makes use of the web infrastructure provided by Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) NITRC.org. Accompanying phenotypic information includes: diagnostic status, dimensional ADHD symptom measures, age, sex, intelligence quotient (IQ), and lifetime medication status. Additionally, preliminary quality control assessments (usable vs. questionable) based upon visual time-series inspection were included for all resting state fMRI scans. The ADHD-200 release data are stored and distributed in two ways: via NITRC Resources (NITRC-R) as tarballs, and via NITRC Image Repository (NITRC-IR3) which supports searches by phenotypic information powered by XNAT  in three continents came together to form the ADHD-200 Consortium  novel strategies for predicting diagnostic status based on an individual's intrinsic functional architecture and brain structure, and (2) novel techniques for identifying brain features that may yield ADHD biomarkers.4, which provided pre-processed data to the broader community so as to bypass technical obstacles to wider participation.For the purposes of the competition, an additional 197 datasets from six imaging sites were released on July 1, 2011 without diagnostic labels . Fifty teams from around the world, representing a diverse array of backgrounds  communicated their intent to compete, eventually yielding 21 submissions. This effort demonstrated the latent interest of the larger scientific community to develop effective prediction methodologies for psychiatric neuroimaging. Additionally, it encouraged additional open neuroscience efforts, such as the ADHD-200 Pre-Processed Initiative by the Neuro BureauIn the current issue, several of the teams that participated in the ADHD-200 competition describe their techniques and results. These descriptions will provide the reader with insight into each team's decision-making process as they developed optimal diagnosis predictions in novel datasets. It is our hope that access to each team's methodology will spark new ideas and collaborations.The winning team for predicting diagnosis was from Johns Hopkins University, and included Brian Caffo, Ciprian Crainiceanu, AniEloyan, Fang Han, Han Liu, John Muschelli, Mary Beth Nebel, and Tuo Zhao. The Hopkins team scored 119 out of 195 points, with one point awarded per correct diagnosis . A half point was awarded for a correct diagnosis of ADHD if the subtype was incorrect.The method developed by the Hopkins team excelled in specificity, i.e., the ability to identify typically developing children (TDC) without falsely classifying them as having ADHD .Participants developed predictive methods that performed significantly above chance for analyzing datasets that were aggregated from multiple centers without prior coordination. These results suggest that progress toward developing effective predictive methods is possible even in less-than-ideal poorly controlled environments. We expect that these results will guide the psychiatric neuroimaging field as it grows. Despite the success of the methods developed in this competition, further development is necessary before the methods can be used in a clinical setting.The winner in the biomarker contest was Che-Wei Chang from National Taiwan University, who brought emerging analytic approaches in computer vision to the study of ADHD-related differences in brain morphometry. By capturing novel aspects of brain anatomy, this effort defined a new feature upon which brain differences can be characterized and classified.Intriguingly, the team from the University of Alberta consisting of Gagan Sidhu, Matthew Brown, Russell Greiner, Nasimeh Asgarian, and Meysam Bastani, did not use imaging data for their prediction model, but rather only phenotypic data of age, sex, handedness, and IQ. While this strategy was not consistent with the intended competition rules, the effort did garner the highest score, 124, and the highest prediction accuracy (62.5%).5.The question regarding whether demographic features are better predictors of ADHD than imaging-based features naturally became a point of discussion in the imaging communityp < 0.001; Test Set: 113 for TDC vs. 103 for ADHD, p < 0.001). These baseline demographic/clinical differences clearly provided sufficient statistical power in the naturalistic/artificial context of a contest to yield substantial predictive power. In the real world, there are more than two options  so that factors such as sex and IQ are inadequate for predicting diagnosis. In situations with limited options, such clues can be usefully exploited – as the Alberta group clearly demonstrated. Russ Poldrack has highlighted an even more significant concern in Tal Yarkoni's blog – what if imaging-based approaches are detecting the neural correlates of these phenotypic variables rather than neural correlates of the disorder itself? When we characterize differences in these populations, demographic factors should be considered, as they are for other diagnostic tests. Additionally, we recommend more careful consideration of such variables in the design of future training and test datasets.In short, the Alberta team's results drew attention to a major challenge faced by the field in the effort to generate predictive tools using existing data – group differences in base phenotypic variables that reflect population characteristics. ADHD in clinically referred samples is much more frequently recognized in boys than girls. As such, all studies have discrepant M:F ratios for ADHD and TDC groups. In the ADHD-200 sample, this discrepancy was the case . Similarly, performance IQ in ADHD is lower on average by 7–10 points than that of comparisons. In the ADHD-200 sample, IQ estimates differed between the TDC and ADHD groups  multimodal imaging dataset  would need to be created and distributed to the scientific community. Unlike the ADHD-200 dataset that incorporated previously collected data, future reference datasets will require coordination of recruitment and phenotyping strategies. While we recognize the costs, large-scale, coordinated multicenter designs, such as the Alzheimer's Disease Neuroimaging Initiative and the Human Connectome Project, are ideal for such endeavors.We note that the primary goal of the ADHD-200 competition was to promote an open science model to foster competitive collaboration among members of the imaging community. The effort also aimed at encouraging the broader scientific community to join us in confronting the challenges of translational psychiatric imaging research. In this regard, this initial effort can already be characterized as a success and the efforts have engendered some momentum for the field. However, the members of the consortium acknowledge that this At a smaller, more feasible scale , would be efforts to harmonize data collection across independent imaging sites through the use of a core phenotypic protocol and open sharing of ADHD datasets along with core protocol-driven measures. Such a core phenotypic protocol could be as limited as that used by the ADHD-200, or more comprehensive with a brief cognitive battery (which would be preferable). Researchers would have the option not to disclose data beyond the core protocol. To accomplish this harmonized approach, community support for the establishment and coordination of a common phenotypic protocol for ADHD would be required, although even this approach would incur some costs.While it is still unknown where open data sharing in psychiatric imaging will lead, it is clear that the field must transition from the current model into one that promotes coordinated, transparent, and open data sharing across laboratories and institutions. The costs of such an effort will not be small, but are dwarfed by the marked impairment and suffering in the lives of millions associated with disorders such as ADHD; failure to invest in such efforts will continue to cost much more in the long run and limit our ability to improve outcomes for those afflicted with neuropsychiatric disorders.


Recent neurobiological models of ADHD suggest that deficits in different neurobiological pathways may independently lead to symptoms of this disorder. At least three independent pathways may be involved: a dorsal frontostriatal pathway involved in cognitive control, a ventral frontostriatal pathway involved in reward processing and a frontocerebellar pathway related to temporal processing. Importantly, we and others have suggested that disruptions in these three pathways should lead to separable deficits at the cognitive level. Furthermore, if these truly represent separate biological pathways to ADHD, these cognitive deficits should segregate between individuals with ADHD. The present study tests these hypotheses in a sample of children, adolescents and young adults with ADHD and controls. 149 Subjects participated in a short computerized battery assessing cognitive control, timing and reward sensitivity. We used Principal Component Analysis to find independent components underlying the variance in the data. The segregation of deficits between individuals was tested using Loglinear Analysis. We found four components, three of which were predicted by the model: Cognitive control, reward sensitivity and timing. Furthermore, 80% of subjects with ADHD that had a deficit were deficient on only one component. Loglinear Analysis statistically confirmed the independent segregation of deficits between individuals. We therefore conclude that cognitive control, timing and reward sensitivity were separable at a cognitive level and that deficits on these components segregated between individuals with ADHD. These results support a neurobiological framework of separate biological pathways to ADHD with separable cognitive deficits. Heterogeneity in the clinical presentation is a key characteristic of Attention-Deficit/Hyperactivity Disorder: symptoms take many forms, ranging from subtle but pervasive attention deficits or dreaminess to impairing hyperactive, impulsive and unpredictable behavior To date, neuropsychological models of ADHD have often focused on cognitive areas with established deficits. As such, deficits in cognitive control or related concepts have been incorporated in most causal models in some form Indeed, theoretical models are now emphasizing evidence from basic human and translational neuroscience supporting a neurobiological separability of brain systems affected in ADHD Researchers have begun to propose such neurobiological models of ADHD, suggesting that deficits in different neurobiological systems may lead to symptoms In this study, we tested some of the predictions of our model by investigating whether separate components of cognitive functions implicated in the model underlie performance on a task battery designed to test them Furthermore, we tested whether individuals with ADHD segregated into the subtypes predicted by the model. As our sample included subjects in a broad age range (6–27 years), we conducted exploratory analyses in a younger and an older group of subjects to investigate whether such subtyping is possible across development. One important difference from the approach by Sonuga-Barke and colleagues The study was approved by the institutional review board at the UMC Utrecht and included 63 participants with ADHD and 86 typically developing controls. After an initial screening of data integrity (see below), valid, high quality data sets were available for 57 subjects with ADHD and 83 controls. Demographic characteristics are listed in Control = 73, nADHD = 52), written informed consent was obtained from both parents after full disclosure of the study purpose and procedure. Children provided written and/or verbal assent. Parents participated in the DISC-IV, parent version For participants below 18 years of age  gave written informed consent and participated in the MINI-Plus abbreviated psychiatric interview in order to confirm ADHD diagnosis or exclude psychiatric (co)morbidity Participants aged 18 years or older  Benefit, which is defined as RTUnexpected GO – RTExpected GO, and represents the response speed benefit when a trial is presented at the expected time Expected-GO, AccuracyUnexpectedGO, AccuracyExpectedNOGO and AccuracyUnexpectedNOGO) and Mean RT for both expected and unexpected go trials . Variability in reaction times was calculated for both expected and unexpected go trials, using the Intraindividual Coefficient of Variation  The first task assessed cognitive control and timing. It was a variation on a go/no-go paradigm, where go (majority) and no-go (minority) events occurred at expected regular 4 sec intervals (majority) and unexpected 2 sec intervals (minority). The paradigm has been described in detail elsewhere The second task was adapted from the MID task to assess sensitivity to reward. As this is the first report of this task, we have included Supporting Information (0 ct and MRT0 ct respectively).The measure of interest from the reward task was the shift in the RT distribution from the no reward condition to the 5 ct and 15 ct conditions, calculated using linear regression of rank ordered reaction times of the reward  on the no reward (0 ct) condition. This measure was chosen as it is minimally affected by differences in intraindividual variability in RTs Expected-GO, AccuracyUnexpectedGO, AccuracyExpectedNOGO and AccuracyUnexpectedNOGO). For the reward task, outliers were mainly defined by omission trialss (i.e. no response whatsoever). Since the calculation of the RT distribution shift metrics on the reward sensitivity task required as complete as possible datasets, this resulted in an exclusion criterion where cases with an omission rate of 20% or more for any of the reward magnitude conditions  were excluded. Using these rules, three controls and six subjects with ADHD were excluded. The exclusion rate does not differ between subjects with ADHD and controls (Fisher’s exact p = .169). Data from the resulting sample were manually inspected and cases that were at the extreme ends of the distributions of any measure were evaluated across all other measures as a final check to identify possible multivariate outliers that had been missed., No additional outliers were found in this way.All statistical analyses were conducted in PASW Statistics 18. In an initial screening step, the 1.5IQR criterion was used to identify subjects that were moderate outliers on one or more measure across both tasks. Their data was used to formulate decision rules to exclude subjects who had not performed the task according to instructions. For the modified go/no-go task, this resulted in a conservative decision excluding only cases with severe disengagement from the task. Cases were excluded when response accuracy was lower than 60% on go-trials (indicating extremely low target detection) or no-go trials (indicating performance at chance level) for more than two of the four accuracy measures . This was based on preliminary analyses showing that age fits were not comparable across task variables and were suggestive of differential age effects between controls and subjects with ADHD .ExpectedGO and AccuracyExpectedNOGO was nominally different between ADHD and control subjects (p = .033). Since this was only 1 of 78 correlations computed, this did not preclude the use of PCA. There were no between-group differences in relationships between any of the task measures and age or IQ. None of the task measures correlated with IQ in either group.As PCA assumes homogeneity of regression slopes between groups, we investigated this by computing correlations between all measures for both groups separately. Only the correlation between MRTth percentile of the distribution for controls. Subjects with ADHD scoring below this cut-off were defined as having a deficit on that component. Since we intended to use different norms for younger and older participants, we conducted PCAs separately for the younger and older age group, with the whole-group median age of 12 years as the split point. There were no demographic differences between the younger and older control groups, although the older ADHD group did show a trend towards lower IQ (p = .081) and had slightly fewer females than the younger ADHD group .All measures were entered in a PCA on the correlation matrix with the extraction criterion set at all components with an Eigenvalue >1 (a default for first-pass runs of PCA). Data from both groups were included. Initial component loadings were rotated using a Varimax rotation with Kaiser normalization in order to enhance interpretability of the component loadings 2 tests can only handle sets of two categories at a time and thus cannot detect interactions  between multiple categories. This analysis aims to reproduce the original data with the most parsimonious model. It begins by assuming all possible interactions between the categorical variables (the saturated model). If this is the model best to fit the data, all deficits would overlap, and all cells would be equally filled. The software then removes all interaction terms not necessary to describe the data through an iterative model building approach. At each step, a statistic (log likelihood) is calculated that indicates how well the current model fits the data. Next, the amount of log likelihood change is computed for removal of each lower order term included in the current model. The term is then removed of which the removal results in the most significant increase in log likelihood of the model (e.g. better model fit). This process continues until no more terms can be deleted  and as such, the most parsimonious model is found. For example, if deficits in timing and reward sensitivity were to co-occur often, a timing by reward sensitivity interaction would remain in the final model in order to describe this effect. In the case of statistical segregation of all deficits , this analysis will yield a model with only the four main effects remaining.We used hierarchical Loglinear Analysis to assess the statistical independence of segregation of deficits within individuals in the ADHD group Finally, we conducted post-hoc analyses comparing clinical measures between ADHD subjects with and without deficits our using Chi-square and independent sample t-tests as appropriate.The results of the PCA are shown in As None of the component scores correlated with IQ in any of the groups .th percentile worst score of the controls) on one or more component. 30 Subjects (52.6%) had deficits on at least one of the three predicted components . Of those, 24 (80%) had a deficit on only one component. There were no individuals with deficits on more than two components , and more overlap between deficits was also observed. However, half of the subjects with any deficit still showed a deficit for only one component  had a deficit  = 11.45, p<.001). Of those, 82.4% has a low score on one of the predicted components only. Across a 10th, 20th, and 30th percentile cutoffs, >50% of controls with any low score across all four components had such a score only for one of these components (The use of the 10mponents .th percentile cutoff). It terminated after 11 iterations with the final model including only the main effects of cognitive control, timing, reward sensitivity and vigilance. This confirms that any overlap of these deficits within subjects can be attributed to chance.Loglinear analysis was used to test the statistical independence of the classification categories in the ADHD group (based on the 102(1) = 4.18, p<.05). There were no differences between these groups on any of the CBCL scales . There was no difference between clinical ADHD subtypes  in mean component score on any of the four components , or in the proportion of subjects that showed a deficit, (χ2(1) = 2.49, p = .289). There was a trend towards more subjects with the inattentive subtype having a deficit on the vigilance component  = 3.72, p = .054). However, we do not want to over-interpret this result given the small numbers.A comparison of clinical characteristics between those subjects with ADHD who did and those who did not reach criterion for a deficit showed that subjects with a deficit had a lower total IQ (t(55) = 2.756, p<.05), and were more likely to be co-morbid for ODD . Earlier studies have suggested that high reward frequency may reduce reward sensitivity problems in ADHD, whereas these are more obvious in designs using lower reward frequencies Interestingly, we found a fourth independent component in our data that was not predicted by the model and that we called vigilance. This had some face validity, as it related to both variability in RT and omission errors during go-trails. In addition, subjects with the innattentive subtype of ADHD were slightly more likely to have a deficit on this component. Subjects with a deficit on this component had a pattern of slow responding and low target detection. This is a pattern of impairments that has received relatively little attention in ADHD theory, although one model, the Cognitive Energetic Model (CEM) did underscore its relevance Although there were no statistical interactions with the other three components , impairment on the vigilance component frequently coincided with other impairments . It is iIt is important to assess how these results map onto clinical heterogeneity in ADHD. First, in line with previous work th percentile also had a deficit on one rather than multiple components Click here for additional data file.Figure S2Task design of the reward task. Please see (TIF)Click here for additional data file.Figure S3Between group analyses of performance on the Reward Sensitivity Task. Please see (TIF)Click here for additional data file.Figure S4th, 20th and 30th percentile cutoffs.Deficit-level scoring in the ADHD and control groups across 10 A deficit has been defined as a score below the 10th percentile worst score of controls, which is essentially an arbitrary cutoff. These figures show the results across 10th, 20th, and 30th percentile cutoffs. The X-axis shows the three cutoffs, the Y-axis represents percentages. The left figure shows data for the ADHD group. The blue line shows the percentage of the total ADHD sample (n = 57) that did not have any component score below the cutoff. The red and green lines refer only to the subgroup that has at least one deficit (e.g. at least one component score below the Xth percentile). The red line shows the percentage of this group that has only one component score below the cutoff (e.g. only one ‘deficit’). The green line shows the percentage that has more than one component score below the cutoff (e.g. only one ‘deficit’). Since the cutoff method by definition categorizes a certain percentage of controls as having a deficit, the right figure shows the same data for the control group. In both groups, across these increasingly lenient cutoffs, a large percentage of the group with any deficit remains to show this for only one of the components.(TIF)Click here for additional data file.Table S1To provide background on our decision not to residualize the task variables for age prior to the PCA, we report the results of linear and quadratic fits on these measures.(DOC)Click here for additional data file.Table S2Demographic characteristics of the sample reported on in (DOC)Click here for additional data file.Table S3th percentile cutoff.Deficit scores in the ADHD group at a 20(DOC)Click here for additional data file.Text S1Please see for further details.(DOC)Click here for additional data file.


The relatively short durations of the initial pivotal randomized placebo-controlled trials involving atomoxetine HCl for the treatment of attention-deficit/hyperactivity disorder (ADHD) provided limited insight into the time courses of ADHD core symptom responses to this nonstimulant, selective norepinephrine reuptake inhibitor. The aim of this analysis was to evaluate time courses of treatment responses or remission, as assessed by attainment of prespecified scores on the ADHD Rating Scale-IV-Parent Version: Investigator Administered and Scored (ADHDRS-IV-PI) and the Clinical Global Impressions-ADHD-Severity (CGI-ADHD-S) scales, during up to 1 year of atomoxetine treatment in children with ADHD.Using pooled data from three Canadian open-label studies involving 338 children ages 6-11 years with ADHD who were treated with atomoxetine for 3, 6 and 12 months, and survival analysis methods for interval-censored data, we estimated the time to: 1) improvement and robust improvement defined by ≥25% and ≥40% reductions from baseline ADHDRS-IV-PI total scores, respectively; and 2) remission using two definitions: a final score of ADHDRS-IV-PI ≤18 or a final score of CGI-ADHD-S ≤2.p = .33).The median time to improvement was 3.7 weeks (~1 month), but remission of symptoms did not occur until a median of 14.3 weeks (~3.5 months) using the most stringent CGI-ADHD-S threshold. Probabilities of robust improvement were 47% at or before 4 weeks of treatment; 76% at 12 weeks; 85% at 26 weeks; and 96% at 52 weeks. Probabilities of remission at these corresponding time points were 30%, 59%, 77%, and 85%  and 8%, 47%, 67%, and 75% . The change from atomoxetine treatment month 5 to month 12 of -1.01 (1.03) was not statistically significant (Reductions in core ADHD symptoms during atomoxetine treatment are gradual. Although approximately one-half of study participants showed improvement at 1 month of atomoxetine treatment, remission criteria were not met until about 3 months. Understanding the time course of children's responses to atomoxetine treatment may inform clinical decision making and also influence the durations of trials comparing the effects of this medication with other ADHD treatments.NCT00191633, NCT00216918, NCT00191880.clinicaltrials.gov:  Both psychostimulants and the selective norepinephrine reuptake inhibitor atomoxetine HCl are recommended psychopharmacological treatment options for children diagnosed with attention-deficit/hyperactivity disorder (ADHD), which is considered to be the most common neurobehavioral disorder affecting children -3. In clOur initial understanding of the time courses of atomoxetine responses was based on randomized placebo-controlled clinical trials with relatively short durations  -8. NotabAlthough attainment of predefined thresholds on validated scales as a measure of symptom remission is a useful barometer of improvement, the time courses of responses to various treatments must be considered when this outcome measure is used to compare interventions; this may be of marked importance when comparing treatments for ADHD--both pharmacological and non-pharmacological --that have slower onsets of actions compared with stimulants. Stimulants are notable within the psychopharmacological armamentarium for the relative short time to peak clinical effects.Improvements in ADHD symptoms have been defined as ≥25% reductions (and robust improvement as ≥40% reductions) on the ADHD Rating Scale-IV-Parent Version: Investigator Administered and Scored (ADHDRS-IV-PI) total score . HoweverThe primary objective of this study was to determine times to response and remission according to predefined thresholds on the ADHDRS-IV-PI and CGI-ADHD-S scales in children treated with atomoxetine at usual clinical dosages ,12. To aIn this retrospective efficacy analysis, data were pooled from three Canadian open-label studies -15. ThesDiagnostic and Statistical Manual of Mental Disorders Text Revision (DSM-IV-TR) ) age  was 8.7 (0.08) years. Of study participants in Study S012, 54.7% were stimulant-naïve compared with 38.1% of those in Study S013 and 100% of those in Study LYCS. Mean (SE) baseline scores were 39.1 (0.43) on the ADHDRS-IV-PI and 4.8 (0.05) on the CGI-ADHD-S (Table The pooled study sample comprised 249 (73.7%) boys and 89 (26.3%) girls whose mean , followed by adverse events (6.5%). Five (1.5%) study participants were lost to follow-up.SE) and median (range) duration of follow-up (weeks) by individual study, as well as for the combined studies.The median number of weeks from baseline to the last available ADHDR-IV-PI assessment for the pooled studies was 13 weeks (~3 months). The follow-up time between the baseline and last available assessment varied according to the duration of each study. Table p = .33).Atomoxetine treatment reduced ADHD symptoms sharply during the first month and was associated with further reductions in subsequent months. Mean ADHDRS-IV-PI total scores leveled out at approximately month 5 Table . The chaAs shown in Table Longer treatment intervals were required to meet definitions of symptom remission. The median time to remission, defined as a total ADHDRS-IV-PI of ≤18, was 8.0 weeks, and the probability of remission did not reach 75% until approximately 24 weeks. To meet the most rigorous criterion for remission (achieving a CGI-ADHD-S score ≤2), the median time was 14.3 weeks. For this endpoint, the probability of remission by 26 weeks was approximately 67%  were associated with a shorter time to improvement and robust improvement; whereas lower baseline ADHDRS-IV-PI total scores were associated with a shorter time to attain remission. The baseline value of CGI-ADHD-S was also a significant predictor of time to remission, with lower values (indicative of less severe ADHD symptomatology) being associated with shorter time required to achieve remission defined as a CGI-ADHD-S score of ≤2.In this study, there was a progressive increase in the likelihood that treatment met predefined thresholds for response and remission with increasing time on atomoxetine. Of interest, children's ADHD symptoms continued to improve up to approximately 5 months. Although there was symptomatic improvement as measured by the ADHDRS-IV-PI total score beyond this time, the change was not statistically significant.The present analysis of three Canadian open-label clinical trials of atomoxetine produced clinically meaningful findings that are consistent with data from two recently reported European randomized, double-blind, placebo-controlled studies that reported increased efficacy of atomoxetine over time ,20. FirsThe gradual and progressive efficacy of atomoxetine in treating core symptoms of ADHD, which was observed in the present study, has potential implications for understanding three previously reported studies comparing the effects of atomoxetine to those of long-acting stimulants -23. The Evidence from these comparator studies, the European studies discussed above ,20, and In our study, baseline illness severity substantially influenced the time to meet predefined measures of improvement. Children initiating atomoxetine therapy with higher symptom severity scores met percentage improvement criteria in a shorter time than children starting with lower baseline symptom severity scores. In contrast, children rated as less severely symptomatic on the CGI-ADHD-S and ADHDRS-IV-PI achieved remission thresholds more rapidly. Of clinical interest, the Integrated Data Exploratory Analysis study, a retrospective analysis of randomized controlled trials having 6- to 9-week durations, did not identify potential baseline (moderator) predictors of response but did find that the only predictor (on-treatment mediator) of a much improved response at trial endpoint (≥40% decrease on the ADHDRS-IV-PI) was being at least minimally improved (≥25% but <40% decrease on the ADHDRS-IV-PI) by treatment week 4 . This fiOur findings are most generalizable to Canadian children ages 6 to 11.5 years who: 1) are of at least average intelligence and whose ADHD symptoms are well above age and gender norms for severity; 2) do not have comorbid conditions such as bipolar disorder, psychoses, pervasive developmental disorders, conduct disorder, or serious suicide risk; and/or 3) are treated with atomoxetine on an outpatient basis. Most study participants (>70%) were boys and had the combined subtype of ADHD. In addition, approximately two-thirds of enrolled children  were previously untreated with ADHD medications and therefore may have been more responsive to medication than children who had received, but failed, prior treatments. The trials were all open label, and this study design feature may have encouraged positive clinician bias in estimating improvements; in Canada, it was considered unethical to include a placebo arm in extended-duration studies of ADHD. The results of the three open-label trials included in our study may have been influenced by initial patient selection and rater drift. Given the characteristics of our sample, the designs of the studies, and the potential advantages to children of clinical trial participation, it is possible that the relatively high rates of response we report with atomoxetine treatment will not be replicated in usual clinical practice.The slow upward titration of atomoxetine and the differences in the titration schedules mandated by the study protocols may have had some impact on early response rates, but it is unlikely that the dosing of atomoxetine during the initial part of the study impacted the major finding of the study, which was that there is slow but progressive improvement over time during atomoxetine treatment long after titration is complete.Only one of the primary studies (Study SO13) analyzed herein included teachers' evaluations, precluding inclusion of teachers' important perspectives on time courses to response or remission with atomoxetine. In Study S013, baseline symptom ratings completed by the teachers were indicative of less severe symptoms than ratings completed by parents, but the progressive improvements during this 6-month study showed that symptom decrements noted by parents paralleled those of teachers . In addiNot all subjects had 12 months of follow-up; the three studies varied in length from 3-12 months and in addition approximately 30% of children did not complete their study. Therefore, we chose methods of analysis  that do not require an equal length of follow-up for each subject and that did not require that all subjects complete the study. These methods estimate response probabilities over time by using the data for each subject up to the point that they either responded or withdrew from the study. We don't know the time by which the drop-outs would have responded but each participant contributes information: for example, a child who discontinued at week 6 prior to responding provides information on the probability of achieving (or not achieving) a response by week 6.When interpreting such curves, it is essential to understand both that there are declining numbers of participants over time, and that the cumulative response probability at a given time point is based not only on those subjects still under observation, but also on the pattern of response and withdrawals at all previous time points. Thus the response probability at a certain time depends directly on those still under observation, and indirectly on those who withdrew or responded earlier.Our findings concerning time courses of response and remission with atomoxetine may inform future study design and clinical decision making. From a clinical perspective, appropriate expectations should be set with children and parents at the time of atomoxetine initiation, because reductions in symptoms are gradual but appear to progress over time. Designs of future comparison studies of medications to treat ADHD should consider differences in time of onset of efficacy (and time to peak response) between atomoxetine and stimulants and/or other treatments.ADHD: attention-deficit/hyperactivity disorder; ADHDRS-IV-PI: ADHD Rating Scale Parent Version: Investigator Administered and Scored; CGI-ADHD-S: Clinical Global Impressions-ADHD-Severity; MAS XR: mixed amphetamine salts extended-release; OROS MPH: osmotic-release oral system methylphenidate;RAD: employee of the study sponsor with stock or equity >$10,000. EM: paid consultant to the study sponsor. CG: no competing interests to disclose. SWG: paid consultant to the study sponsor and its affiliates, as well as BioBehavioral Diagnostics. MDW: advisory boards and speakers' bureaus, consultant, grant and/or other research support, study sponsor, Abbott, Janssen, Purdue Pharma, Shire, Takeda; speakers' bureau, Novartis; travel support from study sponsor to present poster at congress.All authors contributed to the conception and design of the study.MDW, AT, and other clinical investigators acquired data. EM conducted the statistical analysis. All authors interpreted the data. RAD, EM and SWG wrote the manuscript, and all authors contributed to the manuscript. All have read and approved the final manuscript except for AT. Dr. Atilla Turgay (deceased) participated in the data analysis and an early draft of the manuscript. RAD had access to all data and takes responsibility for the study and its report (study guarantor).


Attention-Deficit/Hyperactivity Disorder (ADHD) and intelligence (IQ) are both heritable phenotypes. Overlapping genetic effects have been suggested to influence both, with neuroimaging work suggesting similar overlap in terms of morphometric properties of the brain. Together, this evidence suggests that the brain changes characteristic of ADHD may vary as a function of IQ. This study investigated this hypothesis in a sample of 108 children with ADHD and 106 typically developing controls, who participated in a cross-sectional anatomical MRI study. A subgroup of 64 children also participated in a diffusion tensor imaging scan. Brain volumes, local cortical thickness and average cerebral white matter microstructure were analyzed in relation to diagnostic group and IQ. Dimensional analyses investigated possible group differences in the relationship between anatomical measures and IQ. Second, the groups were split into above and below median IQ subgroups to investigate possible differences in the trajectories of cortical development. Dimensionally, cerebral gray matter volume and cerebral white matter microstructure were positively associated with IQ for controls, but not for ADHD. In the analyses of the below and above median IQ subgroups, we found no differences from controls in cerebral gray matter volume in ADHD with below-median IQ, but a delay of cortical development in a number of regions, including prefrontal areas. Conversely, in ADHD with above-median IQ, there were significant reductions from controls in cerebral gray matter volume, but no local differences in the trajectories of cortical development.In conclusion, the basic relationship between IQ and neuroanatomy appears to be altered in ADHD. Our results suggest that there may be multiple brain phenotypes associated with ADHD, where ADHD combined with above median IQ is characterized by small, more global reductions in brain volume that are stable over development, whereas ADHD with below median IQ is associated more with a delay of cortical development. Attention-Deficit/Hyperactivity Disorder (ADHD) is associated with substantial heterogeneity in terms of its cognitive correlates, changes in brain development, and genetic influences ADHD and IQ are both highly heritable phenotypes, with heritability estimates between 70–80% for both The relationship between ADHD and IQ is also relevant clinically: An average reduction in IQ of 9 scale points has been reported across studies Based on these findings, it has been suggested that the shared genetic effects associated with both IQ and ADHD may be reflected in shared neuroanatomical changes Cortical thickness has proven to be particularly sensitive to developmental effects in both longitudinal and cross-sectional studies Despite accumulating evidence that IQ is phenotypically important in ADHD, it is still often treated as nuisance variance or included as a covariate in analyses. However, actively investigating how differences in IQ relate to the neuroanatomical signature of disorders may be a more informative approach, in particular for ADHD, where changes in IQ are part of the clinical phenotype. Therefore, we set out to investigate the relationship of IQ and brain development in a sample of children with ADHD and typically developing controls. As overlapping genetic effects have been shown to influence both ADHD and IQ, and both are associated with brain changes, we hypothesized that IQ would act as a statistical moderator of brain differences associated with ADHD. Note that we did not test whether a difference in IQ precedes or causes brain changes in ADHD. We simply tested whether neuroanatomical differences associated with ADHD vary as a function of IQ. Specifically, we hypothesized that changes in the developmental pattern of cortical thickness would be greatest in children with ADHD and below median IQ, as ADHD is associated with a delay in cortical peak thickness A total of 214 children aged 6 to 15 years participated in this study. A subset of 200 children , participated in a structural MRI session. In order to assess the developmental trajectory of cortical thickness in subgroups differing in IQ, we performed a group split at the median IQ for the whole group (IQ = 102) to form four subgroups . There were no differences in age, gender, or hand preference between these subgroups. The below median and above median IQ groups of controls and subjects with ADHD did not differ in mean IQ. We acquired Diffusion Tensor Imaging (DTI) scans from a smaller subsample, largely overlapping with the first sample . These data were used to assess the effect of IQ on overall cerebral white matter microstructure. The institutional review board of the UMC Utrecht approved the study. Written informed consent was obtained from the parents of all subjects after full disclosure of the study purpose and procedure. Children provided written and/or verbal assent. The DISC-IV, parent version We established the history of medication use for subjects with ADHD by reviewing the medical files. We were able to do so reliably for 83% of subjects with ADHD. Children on medication were asked not to take their medication on the day of testing. Most co-operated with this request, except for a small minority of children who were either on atomoxetine, or whose parents were not willing to abstain from medication.2 and 8 diffusion-unweighted volumes with b-factor 0 s/mm2; parallel imaging SENSE factor 2.5; flip angle 90°; 60 slices of 2.5 mm; no gap; 96×96 acquisition matrix; reconstruction matrix 128×128; FOV 240 mm; TE 88 ms; TR 9822 ms). For the analysis of the 3D-FFE scan, a mask of the intracranial space was required. For 126 subjects  this mask was based on a T2-weighted dual echo turbo spin echo scan with 65 to 75 3.0-mm contiguous coronal slices of the whole head . For 62 subjects , the diffusion unweighted volume of the DTI scan was used to define the intracranial mask. Previous work has shown that the definition of intracranial volume is comparable using these two methods MRI scans were acquired on a 1.5-T scanner . A T1-weighted three-dimensional (3D) fast field echo scan of the whole head was acquired with 130 to 150 1.5-mm contiguous coronal slices  or 160 to 180 1.2-mm contiguous coronal slices  . DTI acquisitions consisted of two transverse single shot echo planar imaging DTI scans  volume of the DTI scan using mutual information as the similarity metric. Using this transformation, the binarized cerebral WM segment from the anatomic processing pipeline described above was spatially aligned with the FA image. Mean FA was measured in this segment.The two DTI scans were simultaneously realigned and corrected for possible gradient-induced distortions The primary analyses treated IQ as a dimensional construct. In order to investigate developmental trajectories, a split into separate below median and above median IQ groups was necessary (similar to Shaw and colleagues (2006)). The cutoff point for these analyses is essentially an arbitrary decision. We chose to use the whole group median IQ of 102 as the cutoff, as this allowed for subgroups of equal size and was close to the defined average IQ of 100.Demographic data were compared between diagnostic groups using independent sample t-tests and chi-square tests, as appropriate. ANOVA was applied across diagnosis-by-IQ groups  with post-hoc t-tests across the four subgroups. Duration of medication use was correlated with age, therefore we calculated a corrected measure of medication use (months of use/(age in months - 60 months)), where 60 months represents the youngest age at which stimulants were prescribed in our sample. Ventricle volumes were log-transformed due to non-normality of the distribution. Age, gender, hand preference and a dummy for T1 slice thickness (1.5 versus 1.2) were used as covariates in all analyses of brain volumes and cortical thickness. All analyses of cortical thickness were corrected using false discovery rate to maintain p<.05 First, volumetric data, cerebral FA, mean cortical thickness and total cortical surface were compared between diagnostic groups using univariate GLM. An age × group term was added to the model to test for group differences in linear age effects. Differences in the relationship between volumetric measures and IQ as a continuous measure were tested by adding a main effect of IQ and an IQ × group interaction to the basic model (including the covariates and diagnostic group main effect). An alpha level of .05 was used for all univariate analyses. These analyses were repeated for the below median and above median IQ subgroups separately.Second, to investigate differences in local cortical thickness between diagnostic groups, thickness was linearly regressed on group, age, gender, hand preference and scan slice thickness at each individual vertex. We investigated IQ effects by conducting a second regression with IQ and an IQ × group interaction added as regressors. We then investigated local differences in the developmental trajectories of cortical thickness: For each vertex, a regression analysis was carried out in the form of a locally-weighted running-line smoother control = 93, nADHD = 85). Density of data points was similarly low at IQ>140, and contained only three data points for the ADHD group. These were outliers at the 1.5 IQR criterion and significantly affected the results. Therefore these cases were excluded from analyses where IQ was treated as a continuous variable .As the density of data points was lower beyond 14 years of age and the smoothing procedure is particularly sensitive to low data densities, only cases below age 14 years were used for the cortical smoothing analyses , children with ADHD and above median IQ only differed from matched controls in terms of the intercept of the developmental curve, fitting with a pattern of decreased cortical thickness that is stable across age see also .Our results suggest that brain differences in ADHD may vary with IQ. First, we found a disruption of the typical association between measures of brain structure and IQ . Second,For children with ADHD in the above median IQ subgroup, the developmental trajectory of cortical thickness did not differ from that of matched controls. However, these trajectories did show a developmental lag in certain areas for children with ADHD in the below median IQ subgroup. In contrast, children with ADHD and above median IQ had a marked reduction in gray matter volume, whereas children with ADHD and below median IQ did not. These findings suggest that differences in gray matter in children with ADHD and above median IQ may be more equally distributed throughout the brain and more developmentally stable than for children with ADHD and below median IQ. This model is visualized in Unlike cerebral gray matter, total cerebellar volume and cerebellar gray matter volume were positively associated with IQ for both controls and subjects with ADHD. This suggests that the genetic pathway that affects the relationship between ADHD and IQ may not mediate cerebellar volume reductions in ADHD. Nongenetic pathways may also be involved: The cerebellum is particularly vulnerable to intrauterine environmental influences and premature birth, as it starts to develop early in intrauterine life but shows a protracted developmental pattern into adulthood In the analyses contrasting the whole ADHD group to the whole control group, we found no differences in cortical thickness. Whereas we are not the first study to report such a negative finding The changes in the developmental trajectories in regions in prefrontal cortex for the below median IQ subgroup are consistent with a delayed maturation of cortical gray matter . HoweverStudying the relationship between brain anatomy and a complex cognitive concept such as IQ is a not straightforward. Performance on tests of intellectual ability is the result of a myriad of cognitive processes, limiting the specificity of conclusions that can be drawn. However, there are also advantages to studying IQ rather than its constituent cognitive processes. The correlation between IQ and brain anatomy is one of the more consistently replicated associations between brain measures and cognition Splitting the group by median IQ was necessary to perform the age-fit analyses on the cortical thickness data for different IQ levels. The choice of cut point was entirely data-driven and should not be taken to imply that ADHD groups are qualitatively different above and below this point. In addition, as a result, some of the subgroups were of modest size. However, An advantage of using a median split is that the effect of increased individual measurement error (induced by using a shortened form of the IQ test) is reduced, as this increase will have only limited effect on the group median.One issue in any study addressing brain development in ADHD is the frequent use of medication and its effects on brain development The clinical validity of ADHD across the IQ spectrum has been a source of debate. Anecdotally, many clinicians report clinically meaningful differences between subjects with ADHD at varying intellectual levels. However, empirical data on this issue is sparse. Studies that have addressed this issue have often focus on the extremes of the spectrum, i.e. intellectual disability on the one hand, or giftedness on the other Our results have implications for how variance in IQ is handled in neuroimaging and cognitive studies of ADHD and reaffirm that IQ should not be used as a covariate Finally, it is important to note that our interpretation of our results assumes neuroanatomy is the moderator between genetic variation and both IQ and ADHD. Behavioral genetic studies have implied pleiotropy: an overlapping set of genes that affect both phenotypes. Our results suggest that that this is reflected by variation in the neurobiological differences associated with ADHD as function of IQ.In sum, we find that IQ is relevant to neuroanatomical changes in ADHD: Differences in the developmental trajectory of cortical gray matter, suggestive of delays, appear to be strongest for children with ADHD and below median IQ, whereas children with ADHD and above median IQ show widespread subtle cortical thinning that appears to be more stable over development. Our findings are based on cross-sectional data, but suggest a model for the relationship between IQ and brain anatomy in ADHD . LongituFigure S1Developmental trajectories in right fusiform gyrus and calcarine cortex. This figure shows differences in the developmental trajectories of cortical thickness for children with ADHD and below median IQ in right fusiform gyrus and calcarine cortex. The changes in trajectory for the calcarine cortex mimick the pattern found in right dorsolateral prefrontal cortex. In fusiform gyrus, the fits suggest greater cortical thickness for children with ADHD and below median IQ that is stable over development.(TIF)Click here for additional data file.Figure S2Subthreshold differences in developmental trajectories of cortical thickness in children with ADHD and above median IQ. Using FDR, there were no significant differences in the developmental trajectories of cortical thickness for the high IQ subgroup. In an exploratory analysis, we thresholded the t-maps at t(96) = 3.87, corresponding to an uncorrected p-value of .0001. Using this threshold, we did not find any significantly different vertices in the left hemisphere. In the right hemisphere, we found three clusters with changes in the developmental trajectories, in the middle occipital gyrus, in the temporal pole and in insular cortex. For each cluster, the difference in the trajectory was mostly attributable to a difference in intercept, consistent with a stable decrease in cortical thickness for children with ADHD and above median IQ. This is consistent with the hypotheses presented in the main paper (in paper .(TIF)Click here for additional data file.


SNAP25 gene and has a hyperactive phenotype similar to that of ADHD. Such mice are 3 fold more active compared to their control littermates. Genetic association studies support a role for allelic variants of the human SNAP25 gene in predisposing to ADHD.The Coloboma mouse carries a ∼2 cM deletion encompassing the SNAP25 gene in 1,107 individuals (339 ADHD trios). To assess the functional relevance of the SNAP25-ADHD associated allele, we performed quantitative PCR on post-mortem tissue derived from the inferior frontal gyrus of 89 unaffected adults. Significant associations with the A allele of SNP rs362990  and three marker haplotypes  were observed. Furthermore, a significant additive decrease in the expression of the SNAP25 transcript as a function of the risk allele was also observed. This effect was detected at the haplotype level, where increasing copies of the ADHD-associated haplotype reduced the expression of the transcript.We performed association analysis across the SNAP25 confers risk to ADHD and reduces the expression of the transcript in a region of the brain that is critical for the regulation of attention and inhibition.Our data show that DNA variation at  Attention deficit hyperactivity disorder (ADHD) is a highly heritable disorder of childhood with significant functional impairment and negative lifetime outcomes across all developmental stages. The disorder is marked by disruption of catecholamine signaling, with mainstay treatments for the disorder targeting the dopamine and noradrenaline transporters and the alpha 2a adrenoreceptor 2+-triggered neurotransmitter exocytosis Synaptosomal-associated protein (SNAP-25) is a presynaptic plasma membrane protein that is specifically and highly expressed in nerve cells. The gene encodes a protein component, which interacts with the membrane associated protein syntaxin, and vesicle-associated membrane protein (VAMP) to form the SNARE complex. This complex interacts with membrane proteins known as synaptotagmin to make up a core complex essential for docking and holding synaptic vesicles at the presynaptic membrane in preparation for CaSNAP25 and the gene encoding phospholipase C beta (PLCB-1) SNAP25 copy) may contribute to the hyperactive behavior of the coloboma. Similarly, NA depletion using DSP-4(N-(2-chloroethyl)-N-ethyl-2 bromobenzylamine hydrochloride) significantly reduced hyperactivity in the Coloboma mouse, supporting the noradrenaline hypothesis The Coloboma mouse bears a semi-dominant mutation (cm/+) in which the heterozygous form results in the mutant type while the homozygous is lethal. The mutation is a ∼2 Cm deletion encompassing genes including SNAP25 gene might confer susceptibility to ADHD. An initial analysis SNAP25 gene Genetic association studies suggest that allelic variations in the SNAP25 influence expression within the IFG, with decreased SNAP-25 expression (as expected from the Coloboma mouse) associated with the ADHD risk variants.Here we sought to provide additional evidence for a role of SNAP-25 in ADHD by performing dense SNP mapping across the gene in nuclear families with ADHD. To assess the functional relevance of ADHD-associated variants, we performed quantitative PCR (qPCR) of the SNAP-25 transcript in a large sample (N = 89) of post-mortem brains from non-clinical individuals. Our qPCR work focused on expression of the transcript in human inferior frontal gyrus (IFG), an area of the cortex that has consistently been implicated in ADHD from both structural and functional imaging analyses SD = 3.67) and were predominantly male (87%). All probands fulfilled DSM-IV diagnostic criteria for ADHD. Of these, N = 44 (13%) had the inattentive subtype, and N = 19 (5.6%) had the hyperactive impulsive subtype; the remainder had the combined subtype (81.4%). One hundred and seventy-nine probands (52.8%) had a comorbid diagnosis of oppositional defiant disorder (ODD) and 34 (10%) fulfilled criteria for comorbid conduct disorder (CD). Frequencies of ADHD subtypes and comorbidities were similar across the recruitment sites. We obtained a written consent from the next of kin, caretakers, or guardians on the behalf of the minors/children participants involved in our study. The consent was in accordance with the declaration of Helsinki and the ethical approvals from the University of Queensland, Australia, Trinity College Dublin, Ireland, and the University of Birmingham, UK”A total sample of 1,017 individuals, comprising 339 Caucasian ADHD probands and their parents (full trios) across three similar sub-samples was investigated. The first cohort comprised 185 full trios recruited throughout Ireland from child psychiatric clinics and schools in West County Dublin and from the Hyperactive and Attention Deficit Children's Support Group of Ireland. Consensus diagnoses were made according to DSM-IV ADHD either with or without comorbidity. These diagnoses were based on all available clinical information and the Child Behaviour Checklist (CBCL), the Conners' Parents and Teachers Rating Scales, and the Comprehensive Teachers Rating Scale (ACTeRS) In addition to the ADHD samples, 89 unaffected Caucasian brain samples  were obtained from the Australian Brain Bank. Of these, 63 (71%) were from male subjects. None of these individuals were diagnosed with ADHD or other psychiatric condition. The mean age of the sample was 51.6 years (SD = 12.2), PH ranged from 5.8–7 and the post-mortem interval (PMI) was 28.2 hours (SD = 14).CTTGAAGCATCCCAGGAAGA and the reverse 5′ GAAGGAAAAATGTTGGGGTTT 3′. The PCR product (214 bp) was then restricted with the enzyme Cac81 and the DNA fragments were fractionated on a 3% agarose gel. The G allele was characterized by 164 bp and 51 bp fragments while the A allele was characterized by 215 bp fragment.Fifteen SNPs were included in the current investigation . SNPs weApproximately 100–200 mg of inferior frontal gyrus (IFG) tissue of each sample was used to extract RNA and DNA using TRIZOL reagent as recommended by the manufacturers (Invitrogen). As PH is considered a good indicator of RNA purity and integrity, this was measured and found to range from 5.8–7. The optical density (OD) at 260/280 of RNA preparations ranged from 2–2.1 indicating good quality of the RNA preparation.SNAP25) and the reference genes β2micoglobulin (β2M) and beta actin (ACTB) gene primers were designed to amplify DNA regions involving exon 7 and 8 of SNAP25, exons 1 and 2 in β2M and exon 3 and 4 of ACTB. The qPCR primers were designed using the INTEGRATED DNA TECHNOLOGIES Oligo Design Tool, which is freely available at http://www.idtdna.com/scitools/scitools.aspx. The SNAP-25-qPCR forward primer sequence was 5′ATGGATGAAAACCTAGAGCAGG 3′ and the reverse was 5′ACACTTAAC CACTTCCCAGC 3′. The β2M forward primer sequence was 5′ GGCATTCCTG AAGCTGACAG 3′ whereas the reverse was 5′TGGATGAAACCCAGACACATAG 3′. The ACTB qPCR forward primer was 5′ACCACACCTTCTACAATGAGC 3′ and the reverse was 5′ GCGTACAGGGATAGCACAG3′. In this context, β2M and ACTB were used as reference genes as they have stable gene expression in various tissues including brain. A standard Invitrogen procedure was used to synthesize first cDNA strands of the samples. Relative quantification was performed using SNAP25 (target gene) compared to β2M and ACTB (reference genes). PCR cycling was performed on a Roche LightCycler-480. Cycling conditions were 95°C for 5 min followed by 45 cycles at 95°C for 10 sec, 60°C for 10 sec and 72°C for 20 sec. This was followed by a melting curve cycle at 95°C for 5 sec and 1 min at 65°C. mRNA relative was expression quantified using normalized threshold cycles (Ct) of target relative to reference genes.To minimize RNA contamination with the DNA, samples were treated with DNASE-I and cleaned using RNeasy Mini Kit as recommended by manufacturers . Furthermore, to prevent any possible interference by DNA contamination when conducting qPCR, the target (http://www.broad.mit.edu/mpg/haploview) using the method of Gabriel et al. Assessment of Hardy Weinberg Equilibrium for all SNPs was undertaken using parental DNA of the ADHD participants. All genotypes were in Hardy Weinberg equilibrium. Genetic association across a 15 SNP array was performed using the transmission disequilibrium test (TDT). Haplotype analysis to test for the transmission of multi locus haplotypes was performed by applying the default block definition in HAPLOVIEW . Permutation testing  revealed that only rs362990  was associated with ADHD, with a trend towards association for rs6108461 (p-corrected = 0.059).Fifteen SNPs were examined at the 2 = 8.37, p-corrected  = 0.019, OR = 1.4). An exploratory haplotype analysis using sliding window of 8 SNPs (∼20 kbp) extending from intron 3 (rs6108461) to the 3UTR (rs8636) enhanced the risk association signal . Finally, a haplotype comprised of the alleles GTC in the second haplotype block . This implies that this haplotype may confer protection against the development of ADHD.Although strong LD is apparent across the gene , three dlock see  was less2 change  = 5.6.2%]. Similar results were found for rs6108461 . The strong LD between these SNPs meant that controlling for one removed the effect of the other in the above analyses. Possession of the ADHD risk haplotype (AAC) was also significantly associated with expression of SNAP-25 in the IFG tissue [2 change  = 6.3%]. As expected, increasing possession (0 vs. 1 vs. 2 copies) of the risk haplotype was associated with decreased expression. Conversely, increasing possession of the protective haplotype was associated with increased SNAP-25 expression [2 change = 7.7%].A significant association between rs362990 and relative expression of the SNAP-25 transcript was observed . SpecifiSNAP25 gene that associate with ADHD are also associated with functional changes in the expression level of the transcript in a region of the brain that is an established pathological locus for ADHD. The current investigation provides further support for the notion that SNAP25 is a genetic risk factor for ADHD. Although nominal associations with two SNAP25 SNPs (rs6108461and rs362990) were observed, only the association with rs362990 survived permutation testing. Haplotype analysis enhanced the association (OR = 1.62), suggesting that the region between intron 3 and the 3′UTR of SNAP25 may harbor an important susceptibility variant for ADHD.The results of the current study show that DNA variants of the SNAP25 an important candidate gene for ADHD.SNAP-25 is important for axonal growth and synaptic plasticity, which are essential steps for wiring the nervous system. Selective inhibition of SNAP-25 expression prevents neurite elongation of cortical neurons SNAP25 gene variants and ADHD. All these studies have examined multiple SNPs and tested for association of single SNPs and haplotypes. The associated SNPs and their position within the SNAP25 gene are presented in SNAP25; see 2 = 12.7, p-corrected = 0.003, OR = 1.62) indicating that this region may harbor a risk/functional variant for ADHD.Our results are consistent with some but not all previous association/linkage studies. Apart from the current investigation, a number of studies have tested for association between SNAP25, several SNPs showed evidence of association with the symptoms of ADHD in a quantitative GWAS SNAP25 markers including rs362562 SNAP25 variations in ADHD.Furthermore, it is important to emphasize that the quartile–quartile (QQ) plot for ADHD genome wide association (GWAS) for SNPs in or near candidate genes SNAP25 variants may increase risk to ADHD. Specifically, the lowered expression of SNAP-25 in regions of the cortex that are critical for attention and inhibition, such as the IFG, may ultimately decrease the efficiency of neurotransmitter release and synaptic function, impairing behavior and cognition and conferring risk to ADHD. It should be noted however that the above hypothesis must be viewed with some caution since the current study did not test the expression of SNAP25 in post-mortem samples derived from individuals with ADHD.Computer simulation SNAP25 mutations introduced to the C terminal of the protein at AA positions 78, 81 and 202 resulted in a near elimination of exocytosis Recent biochemical analyses have shown that SNAP-25 amino acids (AA) 7–83 and 141–204 are essential motifs that are spontaneously assembled into helical SNARE complexes with Syntaxin1 and synaptobrevin 2 motifs SNAP25 as a susceptibility locus for ADHD. We hypothesize that the region between intron 3 and the 3UTR of SNAP25 may harbor functional variants that confer risk to ADHD. Finally, we stress the importance of independent replication of our findings preferably in different ethnic samples.In summary, this study provides support for the involvement of


Empirical evidence suggests that prenatal growth is associated with attention deficit/hyperactivity disorder (ADHD) and its symptoms. Data on the importance of postnatal growth is, however scanty. We studied whether pre- and postnatal growth up to 56 months is associated with symptoms of ADHD in children.A longitudinal regional birth cohort study comprising 893 children followed up to 56 months. The associations between pre- and postnatal growth and parent-rated ADHD symptoms of the child were analyzed with multiple linear regression analyses and repeated-measures analyzes of covariance.Children born lighter, thinner, shorter, and with a smaller head circumference, adjusted for length of gestation, received higher parent-rated ADHD symptoms scores at 56 months. Further, smaller head circumference throughout the period of growth from birth up to 56 months was related to higher ADHD symptoms scores. The associations changed only little after adjusting for several pre- and neonatal factors. The associations were not modified by sex and there were no evidence of non-linear associations.Slower prenatal growth in weight, body-mass index, length, and head circumference may pose a risk for higher ADHD symptoms in childhood. The consistently smaller head circumference from birth up to 56 months characterizing children with higher ADHD symptoms may point to a lack of catch-up growth in head circumference in childhood as a predisposing factor. Thus, it remains unknown if postnatal growth is etiologically associated with ADHD or is a consequence of the stimulant-medication  to op = .07). Hack et al. [With regard to postnatal growth, to our knowledge there are only three prior studies focusing on postnatal head circumference and ADHD or its symptoms. Ptcak et al.  suggestek et al.  found amk et al.  failed tThe mechanisms explaining the association between postnatal head circumference and ADHD symptoms remain speculative. Head circumference has been shown to correlate highly with total brain volume (from 1.7 to six years r = .93) . Smallersee [We also found that lower relative BMI at 56 months and slower gain in BMI from 20 to 56 months was related to higher ADHD symptoms. Earlier studies have shown that ADHD or its symptoms are associated with higher BMI ,18,19 rasee .Earlier studies on height and ADHD have been mainly cross-sectional and inconsistent in their findings -16,43. TIn summary, the results point to the importance of a slower prenatal growth. The consistently smaller head circumference from birth to 56 months characterizing children with higher ADHD symptoms scores may point to the importance of a lack of catch-up growth in head in childhood as another predisposing factor. However, it should be kept in mind that the associations we found were relatively modest in effect size. Measurements of body size are nevertheless crude measurements of environmental conditions early in life, and any association may give a hint of a biologically significant relationship. Further, studies are clearly needed to illuminate the association between growth and ADHD throughout the entire period of growth.The authors declare that they have no competing interests.KH and KR made substantial contributions to analysis and interpretation of data and have been involved in drafting the manuscript and revising it critically for important intellectual content. A-KP, SA, EK, JGE have contributed to analysis and interpretation of data, and have been involved in revising manuscript critically for important intellectual content; DW has made substantial contributions to conception and design, and to interpretation of data, and has revised manuscript critically for important intellectual content. TV, AL have made substantial contributions acquisition of data and have been involved in revising manuscript critically for important intellectual content. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2431/11/84/prepub


Low birth weight (LBW) is a major cause of neonatal deaths in developing countries including Nepal. Its social determinants in Nepal have rarely been identified. This study aimed to identify the factors associated with low birth weight among under-five children comparing data from the Nepal Demographic and Health Surveys (NDHS) of 2006 and 2011.Pooled data from the Nepal Demographic and Health Surveys (NDHS) of 2006 and 2011 were analysed initially and the two survey data were then compared separately. The association between LBW and socio-demographic and health related factors were analysed using multiple logistic regression analysis with a stepwise backward elimination procedure. Complex Sample Analysis method was used to account for study design and sampling.A total of 2845 children, 923 children in 2006 and 1922 children in 2011, had their birth weight recorded. The mean birth weight was 3024 (SD = 654.5) grams. A total of 12.1% ; 10.6%-13.7%) children had low birth weight (<2500 grams) at the time of birth. Attending antenatal care was found to be consistently associated with low birth weight for the pooled survey data, and both 2006 and 2011 survey data, respectively. Not attending antenatal care increased the odds of having a LBW infant by more than two times . Iron supplementation, which is an integral part of antenatal care in Nepal, was also significantly associated with birth weight for combined and individual surveys. Mothers not consuming iron supplementation during their pregnancy were more likely to have LBW infants . Residing in the Far-western and Eastern region were also significant risk factors for LBW in the pooled dataset and in 2011 survey.The current study indicated there was no significant decrease in the LBW prevalence and there is a need of targeted interventions aimed at decreasing the high rate of LBW through increasing antenatal care and consumption of iron supplementation during pregnancy. Low birth weight (LBW) is one of the risk factors for neonatal mortality which increases the odds of deaths by 20-30 times . A birthThe prevalence of LBW is around 15% in developing countries . HoweverRisk factors for LBW have been of interest for researchers over a long period. As many as 50 risk and protective factors have been identified by different reviews on LBW infants. Genetic make up, demographic factors, maternal nutritional factors, obstetric factors, maternal health condition and service utilisation are some of the factors that have been of recent interest ,9. MaterNepal is one of the exemplary countries successful in reducing the child and maternal mortality in this century. However, recent Nepal Demographic and Health Survey (NDHS) 2011 showed that neonatal death rate remained stagnant (33 per 1000 births) since 2006 despite having tremendous efforts from the Government of Nepal to reduce neonatal, infant and child deaths ,13. In pThe Nepal Demographic and Health Surveys (NDHS) of 2006 and 2011 ,13 were This study utilised the 2006 and 2011child datasets that contained information on under-five children. The datasets contained information on the child, mother, father, and household characteristics necessary for further analysis. Only those cases with recorded birth weight were included in the analysis. Multiple births were excluded from the study as this have been reported to be a known risk factors of LBW and may mask the effect of other socio-demographic variables due to its stronger effect on birth weight ,15. The To conceptualise the analysis, we adapted the framework used by Dharmalingam et al. . Figure Birth weight <2500 grams is defined as LBW. Birth weights recalled by mothers or recorded from the birth card were included for analysis in this study. Birth weight was categorised into LBW (<2500grams) and normal birth weight (>=2500grams), which serves as the dependent variable in our study.2) (ii) normal (18.5-23.00 kg/m2) and (iii) overweight (>23.00 kg/m2)  was also obtained for the data analysis.Interactions between the survey period (depicted by an indicator variable: 0=2006 survey and 1=2011 survey data) and significant determinants found in Model 1 (pooled data) were then assessed. The interaction term was found significant; indicating the effects of those significant determinants on LBW are significantly different for the two surveys. Next we tested wether the associations from Model 1 were different by survey period (i.e. NDHS 2006 and 2011) thus additional models were run for NDHS 2006 (Model 2) and NDHS 2011 (Model 3). We tested the multicollinearity issue among the independent variables. ANC was highly correlated with iron supplementation during pregnancy (r = 0.88). Therefore, while building models, ANC was used along with other independent variables. In the next step, iron supplementation was introduced into the model replacing ANC from each models to check if iron supplementation was still statistically significant. Given the large number of variables, the adjusted odds ratios and their 95% confidence intervals (CI) were only reported for statistically significant variables in the final model. Additionally, independent sample t-test was used to analyse the difference between mean birth weight in 2006 and 2011. A Chi-square test  grams. In 2006, it was 3012 (SD = 667) grams and in 2011, it was 3030 (SD = 649) grams. There was no significant difference in the mean birth weight between 2006 and 2011. The overall prevalence of LBW was 12.1% (95% CI: 10.6%-13.7%) while 13.2% (95% CI: 10.6%-16.3%) in 2006 survey and 11.5% (95% CI: 9.7%-13.6%) in 2011 survey. No statistically significant difference was found in the prevalence of LBW during the two surveys .Table The majority of children (82.5%) were born to mothers between the ages of 20-29 years. Six in every ten (62.9%) of the mothers attended four or more antenatal care (ANC) visits during their pregnancy. The majority (77.7%) had consumed iron supplementation at some time point of their pregnancy. Only small number (2.4%) of mothers reported smoking at the time of survey. About a quarter (22.8%) of mothers did not have formal education. The majority (58.7%) were from rural areas (58.7%). The majority of children were from Central (26.5%), Western (19.5%) and Eastern (22.7%) regions; only 16.5% and 14.8% were from Mid-western and Far-western regions, respectively.2) and binary regression analyses are presented in Table All the factors included in Figure Effect modification was assessed by including interaction terms between the indicator variable (representing the survey years) and the significant factors obtained into Model 1 (pooled data). Amongst the four interactions considered, only education of mothers was found to be significantly interacted with years of survey: ANC*Year of survey , Iron*Year of survey , Region*Year of survey , Education of mothers* Years of survey . As the year of survey significantly modified the effect of one of the determinants on LBW, we built additional regression models stratified by period (year): Model 2 for the 2006 survey and Model 3 for the 2011 survey. Similar to Model 1, ANC visits and iron consumption were statistically significant factors associated with LBW in both 2006 (Model 2) and 2011 (Model 3) surveys. However, maternal education was found significant only for 2006 survey data suggesting the mothers who only gained primary education had two times [95% CI (1.224-3.650)] higher chance of having a LBW infants compared to those who had completed secondary education. In 2011 survey, the effect of region was similar to what we found for the pooled data, i.e. mothers who lived in the Eastern  and Far-western region  were more likely to have LBW infants compared to mothers from Central region. Overall, the protective effect of attending ANC visits and iron consumption during pregnancy in preventing LBW was proved in all three models.The Government of Nepal has committed to achieve Millennium Development Goals (MDG) and has achieved a significant progress in maternal and child survival goals. However, the on-going challenge remains in reducing the stagnant newborn mortality rate as it still remained unchanged since 2006 . LBW is This study revealed that the prevalence of LBW has not been significantly reduced over NDHS 2006 and the 2011. Likewise, there was also no increase in the birth weight. The Nepalese mothers, generally, are the cohorts of the children when there used to be a very high under nutrition. Until today, four in ten children aged under five years suffer from underweight or stunting ,13. The The association of the use of recommended ANC service and iron supplementation consumption during pregnancy with LBW are two important findings of this study. Both factors were found to be protective against LBW consistently in the pooled, 2006 and 2011 survey datasets. ANC visits are likely to influence in improvements in dietary practices, monitor and encourage recommended weight gain during pregnancy and improve neonatal outcomes ,27. The Education of mother has been found to be a significant determinant of LBW in the pooled, and 2006 survey data similar to studies from India  and PakiThis study reported a higher risk of LBW in the Far-western region and the Eastern regions of Nepal than other regions for pooled and the 2011 survey data. The Far-western region is characterised by its difficult terrain, less access to transportation infrastructure and lesser livelihood opportunity. It is economically and socially under developed area having less access to health care, and food insecurity in high hills and mountain in most times of the year . Such adThis study has some limitations. The cross sectional nature of the study prevents it from developing causal inferences. There is possibility that some responses may suffer from recall bias and social desirable responses. While some birth weights were noted on records, many birth weights relied on the mother’s recall of her baby’s weight. Not all the mothers were able to report the birth weight of their child which may have led to an underestimate of the LBW problem. However, comparatively large samples of the children allow this study to reflect national scenario of Nepal.The current study indicates the need for targeted interventions aimed at decreasing the high rate of LBW in Nepal. Even if the ANC may not have direct causal link to LBW, it will lead to better nutritional status of mothers and the adoption of healthy behaviours that can influence LBW rates ,27,38. TThis study found that the LBW prevalence was similar in 2006 and 2011 surveys with no significant change in birth weight. There is an urgent need for intervention to reduce the prevalence of LBW if Nepal is to reduce newborn mortality and keep the current progress on child survival. A greater promotion of utilisation of antenatal care and consumption of iron supplementation is likely to contribute in reduction of LBW in Nepal. Future observational studies should examine other modifiable risk factors of LBW such as medical service utilization, food security and other health related factors.The author declares that he has no competing interest.VK conceived the study, performed statistical analysis, interpreted the result, and wrote the manuscript. KS and YZ supervised analysis, and contributed in manuscript revision. All authors agreed on the final version of the manuscript.VK holds an MPH degree. He has been working in child health programs in Nepal for more than five years. Newborn care and child nutrition is the focus of his work in Nepal and MPH studies. YZ is a senior lecturer in the School of Public Health and teaches in the postgraduate programs. She has an MSc and PhD in statistics. KS is a senior lecturer in the School of Public Health and coordinates the MPH/DrPH programs. She has an MSc and PhD in Behavioural Sciences.


Background. Attention-deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders among children. The aim of this study was to evaluate risk factors for ADHD in children. Method. In this case-control study, 404 children between 4 and 11 years old were selected by cluster sampling method from preschool children (208 patients as cases and 196 controls). All the participants were interviewed by a child and adolescent psychiatrist to survey risk factors of ADHD. Results. Among cases, 59.3% of children were boys and 38.4% were girls, which is different to that in control group with 40.7% boys and 61.6% girls. The chi-square showed statistically significance . The other significant factors by chi-square were fathers' somatic or psychiatric disease , history of trauma and accident during pregnancy , abortion proceeds , unintended pregnancy , and history of head trauma . Conclusions. Findings of our study suggest that maternal and paternal adverse events were associated with ADHD symptoms, but breast feeding is a protective factor. Attention-deficit hyperactivity disorder (ADHD) is one of the most common behavioral and neurodevelopmental disorders which is characterized by hyperactivity, impulsivity, and inattention in children and adolescents . PrevaleAlthough the association of breastfeeding and ADHD is not established, some studies have shown that ADHD is more common in children with lower rates of breast feeding . The rolThis case-control study was carried out among 404 children aged 4–11 years that were selected by cluster sampling in southern Iran in 2012. This study was approved by the ethics committee of Hormozgan University of Medical Sciences (HUMS). Informed consent was obtained from their parents or legal guardians. In this study, 935 preschool children were selected using cluster sampling and all of them were screened by Conners' parents and teachers rating scales. Out of them, 271 children were positive for ADHD according to Conners' questionnaire and those who were positive for ADHD (271) were interviewed by a child and adolescent psychiatrist and 208 children who met DSM-IV criteria for ADHD were enrolled in this study.This questionnaire had 28 items with answers based on Likert scales. The total possible score was 84 and children with a parent or teacher questionnaire total score above 70 were considered as positive and referred to Ebne-Sina Hospital, the only behavioral and neuroscience center in Hormozgan Province.All of the selected children were interviewed by a specialist in child and adolescent psychiatry. The cases were selected among patients who met the diagnostic criteria of DMS-IV for ADHD and controls were chosen among healthy children. The participants in each group were matched for age. Their parents were asked to answer the questionnaire including maternal and pregnancy related risk factors for ADHD such as the mother age at pregnancy, thyroid disease, the length of pregnancy, exposure to X-ray, infectious disease, eclampsia, abnormal uterine bleeding, abdominal trauma, medication intake, alcohol and tobacco consumption during pregnancy, and previous abortion, as well as neonatal and infantile related risk factors such as unwanted pregnancy, type of delivery, hyperbilirubinemia, phototherapy, blood exchange, low birth weight (less than 2.5 kg), feeding (breastfeeding or formula feeding), childhood asthma, dysentery, epilepsy, head trauma, and thyroid disease. In addition, parents' educational level, economic level, and presence of psychiatric disease in children and their parents were documented.P < 0.05 was considered significant.Data was analyzed using statistical procedures for social sciences  by descriptive analysis such as mean, standard deviation and percentile frequency. Differences between case and control groups were analyzed by chi-square test. A value of P > 0.05). A total of 404 children met the enrollment criteria, including 208 children as case group and 196 as controls. The mean age of participants in case and control groups was 6.48 ± 1.95 and 5.77 ± 1.232 years, respectively. There was no significant difference between both groups ).Among ADHD children, 150 (59.3%) were boys and 58 (38.4%) were girls. Among controls, 103 (40.7%) and 93 (61.6%) were boys and girls, respectively. Chi-square demonstrated that ADHD was significantly higher among boys than girls (The results showed that a history of trauma to abdomen during pregnancy was significantly higher among mothers who had children with ADHD. Maternal, pregnancy, neonatal risk factors for ADHD, and maternal and paternal history of psychiatric disorders are listed in Tables As shown in This cross-sectional case-control study was performed to determine the risk factors of ADHD.Symptoms of ADHD almost present in early childhood after 3 years of age and before 7. It may be continued to adulthood and affect the interpersonal impairments, academic performance, and work and family problems. Untimely diagnosis and treatment of this disorder can lead to cognitive and behavioral impairments. In recent years, numerous investigations have been done to identify the etiology of this disorder and the related factors.In the current study, ADHD was diagnosed three times in male children compared to females. This finding was consistent with the results of other studies conducted by Kim et al.  and CantThere are converging lines of evidence regarding the role of genetic factors in developing ADHD . Our finControversial findings have been reported about the role of thyroid hormones in ADHD , 24, whiAlcohol exposure during pregnancy is related to increased risk of neurodevelopmental and conductive disorders. This association is well documented . Our resThere are few studies regarding the association of preeclampsia in pregnancy and ADHD in their children. Some studies have shown that pregnancy complications are associated with increased risk of offspring ADHD , 31. HowVarious studies demonstrated that low birth weight among ADHD children was significantly higher than normal children , 32. ButThe results of our study demonstrated that prevalence of ADHD among formula-fed patients was significantly higher than breast-fed children. This finding is consistent with the results of other studies. Some lines of evidence demonstrated that fatty acid compositions of human breast milk such as docosahexaenoic acid and arachidonic acid have an important role in brain development , 34. TheOur results showed that epilepsy and trauma to head were significantly higher among ADHD children than control group. On the other hand, the difference of the frequency of childhood asthma, dysentery, and thyroid disease was not significant between both groups. Our result was consistent with a study conducted by Gerring and colleagues that reported that ADHD was more frequent among patients who presented with moderate and severe head injuries . FurtherAlthough in this study we tried to eliminate the overestimation or underestimation of ADHD by parents of children, whoever they are, a limitation in our study could be the probable recall bias by the parents. In conclusion, our study demonstrated that parental psychiatric disorders, previous abortion, unwanted pregnancy, cesarean delivery, maternal alcohol and tobacco exposure during pregnancy, epilepsy, and head trauma were significantly more among ADHD children than control group. Accordingly, avoiding alcohol, tobacco, and unnecessary X-ray during pregnancy and elective cesarean delivery are recommended as well as feeding by breast milk instead of formula. However, it must be taken into consideration that alcohol and tobacco exposure, previous abortion, and unwanted pregnancy may be results of maternal and paternal ADHD.