In this, the largest study to date of brain asymmetry in ASD, we mapped differences in brain asymmetry between participants with ASD and controls, in a collection of 54 international data sets via the ENIGMA Consortium. We had 80% statistical power to detect Cohen’s d effect sizes in the range of 0.12–0.13. We found significantly altered asymmetries of seven regional cortical thickness asymmetries in ASD compared with controls, predominantly involving medial frontal, orbitofrontal, inferior temporal, and cingulate regions. The magnitude of all regional thickness asymmetries was decreased in ASD compared with controls, whether it was reduced leftward, reduced rightward, or reversed average asymmetry. Rightward asymmetry of the medial orbitofrontal surface area was also decreased in individuals with ASD, as was leftward asymmetry of the lateral orbitofrontal surface area. In addition, individuals with ASD showed an increase in leftward asymmetry of putamen volume, compared with controls.

Previous MRI studies of cerebral cortical asymmetries in ASD, based on much smaller data sets, and using diverse methods for image analysis, suggested variable case–control differences29,30, or no differences27,31. Our findings partly support a previously reported, generalized reduction of leftward asymmetry29, as six of the nine significantly altered cortical regional asymmetries (thickness or surface area) involved decreased leftward asymmetries. However, three of the nine significantly altered cortical regional asymmetries involved shifts leftwards in ASD, either driven by a more prominent increase on the left side in ASD (i.e., medial orbitofrontal surface area), or by more prominent right- than left-side decreases in ASD (i.e., fusiform- and inferior temporal thickness). Thus, the directional change of asymmetry can depend on the specific region, albeit that the overall magnitude of asymmetry is most likely to be reduced in ASD.

The significant associations of diagnosis with asymmetry in the present study were all weak (Cohen’s d = −0.13–0.12), indicating that altered structural brain asymmetry is unlikely to be a useful predictor for ASD. Prior studies using smaller samples were underpowered in this context. However, the effect sizes were comparable to those reported by recent, large-scale studies of bilateral disorder-related changes in brain structure, in which asymmetry was not studied, including for ASD14 as well as attention-deficit hyperactivity disorder (ADHD)38,46, schizophrenia37, obsessive compulsive disorder (OCD)32,33, posttraumatic stress disorder34, and major depressive disorder35,36. It has become increasingly clear that anatomical differences between ASD and control groups are very small relative to the large within-group variability that is observed47.

Our findings may inform understanding of the neurobiology of ASD. Multi-regional reduction of cortical thickness asymmetry in ASD fits with the concept that laterality is an important organizing feature of the healthy human brain for multiple aspects of complex cognition, and is susceptible to disruption in disorders (e.g., 16,48). Left–right asymmetry facilitates the development of localized and specialized modules in the brain, which can then have dominant control of behavior49,50. Notably, some of the cortical regions highlighted here are involved in diverse social cognitive processes, including perceptual processing (fusiform gyri), cognitive and emotional control (anterior cingulate), and reward evaluation (orbitofrontal cortex, ventral striatum)51. However, the roles of these brain structures are by no means restricted to social behavior. As we found altered asymmetry of various additional regions, our findings suggest broader disruption of lateralized neurodevelopment as part of the ASD phenotype. We note that many of the regions that showed significant case–control differences in asymmetry, including medial frontal, anterior cingulate, and inferior temporal regions, overlap with the default mode network (DMN). The DMN comprises various cortical regions located in temporal (medial and lateral), parietal (medial and lateral) and prefrontal (medial) cortices52. DMN network organization has shown evidence for differences in ASD11,53,54,55, including alterations in functional laterality56. Our findings may therefore further support a role of altered lateralization of the DMN in ASD, warranting further investigations in this direction.

The medial orbitofrontal cortex was the only region that showed significantly altered asymmetry of both thickness and surface area in ASD, suggesting that disrupted laterality of this region might be particularly important in ASD. The orbitofrontal cortex may be involved in repetitive and stereotyped behaviors in ASD, owing to its roles in executive functions57. Prior studies have reported lower cortical thickness in the left medial orbitofrontal gyrus in ASD58, altered patterning of gyri and sulci in the right orbitofrontal cortex59, and altered asymmetry in frontal regions globally25,31. These studies were in much smaller sample sizes than used here.

As regards the fusiform cortex, a previous study by Dougherty et al.30 reported an association between higher ASD symptom severity and increased rightward surface area asymmetry, but not thickness asymmetry. The fusiform gyrus is involved in facial perception and memory among other functions, which are important for social interactions60. Here we report an asymmetry change in fusiform thickness in ASD that was significant after multiple testing correction, but there was also a nominally significant rightward change of surface area asymmetry in ASD (i.e., that did not survive multiple testing correction). This underlines that separate analyses of regional cortical thickness and surface area are well motivated, as they can vary relatively independently61.

The altered volume asymmetry of the putamen in ASD may be related to its role in repetitive and restricted behaviors in ASD. One study reported that differences in striatal growth trajectories were correlated with circumscribed interests and insistence on sameness62. The striatum is connected with lateral and orbitofrontal regions of the cortex via lateral–frontal–striatal reward and top-down cognitive control circuitry that might be dysfunctional in ASD63. For example, individuals with ASD have shown decreased activation of the ventral striatum and lateral inferior/orbitofrontal cortex during outcome anticipation, and of dorsal striatum and lateral–frontal regions during sustained attention and inhibitory control, compared with typically developing controls11,55,63.

Although the reasons for asymmetrical alterations in many of the structures implicated here are unclear, our findings suggest altered neurodevelopment affecting these structures in ASD. Further research is necessary to clarify the functional relevance and relationships between altered asymmetry and ASD. The findings we report in this large–scale study sometimes did not concur with prior, smaller studies. This may be owing to limited statistical power in the earlier studies, whereas low power reduces the likelihood that a statistically significant result reflects a true effect40. However, the cortical atlas that we used did not have perfect equivalents for regions defined in many of the earlier studies, and we did not consider gyral/sulcal patterns, or gray matter volumes as such. Furthermore, discrepancies with earlier studies may be related to age differences, and differences in clinical features of the disorder arising from case recruitment and diagnosis.

We included subjects from the entire ASD severity spectrum, with a broad range of ages, IQs, and of both sexes. Only one effect of diagnosis on regional asymmetry was influenced by sex, i.e., the rostral anterior cingulate thickness asymmetry, which was altered in males but not in females. This same regional asymmetry was primarily altered in lower versus higher IQ cases. This may therefore be an alteration of cortical asymmetry that is relatively specific to an ASD subgroup, i.e., lower-performing males. In controls, a different asymmetry (i.e., superior frontal thickness AI) showed a nominally significant association with IQ, which may point to different brain-IQ associations in ASD and controls. However, we cannot make strong interpretations based on these exploratory, secondary analyses without multiple testing correction.

As regards symptom severity, thickness asymmetry of the isthmus of the cingulate was associated with the ADOS score, such that the lower severity cases tended to have the most altered asymmetry. Again, this post hoc finding remains tentative in the context of multiple testing, and is reported here for descriptive purposes only. It is clear that most of the AIs that showed significant changes in ASD were not correlated with ADOS scores.

We found no evidence that medication use affected any of the asymmetries altered in ASD, although our medication variable was rudimentary. The role of specific medication usage should be investigated in future studies. As mentioned above, data on comorbidities were only available for 54 of the ASD subjects, precluding a high-powered analysis of this issue. This is a limitation of the study. We did not analyze handedness in the present study, as this had no significant effect on the same brain asymmetry measures as analyzed here, in studies of healthy individuals comprising more than 15,000 participants43,44.

In contrast to some prior studies of ASD, we did not adjust for IQ as a covariate effect in our main, case–control analysis. Rather, we carried out post hoc analysis of possible associations between IQ and brain asymmetries, separately in cases and controls. This was because lower average IQ was clearly part of the ASD phenotype in our total combined data set (Supplementary Fig. 1D), so that including IQ as a confounding factor in case–control analysis might have reduced the power to detect an association of diagnosis with asymmetry. This would occur if underlying susceptibility factors contribute both to altered asymmetry and reduced IQ, as part of the ASD phenotype.

The Desikan–Killiany atlas64 was derived from manual segmentations of sets of reference brain images. Accordingly, the mean regional asymmetries in our samples partly reflect left–right differences present in the reference data set used to construct the atlas. For detecting cerebral asymmetries with automated methods, some groups have chosen to work from artificially created, left–right symmetrical atlases, e.g., ref. 65. However, our study was focused on comparing relative asymmetry between groups. The use of a ‘real-world’ asymmetrical atlas had the advantage that regional identification was likely to be more accurate for structures that are asymmetrical both in the atlas and, on average, in our data sets. By defining the regions of interest in each hemisphere based on each hemisphere’s own particular features, such as its sulcal and gyral geometry, we could then obtain the corresponding relationships between hemispheres. To this end, we used data from the automated labeling program within FreeSurfer for subdividing the human cerebral cortex. The labeling system incorporates hemisphere-specific information on sulcal and gyral geometry with spatial information regarding the locations of brain structures, and shows a high accuracy when compared with manual labeling results64. Thus, reliable measures of each region can be extracted for each subject, and regional asymmetries then accurately assessed.

Although a single image analysis pipeline was applied to all data sets, heterogeneity of imaging protocols was a feature of this study. There were substantial differences between data sets in the average asymmetry measured for some regions, which may be owing in part to different scanner characteristics, as well as differences in patient profiles. We corrected for ‘data set’ as a random effect in the analysis, and sensitivity analysis based on the subset of 3 T acquired data showed similar results to the primary analysis. However, it is possible that between-data set variability resulted in reduced statistical power, relative to a hypothetical, equally sized, single-center study. In reality, no single centre has been able to collect such large samples alone. As long as researchers publish many separate papers based on single data sets, collected in particular ways, the field overall has the same problem. In this case, multi-centre studies can better represent the real-world heterogeneity, with more generalizable findings than single-centre studies66. The primary purpose of our study, based on 54 data sets that were originally collected as separate studies, was to assess the total combined evidence for effects over all of these data sets, whereas allowing for heterogeneity between data sets through the use of random intercepts, and finally adding sensitivity and secondary analyses with respect to relevant variables.

The cross-sectional design limits our capacity to make causal inferences between diagnosis and asymmetry. ASD is highly heritable, with meta-analytic heritability estimates ranging from 64 to 91%67. Likewise, some of the brain asymmetry measures examined here have heritabilities as high as roughly 25%43,44. Future studies are required to investigate shared genetic contributions to ASD and variation in brain structural asymmetry. These could help to disentangle cause-effect relations between ASD and brain structural asymmetry. Given the high comorbidity of ASD with other disorders, such as ADHD, OCD, and schizophrenia68, cross-disorder analyses incorporating between-disorder genetic correlations may be informative.

In conclusion, large-scale analysis of brain asymmetry in ASD revealed primarily cortical thickness effects, but also effects on orbitofrontal cortex asymmetry, and putamen asymmetry, which were significant but very small. Our study illustrates how high-powered and systematic studies can yield much needed clarity in human clinical neuroscience, where prior smaller and more methodologically diverse studies were inconclusive.