Abstract
Background
People with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) have abnormalities in frontal, temporal, parietal and striato-thalamic networks. It is unclear to what extent these abnormalities are distinctive or shared. This comparative meta-analysis aimed to identify the most consistent disorder-differentiating and shared structural and functional abnormalities.
Methods
Systematic literature search was conducted for whole-brain voxel-based morphometry (VBM) and fMRI studies of cognitive control comparing people with ASD or ADHD with typically developing controls. Regional gray matter volume (GMV) and fMRI abnormalities during cognitive control were compared in the overall sample and in age-, sex- and IQ-matched subgroups with seed-based d mapping meta-analytic methods.
Results
Eighty-six independent VBM (1533 ADHD and 1295 controls; 1445 ASD and 1477 controls) and 60 fMRI datasets (1001 ADHD and 1004 controls; 335 ASD and 353 controls) were identified. The VBM meta-analyses revealed ADHD-differentiating decreased ventromedial orbitofrontal (z = 2.22, p < .0001) but ASD-differentiating increased bilateral temporal and right dorsolateral prefrontal GMV (zs ≥ 1.64, ps ≤ .002).The fMRI meta-analyses of cognitive control revealed ASD-differentiating medial prefrontal underactivation but overactivation in bilateral ventrolateral prefrontal cortices and precuneus (zs ≥ 1.04, ps ≤ .003). During motor response inhibition specifically, ADHD relative to ASD showed right inferior fronto-striatal underactivation (zs ≥ 1.14, ps ≤ .003) but shared right anterior insula underactivation.
Conclusions
People with ADHD and ASD have mostly distinct structural abnormalities, with enlarged fronto-temporal GMV in ASD and reduced orbitofrontal GMV in ADHD; and mostly distinct functional abnormalities, which were more pronounced in ASD.
1. Introduction
Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are distinct neurodevelopmental conditions. ADHD is characterized by age-inappropriate symptoms of inattention, hyperactivity and impulsivity, whereas ASD is characterized by difficulties in social interaction/communication and stereotypical repetitive behavior (American Psychiatric Association (APA), 2013). The estimated prevalence of ADHD (5-7%) is higher than ASD (1-2%) worldwide (Elsabbagh et al., 2012; Polanczyk, Willcutt, Salum, Kieling, & Rohde, 2014). In both disorders, brain structural abnormalities have been observed in frontal, temporal, parietal and striato-thalamic networks (Ecker, 2017; Norman et al., 2016), although the extent of their overlap is poorly understood. Furthermore, both disorders are associated with cognitive control deficits (F. Craig et al., 2016), although ASD relative to ADHD seem less severely impaired (see meta-analyses Lipszyc & Schachar, 2010; Willcutt, Sonuga-Barke, Nigg, & Sergeant, 2008) and display more heterogeneous performance (Geurts, van den Bergh, & Ruzzano, 2014; Kuiper, Verhoeven, & Geurts, 2016). Exploring abnormalities in brain structure and cognitive control function could help elucidate the disorder-differentiating and shared difficulties in ASD and ADHD.
In ADHD, brain abnormalities are thought to represent delayed maturation in brain structure and function mediating late-developing cognitive functions such as cognitive control (Hoogman et al., 2017; Shaw et al., 2007; Sripada, Kessler, & Angstadt, 2014). Correspondingly, previous voxel-based morphometry (VBM) meta-analyses have revealed reduced gray matter volume (GMV) in ventromedial orbitofrontal cortex (vmOFC) and basal ganglia (BG) (Frodl & Skokauskas, 2012; Nakao, Radua, Rubia, & Mataix-Cols, 2011; Norman et al., 2016). In addition, mega-analytic studies by the ENIGMA consortium of subcortical and cortical structure involving over 1700 and 2200 individuals with ADHD, respectively, found reduced GMV in BG, amygdala and hippocampus (Hoogman et al., 2017), as well as reduced cortical surface areas and thickness in several frontal, temporal and parietal regions (Hoogman et al., 2019).
Converging with these findings are meta-analytic reports of underactivation in lateral and medial frontostriatal networks in ADHD relative to controls during cognitive control such as in right inferior frontal gyrus (IFG)/anterior insula (AI), supplementary motor area (SMA)/anterior cingulate cortex (ACC), and the striatum (Hart, Radua, Nakao, Mataix-Cols, & Rubia, 2013; McCarthy, Skokauskas, & Frodl, 2014; Norman et al., 2016). The role of right IFG as potential biomarker of ADHD in particular has been discussed in several studies and a meta-analysis showing disorder-specificity of underactivation in right and/or left IFG during cognitive control in ADHD children relative to pediatric bipolar disorder (Passarotti, Sweeney, & Pavuluri, 2010), obsessive-compulsive disorder (Norman et al., 2016) and conduct disorder (Rubia, 2018; Rubia, Halari, et al., 2010).
In ASD, findings from neuroimaging, post-mortem and head circumference development studies have led to the “early brain overgrowth theory” (Courchesne, Campbell, & Solso, 2011; Lange et al., 2015; Redcay & Courchesne, 2005; Schumann et al., 2010), which, however, has been contested in recent studies (Ecker, Schmeisser, Loth, & Murphy, 2017; Raznahan et al., 2013). Meta-analytic and mega-analytic findings have varied but GMV reduction in the striatum and amygdala/hippocampus were among the more frequently reported (Cauda et al., 2011; DeRamus & Kana, 2015; Nickl-Jockschat et al., 2012; van Rooij et al., 2018; Via, Radua, Cardoner, Happe, & Mataix-Cols, 2011). Another recent meta-analysis of VBM studies in over 900 ASD patients, however, primarily found cortical abnormalities such as GMV decrease in medial prefrontal cortex (mPFC) and posterior insula; and increase in left anterior temporal, right inferior temporo-parietal, left dorsolateral prefrontal (dlPFC) and precentral cortices (Carlisi et al., 2017). Furthermore, the ENIGMA mega-analysis has found cortical thickness increase in frontal and decrease in temporal areas (van Rooij et al., 2018).
The whole-brain meta-analysis of fMRI studies of cognitive control in ASD (Carlisi et al., 2017) has found underactivation in salience and executive network areas such as mPFC, left dorsolateral and right ventrolateral prefrontal cortices (vlPFC)/AI, left cerebellum, inferior parietal lobe (IPL) and right premotor area; and overactivation in right temporo-parietal regions including default mode network (DMN) areas.
While these meta-analytic findings suggest partially overlapping brain abnormalities in ASD and ADHD, only direct comparisons can elucidate their differences and commonalities. Among such studies, a VBM study showed reduced GMV in ADHD relative to ASD in right posterior cerebellum and ASD-differentiating reduced left middle/superior temporal gyri (M/STG) (Lim et al., 2015). Furthermore, fMRI studies have shown ASD-differentiating mPFC underactivation during reward reversal (Chantiluke, Barrett, Giampietro, Brammer, Simmons, Murphy, et al., 2015), shared dlPFC underactivation during sustained attention and working memory (Chantiluke, Barrett, Giampietro, Brammer, Simmons, & Rubia, 2015; Christakou et al., 2013); and shared precuneus abnormalities during sustained attention, reward reversal and the resting state (Chantiluke, Barrett, Giampietro, Brammer, Simmons, Murphy, et al., 2015; Christakou et al., 2013; Di Martino et al., 2013). Importantly, during successful motor inhibition, ADHD-differentiating underactivation was observed in left vlPFC and BG and ASD-differentiating overactivation in bilateral IFG (Chantiluke, Barrett, Giampietro, Santosh, et al., 2015). Few imaging studies, however, have directly compared the two disorders and their relatively small sample sizes have limited statistical power.
We therefore conducted meta-analyses of structural and functional abnormalities related to functions that are commonly impaired in ASD and ADHD, i.e., cognitive control. The aim of the study was to establish the most consistently disorder-differentiating and shared structural and functional deficits, which is important for developing disorder-specific or transdiagnostic treatment. Guided by previous comparative studies and meta-analyses in ASD and/or ADHD, we hypothesized that these disorders are characterized by both shared abnormalities in salience, cognitive control, and default mode networks as well as disorder-differentiating impairments. Structurally, we hypothesized ADHD-differentiating reduced GMV in BG/insula relative to ASD (Lim et al., 2015; Norman et al., 2016), and ASD-differentiating increased GMV in frontal and temporo-parietal regions (Carlisi et al., 2017; Lim et al., 2015). During cognitive control, we expected ADHD-differentiating reduced activation in right IFG and BG relative to ASD (Chantiluke, Barrett, Giampietro, Santosh, et al., 2015; Norman et al., 2016; Passarotti et al., 2010; Rubia et al., 2009), ASD-differentiating underactivation relative to ADHD in the medial frontal part of the cognitive control network (Carlisi et al., 2017; Chantiluke, Barrett, Giampietro, Brammer, Simmons, Murphy, et al., 2015), and shared abnormal overactivation in precuneus in both disorders (Chantiluke, Barrett, Giampietro, Brammer, Simmons, Murphy, et al., 2015; Christakou et al., 2013; Di Martino et al., 2013).
2. Methods
2.1. Publication Search and Study Inclusion
Systematic literature search was conducted for peer-reviewed English language publications in PubMed, Scopus, Web of Science and ScienceDirect until 30th November 2018, for whole-brain fMRI or VBM studies in ASD and ADHD. Manual search was conducted in reference lists of previous meta-analyses. Included studies compared the ASD or ADHD groups against typically developing (TD) controls on GMV or on cognitive-control fMRI BOLD signal from stop-signal, go/no-go, switch, Stroop, Simon and flanker, using predefined inhibitory contrasts (Supplement 1). Only whole-brain neuroimaging data were included, to prevent bias towards specific neuroanatomy (Friston, Rotshtein, Geng, Sterzer, & Henson, 2006). Excluded studies used region of interest (ROI) approaches or involved <10 patients, which were deemed lacking in statistical power (Desmond & Glover, 2002; Nakao et al., 2011; Thirion et al., 2007). Studies that potentially report duplicate data from other publications (including mega-analyses), have no TD controls, or have provided no peak coordinates, were excluded. When samples overlapped, the largest sample was included. Mean age, proportion of males, mean IQ, cognitive control tasks, current and lifetime psychostimulant use (i.e., proportion of participants being prescribed the medication) and effect size and coordinate location of peaks for regional GMV and activation differences were extracted from each study and authors were contacted for missing information. Reports of exclusion or inclusion of people with co-occurring ASD or ADHD in the counterpart disorder groups were also extracted from the literature. Data were extracted from all studies by SL, 64% of which were also extracted by LN and CC who achieved 100% agreement. This meta-analysis was not pre-registered in a time-stamped, institutional registry prior to the research being conducted.
2.2. Meta-Analyses
The anisotropic seed-based d Mapping (AES-SDM) meta-analytic software (www.sdmproject.com; Supplement 1) employed in previous meta-analyses of ASD and ADHD (Carlisi et al., 2017; Frodl & Skokauskas, 2012; McCarthy et al., 2014; Norman et al., 2016) was used for the present voxel-wise meta-analyses. The software can accommodate statistical parametric maps or peak coordinates and effect sizes (t-scores) data. For the latter, AES-SDM computes signed (positive/negative) effect sizes and variance maps of brain regional differences between clinical and control groups by convolving an anisotropic non-normalized Gaussian kernel with Hedges effect size of each peak. Voxels correlated with the peak values are assigned higher effect sizes. Maps are then combined across studies based on random-effect model, accounting for sample size, within-study variability and between-study heterogeneity. Correlated datasets (e.g., when the same group of participants completed several cognitive tasks) were included in the meta-analysis as a single set (Norman et al., 2016), adjusted for shared variance of brain activation or structure across datasets. The Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines were followed (Stroup et al., 2000).
Age, sex and IQ were compared across patient and control groups prior to meta-analyses in STATA14 (StataCorp, 2015), using sample-size weighted F-statistics. First, VBM meta-analyses were conducted in each disorder relative to TD controls and then between the two disorders. Second, equivalent fMRI meta-analyses were conducted to examine the neural impairments observed during cognitive control within and between disorders using all available data. To explore specific cognitive-control subconstructs (e.g., Bari & Robbins, 2013), we conducted fMRI sub-meta-analyses of prepotent motor response inhibition, including studies employing the go/no-go and stop-signal tasks only. No other cognitive control subconstructs were explored due to insufficient data. To find the most consistent disorder-differentiating neural abnormalities regardless of demographic characteristics, we conducted sensitivity meta-analyses in an age-, sex- and IQ-matched subgroup, instead of covarying group differences in these variables that could result in false positives (Dennis et al., 2009; Miller & Chapman, 2001). An iterative algorithm computed group differences in age, sex and IQ for all possible combinations of studies, selecting the largest subgroups with statistically nonsignificant group differences in the three variables (Carlisi et al., 2017). Findings surviving the sensitivity analyses in the matched subgroups are the only ones reported and discussed.
Furthermore, conjunction analyses were used to asses overlapping fMRI and GMV abnormalities within and across disorders (using threshold p < .005). To assess consistency of findings across age groups, supplementary age-stratified sub-meta-analyses were conducted in overall pediatric or adult age groups and in their respective age-, sex- and IQ-matched subgroups, where possible (Supplements 3 and 4). These were complemented with a series of sample-size weighted correlational analyses between the mean sample age and extracted effect sizes of brain abnormalities averaged over 10-mm radius spheres centered at the peaks of disorder-differentiating or shared abnormalities, applying Bonferroni multiple comparison correction (Supplement 5). Non-parametric correlations were chosen due to the bimodal distribution of age among studies. Following previous meta-analyses (Hart et al., 2013; Norman et al., 2016), psychostimulant effects were explored in separate meta-regressions between brain structure/function and lifetime psychostimulant exposure, i.e., proportions of samples who have ever been exposed to psychostimulants, or current psychostimulant exposure, in brain regions that are found impaired in ADHD (Supplement 6). Supplementary meta-analyses covarying for task types (motor response inhibition, cognitive interference, motor switching, combination of tasks) and behavioral task performance (impaired, unimpaired) were also conducted to assess their impacts on disorder-differentiating findings (Supplement 7).
A statistical threshold p < .005 was used for all meta-analyses (Radua & Mataix-Cols, 2009; Radua, Mataix-Cols, et al., 2012), and a reduced threshold p < .0005 and a cluster extent 20 voxels was used in the meta-regressions to control for false positives (Radua, Borgwardt, et al., 2012). Egger’s tests assessed potential publication bias in the disorder-differentiating and shared findings. Jack-knife sensitivity analyses examined replicability of findings through iterative whole-brain meta-analyses, leaving one dataset out at a time (Supplement 8; Radua & Mataix-Cols, 2009).
3. Results
3.1. Search Results and Sample Characteristics
The systematic literature search identified 140 VBM and fMRI articles of cognitive control in ASD and ADHD. Accounting for datasets of same clinical participants and, where possible, separating data from pediatric and adult participants resulted in 86 independent VBM datasets (38 datasets including 1533 ADHD participants and 1295 controls; 48 datasets involving 1445 ASD participants and 1477 controls) and 60 independent cognitive-control fMRI datasets (42 datasets involving 1001 ADHD participants and 1004 controls; 18 datasets involving 335 ASD participants and 353 controls) (Tables 1-2). Groups did not differ in age in VBM, F(3, 83) = 1.24, p = .30, and fMRI datasets, F(3, 56) = .68, p =.57, but did in sex in VBM, F(3, 83) = 6.03, p = .0009, and fMRI datasets, F(3, 56) = 5.93, p = .0014; and in IQ in VBM, F(3, 67) = 16.2, p < .0001) and fMRI datasets F(3, 45) = 15.5, p < .0001. See Table 3 for demographic characteristics.
Table 1. Sample Characteristics and Summary Findings of VBM and fMRI Cognitive Control Studies in ADHD.
| Source | Age group | Task | Patients | Controls | Summary findings | ||||
|---|---|---|---|---|---|---|---|---|---|
| N (% male) | Age, y | IQ | N (% male) | Age, y | IQ | ||||
| (A) VBM studies in ADHD | |||||||||
| Ahrendts et al. (2011) | Adult | -- | 31 (65) | 31.2 | n/a | 31 (65) | 31.5 | n/a | ADHD<TD: L/R occipital lobe |
| Montes et al. (2010) | Adult | -- | 20 (50) | 29.0 | 102.9 | 20 (50) | 27.6 | 100.2 | ADHD<TD: R caudate |
| Amico, Stauber, Koutsouleris, and Frodl (2011) | Adult | -- | 20 (75) | 33.6 | n/a | 20 (75) | 34.7 | n/a | -- |
| Bonath, Tegelbeckers, Wilke, Flechtner, and Krauel (2016) | Pediat. | -- | 18 (100) | 13.6 | 106.8 | 18 (100) | 14.1 | 108.1 | ADHD<TD: L/R cerebellum, occipital cortex, L/R hippocampus/ amygdala complex, L ACC |
| Bralten et al. (2016) | Mixed | -- | 307 (68) | 17.1 | 97.1 | 196 (49) | 16.7 | 106.6 | ADHD<TD: L pre-CG, L OFC, R frontal pole, L paracingulate/cingulate cortex/frontal pole, bilateral medial FG/ paracingulate/cingulate/ subcallosal cortices |
| Brieber et al. (2007) | Pediat. | -- | 15 (100) | 13.1 | 104.1 | 15 (100) | 13.3 | 107.7 | ADHD<TD: L/R caudate nucleus, L/R hippocampus, R MFG, L insula, L STG, L MOG; ADHD>TD: L/R SPL, post-CG, L middle cingulate, precuneus, L IPL |
| Carmona et al. (2005) | Pediat. | -- | 25 (84) | 10.8 | n/a | 25 (84) | 11.2 | n/a | ADHD<TD: L pre-/post-CG, paracentral lobule, R rectal gyrus, R inferior and superior orbital frontal, L/R cerebellum |
| Depue, Burgess, Bidwell, Willcutt, and Banich (2010) | Adult | -- | 31 (61) | 20.0 | 114.2 | 21 (65) | 19.3 | 112.6 | -- |
| Gehricke et al. (2017) | Adult | -- | 32 (81) | 25.3 | n/a | 40 (83) | 23.9 | n/a | ADHD<TD: R M/IFG, R S/MTG, L caudate head, L PHG |
| He et al. (2015) | Pediat. | -- | 37 (100) | 9.9 | n/a | 35 (100) | 10.7 | n/a | ADHD<TD: R OFC, R PMC, L PCC, L posterior midcingulate cortex |
| Iannaccone, Hauser, Ball, et al. (2015) | Pediat. | -- | 18 (50) | 14.5 | 114.5 | 18 (61) | 14.8 | 108.5 | ADHD<TD: R medial SFG/ACC/SFG, L SMA/cingulate gyrus, L cerebellum; ADHD>TD: R pre- and post-CG |
| Jagger-Rickels, Kibby, and Constance (2018) | Pediat. | -- | 41 (44) | 9.6 | n/a | 32 (56) | 9.7 | n/a | ADHD<TD: L MTG/STS, R STS L/R caudate/ putamen, L/R SFG, L/R insula, L/R thalamus, R medial OFC, L MFG, L/R occipital lobe, L medial SFG, L pre-CG, R post cingulate/anterior lingual, R calcarine/cuneus, L supramarginal gyrus |
| Johnston et al. (2014) | Pediat. | -- | 34 (100) | 12.5 | 99.8 | 34 (100) | 13.2 | 103.7 | ADHD<TD: Brainstem, putamen |
| Kappel et al. (2015) | Pediat. | -- | 14 (71) | 9.8 | 104.6 | 10 (80) | 11.0 | 111.9 | ADHD<TD: R STG, R Heschl’s gyrus, R rolandic operculum; ADHD>TD: L paracentral lobule, L/R middle orbital gyrus, R FFG, L rectal gyrus |
| Adult | -- | 16 (94) | 23.5 | 97.8 | 20 (100) | 23.7 | 108.4 | ADHD<TD: R supramarginal gyrus, R precuneus, L OFG, R hippocampus, L rectal gyrus | |
| Kaya et al. (2018) | Pediat. | -- | 19 (74) | 10.3 | 113.5 | 18 (67) | 10.2 | 119.7 | ADHD>TD: L/R SFG, R MFG, L/R SMA, L/R pre-CG, L post-CG, R MOG, L cuneus |
| Kobel et al. (2010) | Pediat. | -- | 14 (100) | 10.4 | n/a | 12 (100) | 10.9 | n/a | ADHD<TD: R S/MTG |
| Kumar, Arya, and Agarwal (2017) | Pediat. | -- | 18 (100) | 9.6 | 92.1 | 18 (100) | 9.7 | 109.7 | ADHD<TD: L OFC, L MFG/ dlPFC, L MTG, L cerebellum |
| Li et al. (2015) | Pediat. | -- | 30 (100) | 10.3 | 121.7 | 30 (100) | 10.3 | 107.1 | ADHD<TD: R insula, R OFC |
| Lim et al. (2015) | Pediat. | -- | 44 (100) | 13.6 | 92.2 | 33 (100) | 14.3 | 110 | ADHD<TD: L/R cerebellum, L IPL, pre-/post-CG |
| Maier et al. (2015) | Adult | -- | 131 (48) | 34.5 | 113.1 | 95 (47) | 37.7 | 121 | -- |
| McAlonan et al. (2007) | Pediat. | -- | 28 (100) | 9.9 | 109.9 | 31 (100) | 9.6 | 116.5 | ADHD<TD: R MFG, L/R SFG/IFG, R globus pallidus/putamen/ thalamus, precuneus, L IPL, L SOG, R cerebellum vermis |
| Moreno-Alcázar et al. (2016) | Adult | -- | 44 (66) | 31.6 | 105.0 | 44 (66) | 32.6 | 106.0 | ADHD<TD: R SMA/SFG, subgenual ACC, R DLPFC |
| Onnink et al. (2014) | Adult | -- | 119 (39) | 36.3 | 107.5 | 107 (42) | 36.9 | 110.2 | -- |
| Overmeyer et al. (2001) | Pediat. | -- | 18 (83) | 10.4 | 99 | 16 (94) | 10.3 | n/a | ADHD<TD: R PCC, R SFG, L/R putamen/globus pallidus |
| Ramesh and Rai (2013) | Pediat. | -- | 15 (26) | 16.8 | n/a | 15 (26) | 16.7 | n/a | ADHD<TD: L cingulate gyrus, R mid-cingulate gyrus, L SFG, L/R medial SFG, L temporal lobe, L MOG, L cuneus, L STG, L supramarginal gyrus |
| Roman-Urrestarazu et al. (2016) | Adult | -- | 49 (76) | 22.2 | 96.6 | 34 (50) | 22.9 | 112.2 | ADHD<TD: L/R caudate |
| Saad et al. (2017) | Pediat. | -- | 16 (75) | 12.8 | n/a | 28 (68) | 13.1 | n/a | -- |
| Pediat. | -- | 18 (72) | 13.7 | n/a | 28 (68) | 13.1 | n/a | -- | |
| Sasayama et al. (2010) | Pediat. | -- | 18 (72) | 10.6 | 90 | 17 (71) | 10.0 | n/a | ADHD<TD: L/R amygdala/temporal pole/OFC, L/R occipital cortex, R STS, L parietal cortex, L MFG, L temporal pole, R rectal gyrus, L PHG |
| Seidman et al. (2011) | Adult | -- | 74 (51) | 37.3 | 116 | 54 (46) | 34.3 | 115.8 | -- |
| Sethi et al. (2017) | Adult | -- | 30 (63) | 33.7 | 109.0 | 30 (63) | 32.6 | 110.1 | ADHD<TD: R IPC |
| Shimada et al. (2015) | Pediat. | -- | 17 (88) | 10.3 | 95.3 | 15 (73) | 12.8 | 104.1 | ADHD<TD: L putamen |
| Stevens and Haney-Caron (2012) | Pediat. | -- | 24 (67) | 15.7 | n/a | 24 (70) | 16.0 | n/a | -- |
| van Wingen et al. (2013) | Adult | -- | 14 (100) | 32.0 | 104 | 15 (100) | 37.0 | 99 | ADHD<TD: R putamen, R cerebellum; ADHD>TD: L/R midbrain, R pre-CG |
| Vilgis, Sun, Chen, Silk, and Vance (2016) | Pediat. | -- | 48 (100) | 12.6 | 92.2 | 31 (100) | 12.8 | 109.6 | ADHD<TD: R SPL, L IPL, L/R S/MFG, medial FG, R MTG |
| Villemonteix et al. (2015) | Pediat. | -- | 33 (55) | 10.3 | 105.6 | 24 (50) | 10.0 | 109.7 | ADHD<TD: R insula, R MTG, |
| Pediat. | -- | 20 (80) | 10.4 | 107.4 | 24 (50) | 10.0 | 109.7 | ADHD<TD: R MFG, R pre-CG | |
| Wang, Jiang, Cao, and Wang (2007) | Pediat. | -- | 12 (100) | 13.4 | n/a | 12 (100) | 13.5 | n/a | ADHD<TD: L SPL, R MFG, R MTG, R BG; ADHD>TD: R occipital lobe |
| P. Yang et al. (2008) | Pediat. | -- | 57 (61) | 11.1 | 97.9 | 57 (60) | 11.7 | n/a | ADHD<TD: L/R caudate, L/R cerebellum, R calcarine sulcus, L cuneus |
| (B) fMRI cognitive control studies in ADHD | |||||||||
| Banich et al. (2009) | Adult | Stroop | 23 (61) | 20.0 | 116 | 23 (57) | 19.0 | 113 | ADHD>TD: R MFG |
| Bhaijiwala, Chevrier, and Schachar (2014) | Pediat. | Stop | 12 (58) | 13.8 | n/a | 12 (50) | 15.4 | n/a | ADHD<TD: R MFG, R MTG, L cerebellum; ADHD>TD: R MFG/SFG |
| Booth et al. (2005) | Pediat. | GNG | 12 (67) | 11.0 | n/a | 12 (58) | 11.7 | n/a | ADHD<TD: L/R pre-CG, L/R caudate, R caudate head, R IFG, L/R thalamus. |
| Carmona et al. (2012) | Adult | GNG | 19 (100) | 33.6 | 110.9 | 19 (100) | 29.4 | 111.7 | ADHD<TD: R STG |
| Chantiluke, Barrett, Giampietro, Santosh, et al. (2015) | Pediat. | Stop | 18 (100) | 14.3 | 95 | 25 (100) | 13.4 | 109 | ADHD<TD:L OFC/STL/putamen/GP, L IPL |
| Chen et al. (2015) | Adult | GNG | 29 (100) | 24.9 | n/a | 25 (100) | 25.6 | n/a | -- |
| Chou, Chia, Shang, and Gau (2015) | Pediat. | Stroop | 42 (81) | 10.5 | 108.5 | 20 (80) | 12.0 | 106.5 | ADHD<TD: R MFG/IFG (atomoxetine group pre-treatment), L IFG, L SPL, R medial FG (methylphenidate group pre-treatment); ADHD>TD: L/R SPL, pre-CG, L dlPFC, L dACC (atomoxetine group pre-treatment), post-CG (methylphenidate group pre-treatment) |
| Congdon et al. (2010) | Adult | Stop | 35 (54) | 30.9 | n/a | 62 (45) | 30.8 | n/a | -- |
| Cubillo et al. (2010) a | Adult | Stop, Switch | 11 (100) | 29.0 | 92 | 14 (100) | 28.0 | 106 | ADHD<TD: L IFC/AI/PMC, R IFC/AI/caudate/thalamus/ putamen/PMC, L/R SMA/ACC (stop), R IFG/insula/caudate/ putamen L IFG/insula/ putamen/pre-/post-CG/inferior parietal (switch) |
| Cubillo et al. (2014) | Pediat. | Stop | 19 (100) | 13.1 | 92 | 29 (100) | 13.8 | 110 | ADHD<TD: L/R IFG, L M/ITG/parietal gyri, R cerebellum/FFG; ADHD>TD: L cerebellum, L/R PCC/occipital gyri, R STG/post-CG/posterior insula/ putamen |
| Cubillo, Halari, Giampietro, Taylor, and Rubia (2011) a | Adult | Simon | 11 (100) | 29.0 | 92 | 15 (100) | 28.0 | 112 | ADHD<TD: L OFC/IFC/mPFC/ACC/ caudate/PMC |
| Dibbets, Evers, Hurks, Marchetta, and Jolles (2009) b | Adult | GNG | 16 (100) | 28.9 | n/a | 13 (100) | 28.1 | n/a | -- |
| Dibbets, Evers, Hurks, Bakker, and Jolles (2010) b | Adult | Switch | 15 (100) | 28.8 | n/a | 14 (100) | 28.6 | n/a | ADHD<TD: R putamen, R dorsal posterior CG, L mPFC, R thalamus, L OFC/ claustrum/post-CG; ADHD>TD: R MTG, R dACC, R precuneus, R lingual gyrus, L pre-CG/SMA, L insula |
| Durston, Mulder, Casey, Ziermans, and van Engeland (2006) | Pediat. | GNG | 11 (100) | 14.0 | 100 | 11 (100) | 15.3 | 106 | -- |
| L. Y. Fan, Chou, and Gau (2017) c | Adult. | Stroop | 12 (42) | 28.9 | 115.8 | 12 (42) | 30.3 | 118.3 | ADHD>TD: R IFG, RACC |
| Adult | Stroop | 12 (42) | 32.5 | 119.9 | 12 (42) | 30.3 | 118.3 | ADHD>TD: R IFG | |
| L. Y. Fan, Shang, Tseng, Gau, and Chou (2018) | Pediat. | Stroop | 27 (89) | 12.1 | 105.2 | 27 (78) | 11.8 | 110.4 | ADHD < TD: R MFG, R post-CG, R SPL |
| Hwang et al. (2015) | Pediat. | Stroop | 26 (65) | 13.9 | 106.4 | 35 (51) | 14.5 | 105.1 | ADHD < TD: Medial FG, cerebellar tonsil |
| Iannaccone, Hauser, Staempfli, et al. (2015) | Pediat. | GNG/Flanker | 18 (50) | 14.5 | 108.5 | 18 (61) | 14.8 | 114.4 | ADHD<TD: R MFG/dlPFC/SFG; ADHD>TD: L/R MTG/STG, L insula/putamen/pre-CG, L ITG/FFG/PHG/MTG, L pons, L amygdala/ hippocampus, L FFG/ lingual gyrus, L post-CG/IPG, R brainstem/ pons/cerebellum |
| Janssen, Heslenfeld, Mourik, Logan, and Oosterlaan (2015) | Pediat. | Stop | 21 (90) | 10.6 | 98.6 | 17 (76) | 10.3 | 108.7 | ADHD<TD: L/R mPFC, R MFG, R IFG, R pre-CG, R ACC, L SFG; ADHD>TD: L/R ACC, L/R MFG, R mPFC, R PCC, R cuneus, L IFG, L post-CG, L pre-CG. |
| Konrad, Neufang, Hanisch, Fink, and Herpertz-Dahlmann (2006) | Pediat. | Flanker | 16 (100) | 10.2 | 103 | 16 (100) | 10.3 | 105 | ADHD<TD: L pre-CG, R putamen; ADHD>TD: L medial SPL |
| Kooistra et al. (2010) | Adult | GNG | 11 (100) | 21.5 | 110 | 11 (100) | 10.1 | 125 | ADHD>TD: L/R ACC, R supramarginal gyrus, L angular gyrus, L/R supramarginal gyrus, R occipital gyrus, L frontal pole, L/R MFG, L/R IFG, L SFG, paracingulate gyrus, medial thalamus, L caudate nucleus, L posterior putamen, L lateral occipital gyrus, L/R pre-CG, R post-CG, R mPFC, L PCC, L STG |
| J. Ma et al. (2012) | Pediat. | GNG | 15 (53) | 9.8 | 100.2 | 15 (53) | 22.3 | 102.6 | ADHD>TD: R ITF, R midbrain, R pre-CG, R calcarine, R IOG, R MOG, L/R cerebellum, L post-CG, L IFG, R hippocampus |
| I. Ma et al. (2016) | Pediat. | Stroop | 25 (76) | 15.4 | 98.3 | 33 (67) | 15.3 | 108.9 | -- |
| Massat et al. (2018) | Pediat. | Stop | 18 (44) | 10.0 | 106.8 | 19 (47) | 10.6 | 114.4 | ADHD>TD: L caudate body, L ventral putamen R dorsal putamen, R insula, L caudate tail, R middle CC, R ACC |
| Passarotti et al. (2010) | Pediat. | Stop | 11 (55) | 13.1 | 107.6 | 15 (48) | 9.9 | 101.2 | ADHD<TD: R MFG, R mPFC, L/R I/SFG, L STG; ADHD>TD: R caudate tail, L caudate, L anterior cerebellar vermis |
| Peterson et al. (2009) | Pediat. | Stroop | 16 (81) | 14.1 | 101.2 | 20 (60) | 14.1 | 118.5 | ADHD<TD: L ACC, insula R, precuneus, thalamus, caudate; ADHD>TD: R SFG, hippocampus, L ACC |
| Rasmussen et al. (2016) | Adult | GNG | 50 (82) | 24.8 | 102.1 | 23 (70) | 24.1 | 109.2 | ADHD<TD: R supramarginal gyrus, superior parietal, angular gyrus, L/R MFG. R frontal pole, L/R SPL, L/R IFG, L/R pre-/post-CG, R caudate/accumbens, L/R precuneus, R OFC, R posterior cingulate gyrus, R insula, R thalamus |
| Rubia, Smith, Brammer, Toone, and Taylor (2005) | Pediat. | Stop | 16 (100) | 13.2 | 100 | 21 (100) | 13.4 | 95 | ADHD<TD: R IFC, OFC, pre-CG, STL |
| Rubia, Halari, Cubillo, et al. (2011) d | Pediat. | Simon | 12 (100) | 13.0 | 90 | 13 (100) | 14.0 | 102 | ADHD<TD: R IFC/inferior parietal, L vmPFC/BG/thalamus, R SMA/ACC/PCC/SPL, L STL/MTL/occipital |
| Rubia, Halari, Mohammad, Taylor, and Brammer (2011) d | Pediat. | Stop | 12 (100) | 13.0 | 90 | 13 (100) | 13.0 | 102 | ADHD<TD: B IFC, insula, ACC, pre-SMA; thalamus; R MTL, occipital, IPL, precuneus, PCC, cerebellum |
| Schulz et al. (2004) | Pediat. | GNG | 10 (100) | 17.9 | 88.4 | 9 (100) | 17.5 | 91.9 | ADHD<TD: R pre-CG, R ITG, L hippocampus, R lingual gyrus, L/R cerebellum; ADHD>TD: L/R MFG, L/R IFG, L medial FG, L ACC, L/R IPL, R precuneus |
| Schulz et al. (2014) | Adult | GNG | 14 (100) | 23.3 | n/a | 14 (100) | 22.8 | n/a | -- |
| Schulz et al. (2017) | Adult | Stroop | 27 (89) | 24.2 | n/a | 28 (86) | 24.6 | n/a | ADHD<TD: L/R cerebellum, L MTG, R lingual gyrus, R IFG, R MTG, R OFG, R SOG, R precuneus, R IPL. R MOG |
| Sebastian et al. (2012) e | Adult | GNG Stroop Stop | 20 (55) | 33.3 | 115.3 | 24 (46) | 17.5 | 115.7 | ADHD<TD: R caudate (GNG), R GP (Stop), R post-CG, L paracentral lobule/mid-CC, L STG/MTG, R temporal pole, R GP (Stroop) |
| Shang, Sheng, Yang, Chou, and Gau (2018) f | Adult | Stroop | 25 (48) | 29.1 | 112.8 | 30 (50) | 28.2 | 115.4 | ADHD<TD: L IFG, L/R caudate nucleus |
| Adult | Stroop | 25 (56) | 28.5 | 113.1 | 30 (50) | 28.2 | 115.4 | ADHD<TD: L IFG, L caudate nucleus, L SPL | |
| Siniatchkin et al. (2012) | Pediat. | GNG | 12 (83) | 9.3 | n/a | 12 (75) | 30.3 | n/a | ADHD<TD: ACC, R dlPFC, R caudate nucleus |
| Smith, Taylor, Brammer, Toone, and Rubia (2006) g | Pediat. | GNG | 17(100) | 12.8 | n/a | 18 (100) | 9.3 | n/a | ADHD<TD: L MFG |
| Stroop | 19 (100) | 12.9 | n/a | 24 (100) | 12.8 | n/a | -- | ||
| Switch | 14 (100) | 13.3 | n/a | 27 (100) | 12.9 | n/a | ADHD<TD: L STG/IFG/pre-CG/insula, R MTG/IFG, IPL/STG | ||
| Spinelli et al. (2011) | Pediat. | GNG | 13 (69) | 10.6 | 109.2 | 17 (47) | 13.3 | 108.8 | ADHD>TD: R pre-CG, MFG |
| Szekely et al. (2018) | Adult | Stop | 64 (56) | 24.0 | n/a | 84 (57) | 24.5 | n/a | -- |
| Tamm, Menon, Ringel, and Reiss (2004) | Pediat. | GNG | 10 (100) | 16.0 | 109.2 | 12 (100) | 10.6 | 111.6 | ADHD<TD: R ACC/SMA/ MFG; ADHD>TD: L M/I/STG |
| Thornton, Bray, Langevin, and Dewey (2018) | Pediat. | GNG | 20 (90) | 12.4 | 109.4 | 20 (40) | 10.6 | 112.6 | -- |
| van Hulst, de Zeeuw, Rijks, Neggers, and Durston (2017) | Pediat. | GNG | 24 (100) | 11.2 | 105.6 | 26 (100) | 10.5 | 117.3 | -- |
| van Rooij et al. (2015) | Pediat. | Stop | 108 (64) | 15.1 | 92.7 | 77 (49) | 16.0 | 109.2 | ADHD<TD: L STG, pre-CG; ADHD>TD: L SMA |
| Adult | Stop | 77 (78) | 20.3 | 99.1 | 45 (33) | 14.6 | 106.4 | ADHD<TD: L SFG, R hippocampal gyrus | |
| Zamorano et al. (2017) | Pediat. | Stroop | 17 (100) | 11.6 | 104.2 | 17 (100) | 11.7 | 109.8 | ADHD>TD: R medial/IFG |
Abbreviations. N=sample size, y=year, pediat=pediatric (child/adolescent) sample, ADHD=attentional-deficit/hyperactivity disorder, TD=typically developing controls, L/R=Left/Right, GNG=Go/No-Go. Brain regions (in alphabetical order): ACC=anterior cingulate cortex, AI=anterior insula, BG=basal ganglia, CC=cingulate cortex, dlPFC=dorsolateral prefrontal cortex, dACC=dorsal ACC, FFG=fusiform gyrus, GP=globus pallidus, I/M/SFG=inferior/middle/superior frontal gyrus, I/SPL=inferior/ superior parietal lobe, medial FG=medial frontal gyrus, mPFC=medial prefrontal cortex, M/STG=middle/superior temporal gyrus, OFC=orbitofrontal cortex, OFG=orbitofrontal gyrus, PCC=posterior cingulate cortex, PHG=parahippocampal gyrus, PMC=premotor cortex, pre-/post-CG=pre-/post-central gyrus, SMA=supplementary motor cortex, STL=superior temporal lobe, STS=superior temporal sulcus, vmPFC=ventromedial prefrontal cortex, a-g datasets were combined, adjusting their variance according to the method outlined in Norman et al. (2016). See Supplement Table S2a for further information.
Table 2. Sample Characteristics and Summary Findings of VBM and fMRI Cognitive Control Studies in ASD.
| Source | Age group | Task | Patients | Controls | Summary findings | ||||
|---|---|---|---|---|---|---|---|---|---|
| N (% male) | Age, y | IQ | N (% male) | Age, y | IQ | ||||
| (A) VBM studies in ASD | |||||||||
| Abell et al. (1999) | Adult | -- | 15 (80) | 28.8 | n/a | 15 (80) | 25.3 | n/a | ASD<TD: R paracingulate, L IFG, L occipito-temporal junction; ASD>TD: L amygdala, L/R anterior cerebellum, L MTG, R ITG |
| Boddaert et al. (2004) | Pediat. | -- | 21 (76) | 9.3 | n/a | 12 (58) | 10.8 | n/a | ASD<TD: L/R STS |
| Bonilha et al. (2008) | Pediat. | -- | 12 (100) | 12.4 | n/a | 16 (100) | 13.2 | n/a | ASD>TD: L/R IFG, L/R cuneus, L/R ACC, R PCC, claustrum, L/R precuneus, L/R STG, L/R MTG, L ITG, thalamus (pulvinar), L/R SFG, L/R SPL, L insula, L putamen, R caudate, L/R FFG, L/R occipital gyrus, L lingual gyrus, L pre-CG, L post-CG, L/R thalamus, L/R PHG, L/R mPFC, R IPL |
| Brieber et al. (2007) | Pediat. | -- | 15 (100) | 14.2 | 106.8 | 15 (100) | 13.3 | 107.7 | ASD<TD: L/R ITG/hippocampus/amygdala, L MOG, L PMC, L hippocampus; ASD>TD: R supramarginal gyrus, L post-CG |
| Cai et al. (2018) | Pediat. | -- | 38 (84) | 9.6 | 75.8 | 27 (96) | 8.3 | 98.6 | ASD<TD: R cerebellum anterior lobe, R precuneus, R angular gyrus; ASD>TD: L ITG, L/R MTG |
| Cheng, Chou, Fan, and Lin (2011) | Pediat. | -- | 25 (100) | 13.7 | 101.6 | 25 (100) | 13.5 | 109.0 | ASD<TD: R IFG, R pre-CG, L post-CG, cuneus, thalamus, lingual gyrus, STG; ASD>TD: ACC, paracentral lobule, SPL, precuneus, MFG, FFG, subcallosal gyrus |
| Contarino, Bulgheroni, Annunziata, Erbetta, and Riva (2016) | Pediat. | -- | 25 (88) | 6.1 | 56 | 25 (65) | 6.1 | 103 | -- |
| M. Craig et al. (2007) | Adult | -- | 14 (0) | 37.9 | 103.4 | 19 (0) | 35.0 | 111.2 | ASD<TD: Cuneus, L ITG/STG, R MTG, R ACC |
| D'Mello, Crocetti, Mostofsky, and Stoodley (2015) | Pediat. | -- | 35 (86) | 10.4 | n/a | 35 (60) | 10.4 | n/a | ASD<TD: R lingual gyrus, R cerebellum, R angular gyrus; ASD>TD: L PCC/precuneus, R SFG, L MOG |
| Ecker et al. (2012) | Adult | -- | 89 (100) | 27.0 | 110 | 89 (100) | 28.0 | 113.0 | ASD<TD: R occipital/ITG/MTG, R cerebellum, R posterior FFG, R lingual gyrus, L/R inferior and R SOG, R cuneus, precuneus, R PCC; ASD>TD: L /R ITG/MTG/STG/ L/R FFG/PHG/insula, L IFG, L putamen/caudate, L thalamus, L/R dlPFC/ MFG/pre-/post-CG, R IPL |
| Foster et al. (2015) | Pediat. | -- | 38 (100) | 12.4 | 102.5 | 46 (100) | 12.6 | 113.1 | ASD<TD: R STG, L/R supramarginal gyrus, L cerebellum; ASD>TD: R central sulcus, L medial FG, L/R IFG, L/R pre-CG, L MFG, L pre-SMA, R SFG, L ACC, L OFC, L ITG/STG. L/R MTG, L Heschl’s gyrus, R lingual gyrus, L FFG, L post-CG, L PCC, L precuneus, R supramarginal/angular gyrus, L IOG, L/R cuneus, L putamen, L caudate |
| Freitag et al. (2008) | Mixed | -- | 15 (87) | 17.5 | 101.2 | 15 (87) | 18.6 | 112.1 | ASD<TD: R intraparietal sulcus |
| Greimel et al. (2013) | Mixed | -- | 47 (100) | 18.3 | 107.5 | 51 (100) | 21.4 | 112.5 | ASD<TD: ACC, L/R posterior STS, R MTG |
| Groen, Buitelaar, van der Gaag, and Zwiers (2011) | Pediat. | -- | 17 (82) | 14.4 | 98.0 | 25 (88) | 15.5 | 105.0 | -- |
| Hyde, Samson, Evans, and Mottron (2010) | Mixed | -- | 15 (100) | 22.7 | 100.4 | 15 (100) | 19.2 | 106.6 | ASD<TD: R post-CG, L/R pre-CG; ASD>TD: Brainstem/midbrain, reticular, medial FG/OFC, L/R MFG |
| Itahashi et al. (2015) | Adult | -- | 46 (100) | 30.2 | 106.0 | 46 (100) | 30.5 | 109.2 | -- |
| Katz et al. (2016) | Adult | -- | 23 (100) | 26.6 | n/a | 32 (100) | 29.8 | n/a | ASD>TD: L/R OFC, ACC |
| Kaufmann et al. (2013) | Pediat. | -- | 10 (80) | 14.7 | 102.3 | 10 (80) | 13.8 | 109.5 | ASD<TD: Lateral portion of R precuneus; ASD>TD: L medial FG, R precuneus |
| Ke et al. (2008) | Pediat. | -- | 17 (82) | 8.9 | 108.8 | 15 (80) | 9.7 | 109.8 | ASD<TD: R PHG; ASD>TD: L/R supramarginal gyrus, post-CG, R MFG, R cerebellum |
| Kosaka et al. (2010) | Adult | -- | 32 (100) | 23.8 | 101.6 | 40 (100) | 22.5 | 109.7 | ASD<TD: R insula, R IFG, R IPL |
| Kurth et al. (2011) | Pediat. | -- | 52 (73) | 11.2 | 102.2 | 52 (73) | 11.1 | 106.0 | ASD<TD: Hypothalamus |
| Kwon, Ow, Pedatella, Lotspeich, and Reiss (2004) | Pediat. | -- | 20 (100) | 13.5 | n/a | 13 (100) | 13.6 | n/a | ASD<TD: R ITG, R entorhinal cortex, R FFG |
| Langen et al. (2009) | Pediat. | -- | 99 (92) | 12.9 | 107.6 | 89 (92) | 12.4 | 110.0 | -- |
| Lim et al. (2015) | Pediat. | -- | 19 (100) | 14.9 | 113.0 | 33 (100) | 14.9 | 110.0 | ASD>TD: L MTG/STG, L medial FG |
| Lin, Ni, Lai, Tseng, and Gau (2015) | Pediat. | -- | 28 (100) | 10.7 | 106.9 | 43 (100) | 10.6 | 115.2 | ASD<TD: R post-CG, precuneus, L MOG; ASD>TD: L subcallosal gyrus, L/R sublobar areas |
| Pediat. | -- | 40 (100) | 14.7 | 101.5 | 18 (100) | 15.5 | 108.7 | -- | |
| Adult | -- | 18 (100) | 22.2 | 99.6 | 29 (100) | 23.4 | 116.8 | ASD>TD: L/R SFG, L MFG | |
| Lin, Tseng, Lai, Chang, and Gau (2017) | Pediat. | -- | 20 (100) | 13.5 | 103.8 | 54 (100) | 12.8 | 112.5 | ASD>TD: L cerebellum crus I |
| McAlonan et al. (2002) | Adult | -- | 17 (90) | 32.0 | 96.0 | 24 (92) | 33.0 | 114.0 | ASD<TD: R cerebellum, L/R lenticular nucleus, R CG, L/R MFG, R SFG, precuneus |
| McAlonan et al. (2008) | Pediat. | -- | 33 (82) | 11.6 | 113.2 | 55 (86) | 10.7 | 117.1 | ASD<TD: L dlPFC, L/R BG, L/R inferior cerebellar vermis, L STS, posterior parietal cortices |
| Mengotti et al. (2011) | Pediat. | -- | 20 (90) | 7.0 | n/a | 22 (91) | 7.7 | n/a | ASD<TD: L SMA, R IFG; ASD>TD: R IPL, L/R ITG, L SPL, R SOG, precuneus |
| Mueller et al. (2013) | Adult | -- | 12 (75) | 35.5 | 111.3 | 12 (67) | 33.3 | 110.8 | ASD<TD: L/R TPJ, L/R temporal lobe, L/R IFG, L MFG, L frontal pole, L/R mPFC, paracingulate gyrus |
| Ni et al. (2018) | Pediat. | -- | 81 (100) | 12.6 | 107.4 | 61 (100) | 12.4 | 112.0 | ASD<TD: L lateral occipital/superior parietal cortex, L frontal pole |
| Pereira et al. (2018) | Mixed | 19 (82) | 17.4 | 99.8 | 25 (66) | 18.5 | 105.8 | ASD<TD: L/R cerebellum, posterior lobe, R cerebellum anterior lobe, L FFG, L/R CG, L paracentral lobule, R MFG, L claustrum, R medial FG, L PHG L amygdala, L post-CG, R SFG; ASD>TD: L/R brainstem, R cerebellum, posterior lobe | |
| Poulin-Lord et al. (2014) | Mixed | -- | 23 (87) | 19.8 | 100.3 | 22 (86) | 22.6 | 107.3 | -- |
| Poustka et al. (2012) | Pediat. | -- | 18 (89) | 9.7 | 111.0 | 18 (89) | 9.7 | 112.8 | -- |
| Radeloff et al. (2014) | Mixed | -- | 34 (91) | 19.1 | 105.7 | 26 (85) | 19.5 | 107.8 | -- |
| Retico et al. (2016) | Pediat. | -- | 76 (50) | 4.6 | 71 | 76 (50) | 4.6 | 73 | ASD<TD: L posterior MTG, L cerebellum, L/R temporal pole; ASD>TD: L precuneus, L PCC, R posterior STG, R MTG/ITG, R mPFC, L SPL, mid-CC |
| Riedel et al. (2014) | Adult | -- | 30 (63) | 35.4 | 124.5 | 30 (63) | 35.5 | 123.6 | -- |
| Riva et al. (2013) | Pediat. | -- | 26 (89) | 5.8 | 51.6 | 21 (62) | 6.8 | n/a | ASD<TD: L/R cerebellum crus II, vermis 8/9, L/R hippocampus, L IFG, L MOG/SOG, L ITG, L SFG. R IOG, R post-CG |
| Rojas et al. (2006) | Mixed | -- | 24 (100) | 20.8 | 94.8 | 23 (100) | 21.4 | 118.7 | -- |
| Sato et al. (2017) | Adult | -- | 36 (69) | 27.0 | 110.4 | 36 (69) | 24.9 | n/a | ASD<TD: R IOG/ITG/MTG, L FFG, L/R amygdala, R PHG, R L/MFG, R SMA, L/R CG, ACC, L/R medial SFG, R OFG |
| Schmitz et al. (2006) | Adult | -- | 10 (100) | 38.0 | 105.0 | 12 (100) | 39.0 | 106 | ASD>TD: L IFG, ACC, R SFG, L/R MFG |
| Toal et al. (2010) | Adult | -- | 65 (88) | 31.0 | 98.0 | 33 (91) | 32.0 | 105 | ASD<TD: R cerebellum/PHG/ FFG, R ITG/STG L PHG/STG/TG/FFG/ cerebellum |
| Waiter et al. (2004) | Pediat. | -- | 16 (100) | 15.4 | 100.4 | 16 (100) | 15.5 | 99.7 | ASD<TD: R thalamus; ASD>TD: L SFG, R FFG, R mPFC, L MTG, R PCC, L/R STG, L lingual gyrus, L IFG, L MFG, L IOG, L PHG |
| Wang et al. (2017) | Pediat. | -- | 31 (100) | 4.8 | 62.5 | 31 (100) | 4.8 | 97.1 | ASD>TD: L STG, L post-CG |
| Wilson, Tregellas, Hagerman, Rogers, and Rojas (2009) | Adult | -- | 10 (80) | 30.1 | 91.5 | 10 (70) | 29.4 | 127.2 | -- |
| Q. Yang et al. (2018) | Pediat. | -- | 16 (63) | 10.4 | 45.3 | 16 (63) | 10.5 | 97.5 | ASD<TD: L cerebellum, L caudate |
| (B) fMRI studies in ASD | |||||||||
| Ambrosino et al. (2014) | Pediat. | GNG | 19 (100) | 11.5 | 112 | 19 (100) | 11.1 | 120 | -- |
| Chantiluke, Barrett, Giampietro, Santosh, et al. (2015) | Pediat. | Stop | 19 (100) | 14.7 | 112 | 25 (100) | 13.4 | 109 | ASD<TD: L IPL; ASD>TD: R IFC, L IFG/MFG |
| Daly et al. (2014) | Adult | GNG | 14 (100) | 31 | 115 | 14 (100) | 31 | 123 | ASD<TD: R IFG, L thalamus; ASD>TD: R caudate, R cerebellum |
| Denisova et al. (2017) | Mixed | Simon | 20 (95) | 22.7 | n/a | 20 (95) | 23.2 | n/a | ASD>TD: R lingual gyrus, R cerebellum, R ITG |
| Duerden et al. (2013) | Adult | GNG | 16 (69) | 27.2 | 112 | 17 (71) | 30.7 | 114 | ASD<TD: R MFG; ASD>TD: R IFG, R FFG |
| J. Fan et al. (2012) | Adult | Flanker | 12 (75) | 30 | 115 | 12 (83) | 28 | 120 | ASD<TD: ACC |
| Gooskens et al. (2018) | Pediat. | Stop | 26 (35) | 11.3 | 108.9 | 53 (45) | 10.8 | 111.9 | -- |
| Kana, Keller, Minshew, and Just (2007) | Adult | GNG | 12 (92) | 26.8 | 110 | 12 (92) | 22.5 | 117 | ASD<TD: L ITG, R PHG, R calcarine sulcus, R PMC, R mid-CC, L/R post-CG, R insula/IFG, L lingual gyrus |
| Kennedy et al. (2006) | Adult | Stroop | 15 (100) | 25.5 | 96 | 14 (100) | 26.1 | n/a | ASD>TD: mPFC, precuneus |
| Prat, Stocco, Neuhaus, and Kleinhans (2016) | Adult | GNG | 16 (63) | 25.3 | 107.4 | 17 (65) | 25.6 | 111.0 | -- |
| Schmitz et al. (2006) | Adult | GNG, Switch, Stroop | 10 (100) | 38 | 105 | 12 (100) | 39 | 106 | ASD>TD: L M/IFG, OFC (GNG), insula (Stroop), and IPL, mesial parietal lobe (Switch) |
| Shafritz, Dichter, Baranek, and Belger (2008) | Adult | Switch | 18 (89) | 22.3 | 103 | 15 (87) | 24.3 | 111 | ASD<TD: L dlPFC, ACC, L intraparietal sulcus, L BG, L insula |
| Shafritz, Bregman, Ikuta, and Szeszko (2015) | Mixed | GNG | 15 (80) | 18.1 | 102 | 15 (80) | 18.4 | 115 | ASD<TD: R IFG/insula |
| Solomon et al. (2014) | Pediat. | Switch | 27 (19) | 15.4 | 108 | 27 (19) | 16.1 | 113 | ASD<TD: L PCC, L lingual gyrus, L MOG |
| Vaidya et al. (2011) | Pediat. | Stroop | 11 (100) | 10.8 | 114 | 14 (100) | 11.0 | 119 | ASD<TD: ACC, L MFG, R caudate |
| van Hulst et al. (2017) | Pediat. | GNG | 26 (100) | 10.8 | 109.6 | 26 (100) | 10.5 | 117.3 | -- |
| Velasquez et al. (2017) | Adult | GNG | 19 (68) | 25.8 | 111 | 22 (73) | 29.0 | 112 | ASD<TD: R ACC, post-CG, R lateral occipital cortex |
| Yerys et al. (2015) | Pediat. | Switch | 20 (80) | 11.3 | 115 | 19 (68) | 11.4 | 120 | ASD>TD: L MFG/ pre-CG, L SFG/ dACC, R IFG |
Abbreviations. N=sample size, y=year, pediat.=pediatric (child/adolescent) sample, ASD=autism spectrum disorder, TD=typically developing controls, L/R=Left/Right, GNG = Go/No-Go. Brain region (in alphabetical order): ACC=anterior cingulate cortex, BG=basal ganglia, dACC=dorsal ACC, dlPFC=dorsolateral prefrontal cortex, FFG=fusiform gyrus, I/M/medial/SFG=inferior/middle/medial/superior frontal gyrus, I/M/STG=inferior/middle/superior temporal gyrus, I/SPL=inferior/superior parietal lobe, mPFC=medial prefrontal cortex, OFC=orbital frontal cortex, OFG=orbital frontal gyrus, PCC=posterior cingulate cortex, pre-/post-CG=pre-/post-central gyrus, PHG=parahippocampal gyrus, PMC=premotor cortex, SMA=supplementary motor area, STS=superior temporal sulcus. See Supplement Table S2b for further information.
Table 3. Characteristics of Overall Sample and Subgroups Matched on Age, Sex and IQ.
| ADHD | ASD | TDADHD | TDASD | |
|---|---|---|---|---|
| (A) Overall Sample | ||||
| VBM | ||||
| N | 1533 | 1445 | 1295 | 1477 |
| % males | 69 | 88 | 67 | 87 |
| Mean age (range), y | 20.9 (6-65) | 17.2 (2-59) | 21.1 (6-65) | 16.4 (2-58) |
| Mean FSIQ (SD) | 103 (7.2) | 99 (17.3) | 110 (10.8) | 108 (5.1) |
| fMRI during Cognitive Control | ||||
| N | 1001 | 335 | 1004 | 353 |
| % males | 77 | 83 | 69 | 80 |
| Mean age (range), y | 18.3 (7-50) | 20.7 (7-52) | 18.6 (7-50) | 19.2 (7-52) |
| Mean FSIQ (SD) | 102 (7.1) | 109 (5.2) | 109 (13.2) | 114 (4.4) |
| fMRI during Motor Response Inhibition | ||||
| N | 745 | 212 | 743 | 232 |
| % males | 77 | 82 | 69 | 78 |
| Mean age (range), y | 18.9 (7-50) | 21.6 (8-52) | 19.0 (7-50) | 19.1 (8-52) |
| Mean FSIQ (SD) | 101 (7.2) | 109 (3.7) | 109 (14.8) | 114 (4.4) |
| (B) Matched Subgroups | ||||
| VBM | ||||
| N | 825 | 758 | 778 | 781 |
| % males | 78 | 80 | 78 | 80 |
| Mean age (range), y | 16.7 (6-65) | 17.4 (3-59) | 17.4 (6-65) | 16.1 (2-58) |
| Mean FSIQ (SD) | 102 (8.3) | 97 (18.0) | 109 (4.7) | 107 (12.6) |
| fMRI during Cognitive Control | ||||
| N | 586 | 335 | 628 | 353 |
| % males | 83 | 83 | 79 | 80 |
| Mean age (range), y | 17.6 (7-50) | 20.7 (7-52) | 17.6 (7-50) | 19.2 (7-52) |
| Mean FSIQ (SD) | 105 (7.7) | 109 (4.8) | 109 (16.0) | 114 (4.1) |
| fMRI during Motor Response Inhibition | ||||
| N | 354 | 212 | 421 | 232 |
| % males | 78 | 82 | 73 | 78 |
| Mean age (range), y | 21.3 (7-50) | 21.6 (8-52) | 21.6 (7-50) | 19.1 (8-52) |
| Mean FSIQ (SD) | 106 (9.6) | 109 (3.6) | 114 (7.8) | 114 (4.3) |
Abbreviations: N = overall number of subjects, TDADHD = typically developing controls in the ADHD studies, TDASD = typically developing controls in the ASD studies, VBM = voxel-based morphometry, fMRI = functional Magnetic Resonance Imaging, % males = proportion of males among the samples, y = year, FSIQ = full scale IQ, and SD = standard deviation. The demographic characteristics presented here were calculated from the independent datasets with non-overlapping participants.
3.2. Disorder-Differentiating and Shared Brain Structure Abnormalities
3.2.1. ADHD VBM
Relative to TD controls, ADHD had reduced GMV in vmOFC/vmPFC/rostral ACC (rACC), extending into right caudate, and in right putamen/posterior insula/STG, left precingulate gyrus (pre-CG), right rostrolateral PFC and left vlPFC/STG/temporal pole (Table 4A-i; Figure 1A-i). Age-stratified analyses showed reduced GMV in right STG/putamen/posterior insula, right caudate and rACC/ventromedial PFC (vmPFC) among pediatric participants and vmOFC/subcallosal gyrus in both age groups (Supplements 3,4). Current and lifetime psychostimulant exposures were associated with increased vmOFC GMV (Supplement 6).
Table 4. Brain Structural and Functional Abnormalities in ADHD relative to ASD.
| Contrasts | MNI coord. x, y, z |
SDM Z p-value |
Voxels No |
BA | |
|---|---|---|---|---|---|
| (A) Structural Abnormalities | |||||
| (i) ADHD (vs. TD) | |||||
| ADHD < TD | |||||
| L/R vmOFC/vmPFC/rACC and R caudate | 2,48,-18 | 2.48 | .00009 | 2902 | 11/10 |
| R putamen/posterior insula/STG | 30,-4,4 | 1.98 | .0009 | 902 | 48/22 |
| L pre-CG | -40,-6,56 | 1.88 | .001 | 110 | 6 |
| R rostrolateral PFC | 28,66,-4 | 1.98 | .0009 | 79 | 11 |
| L vlPFC/STG/temporal pole | -26,16,-24 | 1.93 | .001 | 57 | 38 |
| ADHD > TD | |||||
| Nil | -- | -- | -- | -- | -- |
| (ii) ASD (vs. TD) | |||||
| ASD < TD | |||||
| L/R dACC/dmPFC | 4,42,18 | 1.61 | .0004 | 575 | 24/32 |
| L cerebellum | -10,-66,-48 | 1.35 | .002 | 113 | |
| R hippocampus/PHG/FFG | 24,-6,-22 | 1.38 | .002 | 104 | 34/20/36 |
| Dorsomedial thalamus | 0,0,20 | 1.39 | .001 | 46 | |
| ASD > TD | |||||
| L anterior I/M/STG/posterior insula | -60,-20,-14 | 2.55 | .000002 | 1828 | 20/21/22/48 |
| L/R PCC/precuneus, | -8,-50,26 | 1.99 | .0003 | 437 | 23/26/7 |
| R MFG/SFG/dlPFC | 24,48,24 | 2.13 | .0001 | 218 | 46 |
| ADHD (vs. TD) vs. ASD (vs. TD) | |||||
| (iii) Overall Sample | |||||
| ADHD (vs. TD) reduced vs. ASD (vs.TD) | |||||
| vmOFC/rACC/L caudate | 4,22,-10 | 2.22 | .00009 | 1543 | 25/11 |
| L post-CG | -42,-14,48 | 1.86 | .0007 | 276 | 6 |
| L MFG/SFG/dlPFC | -14,58,14 | 1.90 | .0006 | 114 | 10 |
| ASD (vs. TD) increased vs. ADHD (vs.TD) | |||||
| L anterior STG/MTG | -46,-16,-2 | 1.91 | .0005 | 601 | 48/22/20/21 |
| R MFG/SFG/dlPFC | 24,46,24 | 1.98 | .0004 | 150 | 46 |
| R MFG/SFG | 18,66,6 | 1.73 | .001 | 91 | 10 |
| L MTG/temporal pole | -46,6,-26 | 1.68 | .002 | 56 | 20 |
| R posterior MTG/STG | 46,-36,4 | 1.64 | .002 | 52 | 21 |
| (iv) Matched Subgroups | |||||
| ADHD (vs. TD) reduced vs. ASD (vs.TD) | |||||
| vmOFC/rACC | 2,48,-18 | 2.03 | .001 | 442 | 11/10 |
| L MOG | -22,-88,10 | 2.02 | .001 | 85 | 18 |
| ASD (vs. TD) reduced vs. ADHD (vs. TD) | |||||
| R PHG/amygdala/FFG/hippocampus | 28,-6,-24 | 1.37 | .0002 | 386 | 28/34/36/35 |
| Dorsomedial thalamus | 2,-6,16 | 1.18 | .0006 | 92 | |
| ASD (vs. TD) increased vs. ADHD (vs. TD) | |||||
| R MTG/STG/angular gyrus | 50, -40 ,8 | 2.28 | .0004 | 463 | 42/40/22/21/39 |
| R MFG/SFG/dlPFC | 24, 44, 24 | 2.00 | .001 | 65 | 46 |
| L anterior MTG/STG | -50, -20, -8 | 1.84 | .003 | 36 | 48/22/21/20 |
| PCC | -6, -50, 28 | 1.84 | .003 | 33 | 23/30 |
| (B) Functional Abnormalities during Cognitive Control | |||||
| (i) ADHD (vs. TD) | |||||
| ADHD < TD | |||||
| R thalamus/caudate | 10,-4,12 | 1.74 | .0001 | 449 | |
| L M/STG/superior temporal pole | -52,-16,-12 | 1.31 | .001 | 285 | 21/22/38 |
| L/R SMA/dmPFC | 4,6,52 | 1.33 | .001 | 130 | 6/24 |
| L IFG/AI/temporal pole | -50,14,4 | 1.25 | .002 | 82 | 45/48 |
| R AI/putamen | 32,20,0 | 1.27 | .002 | 65 | 48 |
| L post-CG | -48,-12,54 | 1.26 | .002 | 53 | 6 |
| L MFG/dlPFC | -30,30,44 | 1.24 | .002 | 35 | 9 |
| R MTG | 46,-2,-24 | 1.18 | .003 | 33 | 21 |
| ADHD > TD | |||||
| Nil | -- | -- | -- | -- | -- |
| (ii) ASD (vs. TD) | |||||
| ASD < TD | |||||
| L/R ACC/midcingulate/dmPFC | 0,34,20 | 2.12 | .000001 | 2708 | 24/10/32 |
| L MFG/dlPFC | -42,34,24 | 1.42 | .0003 | 370 | 45/46/48 |
| R MFG/dlPFC | 42,10,48 | 1.28 | .0007 | 113 | 9 |
| L IPL | -34,-46,52 | 1.11 | .002 | 91 | 40 |
| L lingual gyrus/cerebellum, lobule IV/V | -14,-48,-10 | 1.08 | .003 | 89 | 30/19 |
| ASD > TD | |||||
| Precuneus/midcingulate | -2,-44,56 | 1.48 | .00007 | 1115 | 7/5/4 |
| R IOG | 36,-70,-6 | 1.37 | .0002 | 226 | 19 |
| L vlPFC/OFC | -30,40,-8 | 1.14 | .0009 | 90 | 47/11 |
| L MFG/rostrolateral PFC | -30,48,10 | 1.14 | .0008 | 81 | 10 |
| R IFG | 42,22,18 | 1.08 | .002 | 64 | 45 |
| ADHD (vs. TD) vs. ASD (vs. TD) | |||||
| (iii) Overall Sample | |||||
| ASD (vs. TD) reduced vs. ADHD (vs. TD) | |||||
| rACC/midcingulate/dmPFC | 0,30,20 | 2.42 | ~0 | 2903 | 24/10/32 |
| L MFG/dlPFC | -44,34,24 | 1.19 | .0004 | 319 | 45/46/48 |
| ASD (vs. TD) increased vs. ADHD (vs. TD) | |||||
| Precuneus | 0,-42,50 | 1.50 | .00009 | 1001 | 7/4 |
| R IOG/FFG | 36,-70,-8 | 1.35 | .0003 | 147 | 19 |
| R IFG | 44,24,18 | 1.27 | .0005 | 138 | 48/45 |
| L vlPFC/OFC | -30,44,-8 | 1.04 | .002 | 32 | 47/11 |
| L MFG | -30,50,8 | 1.02 | .003 | 25 | 10 |
| (iv) Matched Subgroups | |||||
| ADHD (vs. TD) reduced vs. ASD (vs. TD) | |||||
| SMA | -4, 12, 54 | -1.33 | .0003 | 353 | 6 |
| L STG/MTG/temporal pole | -40, -6, -12 | -1.01 | .003 | 40 | 48/21/38 |
| ASD (vs. TD) reduced vs. ADHD (vs. TD) | |||||
| rACC/dmPFC | 2,28,20 | 2.30 | .0000002 | 2815 | 24/32/9/10 |
| L MFG/dlPFC | -40,34,28 | 1.03 | .001 | 211 | 45/45/48 |
| L IPL/SPL | -32,-46,54 | 1.06 | .0009 | 192 | 40/7/2 |
| ASD (vs. TD) increased vs. ADHD (vs. TD) | |||||
| Precuneus/PCC | -2, -40, 50 | 1.25 | .0006 | 433 | 5/4 |
| R FFG/IOG/ITG | 36, -70 ,-8 | 1.49 | .0001 | 247 | 19/37 |
| R IFG | 44, 24, 18 | 1.24 | .0007 | 88 | 48/45 |
| L vlPFC/OFC | -30, 40, -8 | 1.10 | .002 | 57 | 11/47 |
| (C) Functional Abnormalities during Motor Response Inhibition | |||||
| (i) ADHD (vs. TD) | |||||
| ADHD < TD | |||||
| L MFG/dlPFC | -30,30,44 | 1.70 | .0002 | 235 | 9/46/8 |
| L anterior MTG/STG | -52,-18,-10 | 1.57 | .0005 | 210 | 22/21/20/48 |
| L post-CG | -48,-10,54 | 1.52 | .0006 | 198 | 6 |
| R IFG | 46,32,22 | 1.54 | .0005 | 188 | 45/48/46 |
| R vlPFC/OFC/AI | 30,36,-16 | 1.30 | .002 | 172 | 47/38 |
| R caudate | 10,20,0 | 1.38 | .001 | 91 | -- |
| ADHD > TD | |||||
| Nil | -- | -- | -- | -- | -- |
| (ii) ASD (vs. TD) | |||||
| ASD < TD | |||||
| R AI/vlPFC | 44,20,0 | 1.42 | .0004 | 491 | 47/45/48 |
| L cerebellum, hemispheric lobule IV & V/ lingual gyrus/FFG | -14,-46,-10 | 1.33 | .001 | 492 | 30/19/18/37 |
| R MFG/dlPFC | 40,12,50 | 1.43 | .0004 | 251 | 9 |
| R PCC/precuneus | 16,-36,44 | 1.35 | .0008 | 58 | 7 |
| ASD > TD | |||||
| L vlPFC/OFC | -28,48,8 | 1.19 | .0003 | 703 | 11/10/46/47 |
| R IOG/FFG/ITG | 36,-72,-8 | 1.50 | .00001 | 489 | 19/37 |
| ADHD (vs. TD) vs. ASD (vs. TD) | |||||
| (iii) Overall Sample | |||||
| ADHD (vs. TD) reduced vs. ASD (vs. TD) | |||||
| R caudate | 10,22,0 | 1.20 | .0008 | 98 | -- |
| R IFG | 42,26,20 | 1.14 | .001 | 97 | 48/45 |
| ASD (vs. TD) reduced vs. ADHD (vs. TD) | |||||
| L lingual gyrus/FFG/cerebellum lobule IV | -18,-54,-12 | 1.17 | .001 | 243 | 19/37/30 |
| rACC/dmPFC | 0,32,22 | 1.09 | .002 | 203 | 24/32 |
| L MFG/IFG | -44,34,16 | 1.11 | .002 | 109 | 45/48 |
| R precuneus | 16,-36,44 | 1.27 | .0008 | 37 | 7 |
| R MFG/dlPFC | 42,10,48 | 1.04 | .003 | 20 | 9/6 |
| ASD (vs. TD) increased vs. ADHD (vs. TD) | |||||
| R IOG/FFG | 36,-70,-8 | 1.84 | .00001 | 417 | 19/37/18 |
| L vlPFC/OFC | -28,42,-14 | 1.21 | .0007 | 372 | 11/47 |
| L MFG/SFG/dlPFC | -30,46,6 | 1.12 | .001 | 183 | 10/46/11 |
| L posterior MTG | -48,-54,4 | 1.16 | .001 | 63 | 37/21 |
| (iv) Matched Subgroups | |||||
| ADHD (vs. TD) reduced vs. ASD (vs. TD) | |||||
| R caudate | 8,20,2 | 1.17 | .001 | 117 | -- |
| R IFG | 42,26,20 | 1.18 | .001 | 74 | 48 |
| ASD (vs. TD) reduced vs. ADHD (vs. TD) | |||||
| L cerebellum lobule IV/FFG/lingual gyrus | -16,-48,-12 | 1.39 | .0003 | 1082 | 19/37 |
| R MFG/dlPFC | 42,14,48 | 1.22 | .0007 | 104 | 9/6 |
| L SPL/precuneus | -18,-70,58 | 1.00 | .002 | 50 | 7 |
| R precuneus | 16,-36,44 | 1.21 | .0008 | 41 | 7 |
| ASD (vs. TD) increased vs. ADHD (vs. TD) | |||||
| R IOG/FFG | 36,-70,-10 | -1.82 | .00001 | 504 | 19/37 |
| L vlPFC/OFC | -28,36,-8 | -1.17 | .001 | 265 | 11/47 |
| L MFG/SFG/dlPFC | -28,54,8 | -1.10 | .002 | 171 | 10/46/11 |
Abbreviations: MNI = Montreal Neurological Institute, SDM = Seed-based d mapping, BA = Brodmann Area, TD = typically developing controls, brain regions (in alphabetical order): AI = anterior insula, dACC = dorsal anterior cingulate cortex, dlPFC = dorsolateral prefrontal cortex, dmPFC = dorsomedial prefrontal cortex, FFG = fusiform gyrus, IFG = inferior frontal gyrus, I/MOG = inferior/middle occipital gyrus, I/M/STG = inferior/middle/superior temporal gyrus, I/SPL = inferior/superior parietal lobe, M/SFG = middle/superior frontal gyrus, PCC = posterior cingulate cortex, PFC=prefrontal cortex, PHG=parahippocampal gyrus, pre-CG=precentral gyrus, rACC = rostral anterior cingulate cortex, SMA = supplementary motor area, vlPFC = ventrolateral prefrontal cortex, vmOFC = ventromedial orbitofrontal cortex, vmPFC = ventromedial prefrontal cortex. Bold prints indicate regions which survived the sensitivity analyses in subgroups matched on age, sex and IQ.
Fig. 1.
Brain abnormalities in the ADHD and ASD groups. Abnormalities in the (A) gray matter volume and brain activations during (B) cognitive control and (C) motor response inhibition. Rows (i) and (ii) show abnormalities in ADHD and ASD relative to typically developing (TD) controls. Abnormalities in ADHD versus ASD, each relative to TD controls are shown in (iii) overall samples and (iv) age-, sex-, and IQ-matched subgroups. In the overall sample, shared reduced rACC/mPFC GMV (MNI coordinates: 0,46,12; 119 voxels), and subthreshold overactivation in ADHD and underactivation in ASD in dmPFC (MNI coordinates: -2, 34, 36; 82 voxels) were observed but did not survive sensitivity analysis. A statistical threshold of p < .005 with a cluster extent of 20 voxels were used in all analyses.
3.2.2. ASD VBM
Relative to TD controls, ASD had GMV reduction in dACC/dorsomedial PFC (dmPFC), left cerebellum, right hippocampus/PHG/FFG and dorsomedial thalamus; and enhancement in left anterior inferior/middle/superior temporal gyri (I/M/STG)/posterior insula, bilateral precuneus/posterior cingulate cortex (PCC) and right middle/superior frontal gyrus (M/SFG) (Table 4A-ii; Figure 1A-ii). Most abnormalities in the overall sample, i.e., in left cerebellum, dorsomedial thalamus, precuneus/PCC and right MFG/SFG/dlPFC GMV were also found among pediatric participants, and enhanced left anterior STG/MTG GMV was found in both age subgroups (Supplements 3,4).
3.2.3. ADHD vs. ASD VBM
People with ADHD, relative to ASD, had consistently disorder-differentiating reduced vmOFC/rACC/left caudate GMV; while people with ASD, relative to ADHD, showed increased GMV in left anterior STG/MTG, right MFG/SFG/dlPFC, and right posterior MTG/STG (Table 4A-iii,iv; Figure 1A-iii,iv). Reduced vmOFC GMV was most consistently ADHD-differentiating in adults, while the medial prefrontal GMV reduction in rACC/dmPFC was more apparent among pediatric participants (Supplements 3,4).
3.3. Disorder-Differentiating and Shared Brain Abnormalities during Cognitive Control
3.3.1. ADHD fMRI
During cognitive control, ADHD relative to TD controls, showed underactivation in right thalamus/caudate, left MTG/STG/superior temporal pole, bilateral SMA/dmPFC, left IFG/AI/temporal pole, right AI/putamen, left postcingulate gyrus (post-CG), left MFG/dlPFC and right MTG (Table 4B-i, Figure 1B-i). Age-stratified analyses showed underactivated dmPFC clusters and left post-CG among pediatric participants, in all regions but the dmPFC and left post-CG in adults; and in left M/STG/temporal pole in both groups (Supplements 3,4). Lifetime psychostimulant exposure was positively associated with activation in left IFG (Supplement 6).
During motor response inhibition specifically, underactivation was found in ADHD relative to TD controls in left MFG/dlPFC, left anterior MTG/STG, left post-CG, right vlPFC/OFC/AI,right IFG, and right caudate (Table 4C-i, Figure 1C-i). Age-stratified analyses showed that underactivation in left MTG/STG was apparent among pediatric participants, underactivation in left MFG/dlPFC was apparent in adults, while underactivation in right vlPFC/AI was apparent in both age groups (Supplements 3,4).
3.3.2. ASD fMRI
During cognitive control, ASD relative to TD controls showed underactivation in ACC/midcingulate/dmPFC, left MFG/dlPFC, right MFG/dlPFC, left IPL and left lingual gyrus/cerebellum; and overactivation in left precuneus/midcingulate, right inferior occipital gyrus (IOG), left vlPFC/OFC, left MFG/rostrolateral PFC and right IFG (Table 4B-ii, Figure 1B-ii). Underactivation in rdACC/dmPFC and left IPL; and overactivation in precuneus, left vlPFC/OFC and left MFG/dlPFC were found in adults (Supplements 3,4). No pediatric sub-meta-analyses were conducted due to insufficient fMRI data.
During motor response inhibition, underactivation was found in ASD relative to TD controls in right AI/vlPFC, left cerebellum, right MFG/dlPFC and right PCC/precuneus; while overactivation was found in left vlPFC/OFC and right IOG/fusiform gyrus (FFG)/ITG (Table 4C-ii; Figure 1C-ii). No age-stratified sub-meta-analyses were conducted due to insufficient data.
3.3.3. ADHD vs. ASD fMRI
During cognitive control, ASD-differentiating underactivation was found in rACC/midcingulate/dmPFC and left MFG/dlPFC; and overactivation was found in precuneus, right IOG/FFG, right IFG and left vlPFC/OFC (Table 4B-iii,iv; Figure 1B-iii,iv). The age-stratified analysis, which was conducted only in adults due to available data, found ASD-differentiating underactivation relative to ADHD in rdACC/dmPFC and overactivation in precuneus and left vlPFC/OFC (Supplement 4). No ADHD-differentiating abnormalities were found.
During motor response inhibition, ADHD-differentiating underactivation was found in right caudate and IFG. Furthermore, ASD-differentiating underactivation was found in left lingual gyrus/FFG/cerebellum, right precuneus and right MFG/dlPFC; while overactivation was found in right IOG/FFG, left vlPFC/OFC and left MFG/SFG/dlPFC (Table 4C-iii,iv; Figure 1C-iii,iv). Underactivation in right AI (MNI coordinates: 40, 20, -6; 51 voxels) was shared between disorders.
3.5. Multimodal VBM and fMRI Analyses
In ADHD relative to TD controls, overlapping reduced GMV and fMRI underactivation was found during cognitive control in right caudate nucleus (MNI coordinates: 10, 10, 8; 194 voxels). In ASD relative to TD controls, reduced GMV and fMRI underactivation was found in dACC/mPFC (MNI coordinates: 0, 40, 16; 575 voxels).
3.6. Controlling for Task Type and Performance in fMRI Meta-Analysis
The disorder-differentiating impairment during cognitive control was unchanged after covarying for task types and performance. During motor response inhibition, ADHD-differentiating underactivation in right IFG and caudate did not survive covarying for task performance (Supplement 7).
3.7. Publication Bias and Jack-Knife Reliability Findings
Egger tests suggest no significant publication bias for the structural, ts(84) = .32-2.29, ps ≥ .1, and functional abnormalities during cognitive control, ts(58) = .04-1.90, ps ≥ .38, and during motor response inhibition specifically, ts(40) = .41-1.37, ps ≥ .99 (Bonferroni adjusted p-values). Jack-knife reliability analyses suggest robust disorder-differentiating findings (Supplement 8).
4. Discussion
We aimed to elucidate the most consistent disorder-differentiating and shared brain abnormalities in ASD and ADHD. The findings revealed predominantly disorder-differentiating abnormalities, particularly striking in the VBM meta-analysis, where we found ADHD-differentiating reduced GMV in vmOFC; and ASD-differentiating increased GMV in bilateral temporal and right lateral prefrontal cortices. In fMRI, the findings overall showed predominantly ASD-differentiating abnormalities, including underactivation in medial frontal and overactivation in bilateral ventrolateral regions and precuneus during cognitive control. During motor response inhibition specifically, ADHD-differentiating underactivation was in right IFG and caudate, while ASD had differentiating underactivation mostly in posterior regions, and overactivation in left dorsal and ventral frontal regions (Figure 2). Both disorders shared underactivation in right AI. The findings overall suggest that people with ADHD and ASD have mostly distinct brain abnormalities.
Fig. 2.
Summary of consistent disorder-differentiating and shared brain abnormalities in ADHD and ASD. Findings of (A) gray matter volume abnormalities and brain activation abnormalities during (B) cognitive control and (C) motor response inhibition. A statistical threshold of p < .005 with a cluster extent of 20 voxels were used in all analyses.
The ADHD-differentiating decreased GMV in vmOFC relative to ASD may be related to common reports of reward-based decision-making neural network dysfunctions in ADHD (e.g., Chantiluke et al., 2014; Cubillo, Halari, Smith, Taylor, & Rubia, 2012; Rubia, 2018; Tegelbeckers et al., 2018), and supports the hypothesis of distinctive reward processing in ASD and ADHD (Kohls et al., 2014; van Dongen et al., 2015). Reduced vmOFC GMV in ADHD compared to TD controls extends relatively recent meta-analytic findings (McGrath & Stoodley, 2019; Norman et al., 2016), and there have been correlational reports between OFC GMV reduction and increased ADHD symptoms in large-scale general population studies (Albaugh et al., 2017; Fuentes et al., 2012; Korponay et al., 2017). Interestingly, our age-stratified results suggest that the ADHD-differentiating deficit features more consistently in adulthood, when co-occurring addiction behavioral problems increase (e.g., Breyer et al., 2009; Ortal et al., 2015), more so in ADHD than in ASD (e.g., Sizoo et al., 2010; Solberg et al., 2019).
The increased left anterior and right posterior temporal GMV in ASD is a consistent VBM meta-analytic finding in ASD relative to TD controls (Carlisi et al., 2017; Cauda et al., 2011; DeRamus & Kana, 2015; Duerden, Mak-Fan, Taylor, & Roberts, 2012). Enhanced left anterior GMV was the only mega-analytic impairment finding in over 400 people with ASD but it did not survive in a smaller age- and sex-matched subgroups (Riddle, Cascio, & Woodward, 2017). Our meta-analysis may have increased statistical power to detect the differences. The ASD-differentiating increased left temporal GMV extends findings from a small VBM ASD/ADHD comparative study (Lim et al., 2015). The left temporal lobe plays roles in semantic and language processing (Binder et al., 2011), while the right posterior temporoparietal cortices plays a role in social interaction and mentalizing, i.e., the ability to attribute mental states in others (Krall et al., 2015). These structures are important for social cognition, which has been thought to be an ASD-specific impairment (Kana et al., 2015; Lombardo, Chakrabarti, Bullmore, & Baron-Cohen, 2011; White, Frith, Rellecke, Al-Noor, & Gilbert, 2014).
The ASD-differentiating increased right dlPFC GMV has not converged in smaller meta-analyses (Carlisi et al., 2017; DeRamus & Kana, 2015), possibly reflecting heterogeneity in findings. Right dlPFC is important for cognitive control, working memory and cognitive flexibility (Szczepanski & Knight, 2014). Neuronal and grey matter overgrowth have been reported in pediatric ASD in small ROI-based neuroimaging and post-mortem studies (Carper & Courchesne, 2005; Courchesne, Mouton, et al., 2011; Mitchell et al., 2009), but they have not been replicated in wider age range of ASD cases with cognitive flexibility deficits (Griebling et al., 2010).
The disorder-differentiating GMV increase in ASD and decrease in ADHD may possibly be related to the disorders’ contrasting developmental trajectories (Courchesne, Campbell, et al., 2011; Shaw et al., 2007). However, while most of our ADHD-differentiating findings showed reduction in structure and function that may possibly reflect delayed maturation in ADHD, the ASD-differentiating enhanced clusters were not found consistently in the pediatric sub-meta-analysis. Recent findings suggest that early brain overgrowth is not a defining characteristic of toddlers with high-risk for developing ASD (Hazlett et al., 2017), and may be specific to boys with regressive autism (Nordahl et al., 2011), who are underrepresented across studies. Thus, our findings are unlikely to reflect early brain overgrowth.
In the fMRI meta-analyses, ASD-differentiating impairments were predominant medial and left middle prefrontal underactivation and in bilateral ventrolateral prefrontal overactivation during cognitive control, while ADHD-differentiating underactivation emerged during motor inhibition in right inferior frontal and striatal regions. The ADHD-differentiating underactivation in right IFG and caudate, key regions implicated in inhibitory control (Rae, Hughes, Weaver, Anderson, & Rowe, 2014; Zhang, Geng, & Lee, 2017), are consistent with previous meta-analytic findings in ADHD during cognitive control (Hart et al., 2013; McCarthy et al., 2014; Norman et al., 2016) and extends the role of these regions, in particular right IFG, as disorder-specific neurofunctional biomarker of ADHD, as it has previously been observed relative to bipolar, obsessive compulsive and conduct disorders (Norman et al., 2016; Passarotti et al., 2010; Rubia, 2018; Rubia, Cubillo, et al., 2010; Rubia, Halari, et al., 2010; Rubia et al., 2009).
The mPFC underactivation in ASD is a consistent meta-analytic finding during cognitive control and related functions (Carlisi et al., 2017; Di Martino et al., 2009; Philip et al., 2012). The region is also implicated in functions such as sensory, emotion and social processing domains typically impaired in ASD (Kana, Patriquin, Black, Channell, & Wicker, 2016; Martinez-Sanchis, 2014; South & Rodgers, 2017). Intriguingly, the mPFC underactivation was ASD-differentiating relative to ADHD. While both medial and lateral PFC are activated during motor inhibition (Rae et al., 2014; Zhang et al., 2017), the activation of mPFC are more reliably tapped by cognitive interference tasks thus it may have closer associations to change or conflict detection (Aron, Herz, Brown, Forstmann, & Zaghloul, 2016). Whether our findings represent dissociated frontal brain functional impairment in ASD from ADHD in sub-constructs of cognitive control should thus be tested further meta-analytically when more fMRI findings are available.
While both disorders show functional abnormalities in prefrontal cortex in both hemispheres, the ASD-differentiating atypical activation was more predominantly on the left, in contrast to the right-lateralized ADHD-differentiating fronto-striatal underactivation, although atypical brain lateralization is not uncommon finding in ASD (Dichter, 2012; Floris & Howells, 2018; Kleinhans, Muller, Cohen, & Courchesne, 2008). Most importantly, comparisons between each disorder relative their respective TD controls show that neural impairment in ADHD is overwhelmingly characterized by fronto-striato-temporal underactivation, which is in line with previous findings of a developmental lag of cognitive control networks (Sripada et al., 2014), whereas the impairments in ASD implicate both under- and overactivation clusters in executive, attentional and, even, fronto-parieto-occipital visuo-perceptual brain regions. Of note, abnormally enhanced recruitment of posterior brain regions in ASD has been observed during working memory (Koshino et al., 2005; Vogan et al., 2014), psychomotor vigilance (Christakou et al., 2013; Murphy et al., 2014) and temporal discounting (Chantiluke et al., 2014; Norman et al., 2017). This thus suggests a complex pattern of neurofunctional abnormality in ASD, with compromised cortical specialization, possibly reflecting both neural impairments inherent to ASD and, possibly, ensuing neuronal compensation (see review Floris & Howells, 2018).
The ASD-differentiating overactivation in PCC/precuneus, which was also unimpaired in ADHD relative to their respective controls, was unexpected, however. The overactivation in precuneus/PCC in ASD, which has been shown in a variety of tasks and resting-state condition (Christakou et al., 2013; Kennedy, Redcay, & Courchesne, 2006; Murdaugh et al., 2012; Spencer et al., 2012), presumably indicates DMN dysregulation. The lack of overactivation in PCC/precuneus in ADHD during cognitive control were apparent in previous meta-analyses (McCarthy et al., 2014; Norman et al., 2016), and could potentially be related to psychostimulant exposure which has shown to normalize DMN functioning (Liddle et al., 2011; Peterson et al., 2009; Rubia et al., 2014).
Finally, shared functional abnormality between disorders was found during motor response inhibition only in right AI, which is part of the salience network (Menon & Uddin, 2010). The limited shared abnormality may seem at odds with typical observation of psychiatric comorbidities in clinical settings, but individuals with ASD and ADHD are typically selectively recruited into imaging studies, excluding specific psychiatric comorbidities, which could have emphasized the differences between disorders, at the cost of their representativeness.
On the other hand, the findings of disorder differences are more likely to be underestimated. First, typically stringent statistical corrections applied by individual studies could have concealed true group differences in coordinate-based meta-analyses. The ADHD-differentiating findings, particularly, are likely underestimated by co-occurring ADHD in the ASD groups due to past preclusion of ADHD diagnosis in ASD (APA, 2000). Studies attempting to include more representative samples of the disorders may have included comorbid ASD and ADHD cases, perhaps at higher rates in ASD than in ADHD, as shown in prevalence studies in community representative samples (Gjevik, Eldevik, Fjæran-Granum, & Sponheim, 2011; Hollingdale, Woodhouse, Young, Fridman, & Mandy, 2019; Salazar et al., 2015; Simonoff et al., 2008). Long-term medication exposure is also likely to have masked the ADHD-differentiating findings.
Other limitations of the meta-analyses include the fewer number of participants with ASD relative to ADHD, which could have reduced power for detecting small ASD-differentiating impairments and increased the probability of false positive findings, particularly in the fMRI meta- and sub-meta-analyses. The representativeness of the meta-analytic findings may also be limited by the fact that many studies, particularly in ADHD, investigated males specifically. In ASD, the majority of studies have focused on adults and predominantly high-functioning individuals only (with few exceptions, i.e., Cai et al., 2018; Contarino et al., 2016; Retico et al., 2016; Riva et al., 2013; Wang et al., 2017; Q. Yang et al., 2018), even though individuals with learning disability made up a significant proportion of the ASD population (e.g., Charman et al., 2011). Furthermore, brain-behavior correlations could not be examined due to variations of disorder trait measures across studies. Finally, the influence of task discrepancies across studies could not be fully assessed due to limited data for the non-motor cognitive control tasks.
5. Conclusions
These comparative meta-analyses of brain structure and function show predominantly disorder-differentiating abnormalities in the form of decreased GMV in vmOFC reward processing regions in ADHD and increased GMV in ASD in frontal and temporal cortices, part of the central executive and social cognition networks. Disorder-differentiating fMRI deficits were predominantly observed in ASD in medial frontal executive, ventrolateral prefrontal and DMN regions when including all cognitive control tasks; and in ADHD in inferior fronto-striatal regions during motor response inhibition only. Shared functional abnormality was limited to right AI during motor response inhibition. Therefore, people with ADHD and ASD appear to have mostly distinct brain structure and cognitive-control functional abnormalities. The findings contribute to the elucidation of the differential brain abnormalities in the two disorders, which is important for the understanding of their underlying pathophysiology and could ultimately aid in the development of future, disorder-differentiated behavioral, pharmacological or neurotherapeutic treatments.
Supplementary Material
Acknowledgement
We thank all authors of the included studies who provided additional data for this meta-analysis. We also thank Toby Wise for insightful discussions on the statistical methods used in this meta-analysis.
Financial support
SL and LN were supported by Medical Research Council and Institute of Psychiatry, Psychology, and Neuroscience (IoPPN) PhD Excellence awards. CC is supported by a Wellcome Trust Sir Henry Wellcome Postdoctoral Fellowship (206459/Z/17/Z). JR receives support from the Instituto de Salud Carlos III and co-funded by European Union (ERDF/ESF, ‘Investing in your future’, Miguel Servet Research Contract CPII19/00009 and Research Project Grant PI19/00394). HH was supported by a grant from the Lila Weston Foundation for Medical Research and the Kids Company to KR. ES and KR receive research support from the National Institute of Health Research (NIHR) Biomedical Research Centre at South London and Maudsley Foundation Trust (IS-BRC-1215-20018). ES furthermore is supported by the NIHR through a program grant (RP-PG-1211-20016) and Senior Investigator Award (NF-SI-0514-10073), the European Union Innovative Medicines Initiative (EU-IMI 115300), Autistica (7237) Medical Research Council (MR/R000832/1, MR/P019293/1), the Economic and Social Research Council (ESRC 003041/1) and Guy’s and St Thomas’ Charitable Foundation (GSTT EF1150502) and the Maudsley Charity. KR and SL are currently supported by an MRC Grant MR/P012647/1 to KR. This study is supported by funding from the UK Department of Health via the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) for Mental Health at South London and the Maudsley National Health Service (NHS) Foundation Trust and the IoPPN, King's College London, which also support both senior authors, ES and KR. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
Footnotes
Conflicts of interest: KR has received grants from Lilly and Shire pharmaceuticals and speaker’s bureau from Shire, Lilly and Medice. Other authors report no biomedical financial interests or potential conflicts of interest.
Ethical standards: The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.
References
- Abell F, Krams M, Ashburner J, Passingham R, Friston K, Frackowiak R, et al. Frith U. The neuroanatomy of autism: A voxel-based whole brain analysis of structural scans. Neuroreport. 1999;10(8):1647–1651. doi: 10.1097/00001756-199906030-00005. [DOI] [PubMed] [Google Scholar]
- Ahrendts J, Rüsch N, Wilke M, Philipsen A, Eickhoff SB, Glauche V, et al. Van Elst L Tebartz. Visual cortex abnormalities in adults with ADHD: A structural MRI study. The World Journal of Biological Psychiatry. 2011;12(4):260–270. doi: 10.3109/15622975.2010.518624. [DOI] [PubMed] [Google Scholar]
- Albaugh MD, Orr C, Chaarani B, Althoff RR, Allgaier N, D'Alberto N, et al. Potter AS. Inattention and reaction time variability are linked to ventromedial prefrontal volume in adolescents. Biological Psychiatry. 2017;82(9):660–668. doi: 10.1016/j.biopsych.2017.01.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ambrosino S, Bos DJ, van Raalten TR, Kobussen NA, van Belle J, Oranje B, Durston S. Functional connectivity during cognitive control in children with autism spectrum disorder: An independent component analysis. Journal of Neural Transmission. 2014;121(9):1145–1155. doi: 10.1007/s00702-014-1237-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- American Psychiatric Association (APA) Diagnostic and Statistical Manual of Mental Disorders: DSM-5. (5th ed.) Washington, DC: APA; 2013. [Google Scholar]
- American Psychiatric Association (APA)(Ed.) Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) Washington, DC: APA; 2000. [Google Scholar]
- Amico F, Stauber J, Koutsouleris N, Frodl T. Anterior cingulate cortex gray matter abnormalities in adults with attention deficit hyperactivity disorder: A voxel-based morphometry study. Psychiatry Research: Neuroimaging. 2011;191(1):31–35. doi: 10.1016/j.pscychresns.2010.08.011. [DOI] [PubMed] [Google Scholar]
- Aron AR, Herz DM, Brown P, Forstmann BU, Zaghloul K. Frontosubthalamic circuits for control of action and cognition. Journal of Neuroscience. 2016;36(45):11489–11495. doi: 10.1523/jneurosci.2348-16.2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Banich MT, Burgess GC, Depue BE, Ruzic L, Bidwell LC, Hitt-Laustsen S, et al. Willcutt EG. The neural basis of sustained and transient attentional control in young adults with ADHD. Neuropsychologia. 2009;47(14):3095–3104. doi: 10.1016/j.neuropsychologia.2009.07.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bari A, Robbins TW. Inhibition and impulsivity: Behavioral and neural basis of response control. Progress in Neurobiology. 2013;108:44–79. doi: 10.1016/j.pneurobio.2013.06.005. [DOI] [PubMed] [Google Scholar]
- Bhaijiwala M, Chevrier A, Schachar R. Withholding and canceling a response in ADHD adolescents. Brain and Behavior. 2014;4(5):602–614. doi: 10.1002/brb3.244. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Binder JR, Gross WL, Allendorfer JB, Bonilha L, Chapin J, Edwards JC, et al. Weaver KE. Mapping anterior temporal lobe language areas with fMRI: A multicenter normative study. NeuroImage. 2011;54(2):1465–1475. doi: 10.1016/j.neuroimage.2010.09.048. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Boddaert N, Chabane N, Gervais H, Good CD, Bourgeois M, Plumet MH, et al. Zilbovicius M. Superior temporal sulcus anatomical abnormalities in childhood autism: A voxel-based morphometry MRI study. NeuroImage. 2004;23(1):364–369. doi: 10.1016/j.neuroimage.2004.06.016. [DOI] [PubMed] [Google Scholar]
- Bonath B, Tegelbeckers J, Wilke M, Flechtner HH, Krauel K. Regional gray matter volume differences between adolescents with ADHD and typically developing controls: Further evidence for anterior cingulate involvement. Journal of Attention Disorders. 2016 doi: 10.1177/1087054715619682. [DOI] [PubMed] [Google Scholar]
- Bonilha L, Cendes F, Rorden C, Eckert M, Dalgalarrondo P, Li LM, Steiner CE. Gray and white matter imbalance - Typical structural abnormality underlying classic autism? Brain and Development. 2008;30(6):396–401. doi: 10.1016/j.braindev.2007.11.006. [DOI] [PubMed] [Google Scholar]
- Booth JR, Burman DD, Meyer JR, Lei Z, Trommer BL, Davenport ND, et al. Mesulam MM. Larger deficits in brain networks for response inhibition than for visual selective attention in attention deficit hyperactivity disorder (ADHD) Journal of Child Psychology and Psychiatry. 2005;46(1):94–111. doi: 10.1111/j.1469-7610.2004.00337.x. [DOI] [PubMed] [Google Scholar]
- Bralten J, Greven CU, Franke B, Mennes M, Zwiers MP, Rommelse NNJ, et al. Buitelaar JK. Voxel-based morphometry analysis reveals frontal brain differences in participants with ADHD and their unaffected siblings. Journal of Psychiatry & Neuroscience. 2016;41(4):272–279. doi: 10.1503/jpn.140377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Breyer JL, Botzet AM, Winters KC, Stinchfield RD, August G, Realmuto G. Young adult gambling behaviors and their relationship with the persistence of ADHD. Journal of Gambling Studies. 2009;25(2):227–238. doi: 10.1007/s10899-009-9126-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Brieber S, Neufang S, Bruning N, Kamp-Becker I, Remschmidt H, Herpertz-Dahlmann B, et al. Konrad K. Structural brain abnormalities in adolescents with autism spectrum disorder and patients with attention deficit/hyperactivity disorder. Journal of Child Psychology and Psychiatry. 2007;48(12):1251–1258. doi: 10.1111/j.1469-7610.2007.01799.x. [DOI] [PubMed] [Google Scholar]
- Cai J, Hu X, Guo K, Yang P, Situ M, Huang Y. Increased left inferior temporal gyrus was found in both low function autism and high function autism. Frontiers in Psychiatry. 2018;9:542. doi: 10.3389/fpsyt.2018.00542. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Carlisi CO, Norman LJ, Lukito SS, Radua J, Mataix-Cols D, Rubia K. Comparative multimodal meta-analysis of structural and functional brain abnormalities in autism spectrum disorder and obsessive-compulsive disorder. Biological Psychiatry. 2017;82(2):83–102. doi: 10.1016/j.biopsych.2016.10.006. [DOI] [PubMed] [Google Scholar]
- Carmona S, Hoekzema E, Antoni Ramos-Quiroga J, Richarte V, Canals C, Bosch R, et al. Vilarroya O. Response inhibition and reward anticipation in medication-naive adults with attention-deficit/hyperactivity disorder: A within-subject case-control neuroimaging study. Human Brain Mapping. 2012;33(10):2350–2361. doi: 10.1002/hbm.21368. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Carmona S, Vilarroya O, Bielsa A, Tremols V, Soliva JC, Rovira M, et al. Bulbena A. Global and regional gray matter reductions in ADHD: A voxel-based morphometric study. Neuroscience Letters. 2005;389(2):88–93. doi: 10.1016/j.neulet.2005.07.020. [DOI] [PubMed] [Google Scholar]
- Carper RA, Courchesne E. Localized enlargement of the frontal cortex in early autism. Biological Psychiatry. 2005;57(2):126–133. doi: 10.1016/j.biopsych.2004.11.005. [DOI] [PubMed] [Google Scholar]
- Cauda F, Geda E, Sacco K, D'Agata F, Duca S, Geminiani G, Keller R. Grey matter abnormality in autism spectrum disorder: An activation likelihood estimation meta-analysis study. Journal of Neurology Neurosurgery and Psychiatry. 2011;82(12):1304–1313. doi: 10.1136/jnnp.2010.239111. [DOI] [PubMed] [Google Scholar]
- Chantiluke K, Barrett N, Giampietro V, Brammer M, Simmons A, Murphy DG, Rubia K. Inverse effect of fluoxetine on medial prefrontal cortex activation during reward reversal in ADHD and autism. Cerebral Cortex. 2015;25(7):1757–1770. doi: 10.1093/cercor/bht365. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chantiluke K, Barrett N, Giampietro V, Brammer M, Simmons A, Rubia K. Disorder-dissociated effects of fluoxetine on brain function of working memory in attention deficit hyperactivity disorder and autism spectrum disorder. Psychological Medicine. 2015;45(6):1195–1205. doi: 10.1017/s0033291714002232. [DOI] [PubMed] [Google Scholar]
- Chantiluke K, Barrett N, Giampietro V, Santosh P, Brammer M, Simmons A, et al. Rubia K. Inverse fluoxetine effects on inhibitory brain activation in non-comorbid boys with ADHD and with ASD. Psychopharmacology. 2015;232(12):2071–2082. doi: 10.1007/s00213-014-3837-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chantiluke K, Christakou A, Murphy CM, Giampietro V, Daly EM, Ecker C, et al. Rubia K. Disorder-specific functional abnormalities during temporal discounting in youth with attention deficit hyperactivity disorder (ADHD), autism and comorbid ADHD and autism. Psychiatry Research. 2014;223(2):113–120. doi: 10.1016/j.pscychresns.2014.04.006. [DOI] [PubMed] [Google Scholar]
- Charman T, Pickles A, Simonoff E, Chandler S, Loucas T, Baird G. IQ in children with autism spectrum disorders: Data from the Special Needs and Autism Project (SNAP) Psychological Medicine. 2011;41(03):619–627. doi: 10.1017/s0033291710000991. [DOI] [PubMed] [Google Scholar]
- Chen C-Y, Yen J-Y, Yen C-F, Chen C-S, Liu G-C, Liang C-Y, Ko C-H. Aberrant brain activation of error processing among adults with attention deficit and hyperactivity disorder. Kaohsiung Journal of Medical Sciences. 2015;31(4):179–187. doi: 10.1016/j.kjms.2015.01.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cheng Y, Chou KH, Fan YT, Lin CP. ANS: Aberrant neurodevelopment of the social cognition network in adolescents with autism spectrum disorders. PLoS One. 2011;6(4) doi: 10.1371/journal.pone.0018905. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chou T-L, Chia S, Shang C-Y, Gau SS-F. Differential therapeutic effects of 12-week treatment of atomoxetine and methylphenidate on drug-naive children with attention deficit/hyperactivity disorder: A counting Stroop functional MRI study. European Neuropsychopharmacology. 2015;25(12):2300–2310. doi: 10.1016/j.euroneuro.2015.08.024. [DOI] [PubMed] [Google Scholar]
- Christakou A, Murphy CM, Chantiluke K, Cubillo AI, Smith AB, Giampietro V, et al. Rubia K. Disorder-specific functional abnormalities during sustained attention in youth with attention deficit hyperactivity disorder (ADHD) and with autism. Molecular Psychiatry. 2013;18(2):236–244. doi: 10.1038/mp.2011.185. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Congdon E, Mumford JA, Cohen JR, Galvan A, Aron AR, Xue G, et al. Poldrack RA. Engagement of large-scale networks is related to individual differences in inhibitory control. NeuroImage. 2010;53(2):653–663. doi: 10.1016/j.neuroimage.2010.06.062. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Contarino VE, Bulgheroni S, Annunziata S, Erbetta A, Riva D. Widespread focal cortical alterations in autism spectrum disorder with intellectual disability detected by threshold-free cluster enhancement. American Journal of Neuroradiology. 2016;37(9):1721–1726. doi: 10.3174/ajnr.A4779. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Courchesne E, Campbell K, Solso S. Brain growth across the life span in autism: Age-specific changes in anatomical pathology. Brain Research. 2011;1380:138–145. doi: 10.1016/j.brainres.2010.09.101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Courchesne E, Mouton PR, Calhoun ME, Semendeferi K, Ahrens-Barbeau C, Hallet MJ, et al. Pierce K. Neuron number and size in prefrontal cortex of children with autism. JAMA. 2011;306(18):2001–2010. doi: 10.1001/jama.2011.1638. [DOI] [PubMed] [Google Scholar]
- Craig F, Margari F, Legrottaglie AR, Palumbi R, de Giambattista C, Margari L. A review of executive function deficits in autism spectrum disorder and attention-deficit/hyperactivity disorder. Neuropsychiatric Disease and Treatment. 2016;12:1191–1202. doi: 10.2147/ndt.s104620. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Craig M, Zaman SH, Daly EM, Cutter WJ, Robertson DMW, Hallahan B, et al. Murphy DGM. Women with autistic-spectrum disorder: magnetic resonance imaging study of brain anatomy. British Journal of Psychiatry. 2007;191:224–228. doi: 10.1192/bjp.bp.106.034603. [DOI] [PubMed] [Google Scholar]
- Cubillo A, Halari R, Ecker C, Giampietro V, Taylor E, Rubia K. Reduced activation and inter-regional functional connectivity of fronto-striatal networks in adults with childhood attention-deficit hyperactivity disorder (ADHD) and persisting symptoms during tasks of motor inhibition and cognitive switching. Journal of Psychiatric Research. 2010;44(10):629–639. doi: 10.1016/j.jpsychires.2009.11.016. [DOI] [PubMed] [Google Scholar]
- Cubillo A, Halari R, Giampietro V, Taylor E, Rubia K. Fronto-striatal underactivation during interference inhibition and attention allocation in grown up children with attention deficit/hyperactivity disorder and persistent symptoms. Psychiatry Research. 2011;193(1):17–27. doi: 10.1016/j.pscychresns.2010.12.014. [DOI] [PubMed] [Google Scholar]
- Cubillo A, Halari R, Smith A, Taylor E, Rubia K. A review of fronto-striatal and fronto-cortical brain abnormalities in children and adults with attention deficit hyperactivity disorder (ADHD) and new evidence for dysfunction in adults with ADHD during motivation and attention. Cortex. 2012;48(2):194–215. doi: 10.1016/j.cortex.2011.04.007. [DOI] [PubMed] [Google Scholar]
- Cubillo A, Smith AB, Barrett N, Giampietro V, Brammer MJ, Simmons A, Rubia K. Shared and drug-specific effects of atomoxetine and methylphenidate on inhibitory brain dysfunction in medication-naive ADHD boys. Cerebral Cortex. 2014;24(1):174–185. doi: 10.1093/cercor/bhs296. [DOI] [PMC free article] [PubMed] [Google Scholar]
- D'Mello AM, Crocetti D, Mostofsky SH, Stoodley CJ. Cerebellar gray matter and lobular volumes correlate with core autism symptoms. NeuroImage: Clinical. 2015;7:631–639. doi: 10.1016/j.nicl.2015.02.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Daly EM, Ecker C, Hallahan B, Deeley Q, Craig M, Murphy C, et al. Murphy DGM. Response inhibition and serotonin in autism: A functional MRI study using acute tryptophan depletion. Brain. 2014;137:2600–2610. doi: 10.1093/brain/awu178. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Denisova K, Zhao G, Wang Z, Goh S, Huo Y, Peterson BS. Cortical interactions during the resolution of information processing demands in autism spectrum disorders. Brain and Behavior. 2017;7(2):e00596. doi: 10.1002/brb3.596. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Dennis M, Francis DJ, Cirino PT, Schachar R, Barnes MA, Fletcher JM. Why IQ is not a covariate in cognitive studies of neurodevelopmental disorders. Journal of the International Neuropsychological Society. 2009;15(03):331. doi: 10.1017/s1355617709090481. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Depue BE, Burgess GC, Bidwell LC, Willcutt EG, Banich MT. Behavioral performance predicts grey matter reductions in the right inferior frontal gyrus in young adults with combined type ADHD. Psychiatry Research: Neuroimaging. 2010;182(3):231–237. doi: 10.1016/j.pscychresns.2010.01.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- DeRamus TP, Kana RK. Anatomical likelihood estimation meta-analysis of grey and white matter anomalies in autism spectrum disorders. NeuroImage: Clinical. 2015;7:525–536. doi: 10.1016/j.nicl.2014.11.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Desmond JE, Glover GH. Estimating sample size in functional MRI (fMRI) neuroimaging studies: statistical power analyses. Journal of Neuroscience Methods. 2002;118(2):115–128. doi: 10.1016/s0165-0270(02)00121-8. [DOI] [PubMed] [Google Scholar]
- Di Martino A, Ross K, Uddin LQ, Sklar AB, Castellanos FX, Milham MP. Functional brain correlates of social and nonsocial processes in autism spectrum disorders: An activation likelihood estimation meta-analysis. Biological Psychiatry. 2009;65(1):63–74. doi: 10.1016/j.biopsych.2008.09.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Di Martino A, Zuo X-N, Kelly C, Grzadzinski R, Mennes M, Schvarcz A, et al. Milham MP. Shared and distinct intrinsic functional network centrality in autism and attention-deficit/hyperactivity disorder. Biological Psychiatry. 2013;74(8):623–632. doi: 10.1016/j.biopsych.2013.02.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Dibbets P, Evers EAT, Hurks PPM, Bakker K, Jolles J. Differential brain activation patterns in adult attention-deficit hyperactivity disorder (ADHD) associated with task switching. Neuropsychology. 2010;24(4):413–423. doi: 10.1037/a0018997. [DOI] [PubMed] [Google Scholar]
- Dibbets P, Evers L, Hurks P, Marchetta N, Jolles J. Differences in feedback- and inhibition-related neural activity in adult ADHD. Brain and Cognition. 2009;70(1):73–83. doi: 10.1016/j.bandc.2009.01.001. [DOI] [PubMed] [Google Scholar]
- Dichter GS. Functional magnetic resonance imaging of autism spectrum disorders. Dialogues in Clinical Neuroscience. 2012;14(3):319–351. doi: 10.31887/DCNS.2012.14.3/gdichter. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Duerden EG, Mak-Fan KM, Taylor MJ, Roberts SW. Regional differences in grey and white matter in children and adults with autism spectrum disorders: An activation likelihood estimate (ALE) meta-analysis. Autism Research. 2012;5(1):49–66. doi: 10.1002/aur.235. [DOI] [PubMed] [Google Scholar]
- Duerden EG, Taylor MJ, Soorya LV, Wang T, Fan J, Anagnostou E. Neural correlates of inhibition of socially relevant stimuli in adults with autism spectrum disorder. Brain Research. 2013;1533:80–90. doi: 10.1016/j.brainres.2013.08.021. [DOI] [PubMed] [Google Scholar]
- Durston S, Mulder M, Casey BJ, Ziermans T, van Engeland H. Activation in ventral prefrontal cortex is sensitive to genetic vulnerability for attention-deficit hyperactivity disorder. Biological Psychiatry. 2006;60(10):1062–1070. doi: 10.1016/j.biopsych.2005.12.020. [DOI] [PubMed] [Google Scholar]
- Ecker C. The neuroanatomy of autism spectrum disorder: An overview of structural neuroimaging findings and their translatability to the clinical setting. Autism. 2017;21(1):18–28. doi: 10.1177/1362361315627136. [DOI] [PubMed] [Google Scholar]
- Ecker C, Schmeisser MJ, Loth E, Murphy DG. Neuroanatomy and neuropathology of autism spectrum disorder in humans. Advances in Anatomy, Embryology and Cell Biology. 2017;224:27–48. doi: 10.1007/978-3-319-52498-6_2. [DOI] [PubMed] [Google Scholar]
- Ecker C, Suckling J, Deoni SC, Lombardo MV, Bullmore ET, Baron-Cohen S, et al. Murphy DGM. Brain anatomy and its relationship to behavior in adults with autism spectrum disorder. Archives of General Psychiatry. 2012;69(2):195–209. doi: 10.1001/archgenpsychiatry.2011.1251. [DOI] [PubMed] [Google Scholar]
- Elsabbagh M, Divan G, Koh Y-J, Kim YS, Kauchali S, Marcín C, et al. Fombonne E. Global prevalence of autism and other pervasive developmental disorders: global epidemiology of autism. Autism Research. 2012;5(3):160–179. doi: 10.1002/aur.239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fan J, Bernardi S, Van Dam NT, Anagnostou E, Gu X, Martin L, et al. Hof PR. Functional deficits of the attentional networks in autism. Brain and Behavior. 2012;2(5):647–660. doi: 10.1002/brb3.90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fan LY, Chou TL, Gau SS. Neural correlates of atomoxetine improving inhibitory control and visual processing in drug-naive adults with attention-deficit/hyperactivity disorder. Human Brain Mapping. 2017;38(10):4850–4864. doi: 10.1002/hbm.23683. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fan LY, Shang CY, Tseng WI, Gau SS, Chou TL. Visual processing as a potential endophenotype in youths with attention-deficit/hyperactivity disorder: A sibling study design using the counting Stroop functional MRI. Human Brain Mapping. 2018;39(10):3827–3835. doi: 10.1002/hbm.24214. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Floris DL, Howells H. Atypical structural and functional motor networks in autism. Progress in Brain Research. 2018;238:207–248. doi: 10.1016/bs.pbr.2018.06.010. [DOI] [PubMed] [Google Scholar]
- Foster NEV, Doyle-Thomas KAR, Tryfon A, Ouimet T, Anagnostou E, Evans AC, et al. Hyde KL. Structural gray matter differences during childhood development in autism spectrum disorder: A multimetric approach. Pediatric Neurology. 2015;53(4):350–359. doi: 10.1016/j.pediatrneurol.2015.06.013. [DOI] [PubMed] [Google Scholar]
- Freitag CM, Konrad C, Häberlen M, Kleser C, von Gontard A, Reith W, et al. Krick C. Perception of biological motion in autism spectrum disorders. Neuropsychologia. 2008;46(5):1480–1494. doi: 10.1016/j.neuropsychologia.2007.12.025. [DOI] [PubMed] [Google Scholar]
- Friston KJ, Rotshtein P, Geng JJ, Sterzer P, Henson RN. A critique of functional localisers. NeuroImage. 2006;30(4):1077–1087. doi: 10.1016/j.neuroimage.2005.08.012. [DOI] [PubMed] [Google Scholar]
- Frodl T, Skokauskas N. Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects. Acta Psychiatrica Scandinavica. 2012;125(2):114–126. doi: 10.1111/j.1600-0447.2011.01786.x. [DOI] [PubMed] [Google Scholar]
- Fuentes P, Barros-Loscertales A, Bustamante JC, Rosell P, Costumero V, Avila C. Individual differences in the behavioral inhibition system are associated with orbitofrontal cortex and precuneus gray matter volume. Cognitive, Affective, & Behavioral Neuroscience. 2012;12(3):491–498. doi: 10.3758/s13415-012-0099-5. [DOI] [PubMed] [Google Scholar]
- Gehricke JG, Kruggel F, Thampipop T, Alejo SD, Tatos E, Fallon J, Muftuler LT. The brain anatomy of attention-deficit/hyperactivity disorder in young adults - A magnetic resonance imaging study. PLoS One. 2017;12(4) doi: 10.1371/journal.pone.0175433. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Geurts HM, van den Bergh SFWM, Ruzzano L. Prepotent response inhibition and interference control in autism spectrum disorders: Two meta-analyses. Autism Research. 2014;7(4):407–420. doi: 10.1002/aur.1369. [DOI] [PubMed] [Google Scholar]
- Gjevik E, Eldevik S, Fjæran-Granum T, Sponheim E. Kiddie-SADS reveals high rates of DSM-IV disorders in children and adolescents with autism spectrum disorders. Journal of Autism and Developmental Disorders. 2011;41(6):761–769. doi: 10.1007/s10803-010-1095-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gooskens B, Bos DJ, Mensen VT, Shook DA, Bruchhage MMK, Naaijen J, et al. consortium, T No evidence of differences in cognitive control in children with autism spectrum disorder or obsessive-compulsive disorder: An fMRI study. Developmental Cognitive Neuroscience. 2018 doi: 10.1016/j.dcn.2018.11.004. 100602. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Greimel E, Nehrkorn B, Schulte-Ruether M, Fink GR, Nickl-Jockschat T, Herpertz-Dahlmann B, et al. Eickhoff SB. Changes in grey matter development in autism spectrum disorder. Brain Structure and Function. 2013;218(4):929–942. doi: 10.1007/s00429-012-0439-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Griebling J, Minshew NJ, Bodner K, Libove R, Bansal R, Konasale P, et al. Hardan A. Dorsolateral prefrontal cortex magnetic resonance imaging measurements and cognitive performance in autism. Journal of Child Neurology. 2010;25(7):856–863. doi: 10.1177/0883073809351313. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Groen WB, Buitelaar JK, van der Gaag RJ, Zwiers MP. Pervasive microstructural abnormalities in autism: A DTI study. Journal of Psychiatry & Neuroscience. 2011;36(1):32–40. doi: 10.1503/jpn.090100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hart H, Radua J, Nakao T, Mataix-Cols D, Rubia K. Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder: exploring task-specific, stimulant medication, and age effects. JAMA Psychiatry. 2013;70(2):185–198. doi: 10.1001/jamapsychiatry.2013.277. [DOI] [PubMed] [Google Scholar]
- Hazlett HC, Gu H, Munsell BC, Kim SH, Styner M, Wolff JJ, et al. Statistical, A Early brain development in infants at high risk for autism spectrum disorder. Nature. 2017;542(7641):348–351. doi: 10.1038/nature21369. [DOI] [PMC free article] [PubMed] [Google Scholar]
- He N, Li F, Li Y, Guo L, Chen L, Huang X, et al. Gong Q. Neuroanatomical deficits correlate with executive dysfunction in boys with attention deficit hyperactivity disorder. Neuroscience Letters. 2015;600:45–49. doi: 10.1016/j.neulet.2015.05.062. [DOI] [PubMed] [Google Scholar]
- Hollingdale J, Woodhouse E, Young S, Fridman A, Mandy W. Autistic spectrum disorder symptoms in children and adolescents with attention-deficit/hyperactivity disorder: A meta-analytical review. Psychological Medicine. 2019:1–14. doi: 10.1017/S0033291719002368. [DOI] [PubMed] [Google Scholar]
- Hoogman M, Bralten J, Hibar DP, Mennes M, Zwiers MP, Schweren LS, et al. Franke B. Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: A cross-sectional mega-analysis. Lancet Psychiatry. 2017;4(4):310–319. doi: 10.1016/S2215-0366(17)30049-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hoogman M, Muetzel R, Guimaraes JP, Shumskaya E, Mennes M, Zwiers MP, et al. Franke B. Brain Imaging of the Cortex in ADHD: A coordinated analysis of large-scale clinical and population-based samples. American Journal of Psychiatry. 2019;176(7):531–542. doi: 10.1176/appi.ajp.2019.18091033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hwang S, White SF, Nolan ZT, Craig Williams W, Sinclair S, Blair RJR. Executive attention control and emotional responding in attention-deficit/hyperactivity disorder - A functional MRI study. NeuroImage: Clinical. 2015;9:545–554. doi: 10.1016/j.nicl.2015.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hyde KL, Samson F, Evans AC, Mottron L. Neuroanatomical differences in brain areas implicated in perceptual and other core features of autism revealed by cortical thickness analysis and voxel-based morphometry. Human Brain Mapping. 2010;31(4):556–566. doi: 10.1002/hbm.20887. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Iannaccone R, Hauser TU, Ball J, Brandeis D, Walitza S, Brem S. Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging. European Child & Adolescent Psychiatry. 2015;24(10):1279–1289. doi: 10.1007/s00787-015-0678-4. [DOI] [PubMed] [Google Scholar]
- Iannaccone R, Hauser TU, Staempfli P, Walitza S, Brandeis D, Brem S. Conflict monitoring and error processing: New insights from simultaneous EEG–fMRI. NeuroImage. 2015;105:395–407. doi: 10.1016/j.neuroimage.2014.10.028. [DOI] [PubMed] [Google Scholar]
- Itahashi T, Yamada T, Nakamura M, Watanabe H, Yamagata B, Jimbo D, et al. Hashimoto R. Linked alterations in gray and white matter morphology in adults with high-functioning autism spectrum disorder: A multimodal brain imaging study. NeuroImage. Clinical. 2015;7:155–169. doi: 10.1016/j.nicl.2014.11.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jagger-Rickels AC, Kibby MY, Constance JM. Global gray matter morphometry differences between children with reading disability, ADHD, and comorbid reading disability/ADHD. Brain and Language. 2018;185:54–66. doi: 10.1016/j.bandl.2018.08.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Janssen TWP, Heslenfeld DJ, Mourik RV, Logan GD, Oosterlaan J. Neural correlates of response inhibition in children with attention-deficit/hyperactivity disorder: A controlled version of the stop-signal task. Psychiatry Research: Neuroimaging. 2015;233(2):278–284. doi: 10.1016/j.pscychresns.2015.07.007. [DOI] [PubMed] [Google Scholar]
- Johnston BA, Mwangi B, Matthews K, Coghill D, Konrad K, Steele JD. Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification. Human Brain Mapping. 2014;35(10):5179–5189. doi: 10.1002/hbm.22542. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kana RK, Keller TA, Minshew NJ, Just MA. Inhibitory control in high-functioning autism: decreased activation and underconnectivity in inhibition networks. Biological Psychiatry. 2007;62(3):198–206. doi: 10.1016/j.biopsych.2006.08.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kana RK, Maximo JO, Williams DL, Keller TA, Schipul SE, Cherkassky VL, et al. Just MA. Aberrant functioning of the theory-of-mind network in children and adolescents with autism. Molecular Autism. 2015;6:59. doi: 10.1186/s13229-015-0052-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kana RK, Patriquin MA, Black BS, Channell MM, Wicker B. Altered medial frontal and superior temporal response to implicit processing of emotions in autism. Autism Research. 2016;9(1):55–66. doi: 10.1002/aur.1496. [DOI] [PubMed] [Google Scholar]
- Kappel V, Lorenz RC, Streifling M, Renneberg B, Lehmkuhl U, Stroehle A, et al. Beck A. Effect of brain structure and function on reward anticipation in children and adults with attention deficit hyperactivity disorder combined subtype. Social Cognitive and Affective Neuroscience. 2015;10(7):945–951. doi: 10.1093/scan/nsu135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Katz J, d'Albis MA, Boisgontier J, Poupon C, Mangin JF, Guevara P, et al. Houenou J. Similar white matter but opposite grey matter changes in schizophrenia and high-functioning autism. Acta Psychiatrica Scandinavica. 2016;134(1):31–39. doi: 10.1111/acps.12579. [DOI] [PubMed] [Google Scholar]
- Kaufmann L, Zotter S, Pixner S, Starke M, Haberlandt E, Steinmayr-Gensluckner M, et al. Marksteiner J. Brief report: CANTAB performance and brain structure in pediatric patients with Asperger syndrome. Journal of Autism and Developmental Disorders. 2013;43(6):1483–1490. doi: 10.1007/s10803-012-1686-6. [DOI] [PubMed] [Google Scholar]
- Kaya BS, Metin B, Tas ZC, Buyukaslan A, Soysal A, Hatiloglu D, Tarhan N. Gray matter increase in motor cortex in pediatric adhd: A voxel-based morphometry study. Journal of Attention Disorders. 2018;22(7):611–618. doi: 10.1177/1087054716659139. [DOI] [PubMed] [Google Scholar]
- Ke X, Hong S, Tang T, Zou B, Li H, Hang Y, et al. Liu Y. Voxel-based morphometry study on brain structure in children with high-functioning autism. Neuroreport. 2008;19(9):921–925. doi: 10.1097/wnr.0b013e328300edf3. [DOI] [PubMed] [Google Scholar]
- Kennedy DP, Redcay E, Courchesne E. Failing to deactivate: Resting functional abnormalities in autism. Proceedings of the National Academy of Sciences of the United States of America. 2006;103(21):8275–8280. doi: 10.1073/pnas.0600674103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kleinhans NM, Muller RA, Cohen DN, Courchesne E. Atypical functional lateralization of language in autism spectrum disorders. Brain Research. 2008;1221:115–125. doi: 10.1016/j.brainres.2008.04.080. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kobel M, Bechtel N, Specht K, Klarhoefer M, Weber P, Scheffler K, et al. Penner I-K. Structural and functional imaging approaches in attention deficit/hyperactivity disorder: Does the temporal lobe play a key role? Psychiatry Research: Neuroimaging. 2010;183(3):230–236. doi: 10.1016/j.pscychresns.2010.03.010. [DOI] [PubMed] [Google Scholar]
- Kohls G, Thönessen H, Bartley GK, Grossheinrich N, Fink GR, Herpertz-Dahlmann B, Konrad K. Differentiating neural reward responsiveness in autism versus ADHD. Developmental Cognitive Neuroscience. 2014;10:104–116. doi: 10.1016/j.dcn.2014.08.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Konrad K, Neufang S, Hanisch C, Fink GR, Herpertz-Dahlmann B. Dysfunctional attentional networks in children with attention deficit/hyperactivity disorder: Evidence from an event-related functional magnetic resonance imaging study. Biological Psychiatry. 2006;59(7):643–651. doi: 10.1016/j.biopsych.2005.08.013. [DOI] [PubMed] [Google Scholar]
- Kooistra L, van der Meere JJ, Edwards JD, Kaplan BJ, Crawford S, Goodyear BG. Preliminary fMRI findings on the effects of event rate in adults with ADHD. Journal of Neural Transmission. 2010;117(5):655–662. doi: 10.1007/s00702-010-0374-y. [DOI] [PubMed] [Google Scholar]
- Korponay C, Dentico D, Kral T, Ly M, Kruis A, Goldman R, et al. Davidson RJ. Neurobiological correlates of impulsivity in healthy adults: Lower prefrontal gray matter volume and spontaneous eye-blink rate but greater resting-state functional connectivity in basal ganglia-thalamo-cortical circuitry. NeuroImage. 2017;157:288–296. doi: 10.1016/j.neuroimage.2017.06.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kosaka H, Omori M, Munesue T, Ishitobi M, Matsumura Y, Takahashi T, et al. Wada Y. Smaller insula and inferior frontal volumes in young adults with pervasive developmental disorders. NeuroImage. 2010;50(4):1357–1363. doi: 10.1016/j.neuroimage.2010.01.085. [DOI] [PubMed] [Google Scholar]
- Koshino H, Carpenter PA, Minshew NJ, Cherkassky VL, Keller TA, Just MA. Functional connectivity in an fMRI working memory task in high-functioning autism. NeuroImage. 2005;24(3):810–821. doi: 10.1016/j.neuroimage.2004.09.028. [DOI] [PubMed] [Google Scholar]
- Krall SC, Rottschy C, Oberwelland E, Bzdok D, Fox PT, Eickhoff SB, et al. Konrad K. The role of the right temporoparietal junction in attention and social interaction as revealed by ALE meta-analysis. Brain Structure and Function. 2015;220(2):587–604. doi: 10.1007/s00429-014-0803-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kuiper MWM, Verhoeven EWM, Geurts HM. The role of interstimulus interval and “stimulus-type” in prepotent response inhibition abilities in people with ASD: A quantitative and qualitative review. Autism Research. 2016;9(11):1124–1141. doi: 10.1002/aur.1631. [DOI] [PubMed] [Google Scholar]
- Kumar U, Arya A, Agarwal V. Neural alterations in ADHD children as indicated by voxel-based cortical thickness and morphometry analysis. Brain and Development. 2017;39(5):403–410. doi: 10.1016/j.braindev.2016.12.002. [DOI] [PubMed] [Google Scholar]
- Kurth F, Narr KL, Woods RP, O'Neill J, Alger JR, Caplan R, et al. Levitt JG. Diminished gray matter within the hypothalamus in autism disorder: A potential link to hormonal effects? Biological Psychiatry. 2011;70(3):278–282. doi: 10.1016/j.biopsych.2011.03.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kwon H, Ow AW, Pedatella KE, Lotspeich LJ, Reiss AL. Voxel-based morphometry elucidates structural neuroanatomy of high-functioning autism and Asperger syndrome. Developmental Medicine & Child Neurology. 2004;46(11):760–764. doi: 10.1017/s0012162204001306. [DOI] [PubMed] [Google Scholar]
- Lange N, Travers BG, Bigler ED, Prigge MBD, Froehlich AL, Nielsen JA, et al. Lainhart JE. Longitudinal volumetric brain changes in autism spectrum disorder ages 6-35 years. Autism Research. 2015;8(1):82–93. doi: 10.1002/aur.1427. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Langen M, Schnack HG, Nederveen H, Bos D, Lahuis BE, de Jonge MV, et al. Durston S. Changes in the developmental trajectories of striatum in autism. Biological Psychiatry. 2009;66(4):327–333. doi: 10.1016/j.biopsych.2009.03.017. [DOI] [PubMed] [Google Scholar]
- Li X, Cao Q, Pu F, Li D, Fan Y, An L, et al. Wang Y. Abnormalities of structural covariance networks in drug-naïve boys with attention deficit hyperactivity disorder. Psychiatry Research. 2015;231(3):273–278. doi: 10.1016/j.pscychresns.2015.01.006. [DOI] [PubMed] [Google Scholar]
- Liddle EB, Hollis C, Batty MJ, Groom MJ, Totman JJ, Liotti M, et al. Liddle PF. Task-related default mode network modulation and inhibitory control in ADHD: effects of motivation and methylphenidate. Journal of Child Psychology and Psychiatry. 2011;52(7):761–771. doi: 10.1111/j.1469-7610.2010.02333.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lim L, Chantiluke K, Cubillo AI, Smith AB, Simmons A, Mehta MA, Rubia K. Disorder-specific grey matter deficits in attention deficit hyperactivity disorder relative to autism spectrum disorder. Psychological Medicine. 2015;45(5):965–976. doi: 10.1017/s0033291714001974. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lin HY, Ni H-C, Lai M-C, Tseng W-YI, Gau SS-F. Regional brain volume differences between males with and without autism spectrum disorder are highly age-dependent. Molecular Autism. 2015;6:29. doi: 10.1186/s13229-015-0022-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lin HY, Tseng WI, Lai MC, Chang YT, Gau SS. Shared atypical brain anatomy and intrinsic functional architecture in male youth with autism spectrum disorder and their unaffected brothers. Psychological Medicine. 2017;47(4):639–654. doi: 10.1017/S0033291716002695. [DOI] [PubMed] [Google Scholar]
- Lipszyc J, Schachar R. Inhibitory control and psychopathology: A meta-analysis of studies using the stop signal task. Journal of the International Neuropsychological Society. 2010;16(06):1064–1076. doi: 10.1017/s1355617710000895. [DOI] [PubMed] [Google Scholar]
- Lombardo MV, Chakrabarti B, Bullmore ET, Baron-Cohen S. Specialization of right temporo-parietal junction for mentalizing and its relation to social impairments in autism. NeuroImage. 2011;56(3):1832–1838. doi: 10.1016/j.neuroimage.2011.02.067. [DOI] [PubMed] [Google Scholar]
- Ma I, van Holstein M, Mies GW, Mennes M, Buitelaar J, Cools R, et al. Scheres A. Ventral striatal hyperconnectivity during rewarded interference control in adolescents with ADHD. Cortex. 2016;82:225–236. doi: 10.1016/j.cortex.2016.05.021. [DOI] [PubMed] [Google Scholar]
- Ma J, Lei D, Jin X, Du X, Jiang F, Li F, et al. Shen X. Compensatory brain activation in children with attention deficit/hyperactivity disorder during a simplified go/no-go task. Journal of Neural Transmission. 2012;119(5):613–619. doi: 10.1007/s00702-011-0744-0. [DOI] [PubMed] [Google Scholar]
- Maier S, Perlov E, Graf E, Dieter E, Sobanski E, Rump M, et al. Elst LT v. Discrete global but no focal gray matter volume reductions in unmedicated adult patients with attention-deficit/hyperactivity disorder. Biological Psychiatry. 2015;80(12):905–915. doi: 10.1016/j.biopsych.2015.05.012. [DOI] [PubMed] [Google Scholar]
- Martinez-Sanchis S. Neurobiological foundations of multisensory integration in people with autism spectrum disorders: The role of the medial prefrontal cortex. Frontiers in Human Neuroscience. 2014;8:970. doi: 10.3389/fnhum.2014.00970. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Massat I, Slama H, Villemonteix T, Mary A, Baijot S, Albajara Saenz A, et al. Peigneux P. Hyperactivity in motor response inhibition networks in unmedicated children with attention deficit-hyperactivity disorder. The World Journal of Biological Psychiatry. 2018;19(2):101–111. doi: 10.1080/15622975.2016.1237040. [DOI] [PubMed] [Google Scholar]
- McAlonan GM, Cheung V, Cheung C, Chua SE, Murphy DGM, Suckling J, et al. Ho TP. Mapping brain structure in attention deficit-hyperactivity disorder: A voxel-based MRI study of regional grey and white matter volume. Psychiatry Research: Neuroimaging. 2007;154(2):171–180. doi: 10.1016/j.pscychresns.2006.09.006. [DOI] [PubMed] [Google Scholar]
- McAlonan GM, Daly E, Kumari V, Critchley HD, van Amelsvoort T, Suckling J, et al. Murphy DGM. Brain anatomy and sensorimotor gating in Asperger's syndrome. Brain. 2002;125:1594–1606. doi: 10.1093/brain/awf150. [DOI] [PubMed] [Google Scholar]
- McAlonan GM, Suckling J, Wong N, Cheung V, Lienenkaemper N, Cheung C, Chua SE. Distinct patterns of grey matter abnormality in high-functioning autism and Asperger's syndrome. Journal of Child Psychology and Psychiatry. 2008;49(12):1287–1295. doi: 10.1111/j.1469-7610.2008.01933.x. [DOI] [PubMed] [Google Scholar]
- McCarthy H, Skokauskas N, Frodl T. Identifying a consistent pattern of neural function in attention deficit hyperactivity disorder: A meta-analysis. Psychological Medicine. 2014;44(4):869–880. doi: 10.1017/s0033291713001037. [DOI] [PubMed] [Google Scholar]
- McGrath LM, Stoodley CJ. Are there shared neural correlates between dyslexia and ADHD? A meta-analysis of voxel-based morphometry studies. Journal of Neurodevelopmental Disorders. 2019;11(1):31. doi: 10.1186/s11689-019-9287-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mengotti P, D'Agostini S, Terlevic R, De Colle C, Biasizzo E, Londero D, et al. Brambilla P. Altered white matter integrity and development in children with autism: A combined voxel-based morphometry and diffusion imaging study. Brain Research Bulletin. 2011;84(2):189–195. doi: 10.1016/j.brainresbull.2010.12.002. [DOI] [PubMed] [Google Scholar]
- Menon V, Uddin LQ. Saliency, switching, attention and control: A network model of insula function. Brain Structure and Function. 2010;214(5–6):655–667. doi: 10.1007/s00429-010-0262-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Miller GM, Chapman JP. Misunderstanding analysis of covariance. Journal of Abnormal Psychology. 2001;110(1):40–48. doi: 10.1037//0021-843x.110.1.40. [DOI] [PubMed] [Google Scholar]
- Mitchell SR, Reiss AL, Tatusko DH, Ikuta I, Kazmerski DB, Botti JA, et al. Kates WR. Neuroanatomic alterations and social and communication deficits in monozygotic twins discordant for autism disorder. American Journal of Psychiatry. 2009;166(8):917–925. doi: 10.1176/appi.ajp.2009.08101538. [DOI] [PubMed] [Google Scholar]
- Montes LGA, Ricardo-Garcell J, de la Torre LB, Alcántara HP, García RBM, Fernández-Bouzas A, Acosta DA. Clinical correlations of grey matter reductions in the caudate nucleus of adults with attention deficit hyperactivity disorder. Journal of Psychiatry and Neuroscience. 2010;35(4):238–246. doi: 10.1503/jpn.090099. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Moreno-Alcázar A, Ramos-Quiroga JA, Radua J, Salavert J, Palomar G, Bosch R, et al. Pomarol-Clotet E. Brain abnormalities in adults with attention deficit hyperactivity disorder revealed by voxel-based morphometry. Psychiatry Research: Neuroimaging. 2016;254:41–47. doi: 10.1016/j.pscychresns.2016.06.002. [DOI] [PubMed] [Google Scholar]
- Mueller S, Keeser D, Samson AC, Kirsch V, Blautzik J, Grothe M, et al. Meindl T. Convergent findings of altered functional and structural brain connectivity in individuals with high functioning autism: A multimodal MRI study. PLoS One. 2013;8(6):e67329. doi: 10.1371/journal.pone.0067329. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Murdaugh DL, Shinkareva SV, Deshpande HR, Wang J, Pennick MR, Kana RK. Differential deactivation during mentalizing and classification of autism based on default mode network connectivity. PLoS One. 2012;7(11):e50064. doi: 10.1371/journal.pone.0050064. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Murphy CM, Christakou A, Daly EM, Ecker C, Giampietro V, Brammer M, et al. Rubia K. Abnormal functional activation and maturation of fronto-striato-temporal and cerebellar regions during sustained attention in autism spectrum disorder. American Journal of Psychiatry. 2014;171(10):1107–1116. doi: 10.1176/appi.ajp.2014.12030352. [DOI] [PubMed] [Google Scholar]
- Nakao T, Radua J, Rubia K, Mataix-Cols D. Gray matter volume abnormalities in ADHD: Voxel-based meta-analysis exploring the effects of age and stimulant medication. American Journal of Psychiatry. 2011;168(11):1154–1163. doi: 10.1176/appi.ajp.2011.11020281. [DOI] [PubMed] [Google Scholar]
- Ni HC, Lin HY, Tseng WI, Chiu YN, Wu YY, Tsai WC, Gau SS. Neural correlates of impaired self-regulation in male youths with autism spectrum disorder: A voxel-based morphometry study. Progress in Neuro-Psychopharmacology & Biological Psychiatry. 2018;82:233–241. doi: 10.1016/j.pnpbp.2017.11.008. [DOI] [PubMed] [Google Scholar]
- Nickl-Jockschat T, Habel U, Michel TM, Manning J, Laird AR, Fox PT, et al. Eickhoff SB. Brain structure anomalies in autism spectrum disorder--a meta-analysis of VBM studies using anatomic likelihood estimation. Human Brain Mapping. 2012;33(6):1470–1489. doi: 10.1002/hbm.21299. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Nordahl CW, Lange N, Li DD, Barnett LA, Lee A, Buonocore MH, et al. Amaral DG. Brain enlargement is associated with regression in preschool-age boys with autism spectrum disorders. Proceedings of the National Academy of Sciences of the United States of America. 2011;108(50):20195–20200. doi: 10.1073/pnas.1107560108. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Norman LJ, Carlisi C, Lukito S, Hart H, Mataix-Cols D, Radua J, Rubia K. Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: A comparative meta-analysis. JAMA Psychiatry. 2016 doi: 10.1001/jamapsychiatry.2016.0700. [DOI] [PubMed] [Google Scholar]
- Norman LJ, Carlisi CO, Christakou A, Chantiluke K, Murphy C, Simmons A, et al. Rubia K. Neural dysfunction during temporal discounting in paediatric attention-deficit/hyperactivity disorder and obsessive-compulsive disorder. Psychiatry Research: Neuroimaging. 2017;269:97–105. doi: 10.1016/j.pscychresns.2017.09.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Onnink AMH, Zwiers MP, Hoogman M, Mostert JC, Kan CC, Buitelaar J, Franke B. Brain alterations in adult ADHD: Effects of gender, treatment and comorbid depression. European Neuropsychopharmacology. 2014;24(3):397–409. doi: 10.1016/j.euroneuro.2013.11.011. [DOI] [PubMed] [Google Scholar]
- Ortal S, van de Glind G, Johan F, Itai B, Nir Y, Iliyan I, van den Brink W. The role of different aspects of impulsivity as independent risk factors for substance use disorders in patients with ADHD: A review. Current Drug Abuse Review. 2015;8(2):119–133. doi: 10.2174/1874473708666150916112913. [DOI] [PubMed] [Google Scholar]
- Overmeyer S, Bullmore ET, Suckling J, Simmons A, Williams SC, Santosh PJ, Taylor E. Distributed grey and white matter deficits in hyperkinetic disorder: MRI evidence for anatomical abnormality in an attentional network. Psychological Medicine. 2001;31(8):1425–1435. doi: 10.1017/s0033291701004706. [DOI] [PubMed] [Google Scholar]
- Passarotti AM, Sweeney JA, Pavuluri MN. Neural correlates of response inhibition in pediatric bipolar disorder and attention deficit hyperactivity disorder. Psychiatry Research: Neuroimaging. 2010;181(1):36–43. doi: 10.1016/j.pscychresns.2009.07.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pereira AM, Campos BM, Coan AC, Pegoraro LF, de Rezende TJR, Obeso I, et al. Cendes F. Differences in cortical structure and functional mri connectivity in high functioning autism. Frontiers in Neurology. 2018;9:539. doi: 10.3389/fneur.2018.00539. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Peterson BS, Potenza MN, Wang Z, Zhu H, Martin A, Marsh R, et al. Yu S. An fMRI study of the effects of psychostimulants on default-mode processing during Stroop task performance in youths with ADHD. American Journal of Psychiatry. 2009;166(11):1286–1294. doi: 10.1176/appi.ajp.2009.08050724. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Philip RCM, Dauvermann MR, Whalley HC, Baynham K, Lawrie SM, Stanfield AC. A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders. Neuroscience & Biobehavioral Reviews. 2012;36(2):901–942. doi: 10.1016/j.neubiorev.2011.10.008. [DOI] [PubMed] [Google Scholar]
- Polanczyk G, Willcutt EG, Salum GA, Kieling C, Rohde LA. ADHD prevalence estimates across three decades: An updated systematic review and meta-regression analysis. International Journal of Epidemiology. 2014;43(2):434–442. doi: 10.1093/ije/dyt261. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Poulin-Lord MP, Barbeau EB, Soulières I, Monchi O, Doyon J, Benali H, Mottron L. Increased topographical variability of task-related activation in perceptive and motor associative regions in adult autistics. NeuroImage: Clinical. 2014;4:444–453. doi: 10.1016/j.nicl.2014.02.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Poustka L, Jennen-Steinmetz C, Henze R, Vomstein K, Haffner J, Sieltjes B. Fronto-temporal disconnectivity and symptom severity in children with autism spectrum disorder. The World Journal of Biological Psychiatry. 2012;13(4):269–280. doi: 10.3109/15622975.2011.591824. [DOI] [PubMed] [Google Scholar]
- Prat CS, Stocco A, Neuhaus E, Kleinhans NM. Basal ganglia impairments in autism spectrum disorder are related to abnormal signal gating to prefrontal cortex. Neuropsychologia. 2016;91:268–281. doi: 10.1016/j.neuropsychologia.2016.08.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Radeloff D, Ciaramidaro A, Siniatchkin M, Hainz D, Schlitt S, Weber B, et al. Freitag CM. Structural alterations of the social brain: A comparison between schizophrenia and autism. PLoS One. 2014;9(9):e106539. doi: 10.1371/journal.pone.0106539. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Radua J, Borgwardt S, Crescini A, Mataix-Cols D, Meyer-Lindenberg A, McGuire PK, Fusar-Poli P. Multimodal meta-analysis of structural and functional brain changes in first episode psychosis and the effects of antipsychotic medication. Neuroscience & Biobehavioral Review. 2012;36(10):2325–2333. doi: 10.1016/j.neubiorev.2012.07.012. [DOI] [PubMed] [Google Scholar]
- Radua J, Mataix-Cols D. Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. British Journal of Psychiatry. 2009;195(5):393–402. doi: 10.1192/bjp.bp.108.055046. [DOI] [PubMed] [Google Scholar]
- Radua J, Mataix-Cols D, Phillips ML, El-Hage W, Kronhaus DM, Cardoner N, Surguladze S. A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. European Psychiatry. 2012;27(8):605–611. doi: 10.1016/j.eurpsy.2011.04.001. [DOI] [PubMed] [Google Scholar]
- Rae CL, Hughes LE, Weaver C, Anderson MC, Rowe JB. Selection and stopping in voluntary action: A meta-analysis and combined fMRI study. NeuroImage. 2014;86:381–391. doi: 10.1016/j.neuroimage.2013.10.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ramesh MG, Rai KS. Region-wise gray matter volume alterations in brain of adolescents with attention deficit hyperactive disorder: A voxel based morphometric analysis. Indian Journal of Physiology and Pharmacology. 2013;57(3):270–279. Retrieved from https://www.researchgate.net/publication/277133055. [Google Scholar]
- Rasmussen J, Casey BJ, van Erp TG, Tamm L, Epstein JN, Buss C, et al. Group, M.T.A.N ADHD and cannabis use in young adults examined using fMRI of a Go/NoGo task. Brain Imaging and Behavior. 2016;10(3):761–771. doi: 10.1007/s11682-015-9438-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Raznahan A, Wallace GL, Antezana L, Greenstein D, Lenroot R, Thurm A, et al. Giedd JN. Compared to what? Early brain overgrowth in autism and the perils of population norms. Biological Psychiatry. 2013;74(8):563–575. doi: 10.1016/j.biopsych.2013.03.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Redcay E, Courchesne E. When is the brain enlarged in autism? A meta-analysis of all brain size reports. Biological Psychiatry. 2005;58(1):1–9. doi: 10.1016/j.biopsych.2005.03.026. [DOI] [PubMed] [Google Scholar]
- Retico A, Giuliano A, Tancredi R, Cosenza A, Apicella F, Narzisi A, et al. Calderoni S. The effect of gender on the neuroanatomy of children with autism spectrum disorders: A support vector machine case-control study. Molecular Autism. 2016;7:5. doi: 10.1186/s13229-015-0067-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Riddle K, Cascio CJ, Woodward ND. Brain structure in autism: A voxel-based morphometry analysis of the Autism Brain Imaging Database Exchange (ABIDE) Brain Imaging and Behavior. 2017;11(2):541–551. doi: 10.1007/s11682-016-9534-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Riedel A, Maier S, Ulbrich M, Biscaldi M, Ebert D, Fangmeier T, et al. Elst LTv. No significant brain volume decreases or increases in adults with high-functioning autism spectrum disorder and above average intelligence: A voxel-based morphometric study. Psychiatry Research: Neuroimaging. 2014;223(2):67–74. doi: 10.1016/j.pscychresns.2014.05.013. [DOI] [PubMed] [Google Scholar]
- Riva D, Annunziata S, Contarino V, Erbetta A, Aquino D, Bulgheroni S. Gray matter reduction in the vermis and crus-II is associated with social and interaction deficits in low-functioning children with autistic spectrum disorders: A VBM-DARTEL study. Cerebellum. 2013;12(5):676–685. doi: 10.1007/s12311-013-0469-8. [DOI] [PubMed] [Google Scholar]
- 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. doi: 10.1186/1471-244x-656. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Roman-Urrestarazu A, Lindholm P, Moilanen I, Kiviniemi V, Miettunen J, Jaaskelainen E, et al. Murray GK. Brain structural deficits and working memory fMRI dysfunction in young adults who were diagnosed with ADHD in adolescence. European Child & Adolescent Psychiatry. 2016;25(5):529–538. doi: 10.1007/s00787-015-0755-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rubia K. Cognitive neuroscience of attention deficit hyperactivity disorder (adhd) and its clinical translation. Frontiers in Human Neuroscience. 2018;12:100. doi: 10.3389/fnhum.2018.00100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rubia K, Alegria AA, Cubillo AI, Smith AB, Brammer MJ, Radua J. Effects of stimulants on brain function in attention-deficit/hyperactivity disorder: A systematic review and meta-analysis. Biological Psychiatry. 2014;76(8):616–628. doi: 10.1016/j.biopsych.2013.10.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rubia K, Cubillo A, Smith AB, Woolley J, Heyman I, Brammer MJ. Disorder-specific dysfunction in right inferior prefrontal cortex during two inhibition tasks in boys with attention-deficit hyperactivity disorder compared to boys with obsessive-compulsive disorder. Human Brain Mapping. 2010;31(2):287–299. doi: 10.1002/hbm.20864. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rubia K, Halari R, Cubillo A, Mohammad AM, Scott S, Brammer M. Disorder-specific inferior prefrontal hypofunction in boys with pure attention-deficit/hyperactivity disorder compared to boys with pure conduct disorder during cognitive flexibility. Human Brain Mapping. 2010;31(12):1823–1833. doi: 10.1002/hbm.20975. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rubia K, Halari R, Cubillo A, Smith AB, Mohammad AM, Brammer M, Taylor E. Methylphenidate normalizes fronto-striatal underactivation during interference inhibition in medication-naive boys with attention-deficit hyperactivity disorder. Neuropsychopharmacology. 2011;36(8):1575–1586. doi: 10.1038/npp.2011.30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rubia K, Halari R, Mohammad AM, Taylor E, Brammer M. Methylphenidate normalizes frontocingulate underactivation during error processing in attention-deficit/hyperactivity disorder. Biological Psychiatry. 2011;70(3):255–262. doi: 10.1016/j.biopsych.2011.04.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rubia K, Halari R, Smith AB, Mohammad M, Scott S, Brammer MJ. Shared and disorder-specific prefrontal abnormalities in boys with pure attention-deficit/hyperactivity disorder compared to boys with pure CD during interference inhibition and attention allocation. Journal of Child Psychology and Psychiatry. 2009;50(6):669–678. doi: 10.1111/j.1469-7610.2008.02022.x. [DOI] [PubMed] [Google Scholar]
- Rubia K, Smith AB, Brammer MJ, Toone B, Taylor E. Abnormal brain activation during inhibition and error detection in medication-naive adolescents with ADHD. American Journal of Psychiatry. 2005;162(6):1067–1075. doi: 10.1176/appi.ajp.162.6.1067. [DOI] [PubMed] [Google Scholar]
- Saad JF, Griffiths KR, Kohn MR, Clarke S, Williams LM, Korgaonkar MS. Regional brain network organization distinguishes the combined and inattentive subtypes of attention deficit hyperactivity disorder. NeuroImage: Clinical. 2017;15:383–390. doi: 10.1016/j.nicl.2017.05.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Salazar F, Baird G, Chandler S, Tseng E, O’sullivan T, Howlin P, et al. Simonoff E. Co-occurring psychiatric disorders in preschool and elementary school-aged children with autism spectrum disorder. Journal of Autism and Developmental Disorders. 2015;45(8):2283–2294. doi: 10.1007/s10803-015-2361-5. [DOI] [PubMed] [Google Scholar]
- Sasayama D, Hayashida A, Yamasue H, Harada Y, Kaneko T, Kasai K, et al. Amano N. Neuroanatomical correlates of attention-deficit-hyperactivity disorder accounting for comorbid oppositional defiant disorder and conduct disorder. Psychiatry and Clinical Neuroscience. 2010;64(4):394–402. doi: 10.1111/j.1440-1819.2010.02102.x. [DOI] [PubMed] [Google Scholar]
- Sato W, Uono S, Kochiyama T, Yoshimura S, Sawada R, Kubota Y, et al. Toichi M. structural correlates of reading the mind in the eyes in autism spectrum disorder. Frontiers in Human Neuroscience. 2017;11:361. doi: 10.3389/fnhum.2017.00361. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schmitz N, Rubia K, Daly E, Smith A, Williams S, Murphy DG. Neural correlates of executive function in autistic spectrum disorders. Biological Psychiatry. 2006;59(1):7–16. doi: 10.1016/j.biopsych.2005.06.007. [DOI] [PubMed] [Google Scholar]
- Schulz KP, Bédard ACV, Fan J, Clerkin SM, Dima D, Newcorn JH, Halperin JM. Emotional bias of cognitive control in adults with childhood attention-deficit/hyperactivity disorder. NeuroImage: Clinical. 2014;5:1–9. doi: 10.1016/j.nicl.2014.05.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schulz KP, Fan J, Tang CY, Newcorn JH, Buchsbaum MS, Cheung AM, Halperin JM. Response inhibition in adolescents diagnosed with attention deficit hyperactivity disorder during childhood: An event-related fMRI study. American Journal of Psychiatry. 2004;161(9):1650–1657. doi: 10.1176/appi.ajp.161.9.1650. [DOI] [PubMed] [Google Scholar]
- Schulz KP, Li X, Clerkin SM, Fan J, Berwid OG, Newcorn JH, Halperin JM. Prefrontal and parietal correlates of cognitive control related to the adult outcome of attention-deficit/hyperactivity disorder diagnosed in childhood. Cortex. 2017;90:1–11. doi: 10.1016/j.cortex.2017.01.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schumann CM, Bloss CS, Barnes CC, Wideman GM, Carper RA, Akshoomoff N, et al. Courchesne E. Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. Journal of Neuroscience. 2010;30(12):4419–4427. doi: 10.1523/jneurosci.5714-09.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sebastian A, Gerdes B, Feige B, Kloeppel S, Lange T, Philipsen A, et al. Tuescher O. Neural correlates of interference inhibition, action withholding and action cancelation in adult ADHD. Psychiatry Research: Neuroimaging. 2012;202(2):132–141. doi: 10.1016/j.pscychresns.2012.02.010. [DOI] [PubMed] [Google Scholar]
- Seidman LJ, Biederman J, Liang L, Valera EM, Monuteaux MC, Brown A, et al. Makris N. Gray matter alterations in adults with attention-deficit/hyperactivity disorder identified by voxel based morphometry. Biological Psychiatry. 2011;69(9):857–866. doi: 10.1016/j.biopsych.2010.09.053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sethi A, Evelyn-Rahr E, Dowell N, Jain S, Voon V, Critchley HD, et al. Cercignani M. Magnetization transfer imaging identifies basal ganglia abnormalities in adult ADHD that are invisible to conventional T1 weighted voxel-based morphometry. NeuroImage: Clinical. 2017;15:8–14. doi: 10.1016/j.nicl.2017.03.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shafritz KM, Bregman JD, Ikuta T, Szeszko PR. Neural systems mediating decision-making and response inhibition for social and nonsocial stimuli in autism. Progress in Neuro-Psychopharmacology & Biological Psychiatry. 2015;60:112–120. doi: 10.1016/j.pnpbp.2015.03.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shafritz KM, Dichter GS, Baranek GT, Belger A. The neural circuitry mediating shifts in behavioral response and cognitive set in autism. Biological Psychiatry. 2008;63(10):974–980. doi: 10.1016/j.biopsych.2007.06.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shang CY, Sheng C, Yang LK, Chou TL, Gau SS. Differential brain activations in adult attention-deficit/ hyperactivity disorder subtypes: A counting Stroop functional MRI study. Brain Imaging and Behavior. 2018;12(3):882–890. doi: 10.1007/s11682-017-9749-0. [DOI] [PubMed] [Google Scholar]
- Shaw P, Eckstrand K, Sharp W, Blumenthal J, Lerch JP, Greenstein D, et al. Rapoport JL. Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proceedings of the National Academy of Sciences of the United States of America. 2007;104(49):19649–19654. doi: 10.1073/pnas.0707741104. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shimada K, Fujisawa TX, Takiguchi S, Naruse H, Kosaka H, Okazawa H, Tomoda A. Ethnic differences in COMT genetic effects on striatal grey matter alterations associated with childhood ADHD: A voxel-based morphometry study in a Japanese sample. The World Journal of Biological Psychiatry. 2015:1–7. doi: 10.3109/15622975.2015.1102325. [DOI] [PubMed] [Google Scholar]
- Simonoff E, Pickles A, Charman T, Chandler S, Loucas T, Baird G. Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample. Journal of the American Academy of Child & Adolescent Psychiatry. 2008;47(8):921–929. doi: 10.1097/CHI.0b013e318179964f. [DOI] [PubMed] [Google Scholar]
- Siniatchkin M, Glatthaar N, von Mueller GG, Prehn-Kristensen A, Wolff S, Knoechel S, et al. Gerber W-D. Behavioural treatment increases activity in the cognitive neuronal networks in children with attention deficit/hyperactivity disorder. Brain Topography. 2012;25(3):332–344. doi: 10.1007/s10548-012-0221-6. [DOI] [PubMed] [Google Scholar]
- Sizoo B, van den Brink W, Koeter M, Gorissen van Eenige M, van Wijngaarden-Cremers P, van der Gaag RJ. Treatment seeking adults with autism or ADHD and co-morbid substance use disorder: prevalence, risk factors and functional disability. Drug and Alcohol Dependence. 2010;107(1):44–50. doi: 10.1016/j.drugalcdep.2009.09.003. [DOI] [PubMed] [Google Scholar]
- Smith AB, Taylor E, Brammer M, Toone B, Rubia K. Task-specific hypoactivation in prefrontal and temporoparietal brain regions during motor inhibition and task switching in medication-naive children and adolescents with attention deficit hyperactivity disorder. American Journal of Psychiatry. 2006;163(6):1044–1051. doi: 10.1176/ajp.2006.163.6.1044. [DOI] [PubMed] [Google Scholar]
- Solberg BS, Zayats T, Posserud MB, Halmoy A, Engeland A, Haavik J, Klungsoyr K. Patterns of psychiatric comorbidity and genetic correlations provide new insights into differences between attention-deficit/hyperactivity disorder and autism spectrum disorder. Biological Psychiatry. 2019;86(8):587–598. doi: 10.1016/j.biopsych.2019.04.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Solomon M, Yoon JH, Ragland JD, Niendam TA, Lesh TA, Fairbrother W, Carter CS. The development of the neural substrates of cognitive control in adolescents with autism spectrum disorders. Biological Psychiatry. 2014;76(5):412–421. doi: 10.1016/j.biopsych.2013.08.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- South M, Rodgers J. Sensory, emotional and cognitive contributions to anxiety in autism spectrum disorders. Frontiers in Human Neuroscience. 2017;11:20. doi: 10.3389/fnhum.2017.00020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spencer MD, Chura LR, Holt RJ, Suckling J, Calder AJ, Bullmore ET, Baron-Cohen S. Failure to deactivate the default mode network indicates a possible endophenotype of autism. Molecular Autism. 2012;3(1):15. doi: 10.1186/2040-2392-3-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spinelli S, Joel S, Nelson TE, Vasa RA, Pekar JJ, Mostofsky SH. Different neural patterns are associated with trials preceding inhibitory errors in children with and without attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry. 2011;50(7):705–715. doi: 10.1016/j.jaac.2011.03.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sripada CS, Kessler D, Angstadt M. Lag in maturation of the brain’s intrinsic functional architecture in attention-deficit/hyperactivity disorder. Proceedings of the National Academy of Sciences of the United States of America. 2014;111(39):14259–14264. doi: 10.1073/pnas.1407787111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- StataCorp, L. P. Stata: Release 14 Base Reference Manual. College Station, TX: Stata Press; 2015. [Google Scholar]
- Stevens MC, Haney-Caron E. Comparison of brain volume abnormalities between ADHD and conduct disorder in adolescence. Journal of Psychiatry and Neuroscience. 2012;37(6):389–398. doi: 10.1503/jpn.110148. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Thacker SB. Meta-analysis of observational studies in epidemiology: A proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;283(15):2008–2012. doi: 10.1001/jama.283.15.2008. [DOI] [PubMed] [Google Scholar]
- Szczepanski SM, Knight RT. Insights into human behavior from lesions to the prefrontal cortex. Neuron. 2014;83(5):1002–1018. doi: 10.1016/j.neuron.2014.08.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Szekely E, Schwantes-An TL, Justice CM, Sabourin JA, Jansen PR, Muetzel RL, et al. Shaw P. Genetic associations with childhood brain growth, defined in two longitudinal cohorts. Genetic Epidemiology. 2018;42(4):405–414. doi: 10.1002/gepi.22122. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tamm L, Menon V, Ringel J, Reiss AL. Event-related fMRI evidence of frontotemporal involvement in aberrant response inhibition and task switching in attention-deficit/hlyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry. 2004;43(11):1430–1440. doi: 10.1097/01.chi.0000140452.51205.8d. [DOI] [PubMed] [Google Scholar]
- Tegelbeckers J, Kanowski M, Krauel K, Haynes JD, Breitling C, Flechtner HH, Kahnt T. Orbitofrontal signaling of future reward is associated with hyperactivity in attention-deficit/hyperactivity disorder. Journal of Neuroscience. 2018;38(30):6779–6786. doi: 10.1523/jneurosci.0411-18.2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Thirion B, Pinel P, Mériaux S, Roche A, Dehaene S, Poline J-B. Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses. NeuroImage. 2007;35(1):105–120. doi: 10.1016/j.neuroimage.2006.11.054. [DOI] [PubMed] [Google Scholar]
- Thornton S, Bray S, Langevin LM, Dewey D. Functional brain correlates of motor response inhibition in children with developmental coordination disorder and attention deficit/hyperactivity disorder. Human Movement Science. 2018;59:134–142. doi: 10.1016/j.humov.2018.03.018. [DOI] [PubMed] [Google Scholar]
- Toal F, Daly EM, Page L, Deeley Q, Hallahan B, Bloemen O, et al. Murphy DGM. Clinical and anatomical heterogeneity in autistic spectrum disorder: A structural MRI study. Psychological Medicine. 2010;40(7):1171–1181. doi: 10.1017/s0033291709991541. [DOI] [PubMed] [Google Scholar]
- Vaidya CJ, Foss-Feig J, Shook D, Kaplan L, Kenworthy L, Gaillard WD. Controlling attention to gaze and arrows in childhood: An fMRI study of typical development and autism spectrum disorders. Developmental Science. 2011;14(4):911–924. doi: 10.1111/j.1467-7687.2011.01041.x. [DOI] [PubMed] [Google Scholar]
- van Dongen EV, von Rhein D, O'Dwyer L, Franke B, Hartman CA, Heslenfeld DJ, et al. Buitelaar J. Distinct effects of ASD and ADHD symptoms on reward anticipation in participants with ADHD, their unaffected siblings and healthy controls: A cross-sectional study. Molecular Autism. 2015;6:48. doi: 10.1186/s13229-015-0043-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- van Hulst BM, de Zeeuw P, Rijks Y, Neggers SFW, Durston S. What to expect and when to expect it: An fMRI study of expectancy in children with ADHD symptoms. European Child & Adolescent Psychiatry. 2017;26(5):583–590. doi: 10.1007/s00787-016-0921-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Busatto GF, et al. Buitelaar JK. Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: Results from the ENIGMA ASD working group. American Journal of Psychiatry. 2018;175(4):359–369. doi: 10.1176/appi.ajp.2017.17010100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- van Rooij D, Hoekstra PJ, Mennes M, von Rhein D, Thissen AJAM, Hestenfeld D, et al. Hartman CA. Distinguishing adolescents with ADHD from their unaffected siblings and healthy comparison subjects by neural activation patterns during response inhibition. American Journal of Psychiatry. 2015;172(7):674–683. doi: 10.1176/appi.ajp.2014.13121635. [DOI] [PMC free article] [PubMed] [Google Scholar]
- van Wingen GA, van den Brink W, Veltman DJ, Schmaal L, Dom G, Booij J, Crunelle CL. Reduced striatal brain volumes in non-medicated adult ADHD patients with comorbid cocaine dependence. Drug and Alcohol Dependence. 2013;131(3):198–203. doi: 10.1016/j.drugalcdep.2013.05.007. [DOI] [PubMed] [Google Scholar]
- Velasquez F, Qin XA, Reilly MA, Neuhaus E, Estes A, Aylward E, Kleinhans NM. Neural correlates of emotional inhibitory control in autism spectrum disorders. Research in Developmental Disabilities. 2017;64:64–77. doi: 10.1016/j.ridd.2017.03.008. [DOI] [PubMed] [Google Scholar]
- Via E, Radua J, Cardoner N, Happe F, Mataix-Cols D. Meta-analysis of gray matter abnormalities in autism spectrum disorder: Should asperger disorder be subsumed under a broader umbrella of autistic spectrum disorder? Archives of General Psychiatry. 2011;68(4):409–418. doi: 10.1001/archgenpsychiatry.2011.27. [DOI] [PubMed] [Google Scholar]
- Vilgis V, Sun L, Chen J, Silk TJ, Vance A. Global and local grey matter reductions in boys with ADHD combined type and ADHD inattentive type. Psychiatry Research: Neuroimaging. 2016;254:119–126. doi: 10.1016/j.pscychresns.2016.06.008. [DOI] [PubMed] [Google Scholar]
- Villemonteix T, De Brito SA, Kavec M, Baleriaux D, Metens T, Slama H, et al. Massat I. Grey matter volumes in treatment naive vs. chronically treated children with attention deficit/hyperactivity disorder: A combined approach. European Neuropsychopharmacology. 2015;25(8):1118–1127. doi: 10.1016/j.euroneuro.2015.04.015. [DOI] [PubMed] [Google Scholar]
- Vogan VM, Morgan BR, Lee W, Powell TL, Smith ML, Taylor MJ. The neural correlates of visuo-spatial working memory in children with autism spectrum disorder: effects of cognitive load. Journal of Neurodevelopmental Disorders. 2014;6:19. doi: 10.1186/1866-1955-6-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Waiter GD, Williams JHG, Murray AD, Gilchrist A, Perrett DI, Whiten A. A voxel-based investigation of brain structure in male adolescents with autistic spectrum disorder. NeuroImage. 2004;22(2):619–625. doi: 10.1016/j.neuroimage.2004.02.029. [DOI] [PubMed] [Google Scholar]
- Wang J, Fu K, Chen L, Duan X, Guo X, Chen H, et al. Chen H. Increased gray matter volume and resting-state functional connectivity in somatosensory cortex and their relationship with autistic symptoms in young boys with autism spectrum disorder. Frontiers in Physiology. 2017;8:588. doi: 10.3389/fphys.2017.00588. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang J, Jiang T, Cao Q, Wang Y. Characterizing anatomic differences in boys with attention-deficit/hyperactivity disorder with the use of deformation-based morphometry. American Journal of Neuroradiology. 2007;28(3):543–547. Retrieved from http://www.ajnr.org/content/28/3/543.long. [PMC free article] [PubMed] [Google Scholar]
- White SJ, Frith U, Rellecke J, Al-Noor Z, Gilbert SJ. Autistic adolescents show atypical activation of the brain's mentalizing system even without a prior history of mentalizing problems. Neuropsychologia. 2014;56:17–25. doi: 10.1016/j.neuropsychologia.2013.12.013. [DOI] [PubMed] [Google Scholar]
- Willcutt EG, Sonuga-Barke EJS, Nigg JT, Sergeant JA. Recent developments in neuropsychological models of childhood psychiatric disorders. In: Banaschewski T, Rohde LA, editors. Advances in Biological Psychiatry. Basel: Karger; 2008. pp. 195–226. Retrieved from https://www.karger.com/Article/Abstract/118526. [Google Scholar]
- Wilson LB, Tregellas JR, Hagerman RJ, Rogers SJ, Rojas DC. A voxel-based morphometry comparison of regional gray matter between fragile X syndrome and autism. Psychiatry Research: Neuroimaging. 2009;174(2):138–145. doi: 10.1016/j.pscychresns.2009.04.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yang P, Wang P-N, Chuang K-H, Jong Y-J, Chao T-C, Wu M-T. Absence of gender effect on children with attention-deficit/hyperactivity disorder as assessed by optimized voxel-based morphometry. Psychiatry Research: Neuroimaging. 2008;164(3):245–253. doi: 10.1016/j.pscychresns.2007.12.013. [DOI] [PubMed] [Google Scholar]
- Yang Q, Huang P, Li C, Fang P, Zhao N, Nan J, et al. Cui LB. Mapping alterations of gray matter volume and white matter integrity in children with autism spectrum disorder: evidence from fMRI findings. Neuroreport. 2018;29(14):1188–1192. doi: 10.1097/wnr.0000000000001094. [DOI] [PubMed] [Google Scholar]
- Yerys BE, Antezana L, Weinblatt R, Jankowski KF, Strang J, Vaidya CJ, et al. Kenworthy L. Neural correlates of set-shifting in children with autism. Autism Research. 2015;8(4):386–397. doi: 10.1002/aur.1454. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zamorano F, Billeke P, Kausel L, Larrain J, Stecher X, Hurtado JM, et al. Aboitiz F. Lateral prefrontal activity as a compensatory strategy for deficits of cortical processing in attention deficit hyperactivity disorder. Scientific Reports. 2017;7(1) doi: 10.1038/s41598-017-07681-z. 7181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhang R, Geng X, Lee TMC. Large-scale functional neural network correlates of response inhibition: An fMRI meta-analysis. Brain Structure and Function. 2017;222(9):3973–3990. doi: 10.1007/s00429-017-1443-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.