Magnetoencephalographic (MEG) Brain Activity During A Mental Flexibility Task PDF

Mogadam et al. Journal of Neurodevelopmental Disorders (2019) 11:19 https://doi.org/10.1186/s11689-019-9280-2 RESEARCH Open Access Magnetoenceph...

Mogadam et al. Journal of Neurodevelopmental Disorders (2019) 11:19 https://doi.org/10.1186/s11689-019-9280-2 RESEARCH Open Access Magnetoencephalographic (MEG) brain activity during a mental flexibility task suggests some shared neurobiology in children with neurodevelopmental disorders Alexandra Mogadam1,2, Anne E. Keller2,3, Paul D. Arnold4,5, Russell Schachar2,6, Jason P. Lerch2,7,8, Evdokia Anagnostou1,2,9 and Elizabeth W. Pang1,2,3* Abstract Background: Children with neurodevelopmental disorders (NDDs) exhibit a shared phenotype that involves executive dysfunctions including impairments in mental flexibility (MF). It is of interest to understand if this phenotype stems from some shared neurobiology. Methods: To investigate this possibility, we used magnetoencephalography (MEG) neuroimaging to compare brain activity in children (n = 88; 8–15 years) with autism spectrum disorders (ASD), attention deficit hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD), as they completed a set-shifting/mental flexibility task. Results: Neuroimaging results revealed a similar parietal activation profile across the NDD, groups suggesting a link to their shared phenotype. Differences in frontal activity differentiated the three clinical groups. Brain-behaviour analyses showed a link with repetitive behaviours suggesting shared dysfunction in the associative loop of the corticostriatal system. Conclusion: Our study supports the notion that NDDs may exist along a complex phenotypic/biological continuum. All NDD groups showed a sustained parietal activity profile suggesting that they share a strong reliance on the posterior parietal cortices to complete the mental flexibility task; future studies could elucidate whether this is due to delayed brain development or compensatory functioning. The differences in frontal activity may play a role in differentiating the NDDs. The OCD group showed sustained prefrontal activity that may be reflective of hyperfrontality. The ASD group showed reduced frontal activation suggestive of frontal dysfunction and the ADHD group showed an extensive hypoactivity that included frontal and parietal regions. Brain-behaviour analyses showed a significant correlation with repetitive behaviours which may reflect dysfunction in the associative loop of the corticostriatal system, linked to inflexible behaviours. Keywords: ASD, ADHD, OCD, MEG, Executive function, Set shifting, TOCS, RBS-R, Corticostriatal projections, Neurodevelopmental disorders * Correspondence: [email protected] 3 Division of Neurology, Hospital for Sick Children, 555 University Avenue, Toronto M5G 1X8, Canada Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Mogadam et al. Journal of Neurodevelopmental Disorders (2019) 11:19 Page 2 of 12 Introduction accuracy making it an excellent complement to fMRI Neurodevelopmental disorders (NDDs) are a heteroge-. Investigations into the fast-paced temporal dimen- neous group of disorders, characterized by compromised sion of brain activity can significantly contribute to our central nervous system development and aberrant brain understanding of the dynamics of cognitive processes function [1–3]. The most common NDDs include autism such as MF, in both typical and non-typical populations. spectrum disorders (ASD), attention deficit hyperactivity In light of emerging evidence of disrupted connectivity disorder (ADHD) and paediatric obsessive-compulsive (as measured by high temporal resolution oscillatory disorder (OCD). While each of these NDDs has its own synchronization) across multiple areas in the brain dur- distinct clinical phenotype (i.e. social communication im- ing tasks of MF in children with ASD [15, 17, 25], we pairments and repetitive behaviours in ASD, dysfunctions decided to employ MEG in our investigation of MF in in attention regulation and hyperactivity in ADHD, and NDDs. impaired control of obsessive thoughts and behaviours in To investigate the neural bases of mental (in) flexibility OCD ); they are often co-morbid and share genetic in children with NDDs, we recruited children with ASD, [5–7], neurobiological and cognitive-behavioural char- ADHD and OCD to complete a set-shifting task in the acteristics, such as impairments in social perception , MEG scanner. As there is increasing research suggesting rigidity and difficulties with attention. that NDDs may exist along a continuum, we hypothe- One cognitive characteristic observed in all three sized that the common behavioural manifestation of groups is that of impaired executive functions, including cognitive inflexibility across ASD, ADHD and OCD mental flexibility (MF). Mental flexibility comprises the groups may be due to some shared underlying neural ability to alter behavioural and thought patterns in substrates, existing along a spectrum. Specifically, based response to environmental changes [10, 11] and is essen- on Ameis et al.’s DTI research, we expected to find tial for adapting to changing surroundings, navigating greater similarities in activity in ASD and ADHD groups, social interactions and learning in academic and work with both groups more affected than the OCD group. environments. This crucial cognitive function can be assessed using a set-shifting task, in which participants Materials and methods are asked to match stimuli, with matching criteria shift- Participants ing every few trials. We recruited 116 children with an NDD between the ages Mental flexibility relies on a network of brain regions of 8–15 years. After data cleaning for artefacts, a total of 88 spanning prefrontal, posterior parietal and insular re- children (38 ASD, 28 ADHD, 22 OCD) were included in gions, the basal ganglia and anterior cingulate cortex our final analyses (see Table 1 for demographics). Partici- (ACC; [11–13]), as well as the temporal pole (TP) and pants were recruited through the Province of Ontario Neu- pre- and supplementary motor regions in typically devel- rodevelopmental Disorders (POND) Network from clinics oping children (TDC; ). Functional neuroimaging at the Holland Bloorview Kids Rehabilitation Hospital studies have investigated the neural correlates of MF in (ASD) and the Hospital for Sick Children (SickKids; ADHD children with ASD [15–17] and adolescents and adults and OCD) in Toronto. Inclusion criteria were a primary with ASD , ADHD [19–21], and OCD [19, 22]. In clinical diagnosis of ASD, ADHD or OCD, normal or comparison with TDC, these studies have found differ- corrected-to-normal vision, ability to comply with neuroim- ences in brain activity associated with MF within these aging protocols and no contradictions for neuroimaging. groups, with one reporting similarities across ADHD Co-morbidities and psychotropic medication use were and OCD , although none have compared all three noted but not excluded. groups together. Similarly, a diffusion tensor imaging Upon enrolment, primary clinical diagnoses were (DTI) study of ASD, ADHD and OCD suggested that confirmed using disorder-specific diagnostic measures: there may also be shared structural deficits in all three Autism Diagnostic Observation Schedule-2 (ADOS; groups, with ASD and ADHD additionally affected. ) and Autism Diagnostic Interview-Revised (ADI-R; Together, this growing body of neurobiological and gen- ) for ASD, Parent Interview for Child Symptoms etic evidence proposes that these NDDs are not separate (PICS; ) for ADHD, and the Child Yale-Brown entities that sometimes co-occur, but in fact, are perhaps Obsessive Compulsive Scale (CYBOCS; ) for OCD. part of a spectrum with shared aetiologies and overlap- ping phenotypes (as discussed by ). Neuropsychological assessments Most studies of MF have used fMRI as their method Full-scale intelligence quotients (FSIQ-2/4: WISC-IV®, of choice. While this tool is excellent for spatial investi- WASI-I/-II®; Full IQ: Stanford Binet Intelligence Scales®) gations, it is more limited in its temporal resolution. and four parent-questionnaires were administered to meas- Magnetoencephalography (MEG) is a neurophysiological ure repetitive (Repetitive Behaviour Scale—Revised (RBS-R; modality which tracks neural activity with millisecond [28, 29])), obsessive-compulsive (Toronto Obsessive- Mogadam et al. Journal of Neurodevelopmental Disorders (2019) 11:19 Page 3 of 12 Table 1 Summary of demographic information and neuropsychological assessments ASD1 ADHD OCD2 Number (N = 88) 38 28 22 Age 12.26 ± 2.19 years 12.13 ± 1.89 years 11.58 ± 2.29 years Male to female 31:7 24:4 14:8 FS-IQ-4/SB-IQ 99 ± 18 (n = 36) 98 ± 17 (n = 22) 117 ± 18 (n = 8) 3 RBS-R total 30 13 30 SWAN-inattention4 5 6 3 SWAN-hyperactivation4 4 4 2 TOCS5 −7 − 26 18 1 28.57% (10/35) of participants with ASD received a secondary diagnosis of ADHD 2 27.27% (6/22) of participants with OCD received a secondary diagnosis of ADHD and 4.55% (1/22) of participants with OCD received a secondary diagnosis of ASD 3 Repetitive Behaviour Scale—Revised (total score and number of endorsed items score) [26, 27] 4 Strengths and Weakness of ADHD Symptoms and Normal Behaviour Rating Scales (inattention and hyperactive sub-measures) 5 Toronto Obsessive-Compulsive Scale Compulsive Scale (TOCS; )) and inattentive and hyper- (heart and eye artefacts) using ICA [37, 38]. The data active [Strengths and Weaknesses of ADHD Symptoms were then averaged, and root mean square (RMS) activ- and Normal Behaviour Rating Scales (SWAN, inattention ity plots, summed over all MEG channels, across time, and hyperactive sub measures; )] behavioural patterns were generated. See Additional file 1, Sections 1-2 and in participants. See Table 1 for group scores. 1-3 for more imaging and pre-processing details, respectively. Task To assess MF in our clinical groups, we employed an MEG analyses MEG-compatible Intra-Extra Dimensional Set Shift task Empirical Bayesian beamforming (EBB; [39, 40]) was (IED-task) previously used in our group to test adults applied to reconstruct sources (12-mm FWHM Gaussian and children. In this task, participants match a kernel smoothing) from 50 to 500 ms post-stimulus on- target stimulus based on a matching rule that changes set, with sliding overlapping time windows (100 ms wide, every few trials. Participants are required to ‘shift’ to the 50 ms overlap), resulting in a total of eight windows of new rule to have a correct match. There were two types interest (i.e. 50–150, 100–200 ms, etc.). Between- and of shifts in our study, ‘extra-dimensional’ and ‘intra- within-group contrasts were conducted using independ- dimensional’, where the former involves a more difficult ent samples t test [SPM(T)], corrected for multiple com- shift between categories (dimensions), while the latter parisons with a modified Bonferroni applied to the p involves an easier shift, within categories. As the extra- value of 0.05. All results report significant corrected dimensional shift is more difficult, it better taps into the brain activity (pcorr < 0.05) which was visualized through mental processes involved in set-shifting; thus, we present MRIcron. the extra-dimensional results only. From here, this is We first conducted a within-group analysis where, for referred to as the ‘Shift’ condition. See Additional file 1, each group, we used a multifactorial design to con- Section 1-1 for full details. trast the Shift with the Non-Shift condition to identify the brain activity associated with shifting. This gener- Behavioural analyses ated, for each group, a list of regions with significantly Accuracy and reaction time for correct Shift and greater activation for the Shift condition within each Non-Shift trials were compared across groups using a time window. linear fixed-effects model in SPSS® (v24), with re- To explore differences between groups, a multifactor- peated measures for shift type, age as a covariate and ial design was used to contrast the images based on an unstructured repeated covariance type. our hypotheses generated from the literature. We tested the following Shift contrasts: OCD > ASD, OCD > Imaging data acquisition and pre-processing ADHD, ADHD > ASD, and ASD > ADHD. MEG data were acquired supine in a 151-channel CTF Omega system (MISL, Coquitlam, Canada). Analyses Brain-behaviour analyses were conducted using SPM12 and FieldTrip. To further probe the cross-diagnosis shared neurobio- Data were filtered (1–50 Hz) and epoched (− 500–1500 logical correlates of mental inflexibility, we investigated ms). Artefacts were rejected (> 2500 fT) and removed brain-behaviour relations collapsed across the group. We Mogadam et al. Journal of Neurodevelopmental Disorders (2019) 11:19 Page 4 of 12 used a linear regression model (controlled for age) to test right temporal lobe (BA 37, 38) and the left parahippo- whether the magnitude and/or latency of brain activity pre- campus (BA 36) from 150 to 350 ms, as well as in the dicted behavioural measures (TOCS, RBS-R and SWAN), right pre- and supplementary motor areas (BA 6) from regardless of clinical group (see Additional file 1, Section 250 to 350 ms. 1–4). The TOCS and RBS-R measure obsessive-compulsive In the ADHD group, similar to the participants with and repetitive behaviours, respectively, and the SWAN ASD, parietal activity was sustained, although the activity measures inattentive and hyperactive behaviours. These was predominantly in the left hemisphere, in both infer- scales were selected as they quantify the severity of behav- ior and superior lobules (BA 7, 39, 40). The right pre- ioural symptoms that may reflect, and/or contribute to, frontal regions (BA 10) were briefly active from 50 to mental inflexibility. 150 ms, and then again later from 250 to 450 ms bilat- erally in inferior, middle and superior frontal gyri (BA 8, Results 10, 46, 47). Similar to ASD, additional activity was found Behavioural results in pre- and supplementary motor areas (BA 6) and the For accuracy, a significant main effect was observed for temporal lobe (BA 37), though in the contralateral ‘Age’ [F (1,82) = 4.187, p = 0.044] but not for ‘Group’ nor hemisphere. ‘Shift Type’. There were no significant interactions. The Finally, children with OCD also displayed early and average age-adjusted accuracy scores per trial type, for sustained bilateral activity in the parietal lobes (BA 7, each group, are contained in Table 2 (upper). 39, 40) from 50 to 400 ms post-stimulus onset. Frontal For reaction time, the fixed effects model revealed a activity was similarly sustained from 50 to 500 ms, also main effect for ‘Shift Type’ [F (2,82) = 7.050, p = 0.001] in both hemispheres, across inferior, middle and su- only. Post hoc analyses, adjusted for multiple compari- perior frontal gyri (BA 9, 10, 11, 44, 46, 47). Bilateral sons using the Bonferroni method, revealed that Non- pre- and supplementary motor areas (BA 6) were also Shift trials were faster than Shift trials [p < 0.001]. There active early on, with the temporal lobe (BA 37, 38) dis- were no significant main effects for ‘Group’ and no playing activity somewhat later, predominantly in the significant interactions. The mean age-adjusted reaction left hemisphere. times by group and trial type can be found in Table 2 (lower). Between-group contrasts As per our a priori hypotheses, we conducted four MEG results between-group comparisons (contrasting the Shift Within-group source level analyses: brain regions involved conditions of two groups at a time; pcorr < 0.05) in set-shifting where significant differences are displayed in Fig. 2 Spatiotemporal activity plots identifying brain regions and delineated in Additional file 1: Table S1. Our a that were significantly more active during set-shifting, priori hypotheses were that the OCD group would for each group, are contained in Fig. 1. For the ASD show greater activations compared to both ASD and group, there was sustained activity in parietal lobes in- ADHD groups. The OCD > ASD contrast (Fig. 2a) volving both inferior and superior lobules (BA 7, 39, 40), revealed significantly greater and significantly more with contributions from both hemispheres. Prefrontal sustained (50–350 ms) activity, mainly in frontal re- activity was dominated by the right inferior frontal gyrus gions (middle frontal gyrus, MFG; BA 10) for the (IFG; BA 44, 45, 47), starting 200 ms post-stimulus on- OCD group. As well, the OCD group showed a brief set, until 450 ms. Additional activity was found in the (100–200 ms) period of greater activity in the right Table 2 Accuracy and reaction times for the set-shifting task, by clinical group Non-shift Intradimensional (easy) shift Extradimensional (hard) shift Mean SE Mean SE Mean SE Accuracy ASD 93.7% 0.8 93.4% 1.1 91.7% 1.2 ADHD 94.2% 0.9 93.2% 1.3 89.7% 1.4 OCD 93.9% 1.0 92.7% 1.5 90.8% 1.6 Reaction time ASD 755 ms 41 834 ms 60 914 ms 43 ADHD 812 ms 48 912 ms 70 948 ms 50 OCD 773 ms 55 865 ms 80 870 ms 58 Mogadam et al. Journal of Neurodevelopmental Disorders (2019) 11:19 Page 5 of 12 Fig. 1 Individual brain activity profiles within each clinical group. For each brain region, the significant activations (p < 0.05corr) associated with set-shifting are shown, for each clinical group. The activity profiles reveal a shared pattern of sustained parietal activity in all three groups, late and limited prefrontal activity in ASD and ADHD, and sustained frontal activity in OCD. Other regions (pre- and supplemental motor cortices and temporal regions) do not show similarities across groups. SPL superior parietal lobule, IPL inferior parietal lobule, DLPFC, dorsolateral prefrontal cortex, VLPFC ventrolateral prefrontal cortex, M/ITG middle/inferior temporal gyrus, TP temporal pole superior temporal gyrus (STG; BA 22) and the left time window in the right IFG/BA 47 (300–450 ms) ACC (BA 25). The OCD > ADHD contrast (Fig. 2b) and right STG/BA 22 (300–500 ms). showed more between-group differences with greater and significantly more sustained (50–300 ms) activity Brain-behaviour analyses in bilateral prefrontal regions (MFG/ IFG; BA 10, 11, To explore potential relationships between brain 45, 46, 47) for OCD. Additional differences were regions involved in MF and continuous behavioural found in the parietal (right angular gyrus; BA 39; measures of clinical symptomology, we regressed peak 50–150 ms) and temporal regions (right STG/BA 22; latency and magnitude during Shift trials with mea- 100–200 ms and left TP/BA 38; 250–350 ms). sures of obsession-compulsion (TOCS), repetitive be- Because we hypothesized that the ASD and ADHD haviours (RBS-R) and attention/hyperactivity (SWAN), groups would be more similar, we conducted con- controlling for age. Only the first two measures trasts in both directions. For the ASD > ADHD (TOCS and RBS-R) showed significant relationships (Fig. 2c) contrast, a few differences were observed (Table 3). with the ASD group showing greater activity in the For the measure of obsessive-compulsive behaviours right parietal (supramarginal gyrus, SMG/BA 40; 50– captured by the TOCS, we found that the peak latency 150 ms and superior parietal lobule, SPL/BA 7; 150– in two frontal regions, the right superior frontal gyrus 300 ms) and left temporal (TP/BA 38; 150–250 ms) (SFG) [F (2,79) = 4.084, p = 0.021; adjusted R2 = 0.071; regions. In the other direction, ADHD > ASD B = − 0.102, p = 0.008] and left IFG pars triangularis [F (2, (Fig. 2d), the differences were very sparse. The 79) = 3.419, p = 0.038; adjusted R2 = 0.056; B = − 0.086, p = ADHD group showed greater activations in a late 0.015], was negatively related with TOCS score. That is, Mogadam et al. Journal of Neurodevelopmental Disorders (2019) 11:19 Page 6 of 12 Fig. 2 Between-group contrast results, p = 0.05corr. Between-group contrasts of brain activity associated with mental flexibility (MF). The first two contrasts, a OCD > ASD and b OCD > ADHD, reveal significantly greater bilateral prefrontal activity in OCD, compared to both ASD and ADHD. The c ADHD>ASD and d ASD > ADHD contrasts show fewer differences, with ASD showing greater parietal activity and reduced frontal activity compared to ADHD faster peak latencies in these two frontal regions were associated with greater RBS-R total scores or greater associated with higher scores, or greater morbidity, re- morbidity. gardless of clinical group, on the TOCS scale. For the measure of repetitive behaviours, we found Discussion that a significant linear regression equation predicted In this study, we used MEG to investigate neural RBS-R total scores based on peak power values, processing involved in a MF task in children with extracted during peak two (150–300 ms), [F (2,79) = ASD, ADHD and OCD. The ease with which partici- 4.059, p = 0.021; adjusted R2 = 0.070]. Peak power pants completed the task (there were no group values in the left angular gyrus were significantly differences, with accuracy at approximately 90%) in- positively related to RBS-R total scores, [B = 72.025, dicated that the children were able to set-shift p = 0.008], indicating increased power/activation was successfully. Table 3 Significant correlations between brain-behaviour measures Behavioural measure Brain region Brain measure β (beta) p TOCS1 Right SFG Peak latency − 0.299 0.008 TOCS Left IFG Peak latency − 0.274 0.015 2 2 RBS-R (total) Left angular gyrus Amplitude 0.294 0.008 1 Toronto Obsessive-Compulsive Scale 2 Repetitive Behaviour Scale—Revised (total score) Mogadam et al. Journal of Neurodevelopmental Disorders (2019) 11:19 Page 7 of 12 Similar regions underlie mental flexibility in all groups display significantly decreased activity in prefrontal re- In both adults and typically developing control chil- gions compared to children with OCD. The OCD > dren (TDC; ), MF processing has been shown to be ADHD contrast revealed the greatest differences, with subsumed by hubs in bilateral fronto-parietal cortices, ADHD showing bilaterally reduced frontal activity across the insula and the ACC, with children drawing on add- the inferior and middle frontal gyri. These findings are itional premotor and temporal lobe regions. Our results consistent with other functional neuroimaging studies in concur with this literature as we found that the NDD ADHD which have shown hypofrontality in MF, as well groups showed recruitment of these brain regions during as executive functions more broadly [19–21, 50–54]. the task. The sustained prefrontal activity in OCD may be re- flective of hyperfrontality, a functional characteristic that Overlapping activation patterns observed in all groups has previously been reported in the OCD literature The literature in both adults and children sug- (, for reviews, please refer to [56, 57]), although not gests that parietal and frontal hubs activate sequentially, necessarily in relation to MF. In adults with OCD, fMRI without overlap, when completing a set-shifting task. In studies show reduced activation in the classic functional the current study, our NDD groups showed extensive hubs of mental flexibility , which related to perform- overlap in the activation pattern of parietal and frontal ance. We were able to hold performance constant regions (Fig. 1), with a distinct absence of the sequential between groups; thus, our increased activation may be progression of activation that is described in the litera- suggestive of a compensatory mechanism recruited to ture. Instead, all three NDD groups showed sustained maintain this high function. Only one study has been SPL and inferior parietal lobule (IPL) activation through- conducted in children with OCD. Using fMRI and a out the processing of the task. This sustained posterior set-shifting task, this study reported a decreased parietal activity suggests that all three clinical groups hemodynamic response in the left IFG in children with share a strong reliance on the posterior parietal cortices OCD; however, they report greater grey matter density to complete the MF task. in dorsolateral prefrontal cortex (BA 10), IFG and ACC According to the posterior-to-anterior theorem of , which may be the neurophysiological mechanism brain development [44, 45], we know that parietal underlying our observation of increased activation in this gyri develop before frontal regions and that often, area. In general, our findings concur with the idea of the posterior parietal cortices play a larger role in dysregulation in the prefrontal regions in OCD; however, mediating executive functions in childhood, until, further studies are needed to understand whether atyp- with increasing age, the frontal areas become more ical activations are symptomatic of dysfunctional pro- developed and can assume their role in processing cessing or indicative of compensatory function. executive functions [46–49]. We speculate that our observation of sustained parietal activation that over- ASD and ADHD differentiated by frontal/parietal laps with the timing of frontal activations suggests abnormalities that children with NDDs need greater assistance We did not have a priori hypotheses as to how the ASD from the parietal regions for their executive func- and ADHD groups would compare with each other; tioning. Future studies should investigate whether thus, we conducted our comparisons in both directions. this observation of high reliance on parietal regions As we would have predicted from Fig. 2, fewer differ- is indicative of delayed brain development or com- ences were found in the ASD > ADHD and ADHD>ASD pensation for prefrontal dysfunction. contrasts, compared to the contrasts with the OCD group. However, a definite pattern emerged where we Prefrontal activation pattern differentiates OCD observed significantly reduced right parietal activation in While the NDD groups all showed a similar pattern of ADHD and reduced right frontal activation in ASD. sustained parietal activity, the pattern of frontal activa- In the ADHD group, this reduced parietal activation tions was significantly different between groups, suggest- compared to ASD, and the reduced frontal activation ing that this may be a distinguishing feature. Based on compared to OCD (described above), is consistent with the pattern of findings by Ameis et al. in the same many studies showing that individuals with ADHD have NDDs, and in line with our within-group results (Fig. 1), extensive hypoactivity that includes the frontal lobes and we expected between-group analyses to show the OCD the parietal regions, as well as the striatum, insula and group to less similar to the other two, and we expected ACC [19–21, 51–53]. ASD and ADHD groups to be more comparable to each In ASD, previous studies have shown dysregulations in other. the connectivity and synchrony of brain regions/net- Our between-group analyses (OCD > ASD and OCD > works involved in MF , as well as an increased reli- ADHD) revealed that children with ASD and ADHD ance on the parietal lobes in set-shifting compared to Mogadam et al. Journal of Neurodevelopmental Disorders (2019) 11:19 Page 8 of 12 TDC [15, 17, 60]. It has been suggested that the brains suggesting greater error-monitoring and a general exces- of individuals with ASD do not develop efficient long- sive concern to perform well in this clinical group. range cortical connections during development, and at In addition to the finding in the prefrontal regions, we the same time, have very well-established short-range also found a significant brain-behaviour relation in the connections (U-fibres), resulting in hyper-connectivity of left angular gyrus of the parietal lobe. Increased peak local brain hubs [61–64]. Such a structural organization power was positively related with total scores on the of the brain would show excessive activation of inter- RBS-R, meaning that increased peak power was associ- parietal cortical networks and result in altered function, ated with increased repetitive behaviour morbidity. as in the case of MF. Because of the disruptions in the These findings would suggest that increased activity in efficiency of long-range connections, it is possible that the left parietal region, a key hub for set-shifting func- the ASD group cannot efficiently recruit their frontal re- tions, would be associated with increased morbidity of gions, and therefore exercise a greater reliance on their repetitive behaviours. Overall, these brain-behaviour re- parietal lobes for successful task execution. sults fit with the between-group comparisons, implying hyperactivity in parietal regions across NDD groups dur- Brain-behaviour analyses ing an MF task. We further speculate that the more a For our first set of MEG analyses (featuring the within- child with an NDD relies on this pronounced involve- and between-group neuroimaging contrasts), we ment/activation of posterior parietal regions during such grouped participants according to their primary diagno- tasks, the greater their mental inflexibility. Neuropsycho- sis. While our MEG contrasts did reveal unique MF logical research in adults and children with ASD exam- group-specific brain activity profiles, we also found strik- ining error type on mental flexibility tasks has found a ing similarities across the NDDs. Increasingly, there is correlation between regressive errors on the task and evidence that NDDs are not separate entities that some- repetitive behaviour symptomology [65, 66]. While the times co-occur, but in fact, may be part of a spectrum participants in our study showed extremely high per- with shared aetiologies and overlapping phenotypes. formance, future neuroimaging studies utilizing more To this end, we conducted brain-behaviour correlations, challenging tasks could assess atypical brain activity, its across all participants, to explore whether measures of interaction with symptomology, error and performance neuromagnetic activity in brain regions involved in set- functioning. shifting predicted inflexibility-related symptom severity, In a review paper examining the neurobiology of re- irrespective of diagnostic group. petitive behaviours, Langen et al. discuss the different We used the RBS-R, TOCS and SWAN as measures of ways in which disruptions to the corticostriatal projec- severity of symptomatic behaviours reflective of, and/or tions can contribute to inflexible behaviours. Corticos- contributing to, mental inflexibility. For both the RBS-R triatal projections are white matter tracts that project and the TOCS, we found that brain activity predicted from the cortex to the striatum of the basal ganglia, a symptom severity, albeit in opposite directions. Bilat- structure important for motor function and more gener- erally in the dorsolateral prefrontal cortices (right SFG ally goal-directed behaviour; disruptions to these projec- and left IFG), we found a negative relation between peak tions have been associated with repetitive, restrictive and latency and TOCS scores, such that faster peak latencies rigid behaviours in various disorders [67, 68]. Repetitive were significantly related to poorer (increased) TOCS behaviour types can be classified according to their scores. As expected, the OCD group had the greatest neuroanatomical substrates, and thus, mapped onto dis- morbidity on the TOCS scale. There are a few different tinct corticostriatal loops. Of particular interest is ways that we could interpret this finding. It is likely that the associative loop, which consists of prefrontal and this brain-behaviour relation is tied to the earlier and posterior parietal projections to the striatum, including generally greater frontal activity observed in the OCD from the regions in which we found significant brain- cohort during the MF task. Consistent with the litera- behaviour relations [67, 70, 71]. Langen et al. propose ture, these findings may suggest that the prefrontal that dysfunctions in the associative loop may present regions are hyperactive in children with OCD-like symp- behaviourally as impulsivity and/or rigidity. In light of toms ( for reviews, please refer to [56, 57]). Another this, it is possible that our brain-behaviour findings possibility is that this brain-behaviour relation indicates are reflective of an underlying corticostriatal dysfunc- that children with NDDs who activate their prefrontal tion, impacting the ability of the involved neural re- regions earlier exhibit behaviours associated with OCD gions to operate efficiently, including those that are symptomology, such as impulsive/compulsive behaviours important in set-shifting, resulting in symptoms of or hyper-performance monitoring. The latter is sup- greater inflexibility. ported by a study reporting hyperactivity in the MFC The other corticostriatal loops are thought to be in- during performance monitoring in children with OCD, volved with other forms of inflexible behaviour, with the Mogadam et al. Journal of Neurodevelopmental Disorders (2019) 11:19 Page 9 of 12 sensorimotor circuit associated with (atypical) stereotyp- evidence that sex impacts the presentation of the ical motor behaviour, and the limbic circuit with NDDs, we chose not to directly compare the con- motivation-associated facets of behaviour and obsessions trols to the NDD groups. Future studies should and compulsions. Different combinations of varia- target recruitment of additional males in the control tions or pathologies in these loops can create unique group so as to maintain comparable sex ratios with and inflexible behavioural patterns across individuals, the NDDs. A second limitation is that while co- creating the complexity and overlap in symptoms that morbidities and medications were noted, they were are observed in disorders where inflexibility and repeti- not factored into the analysis. Finally, while we tive behaviours are of issue. Langen et al. propose interpret our significant brain-behaviour results, it that these behavioural patterns fall along a continuum, should be noted that these values account for only a where, for example, a more pronounced abnormality in small part of the variance. Future studies, possibly prefrontal or posterior parietal regions of the corticos- with much larger sample sizes and incorporating an triatal system may result in symptomology that would age- and sex-matched control group, should attempt look more like the OCD or ASD cohorts, respectively. In to address these limitations. Despite these limita- this manner, a child with OCD who displays prefrontal tions, we believe in the value of these findings and over-engagement during a challenging MF exercise may hope they may be hypothesis-generating for other perform below average, similar to a child with ASD, who groups working in this field. displays less and later prefrontal engagement. Even if disruptions occur at different locations, or in different Conclusion loops, as both children most likely have impairments in In conclusion, while children with ASD, ADHD and their corticostriatal projections, their behavioural output OCD behaviourally share the same impairment in MF, looks similar. using MEG, we found a pattern of similarities and differ- Research into the structural and functional health of ences in the neurobiological bases supporting this execu- frontostriatal regions in NDDs shows ubiquitous atypi- tive function. We observed that the three groups share calities. Children with ASD and ADHD show decreased neurofunctional characteristics in the parietal regions, white matter integrity along the corticostriatal tracts but differ primarily in the frontal lobes. We observed compared to controls. Other studies have linked that the NDD groups showed an absence of sequential atypical frontostriatal (micro) structures to repetitive be- brain activations, but instead, they showed sustained haviours in ASD [73–75] and to more errors and more parietal activation which overlapped with frontal activa- trials to complete a set-shifting task in ADHD. A tion. This finding suggests that the three clinical groups recent fMRI study in children with ASD and OCD found share a delay or irregularity in brain development; a lon- a relation between increased functional connectivity gitudinal study with a control group is recommended to across frontostriatal regions during resting state and in- draw firmer conclusions within this domain. ASD and creased morbidity on their measure of repetitive behav- ADHD groups seemed more affected than the OCD with iours , although this may reverse in adulthood. limited and late frontal lobe activations; however, it To further explore the health and function of corticos- remains to be seen whether the sustained prefrontal triatal fibres in children with NDDs, future studies may engagement in OCD is typical. Analyses linking brain consider taking a multi-model imaging approach, explor- activity with behavioural symptom measures revealed ing white matter integrity of the loops and whether any significant relations between the activity in prefrontal correlations can be found with MF function. As well, it and parietal regions, and morbidity as measured on be- would be of interest to take a blind approach to the ana- havioural scales of repetitive and stereotypical behav- lysis and see whether primary diagnoses hold. iours, as well as obsessive-compulsive behaviours. These, in turn, may be reflective of a dysfunction in the associa- Limitations tive loop of the corticostriatal system, which has been There are some important limitations to consider. associated with inflexible behaviours, and has been We are not able to draw direct comparisons between found to be affected in children with NDDs. These find- the NDD groups and typically developing children as ings raise the possibility that this neural system may be a there was not a control group in this study. Our target for intervention. Finally, we believe our findings previous work in typically developing children are in line with new thinking that the NDDs exist along used the identical task, acquisition parameters and a complex continuum, where, despite the differing core processing pipeline of this study; however, there was phenotypic characteristics of the existing diagnostic an equally balanced sex ratio, whereas in the current groupings, NDDs appear to share some ‘deeper’ facets study, the cohorts are male-dominated, as would be that become evident when probing genetic or neurobio- expected in NDDs [79–81]. Because of the increasing logical underpinnings of the disorders. Mogadam et al. Journal of Neurodevelopmental Disorders (2019) 11:19 Page 10 of 12 Additional file Received: 11 October 2018 Accepted: 29 July 2019 Additional file 1: Supplement 1. (PDF 212 kb) References 1. American Psychiatric Association. Diagnostic and statistical manual of Acknowledgements mental disorders [Internet]. Fifth Edition: American Psychiatric Association; The authors thank the many children and families involved in the study, as 2013. well as the many members of the POND recruiting, diagnosis, assessment 2. Bishop D, Rutter M. Neurodevelopmental disorders: conceptual issues. and imaging teams who made this work possible. Additionally, we would In: Rutter’s child and adolescent psychiatry. 5th ed. Hoboken: Blackwell like to thank Amanda Robertson, MyLoi Huynh, Olivia Dell’Unto, Julie Sato Publishing Inc; 2008. p. 32–41. and Veronica Yuk who assisted with the study. 3. World Health Organization. Multiaxial classification of child and adolescent psychiatric disorders: the ICD-10 classification of mental and behavioral Authors’ contributions disorders in children and adolescents. Cambridge: Cambridge University AM conducted the study and co-wrote the manuscript draft as part of her Press; 1996. graduate thesis at the University of Toronto. AEK developed the neuroimaging 4. Leyfer OT, Folstein SE, Bacalman S, Davis NO, Dinh E, Morgan J, et al. analysis pipeline for this study and assisted with neuroimaging statistics. PDA Comorbid psychiatric disorders in children with autism: interview consulted on the study design and recruited and oversaw the cohort of development and rates of disorders. J Autism Dev Disord. 2006;36(7):849–61. children with OCD. RS consulted on the study design, recruited and oversaw 5. Gazzellone MJ, Zarrei M, Burton CL, Walker S, Uddin M, Shaheen SM, et al. the cohort of children with ADHD. JPL helped to design the study protocol and Uncovering obsessive-compulsive disorder risk genes in a pediatric cohort neuroimaging details. EA helped to design the study, secured funding and by high-resolution analysis of copy number variation. J Neurodev Disord. recruited and oversaw the cohort of children with ASD. EWP supervised the 2016;8:–36. https://doi.org/10.1186/s11689-016-9170-9. study and co-wrote the manuscript draft with the first author. All authors 6. Lionel AC, Crosbie J, Barbosa N, Goodale T, Thiruvahindrapuram B, Rickaby J, contributed to the manuscript draft and approve its submission. et al. Rare copy number variation discovery and cross-disorder comparisons identify risk genes for ADHD. Sci Transl Med. 2011;3(95):95ra75. 7. Lionel AC, Tammimies K, Vaags AK, Rosenfeld JA, Ahn JW, Merico D, et al. Funding Disruption of the ASTN2/TRIM32 locus at 9q33.1 is a risk factor in males for The MEG neuroimaging component of this work was completed as part of autism spectrum disorders, ADHD and other neurodevelopmental Alexandra Mogadam’s graduate thesis and was supported by the Province of phenotypes. Hum Mol Genet. 2014;23(10):2752–68. Available from: https:// Ontario Neurodevelopmental Disorders (POND) Network, in partnership with academic.oup.com/hmg/article/23/10/2752/615766/Disruption-of-the- the Ontario Brain Institute (OBI). Recruitment of the clinical groups is ASTN2-TRIM32-locus-at-9q33-1-is. supported as follows: Paul D. Arnold holds the Alberta Innovates Health 8. Ameis SH, Lerch JP, Taylor MJ, Lee W, Viviano JD, Pipitone J, et al. A Solutions (AIHS) Translation Health Chair in Child and Youth Mental Health; diffusion tensor imaging study in children with ADHD, autism spectrum Russell Schachar receives funding from DNA Genotek, Canadian Institutes of disorder, OCD, and matched controls: distinct and non-distinct white matter Health Research and TD Bank. disruption and dimensional brain-behavior relationships. Am J Psychiatry. 2016;173(12):1213–22. Availability of data and materials 9. Baribeau DA, Doyle-Thomas KAR, Dupuis A, Iaboni A, Crosbie J, McGinn H, The dataset supporting the conclusions of this article is available in the et al. Examining and comparing social perception abilities across childhood- Brain-CODE repository (https://www.braincode.ca). onset neurodevelopmental disorders. J Am Acad Child Adolesc Psychiatry. 2015;54(6):479–86 e1. 10. Armbruster DJN, Ueltzhöffer K, Basten U, Fiebach CJ. Prefrontal cortical Ethics approval and consent to participate mechanisms underlying individual differences in cognitive flexibility and The study was approved by the SickKids Research Ethics Board, and stability. J Cogn Neurosci. 2012;24(12):2385–99. informed written consent and verbal assent were obtained from parents and 11. Dajani DR, Uddin LQ. Demystifying cognitive flexibility: implications for children, respectively. clinical and developmental neuroscience. Trends Neurosci. 2015;38(9):571–8. 12. Kim C, Cilles SE, Johnson NF, Gold BT. Domain general and domain preferential brain regions associated with different types of task switching: a Consent for publication meta-analysis. Hum Brain Mapp. 2012;33(1):130–42. Available from: http:// All authors approve the publication of this manuscript. www.ncbi.nlm.nih.gov/pmc/articles/PMC3421461/. 13. Niendam TA, Laird AR, Ray KL, Dean YM, Glahn DC, Carter CS. Meta-analytic Competing interests evidence for a superordinate cognitive control network subserving diverse Russell Schachar provides consultation to Highland Therapeutics/Ironshore executive functions. Cogn Affect Behav Neurosci. 2012;12(2):241–68. Pharmaceuticals, has stock in ehave biotechnology company and previously 14. Mogadam A, Keller AE, Taylor MJ, Lerch JP, Anagnostou E, Pang EW. Mental was a consultant to Lilly Corp and Purdue Pharma. Evdokia Anagnostou flexibility: an MEG investigation in typically developing children. Brain Cogn. provides consultation to Roche, receives in-kind support from AMO Pharma 2017. Available from: http://www.sciencedirect.com/science/article/pii/S02 and Clinical Research Associates, receives royalties from APPI, Springer and 78262617301197. an honoraria from Wiley. Alexandra Mogadam, Anne E. Keller, Jason P. Lerch 15. Doesburg SM, Vidal J, Taylor MJ. Reduced theta connectivity during set- and Elizabeth W. Pang report no conflicts of interest. shifting in children with autism. Front Hum Neurosci. 2013;7:785. 16. Taylor MJ, Donner EJ, Pang EW. fMRI and MEG in the study of typical and Author details atypical cognitive development. Neurophysiol Clin Neurophysiol. 2012;42(1– 1 Institute of Medical Science, Faculty of Medicine, University of Toronto, 2):19–25. Toronto, Canada. 2Neurosciences and Mental Health, SickKids Research 17. Velazquez JLP, Barcelo F, Hung Y, Leshchenko Y, Nenadovic V, Belkas Institute, Toronto, Canada. 3Division of Neurology, Hospital for Sick Children, J, et al. Decreased brain coordinated activity in autism spectrum 555 University Avenue, Toronto M5G 1X8, Canada. 4Mathison Centre for disorders during executive tasks: reduced long-range synchronization Mental Health Research and Education, University of Calgary, Calgary, in the fronto-parietal networks. Int J Psychophysiol. 2009;73(3):341–9. Canada. 5Genetics and Genome Biology, SickKids Research Institute, Toronto, 18. D’Cruz A-M, Mosconi MW, Ragozzino ME, Cook EH, Sweeney JA. Alterations Canada. 6Department of Psychiatry, Faculty of Medicine, University of in the functional neural circuitry supporting flexible choice behavior in Toronto, Toronto, Canada. 7Mouse Imaging Centre, Hospital for Sick Children, autism spectrum disorders. Transl Psychiatry. 2016;6(10):e916. Available from: Toronto, Canada. 8Department of Medical Biophysics, Faculty of Medicine, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5315543/. University of Toronto, Toronto, Canada. 9Holland Bloorview Kids 19. Rubia K, Cubillo A, Smith AB, Woolley J, Heyman I, Brammer MJ. Disorder- Rehabilitation Hospital, Toronto, Canada. specific dysfunction in right inferior prefrontal cortex during two inhibition Mogadam et al. Journal of Neurodevelopmental Disorders (2019) 11:19 Page 11 of 12 tasks in boys with attention-deficit hyperactivity disorder compared to boys 41. Brookes MJ, Hall EL, Robson SE, Price D, Palaniyappan L, Liddle EB, et al. with obsessive–compulsive disorder. Hum Brain Mapp. 2010;31(2):287–99. Complexity measures in magnetoencephalography: measuring “disorder” in 20. Rubia K, Halari R, Cubillo A, Mohammad A-M, Scott S, Brammer M. Disorder- schizophrenia. PLOS ONE. 2015;10(4):e0120991. Available from: http:// specific inferior prefrontal hypofunction in boys with pure attention-deficit/ journals.plos.org/plosone/article?id=10.1371/journal.pone.0120991. hyperactivity disorder compared to boys with pure conduct disorder during 42. Rorden C, Karnath H-O, Bonilha L. Improving lesion-symptom mapping. J cognitive flexibility. Hum Brain Mapp. 2010;31(12):1823–33. Cogn Neurosci. 2007;19(7):1081–8. 21. Smith AB, Taylor E, Brammer M, Toone B, Rubia K. Task-specific 43. Penny WD, Holmes AP, Friston KJ. Random effects analysis. In: Human brain hypoactivation in prefrontal and temporoparietal brain regions during function. 2nd ed; 2003. p. 843–50. motor inhibition and task switching in medication-naive children and 44. Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, et al. adolescents with attention deficit hyperactivity disorder. Am J Psychiatry. Dynamic mapping of human cortical development during childhood 2006;163(6):1044–51. through early adulthood. Proc Natl Acad Sci U S A. 2004;101(21):8174–9. 22. Britton JC, Rauch SL, Rosso IM, Killgore WDS, Price LM, Ragan J, et al. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC419576/. Cognitive inflexibility and frontal-cortical activation in pediatric obsessive- 45. Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL, compulsive disorder. J Am Acad Child Adolesc Psychiatry. 2010;49(9):944–53. Toga AW. Mapping cortical change across the human life span. Nat 23. Hansen PC, Kringelbach ML, Salmelin R. Introduction. In: Hansen P, Neurosci. 2003;6(3):309–15. Kringelbach ML, Salmelin R, editors. MEG: an introduction to methods. 46. Bunge SA, Dudukovic NM, Thomason ME, Vaidya CJ, Gabrieli JDE. Immature Oxford, New York: Oxford University Press; 2010. p. vii–x. frontal lobe contributions to cognitive control in children. Neuron. 2002; 24. Hari R, Salmelin R. Magnetoencephalography: from SQUIDs to neuroscience: 33(2):301–11. Available from: http://www.cell.com/neuron/abstract/S0896-62 Neuroimage 20th anniversary special edition. NeuroImage. 2012;61(2):386–96. 73(01)00583-9. 25. Mišić B, Doesburg SM, Fatima Z, Vidal J, Vakorin VA, Taylor MJ, et al. 47. Casey BJ, Galvan A, Hare TA. Changes in cerebral functional organization Coordinated information generation and mental flexibility: large-scale during cognitive development. Curr Opin Neurobiol. 2005;15(2):239–44. network disruption in children with autism. Cereb Cortex. 2015;25(9): 48. Crone EA, Zanolie K, Leijenhorst LV, Westenberg PM, Rombouts SARB. 2815–27. Neural mechanisms supporting flexible performance adjustment during 26. Ickowicz A, Schachar RJ, Sugarman R, Chen SX, Millette C, Cook L. The development. Cogn Affect Behav Neurosci. 2008;8(2):165–77. Available from: parent interview for child symptoms: a situation-specific clinical research http://link.springer.com/article/10.3758/CABN.8.2.165. interview for attention-deficit hyperactivity and related disorders. Can J 49. Rubia K, Smith AB, Woolley J, Nosarti C, Heyman I, Taylor E, et al. Progressive Psychiatry. 2006;51(5):325–8. https://doi.org/10.1177/070674370605100508. increase of frontostriatal brain activation from childhood to adulthood 27. Scahill L, Riddle MA, McSwiggin-Hardin M, Ort SI, King RA, Goodman WK, during event-related tasks of cognitive control. Hum Brain Mapp. 2006; et al. Children’s Yale-Brown obsessive compulsive scale: reliability and 27(12):973–93. validity. J Am Acad Child Adolesc Psychiatry. 1997;36(6):844–52. 50. Booth JR, Burman DD, Meyer JR, Lei Z, Trommer BL, Davenport ND, 28. Bodfish JW, Symons FJ, Parker DE, Lewis MH. Varieties of repetitive behavior et al. Larger deficits in brain networks for response inhibition than for in autism: comparisons to mental retardation. J Autism Dev Disord. 2000; visual selective attention in attention deficit hyperactivity disorder 30(3):237–43. (ADHD). J Child Psychol Psychiatry. 2005;46(1):94–111. 29. Bodfish JW, Symons FJ, Lewis MH. The Repetitive Behavior Scale: a test 51. Cherkasova MV, Hechtman L. Neuroimaging in attention-deficit manual. Morganton: Western Carolina Center Research Reports; 1999. hyperactivity disorder: beyond the frontostriatal circuitry. Can J Psychiatry 30. Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, DiLavore PC, et al. The Rev Can Psychiatr. 2009;54(10):651–64. Autism Diagnostic Observation Schedule—Generic: a standard measure of 52. Cubillo A, Halari R, Ecker C, Giampietro V, Taylor E, Rubia K. Reduced social and communication deficits associated with the spectrum of autism. activation and inter-regional functional connectivity of fronto-striatal J Autism Dev Disord. 2000;30(3):205–23. networks in adults with childhood attention-deficit hyperactivity disorder 31. Lord C, Rutter M, Couteur AL. Autism Diagnostic Interview-Revised: a (ADHD) and persisting symptoms during tasks of motor inhibition and revised version of a diagnostic interview for caregivers of individuals cognitive switching. J Psychiatr Res. 2010;44(10):629–39. with possible pervasive developmental disorders. J Autism Dev Disord. 53. Dickstein SG, Bannon K, Castellanos FX, Milham MP. The neural correlates of 1994;24(5):659–85. attention deficit hyperactivity disorder: an ALE meta-analysis. J Child Psychol 32. Park LS, Burton CL, Dupuis A, Shan J, Storch EA, Crosbie J, et al. The Toronto Psychiatry. 2006;47(10):1051–62. Obsessive-Compulsive Scale: psychometrics of a dimensional measure of 54. Rubia K, Overmeyer S, Taylor E, Brammer M, Williams SCR, Simmons A, et al. obsessive-compulsive traits. J Am Acad Child Adolesc Psychiatry. 2016;55(4): Hypofrontality in attention deficit hyperactivity disorder during higher-order 310–8 e4. motor control: a study with functional MRI. Am J Psychiatry. 1999;156(6):891–6. 33. Swanson JM, Schuck S, Porter MM, Carlson C, Hartman CA, Sergeant JA, 55. Fitzgerald KD, Stern ER, Angstadt M, Nicholson-Muth KC, Maynor MR, Welsh et al. Categorical and dimensional definitions and evaluations of symptoms RC, et al. Altered function and connectivity of the medial frontal cortex in of ADHD: history of the SNAP and the SWAN Rating Scales. Int J Educ pediatric obsessive-compulsive disorder. Biol Psychiatry. 2010;68(11):1039–47. Psychol Assess. 2012;10(1):51–70. Available from: http://www.ncbi.nlm.nih. 56. Aylward EH, Harris GJ, Hoehn-Saric R, Barta PE, Machlin SR, Pearlson GD. gov/pmc/articles/PMC4618695/. Normal caudate nucleus in obsessive-compulsive disorder assessed by 34. Oh A, Vidal J, Taylor MJ, Pang EW. Neuromagnetic correlates of intra- and quantitative neuroimaging. Arch Gen Psychiatry. 1996;53(7):577–84. extra-dimensional set-shifting. Brain Cogn. 2014;86:90–7. 57. Menzies L, Chamberlain SR, Laird AR, Thelen SM, Sahakian BJ, Bullmore 35. Statistical Parametric Mapping. London: Wellcome Trust Centre for ET. Integrating evidence from neuroimaging and neuropsychological Neuroimaging. 2014. studies of obsessive-compulsive disorder: the orbitofronto-striatal model 36. Oostenveld R, Fries P, Maris E, Schoffelen J-M. FieldTrip: open source software revisited. Neurosci Biobehav Rev. 2008;32(3):525–49. for advanced analysis of MEG, EEG, and invasive electrophysiological data. 58. Chamberlain SR, Menzies L, Hampshire A, Suckling J, Fineberg NA, del Comput Intell Neurosci. 2010;2011:e156869. Available from: https://www. Campo N, et al. Orbitofrontal dysfunction in patients with obsessive- hindawi.com/journals/cin/2011/156869/abs/. compulsive disorder and their unaffected relatives. Science. 2008; 37. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of 321(5887):421–2. single-trial EEG dynamics including independent component analysis. 59. Gu B-M, Park J-Y, Kang D-H, Lee SJ, Yoo SY, Jo HJ, et al. Neural correlates of J Neurosci Methods. 2004;134(1):9–21. cognitive inflexibility during task-switching in obsessive-compulsive disorder. 38. Kovacevic N, McIntosh AR. Groupwise independent component Brain. 2008;131(1):155–64 Available from: http://brain.oxfordjournals.org/ decomposition of EEG data and partial least square analysis. NeuroImage. content/131/1/155. 2007;35(3):1103–12. 60. Schmitz N, Rubia K, Daly E, Smith A, Williams S, Murphy DGM. Neural 39. Friston K, Henson R, Phillips C, Mattout J. Bayesian estimation of evoked correlates of executive function in autistic spectrum disorders. Biol and induced responses. Hum Brain Mapp. 2006;27(9):722–35. Psychiatry. 2006;59(1):7–16. 40. Mattout J, Phillips C, Penny WD, Rugg MD, Friston KJ. MEG source 61. Ecker C, Bookheimer SY, Murphy DGM. Neuroimaging in autism spectrum localization under multiple constraints: an extended Bayesian framework. disorder: brain structure and function across the lifespan. Lancet Neurol. NeuroImage. 2006;30(3):753–67. 2015;14(11):1121–34. Mogadam et al. Journal of Neurodevelopmental Disorders (2019) 11:19 Page 12 of 12 62. Keown CL, Shih P, Nair A, Peterson N, Mulvey ME, Müller R-A. Local functional overconnectivity in posterior brain regions is associated with symptom severity in autism spectrum disorders. Cell Rep. 2013;5(3):567–72. Available from: http://www.sciencedirect.com/science/article/pii/S2211124 71300572X. 63. Maximo JO, Keown CL, Nair A, Müller R-A. Approaches to local connectivity in autism using resting state functional connectivity MRI. Front Hum Neurosci. 2013;7. Available from: http://journal.frontiersin.org/article/10.3389/ fnhum.2013.00605/full. 64. Zeeland AAS-V, Abrahams BS, Alvarez-Retuerto AI, Sonnenblick LI, Rudie JD, Ghahremani D, et al. Altered functional connectivity in frontal lobe circuits is associated with variation in the autism risk gene CNTNAP2. Sci Transl Med. 2010;2(56):56ra80. 65. D’Cruz A-M, Ragozzino ME, Mosconi MW, Shrestha S, Cook EH, Sweeney JA. Reduced behavioral flexibility in autism spectrum disorders. Neuropsychology. 2013;27(2):152–60. 66. Miller HL, Ragozzino ME, Cook EH, Sweeney JA, Mosconi MW. Cognitive set shifting deficits and their relationship to repetitive behaviors in autism spectrum disorder. J Autism Dev Disord. 2015;45(3):805–15. 67. Haber SN. Corticostriatal circuitry. Dialogues Clin Neurosci. 2016;18(1):7–21. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4826773/. 68. Middleton FA, Strick PL. Basal ganglia output and cognition: evidence from anatomical, behavioral, and clinical studies. Brain Cogn. 2000;42(2):183–200. 69. Langen M, Durston S, Kas MJH, van Engeland H, Staal WG. The neurobiology of repetitive behavior: … and men. Neurosci Biobehav Rev. 2011;35(3):356–65. 70. Jarbo K, Verstynen TD. Converging structural and functional connectivity of orbitofrontal, dorsolateral prefrontal, and posterior parietal cortex in the human striatum. J Neurosci. 2015;35(9):3865–78. 71. Alexander GE, DeLong MR, Strick PL. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci. 1986;9(1):357–81. Available from: https://www.annualreviews.org/doi/1 0.1146/annurev.ne.09.030186.002041. 72. Osmon DC, Smerz JM. Neuropsychological evaluation in the diagnosis and treatment of Tourette’s syndrome. Behav Modif. 2005;29(5):746–83. 73. Hollander E, Anagnostou E, Chaplin W, Esposito K, Haznedar MM, Licalzi E, et al. Striatal volume on magnetic resonance imaging and repetitive behaviors in autism. Biol Psychiatry. 2005;58(3):226–32. 74. Langen M, Bos D, Noordermeer SDS, Nederveen H, van Engeland H, Durston S. Changes in the development of striatum are involved in repetitive behavior in autism. Biol Psychiatry. 2014;76(5):405–11. 75. Rojas DC, Peterson E, Winterrowd E, Reite ML, Rogers SJ, Tregellas JR. Regional gray matter volumetric changes in autism associated with social and repetitive behavior symptoms. BMC Psychiatry. 2006;6:56. https://doi. org/10.1186/1471-244X-6-56. 76. Chiang H-L, Chen Y-J, Shang C-Y, Tseng W-YI, Gau SS-F. Different neural substrates for executive functions in youths with ADHD: a diffusion spectrum imaging tractography study. Psychol Med Camb. 2016;46(6):1225–38. 77. Akkermans SEA, Rheinheimer N, Bruchhage MMK, Durston S, Brandeis D, Banaschewski T, et al. Frontostriatal functional connectivity correlates with repetitive behaviour across autism spectrum disorder and obsessive- compulsive disorder. Psychol Med. 2018;26:1–9. 78. Vaghi MM, Vértes PE, Kitzbichler MG, Apergis-Schoute AM, van der Flier FE, Fineberg NA, et al. Specific frontostriatal circuits for impaired cognitive flexibility and goal-directed planning in obsessive-compulsive disorder: evidence from resting-state functional connectivity. Biol Psychiatry. 2017;81(8):708–17. 79. Jacquemont S, Coe BP, Hersch M, Duyzend MH, Krumm N, Bergmann S, et al. A higher mutational burden in females supports a “female protective model” in neurodevelopmental disorders. Am J Hum Genet. 2014;94(3):415–25. Available from: http://www.sciencedirect.com/science/article/pii/S0002929714 000597. 80. Valera EM, Brown A, Biederman J, Faraone SV, Makris N, Monuteaux MC, et al. Sex differences in the functional neuroanatomy of working memory in adults with ADHD. Am J Psychiatry. 2010;167(1):86–94. 81. Werling DM, Geschwind DH. Sex differences in autism spectrum disorders. Curr Opin Neurol Curr Opin Neurol. 2013;26(2):146–53. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.