Archival Report

# Decomposing the Brain in Autism: Linking Behavioral Domains to Neuroanatomical Variation and Genomic Underpinnings

Hanna Seelemeyer, Caroline Gurr, Johanna Leyhausen, Lisa M. Berg, Charlotte M. Pretzsch, Tim Schäfer, Bassem Hermila, Christine M. Freitag, Eva Loth, Bethany Oakley, Luke Mason, Jan K. Buitelaar, Christian F. Beckmann, Dorothea L. Floris, Tony Charman, Tobias Banaschewski, Emily Jones, Thomas Bourgeron, on behalf of the EU-AIMS LEAP Group, Declan Murphy, and Christine Ecker

## ABSTRACT

**BACKGROUND:** Autism is accompanied by highly individualized patterns of neurodevelopmental differences in brain anatomy. This variability makes the neuroanatomy of autism inherently difficult to describe at the group level. Here, we examined interindividual neuroanatomical differences using a dimensional approach that decomposed the domains of social communication and interaction (SCI), restricted and repetitive behaviors (RRBs), and atypical sensory processing (ASP) within a neurodiverse study population. Moreover, we aimed to link the resulting neuroanatomical patterns to specific molecular underpinnings.

**METHODS:** Neurodevelopmental differences in cortical thickness (CT) and surface area (SA) were correlated with SCI, RRB, and ASP domain scores by regression of a general linear model in a large neurodiverse sample of 288 autistic individuals and 140 nonautistic individuals, ages 6 to 30 years, recruited within the European Autism Interventions Longitudinal European Autism Project (EU-AIMS LEAP). The domain-specific patterns of neuroanatomical variability were subsequently correlated with cortical gene expression profiles via the Allen Human Brain Atlas.

**RESULTS:** Across groups, behavioral variations in SCI, RRBs, and ASP were associated with interindividual differences in CT and SA in partially non-overlapping frontoparietal, temporal, and occipital networks. These domain-specific imaging patterns were enriched for genes that 1) are differentially expressed in autism, 2) mediate typical brain development, and 3) are associated with specific cortical cell types. Many of these genes were implicated in pathways governing synaptic structure and function.

**CONCLUSIONS:** Our study corroborates the close relationship between neuroanatomical variation and interindividual differences in autism-related symptoms and traits within the general framework of neurodiversity and links domain-specific patterns of neuroanatomical differences to putative molecular underpinnings.

https://doi.org/10.1016/j.bpsc.2024.12.003

Autism is a common neurodevelopmental condition characterized by differences in social communication and interaction (SCI) as well as restricted and repetitive behaviors (RRBs) (1). Atypical sensory processing (ASP), though traditionally categorized under RRBs, has increasingly gained recognition as a core feature of autism, both in recent research and within the autistic community (2). These core features are accompanied by an atypical development of the brain (3). To elucidate the neuroanatomical underpinnings of autism, most structural magnetic resonance imaging (sMRI) studies have employed traditional case-control designs. However, recent findings promote a more dimensional understanding of neurodevelopmental conditions within the general framework of neurodiversity (4). Yet, few neuroimaging studies have employed dimensional approaches to characterize the

neuroanatomy of autistic traits and to relate imaging-derived phenotypes (IDPs) to putative underlying mechanisms.

The neural systems underpinning autism have been extensively studied, mostly using between-group designs, and include frontotemporal, frontoparietal and occipital brain regions (5) that are functionally linked to autistic traits [e.g., (6,7)]. However, regional neuroanatomical differences in autism typically have small effect sizes (8) and reflect highly variable patterns of neurodevelopmental deviations across individuals (9,10). This makes the neuroanatomy of autism inherently difficult to describe and highlights the need for employing alternative analytical approaches that also account for interindividual heterogeneity [e.g., (11)] and represent the different behavioral domains of autism. A number of existing studies therefore employ dimensional approaches to dissect the neuroanatomy of autistic traits
into distinct regional networks, mapping onto different behavioral
domains [e.g., (6,12)]. Here, instead of encoding autism as a
categorical fixed effect, autism-associated behaviors and traits
are conceptualized as continuous variables to assess brain-
behavior correlations [see (6,13–15)]. This approach has several
advantages as it can accommodate interindividual variations in
symptom severity and profile extending into the subclinical range
[see Rolland et al. (16)], while also maximizing the sample size to
enable the detection of more subtle effects.

Along these lines, Prigge et al. (6) have shown that interin-
dividual differences in the SCI and RRB domains, assessed via
the Social Responsiveness Scale (SRS), are associated with
regional variations in cortical thickness (CT) in frontal and tem-
poral lobes as well as in the cingulate cortex. More specifically,
SCI scores were associated with increased CT in superior
frontal and posterior cingulate, while RRB scores were posi-
tively correlated with CT in the right frontal pole (6). Other studies
have investigated neuroanatomical correlates of the SRS in
predominantly non-autistic cohorts highlighting higher autistic
traits to be associated with cortical thinning in superior temporal
and temporoparietal regions (17,18). Additionally, measures of
visual sensory sensitivity as assessed by the Adolescent/Adult
Sensory Profile significantly correlate with interindividual varia-
tions in right lingual and right lateral orbitofrontal CT in autistic
adults (15). Last, there is also one study reporting an association
between RRBs and measures of surface area (SA) in intra-
parietal sulcus and left superior frontal gyrus in a co-twin control
design (19). Taken together, these studies support current
clinical trends toward promoting a more dimensional concep-
tualization of neurodiversity along the autism spectrum on the
level of neurobiology (20). However, most of these studies were
restricted to a single behavioral domain or cortical feature and
so did not fully capture the broader autistic phenotype.

Therefore in the present study, we decomposed the
behavioral autism phenotype into the 3 distinct quantitative
behavioral domains of SCI, RRB, and ASP in a large and
clinically well-characterized cohort recruited within the Euro-
pean Autism Interventions Longitudinal European Autism
Project (EU-AIMS LEAP). Interindividual variability in these
domains were subsequently linked to interindividual differ-
ences in brain anatomy characterized by measures of SA and
CT (21). The LEAP sample is particularly well suited for this
purpose as it provides deep phenotypic assessments and
structural neuroimaging data for more than 700 individuals,
including male and female participants that span a broad range
of intellectual abilities and clinical phenotypes (22,23). Initially,
we employed a dimensional approach to establish brain-
behavioral correlations across the entire cohort, i.e., across
diagnostic categories, to maximize statistical power (24).
Subsequently, an imaging transcriptomics approach
leveraging the spatial gene expression data provided by the
Allen Human Brain Atlas (AHBA) (25) was used to link macro-
scopic differences in brain structure to putative mechanisms.

METHODS AND MATERIALS

Participants

This study used data provided by EU-AIMS LEAP, a multi-
center, transdisciplinary project to identify stratification

biomarkers for autism (www.eu-aimseuwww.eu-aimseu).
The LEAP sample provided a wide range of dimensional
measures of core autism-related features as well as usable
sMRI data for 638 individuals. For details about the quality
assessment of the MRI data and clinical characteristics of
excluded participants, see the Supplement. Of the 638 in-
dividuals, 428, ages 6 to 30 years, had complete clinical data
in all 3 behavioral domains (SCI, RRB, and ASP); 288 of
these participants had a clinical diagnosis of autism (83 fe-
male and 205 male), and 140 served as nonautistic control
participants (53 female, 87 male) (Table 1). The subsample
including autistic individuals was further clinically charac-
terized using gold standard diagnostic tools, such as the
Autism Diagnostic Observation Schedule, Second Edition
(26), and the Autism Diagnostic Interview-Revised (26). The
sample and data collection procedure have been described
in detail elsewhere (22,23). Interindividual variability in
autism-related symptoms and traits was assessed in the 3
core domains of SCI, RRB, and ASP. To represent the SCI
domain, we accumulated scores of the 4 SCI subscales
(Social Cognition, Social Communication, Social Aware-
ness, and Social Motivation) of the second edition of the
SRS (SRS-2) (27), as suggested by Prigge et al. (6). The SRS-
2 is a well-established and well-validated measure of overall,
as well as SCI, symptom severity (6,28). The RRB domain
was measured by the Repetitive Behavior Scale-Revised
(RBS-R) (29), a questionnaire assessing restricted and re-
petitive patterns of behavior, interests, or activities, which
has repeatedly shown good psychometric properties (30).
Further details on our choice of clinical measures are pro-
vided in the Supplement. All analyses were also conducted
using the SRS RRB subscale for comparison (Figure S10).
To quantify ASP, we used the Short Sensory Profile (SSP)
(31), the most commonly used and psychometrically
well-established measure of sensory symptoms in autism
(32). The SSP was inverse-coded for all analyses to be
directionally aligned with the SRS and RBS-R, where
higher values indicate higher autistic traits in the behavioral
score.

All questionnaires were filled out by caregivers. Missing data
in all 3 behavioral domain measures were imputed where
possible to achieve a maximum sample size using an extra
trees regression approach. The imputation process is
described in detail elsewhere (33). A full clinical characteriza-
tion, medication status, and inclusion and exclusion criteria are
provided in the Supplement. All participants (or guardians in
the case of minors) gave informed written consent, and the
study was approved by an independent ethics committee at
each site.

MRI Data Acquisition and Cortical Surface
Reconstruction

All participants underwent high-resolution sMRI scanning in 3T
scanners at 6 different locations (University of Cambridge and
King's College London, United Kingdom; Mannheim Univer-
sity, Germany; Radboud University and Utrecht University, the
Netherlands; Rome University, Italy). T1-weighted volumetric
images were acquired with full head coverage, at 1.2-mm
thickness with 1.2 × 1.2-mm in-plane resolution (Table S2).
Neuroanatomy of Behavioral Domains in Autism

<table><caption>Table 1. Clinical and Demographic Sample Characteristics and Between-Group Comparison</caption><thead><tr><th>Variable</th><th>Autistic Group, n = 288</th><th>Nonautistic Group, n = 140</th><th>Statistics</th></tr></thead><tbody><tr><td>Sex</td><td></td><td></td><td>χ<sup>2</sup><sub>1</sub> = 3.14, p = .08</td></tr><tr><td>Female</td><td>83 (28.82%)</td><td>53 (37.86%)</td><td></td></tr><tr><td>Male</td><td>205 (71.18%)</td><td>87 (62.14%)</td><td></td></tr><tr><td>Site</td><td></td><td></td><td>χ<sup>2</sup><sub>5</sub> = 20.52, p &lt; .001<sup>a</sup></td></tr><tr><td>Cambridge</td><td>41 (14.24%)</td><td>13 (9.29%)</td><td></td></tr><tr><td>London</td><td>100 (34.72%)</td><td>38 (27.14%)</td><td></td></tr><tr><td>Mannheim</td><td>17 (5.90%)</td><td>19 (13.57%)</td><td></td></tr><tr><td>Nijmegen</td><td>93 (32.29%)</td><td>55 (39.29%)</td><td></td></tr><tr><td>Rome</td><td>16 (5.56%)</td><td>0 (0.00%)</td><td></td></tr><tr><td>Utrecht</td><td>21 (7.29%)</td><td>15 (10.71%)</td><td></td></tr><tr><td>Age, Years</td><td>17.09 (5.43)</td><td>13.66 (3.76)</td><td>t<sub>376.36</sub> = 7.60, p &lt; .001*</td></tr><tr><td>FSIQ</td><td>98.35 (20.46)</td><td>102.48 (20.40)</td><td>t<sub>276.15</sub> = -1.96, p = .05</td></tr><tr><td>ADI-R</td><td></td><td></td><td></td></tr><tr><td>Social interaction</td><td>16.97 (6.68)</td><td>-</td><td></td></tr><tr><td>Communication</td><td>13.55 (5.65)</td><td>-</td><td></td></tr><tr><td>RRB</td><td>4.30 (2.67)</td><td>-</td><td></td></tr><tr><td>ADOS<sub>CSS</sub></td><td>5.52 (2.75)</td><td>-</td><td></td></tr><tr><td>Total BV, L</td><td>1.19 (0.13)</td><td>1.19 (0.14)</td><td>t<sub>253.49</sub> = -0.20, p = .84</td></tr><tr><td>Mean CT, mm</td><td>2.69 (0.13)</td><td>2.73 (0.09)</td><td>t<sub>360.74</sub> = -3.79, p &lt; .001*</td></tr><tr><td>Total SA, m<sup>2</sup></td><td>0.23 (0.02)</td><td>0.23 (0.03)</td><td>t<sub>267.06</sub> = -1.60, p = .11</td></tr><tr><td>SRS<sub>SCI</sub></td><td>64.65 (18.10)</td><td>48.79 (10.90)</td><td>t<sub>407.13</sub> = 11.26, p &lt; .001*</td></tr><tr><td>RBS-R</td><td>16.34 (13.95)</td><td>2.49 (8.18)</td><td>t<sub>411.4</sub> = 12.89, p &lt; .001*</td></tr><tr><td>SSP<sub>inverted</sub></td><td>49.54 (25.85)</td><td>17.04 (18.03)</td><td>t<sub>374.48</sub> = 15.09, p &lt; .001*</td></tr></tbody></table>

Values are presented as n (%) or mean (SD). SSP scores were inverted so that higher scores indicate higher symptom severity.

*Statistically significant.

ADI-R, Autism Diagnostic Interview-Revised; ADOS<sub>CSS</sub>, Autism Diagnostic Observation Schedule Calibrated Severity Score; BV, brain volume; CT, cortical thickness; FSIQ, Full Scale IQ; RBS-R, Repetitive Behavior Scale-Revised; RRB, restricted and repetitive behavior; SA, surface area; SRS<sub>SCI</sub>, Social Responsiveness Scale-Social Communication and Interaction Subscale; SSP, Short Sensory Profile.

Initially, usable sMRI data were available for 709 individuals. For each T1-weighted image, vertexwise models of the cortical surface were derived using FreeSurfer software version 6.0.0 (https://surfer.nmr.mgh.harvard.edu/) as described in (34,35). The surface reconstruction underwent thorough multirater quality assessment, resulting in a final sample of 638 usable MRI scans (see the Supplement). CT was computed as the closest distance between the inner (i.e., white) and outer (i.e., pial) matter boundary at each vertex (36), and SA was computed as the interface between gray and white matter (37). We also computed mean CT (CT₀) and total SA (SA₀) for each participant.

## Statistical Analyses

Statistical analyses were conducted using the SurfStat toolbox (http://www.math.mcgill.ca/keith/surfstat) for MATLAB, version R2022a (The MathWorks, Inc.; https://www.mathworks.com) and R (R Project for Statistical Computing; https://www.r-project.org). The main effect of all 3 behavioral domain scores in CT and SA was examined by regression of a general linear model with the following: 1) SCI, RRB, and ASP scores; linear age; full-scale IQ (38); and total brain (CT₀ or SA₀, respectively) as continuous covariates; and 2) sex and acquisition site as fixed-effect factors. For measures of CT, we also included a quadratic age term,

as CT has been shown to follow a quadratic developmental trajectory (28,39):

$$Y_i = \beta_0 + \beta_1 \text{SCI} + \beta_2 \text{RRB} + \beta_3 \text{ASP} + \beta_4 \text{Sex} + \beta_5 \text{Age} \\ + [\beta_6 \text{Age}^2] + \beta_7 \text{IQ} + \beta_8 \text{Site} + \beta_9 \text{SA}_0[\text{CT}_0] + \varepsilon_i$$

where εᵢ is the residual error at vertex i. The main effect of the target predictor, i.e., SCI, RRB, or ASP, was estimated from the coefficient β₁, β₂, or β₃, respectively. All continuous covariates were mean-centered across groups and z-standardized to make scores comparable across measures. To further investigate the effect of site, age, and group, the analyses were repeated using ComBat batch correction (40), repeated in age-stratified subgroups, and repeated covarying for group (Figures S1, S2, and S4). To account for possible multicollinearity between predictors, the main effect of each behavioral measure was also examined in isolation, i.e., without covarying for any other behavioral domain score (Figure S3). Lastly, the effect of medication status was investigated both by examining its main effect and by repeating the analyses when covarying for medication status (see Figure S5). Corrections for multiple comparisons were performed using random-field theory-based cluster analysis with a cluster-based significance threshold (p<clust) of .05 (2-tailed) (41). Notably, all statistical analyses
were conducted across diagnostic groups, i.e., across autistic participants and nonautistic control participants. To allow for a direct comparison between a dimensional and a categorical approach, we conducted an additional between-group comparison (Figure S6).

## Gene Expression Decoding Analyses

To link the neuroanatomical findings to putative genomic underpinnings, an imaging transcriptomics approach was used. To this aim, we performed a gene expression decoding analysis (GEDA) using a linear mixed-effects (LME) model, as described in detail by C. Ecker, Ph.D., et al. (unpublished data, 2024). In brief, the IDPs are aligned with a surface representation of 15,633 genes at 1670 sampling points of the AHBA (25), mapped onto the FreeSurfer fsaverage6 standard-space template and then correlated with the normalized gene expression data from the AHBA using an LME model with random intercept and slope (42). The resulting gene lists were then tested for an enrichment of differentially expressed genes (DEGs) that are upregulated or downregulated in postmortem cortical tissue (43) and genes of differentially expressed correlated modules in autism (44). Next, to test for an enrichment of genes underpinning typical brain development, we used the human brain transcriptome data set provided by Kang et al. (45), which includes transcriptome profiles of 16 different brain regions ranging from prenatal development to late adulthood. Lastly, we performed a cell type enrichment analysis using the single-cell transcriptomic atlas of human neocortical development provided by Polioudakis et al. (46). All enrichment testing was performed using the GeneOverlap package in R (47), which generated enrichment odds ratios and false discovery rate (FDR)-corrected *p* values. Only comparisons with *p*<sub>FDR</sub> < .05 were interpreted further. The GEDA and gene enrichment analysis are described in detail in the Supplement.

## RESULTS

### Participant Demographics and Clinical Characterization

Autistic participants and nonautistic control participants did not differ in terms of sex ($\chi^2_1$ = 3.14, *p* = .08), full-scale IQ ($t_{276.15}$ = -1.96, *p* = .05), total brain volume ($t_{253.49}$ = -0.20, *p* = .84), and total SA ($t_{267.06}$ = -1.60, *p* = .11). Autistic participants were significantly older than nonautistic control participants ($t_{376.36}$ = 7.60, *p* < .001) and had significantly lower mean CT ($t_{360.74}$ = -3.79, *p* < .001). The groups differed regarding the distribution of acquisition site ($\chi^2_5$ = 20.52, *p* < .001). We therefore covaried for these measures in all subsequent analyses. As expected, there were significant differences in total SRS ($t_{67.46}$ = -2.45, *p* = .02), RBS-R ($t_{56.90}$ = -3.32, *p* < .001), and SSP ($t_{61.78}$ = -2.57, *p* = .01) scores between autistic individuals with and without intellectual disability (ID) (Figure S8). To ensure that ID did not drive the observed neuroanatomical findings, we repeated the analyses controlling for ID. The results remained stable and consistent with the original findings, indicating that ID did not significantly affect the brain-behavior correlations (Figure S9). Moreover, as there were floor effects in the RBS-R, a 2-part

hurdle model, which combines the binary prediction that a value is nonzero and a truncated count model for nonzero values, was additionally tested to ensure the robustness of our results (Table S5 and Figure S12). As expected, autistic participants had significantly higher scores in all 3 behavioral domains relative to nonautistic control participants (Table 1).

### Vertex-Level Association Between Neuroanatomical and Behavioral Variation in the Domains of SCI, RRB, and ASP

Across groups, behavioral variations in the SCI, RRB, and ASP domains were associated with interindividual differences for both measures of CT and SA (Figure 1). More specifically, increased SCI scores were associated with decreased CT in the fusiform and parahippocampal gyri (approximate Brodmann areas [BAs] 18/19/34/37). Significant negative associations were also observed in the RRB domain, where increased RRB scores were linked with decreased CT in the primary motor and somatosensory cortex and in the superior parietal and supramarginal gyrus (BAs 1–6/40). Positive brain-behavior associations, on the other hand, were found for ASP domain scores, where increased scores were accompanied by increased CT in the inferior, middle, and superior temporal gyrus as well as in inferior parietal and lateral occipital cortices (BAs 19–22/38/40) (Figure 1A; Table S3; for unthresholded maps, see Figure S7A).

For measures of SA, SCI scores were significantly positively correlated with SA in predominantly frontal areas, such as the superior, rostral, and caudal middle frontal gyrus; pars opercularis and triangularis; medial orbital frontal cortex; and frontal pole (BAs 10–12/18/38/44–46). Conversely, SCI was negatively associated with SA in temporal regions, including inferior and middle temporal gyrus, lingual gyrus, and isthmus cingulate cortex (BAs 18–21). In the case of RRB scores, positive associations with SA were observed in precuneus and pericalcarine cortex, insula, and supramarginal gyrus (BAs 7/13–16/23/31/40). However, RRB scores were negatively associated with SA in the lingual gyrus (BAs 7/18/19). For ASP scores, a positive association with SA was found predominantly in occipital and temporal regions, such as the entorhinal cortex, parahippocampal gyrus, lingual gyrus, and fusiform gyrus (BAs 18/19/37/40). Conversely, ASP scores exhibited a negative association with SA in a widespread, predominantly left hemisphere network, including supramarginal and postcentral gyrus, pars opercularis, pars triangularis, supramarginal and precentral gyrus, and caudal and rostral middle frontal gyrus (BAs 1–4/10/13–16/21/38/44–46) (Figure 2B; Table S4; for unthresholded maps, see Figure S7B). All of the above effects remained stable when covarying for group, when covarying for medication status, and when using ComBat batch correction (Figures S1, S4, and S5).

### Gene Set Enrichment Analyses

Overall, we observed a significant enrichment of 1) genes with differential expression in autism (43,44), 2) genes mediating typical brain development (45), and 3) genes associated with different cell types in the developing cortex (46) for all 3 domain-related brain phenotypes. The results of all enrichment analyses are shown in Figures 2 and 3.
Neuroanatomy of Behavioral Domains in Autism

A

<figure><img src="image_1.png" /></figure>

B

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Vertexwise association between neuroanatomical features and clinical measures of behavioral domains. Panel (A) shows clusters with increased (orange to yellow) and decreased (blue to cyan) cortical thickness in relation to the severity in the dimensions of social communication and interaction (SCI), restricted and repetitive behaviors (RRBs), and atypical sensory processing (ASP) (random field theory-based cluster-corrected *p* < .05, 2-tailed). Panel (B) shows clusters with increased (orange to yellow) and decreased (blue to cyan) surface area in relation to their symptom severity in the dimensions of SCI, RRB, and ASP (random field theory-based cluster-corrected *p* < .05, 2-tailed). L, left; R, right.</figcaption></figure>

In particular, across domains and features, we observed a strong enrichment of DEGs in autism (48), rather than autism likelihood genes. More specifically, in CT we found a strong enrichment of coexpression modules downregulated in autism (e.g., CTX.down.M4, CTX.down.M10, or ASD.DEGs.down). These gene modules have previously been implicated in synaptic transmission (44) and receptor signaling (43). Additionally, one coexpression module was upregulated in autism (CTX.up.M20) that has previously been linked to developmental processes and the regulation of cell differentiation (44) (Figure 2). Furthermore, we found an enrichment for coexpression modules underpinning typical brain development during the first decade of life (45). We observed a significant enrichment for coexpression modules representing neuron development (modules 8 and 10), synaptic transmission (modules 2, 15, and 24), cell adhesion and signaling (module 16), and ZNF and SOX transcription factors (module 20) in the SA, but not in the CT, neuroanatomical maps. Modules 10 and 15 have their highest expression during childhood (Figure 2C). Finally, our virtual histology approach revealed an enrichment of predominantly excitatory, rather than inhibitory, cell types for our neuroanatomical phenotypes (Figure 3).

DISCUSSION

Our study examined the neuroanatomical underpinnings of interindividual differences in autistic traits in the domains of SCI, RRB, and ASP along a continuum, spanning the typically developing brain as well as the brain in autism. In our neurodiverse cohort, we observed a significant association between domain scores and neuroanatomical variability in partially non-overlapping frontoparietal, temporal, and occipital networks. Moreover, these networks were transcriptomically enriched for genes known to be upregulated or downregulated in the brain

in autism during childhood and adolescence, particularly in the neural systems underpinning RRB and ASP. In addition, our study established a transcriptomic association between neuroanatomical markers and gene coexpression modules mediating typical brain development as well as genes expressed in specific cell types of the developing cortex. Taken together, our study thus provides important novel insights into the cortical systems underpinning autism traits and their putative molecular mechanisms.

Our study builds on previous case-control studies by examining interindividual variability in CT and SA in relation to 3 key behavioral domains characterizing autism (i.e., SCI, RRB, and ASP) (17,18). Here, we highlight domain-specific as well as domain-overarching patterns of brain-behavioral association spanning across diagnostic categories. Across domains, we observed neuroanatomical variation in brain regions, such as the prefrontal cortex, fusiform gyrus, and superior and middle temporal gyrus, that have previously been established as core autism regions [see (8,49)]. In particular, there was a strong association between the RRB and ASP domains and SA variability in the supramarginal gyrus, which has previously been implicated in autism by structural (50) and functional (51) neuroimaging studies. Functionally, the supramarginal gyrus plays a crucial role in sensory integration (52,53), motor planning (54), and social functions (55), which are known to be altered in autism. Moreover, we observed significant brain-behavior correlations in social brain regions, such as the fusiform and parahippocampal gyrus; the superior, inferior, and middle temporal gyrus; and the inferior parietal cortex (56), which were significantly correlated with the SCI and ASP domains. Notably, we also found significant associations in executive brain areas of the frontal cortex, such as superior and middle frontal gyrus, that are relevant to higher cognitive control, such as executive functions (57) and decision making
A Gene Set Enrichment Analyses

<figure><img src="image_3.png" /></figure>

B Gene Set Enrichment of Genes Mediating Typical Brain Development

<figure><img src="image_4.png" /></figure>

C Spatiotemporal Expression Profiles

<figure><img src="image_5.png" /></figure>

**Figure 2.** Gene set enrichment analyses. Panel (A) shows significant odds ratios (ORs) at a false discovery rate-corrected threshold of 0.5 resulting from the gene set enrichment analyses for genes expressed in the different output maps of Social Responsiveness Scale, Repetitive Behavior Scale, and Short Sensory Profile for cortical thickness (CT) and surface area (SA), respectively. Gene sets were subdivided into sets with differential gene expression in autism and sets representing autism likelihood genes that contain either common variants or rare de novo variants. Gene sets are annotated and labeled based on their original publication. Enrichment ORs of gene sets that are significantly enriched across behavioral domains are highlighted in green, enrichment ORs that are significantly enriched only in distinct behavioral domains are highlighted in red. Panel (B) shows set enrichment of genes mediating typical brain development as reported in the spatiotemporal transcriptome data set provided by Kang *et al.* (45). Set names contain their respective coexpression module label (e.g., M1), followed by their functional description based on their Gene Ontology term enrichment. Panel (C) shows spatiotemporal expression profiles of brain gene
Neuroanatomy of Behavioral Domains in Autism

(58), and in prosocial behavior (59). A combination of shared and distinct brain regions thus contributes to the highly heterogeneous clinical phenotype of autism across symptom domains, which substantiates the concept of autism as a condition affecting neural systems (60,61).

Moreover, our findings are in line with previous studies highlighting the occurrence of subthreshold autism symptoms in the general population [i.e., a broader autistic phenotype (17,18,62,63)] that map onto trait-specific neural systems (64). Such a dimensional conceptualization of autism-related neurodiversity has also recently been employed by genetic studies that quantify genetic variants associated with autism across diagnostic categories (16). Moreover, there are twin studies supporting the notion of a shared etiology between subclinical and clinically relevant levels of autistic traits that seem to be driven by genetics independent of diagnostic labels (65,66). These findings thus align with the emerging paradigm shift in clinical research toward a dimensional conceptualization of neurodiversity and mental health (4,67). Our supplementary analyses of categorical between-group contrasts also show that dimensional approaches may be more sensitive in identifying brain-behavioral associations compared with categorical approaches (Figure S4) and provide a more fine-grained cartographical representation of domain-specific and -overarching networks. Such a fine-grained delineation of the neural systems underlying autistic traits is also crucial for linking IDPs to putative molecular mechanisms that might contribute to autism heterogeneity. Investigating the neuroanatomy underlying the heterogeneous behavioral phenotypes of autistic traits and the mechanisms driving symptom profiles is crucial to understanding the heterogeneity of clinical phenotypes and ultimately informing individualized support strategies.

To link neuroanatomical variability associated with domain scores to putative molecular mechanisms, we used the AHBA, which allowed us to identify genes with a cortical expression signature resembling our imaging findings. In line with previous publications [e.g., (9,68)], we observed a significant enrichment for genes known to be associated with autism, particularly for genes that are downregulated in cortical brain tissue (43,44). Many of these genes underpin the development of synaptic structure and functioning, hence suggesting a link between synaptic dysregulation and altered cortical morphometry (68). Moreover, our findings align with previous genomic studies highlighting the pleiotropic effects of regulatory processes, such as altered transcription and splicing, as key contributors to autism heterogeneity (68,69). We also observed that our autism-related neuroimaging patterns were transcriptomically enriched for coexpression modules mediating typical brain development (e.g., genes modulating synaptic transmission or

neuron development) (45). Notably, such enrichment was predominantly observed for measures of SA, but not CT, suggesting that measures of SA may be under closer genetic and/or transcriptional control than measures of CT. Genetic influences on CT, on the other hand, might be more dependent on other genomic or environmental processes, as has been suggested in previous studies (70). Here, it is also important to note that CT and SA represent different aspects of the cortical architecture and are therefore also linked to different developmental mechanisms. For example, CT has been linked to dendritic arborization (71,72) and SA has been linked to radial units and minicolumnar organization (73,74). It is therefore not surprising that patterns of CT and SA are transcriptomically enriched for distinct gene sets. Lastly, we observed a significant enrichment especially of excitatory neuronal signals, which aligns with the notion of an imbalance between excitation and inhibition that may constitute a common neural mechanism for autism (75). Aligning imaging phenotypes with gene expression signatures is therefore important as it allows us to link macroscopic brain organization to microscopic mechanisms and to inform future studies designed to parse autism heterogeneity on the etiological and phenotypic level.

This study has additional limitations. Most importantly, the AHBA, while it is the most comprehensive gene expression atlas to data, has some methodological limitations. It comprises 6 brains of exclusively adult nonautistic donors, thereby forming a relatively homogeneous and small sample. We acknowledge the importance of repeating the analyses in high-resolution age-specific gene expression datasets once they become available. Moreover, effect sizes when studying neuroanatomy in autism are traditionally small (8). Even with the high resolution of the present study with its large sample size and inclusion of different behavioral measures, it remains unclear how robust the reported associations between brain and behavior are. It is therefore important to replicate our present findings in an independent sample [e.g., as suggested by (76)]. We have not yet done so as other publicly available datasets do not possess the same breadth of phenotyping, sample heterogeneity, and age range from childhood to adulthood as the LEAP cohort. In the future it will also be important to extend the study of neuroanatomical underpinnings of SCI, RRB, and ASP to other neuro-developmental conditions in which they occur (e.g., RRBs in obsessive-compulsive disorder and Tourette syndrome or ASP in attention-deficit/hyperactivity disorder) implementing a transdiagnostic approach.

In summary, our dimensional approach to examining the relationship between interindividual variation in autism traits and neuroanatomical diversity and their link to putative

modules significantly enriched in the t-map of the association between SA and the behavioral domains for module 10 (left panel) enriched for genes implicated in neuronal development and for module 15 also enriched for synaptic transmission genes. The x-axis shows the developmental time frame, and the y-axis shows the different brain regions. \*p < .05 (FDR-corrected), \*\*p < .001 (FDR-corrected). A1C, primary auditory (A1) cortex; AMY, amygdala; ASD, autism spectrum disorder; ASP, atypical sensory processing; CBC, cerebellar cortex; CTX, cortex; DEG, differentially expressed gene; DFC, dorsolateral prefrontal cortex; down, down-regulated expression in autism; FDR, false discovery rate; HIP, hippocampus; IPC, posterior inferior parietal cortex; ITC, inferior temporal cortex; M1C, primary motor (M1) cortex; MD, mediodorsal nucleus of the thalamus; MFC, medial prefrontal cortex; OFC, orbital prefrontal cortex; pcw, postconception weeks; RRB, restricted and repetitive behavior; S1C, primary somatosensory (S1) cortex; SCI, social communication and interaction; SFARI, Simons Foundation Autism Research Initiative; STC, superior temporal cortex; STR, striatum; up, up-regulated expression in autism; V1C, primary visual (V1) cortex; VFC, ventrolateral prefrontal cortex.
A Germinal Zones of the Developing Cortex

<figure><img src="image_6.png" /></figure>

B Cortical Thickness

<figure><img src="image_7.png" /></figure>

C Surface Area

<figure><img src="image_8.png" /></figure>

**Figure 3.** Cell-type enrichment analyses. Panel (A) is a schematic illustration of cell types in germinal zones of the developing cortex, adapted from Polioudakis et al. (46) and the t-maps from the previous neuroanatomical analyses (Figure 1). Panel (B) shows the cell-type enrichment odds ratios (ORs) and associated -log10(q) values for gene sets expressed for social communication and interaction (SCI), restricted and repetitive behaviors (RRB), and atypical
Neuroanatomy of Behavioral Domains in Autism

genomic mechanisms represents a step forward toward
capturing autism heterogeneity along a gradient, spanning the
typically developing brain as well as the brain in autism. Our
approach thus aligns with recent research efforts conceptu-
alizing autism within the general framework of neurodiversity,
thereby contributing to the emerging paradigm shift toward a
more dimensional conceptualization of mental health that
goes beyond diagnostic categories.

ACKNOWLEDGMENTS AND DISCLOSURES

This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (Grant No. 115300 [for project EU-AIMS] and Grant No. 777394 [for project Autism Innovative Medicine Studies-2-Trials]). This Joint Undertaking is supported by the European Union's Horizon 2020 research and innovation program; European Federation of Pharmaceutical Industries and Associations; and Autism Speaks, Autistica, and Simons Foundation Autism Research Initiative. Any views expressed are those of the authors and not necessarily those of the funders (Innovative Health Initiative 2 Joint Undertaking).

This work was supported by Horizon Europe (Grant No. 101057385), UK Research and Innovation under the United Kingdom government's Horizon Europe funding guarantee (Grant No.10039383 [for Risk and Resilience in Developmental Diversity and Mental Health]), German Research Foundation under the Heisenberg Programme (Grant Nos. EC480/1-1 and EC480/2-1 [to CE]), Dynamic Network Approach of Mental Health to Stimulate Innovations for Change center funded by the Landes-Offensive zur Entwicklung wissenschaftlich-ökonomischer Exzellenz program of the Hessian Ministry of Science and Arts (Grant No. LOEWE1/16/519/03/09.001(0009)/98), and National Institute for Health and Care Research Maudsley Biomedical Research Centre (to DM).

We gratefully acknowledge the contributions of the EU-AIMS LEAP Group: Jumana Ahmad, Sara Ambrosino, Bonnie Auyeung, Tobias Banaschewski, Simon Baron-Cohen, Sarah Baumeister, Christian F. Beckmann, Sven Bölte, Thomas Bourgeron, Carsten Bours, Michael Brammer, Daniel Brandeis, Claudia Brogna, Yvette de Bruijn, Jan K. Buitelaar, Bhismadev Chakrabarti, Tony Charman, Ineke Cornelissen, Daisy Crawley, Flavio Del l'Acqua, Guillaume Dumas, Sarah Durston, Christine Ecker, Jessica Faulk- ner, Vincent Frouin, Pilar Garcés, David Goyard, Lindsay Ham, Hannah Hayward, Joerg Hipp, Rosemary Holt, Mark H. Johnson, Emily J.H. Jones, Prantik Kundu, Meng-Chuan Lai, Xavier Liogier D'ardhuy, Michael V. Lom- bardo, Eva Loth, David J. Lythgoe, René Mandl, Andre Marquand, Luke Mason, Maarten Mennes, Andreas Meyer-Lindenberg, Carolin Moessnang, Nico Bast, Declan G.M. Murphy, Bethany Oakley, Laurence O'Dwyer, Marianne Oldehinkel, Bob Oranje, Gahan Pandina, Antonio M. Persico, Barbara Ruggeri, Amber Ruigrok, Jessica Sabet, Roberto Sacco, Antonia San José Cáceres, Emily Simonoff, Will Spooren, Julian Tillmann, Roberto Toro, Heike Tost, Jack Waldman, Steve C.R. Williams, Caroline Wooldridge, and Marcel P. Zwiers. The primary contact for the EU-AIMS LEAP Group is Declan Murphy (Email: pa-dmurphy@kcl.ac.uk).

CMF receives research funding from the German Research Foundation, German Ministry for Science and Education, and European Commission. She receives royalties for books on autism and attention-deficit/ hyperactivity disorder and has been consultant to Desitin Arzneimittel GmbH and F. Hoffman-La Roche Ltd. in 2017. JKB has been in the past 3 years a consultant to/member of advisory board of/speaker for Janssen- Cilag BV, Takeda/Shire, Roche, Novartis, MEDICE Arzneimittel Pütter GmbH & Co. KG, Angelini Pharma, and Servier. He is not an employee of any of these companies and not a stock shareholder of any of these companies.

He has no other financial or material support, including expert testimony, patents, and royalties. TC has received research grant support from the Medical Research Council (United Kingdom), National Institute for Health Research, UKRI Innovate UK, Horizon Europe, Horizon 2020, Innovative Health Initiative (European Commission), James S. McDowell Foundation, Autistica, Epilepsy Research UK, and Baily Thomas Charitable Fund. He has served as a consultant to F. Hoffmann-La Roche Ltd. and received royalties from Sage Publishing and Guilford Press. TBa has served in an advisory or consultancy role for Eye Level, InfectoPharm Arzneimittel und Consilium GmbH, MEDICE Arzneimittel Pütter GmbH & Co. KG, Neurim Pharmaceu- ticals, Oberberg GmbH, and Takeda. He received conference support or speaker's fee from Janssen-Cilag, MEDICE Arzneimittel Pütter GmbH & Co. KG, and Takeda. He received royalties from Hogrefe Publishing Group, W. Kohlhammer Verlag, CIP-Medien, and Oxford University Press. DM has received consultancy fees from Roche and Servier and grant support from the Medical Research Council (United Kingdom), National Institute for Health Research, Horizon 2020, and Innovative Medicines Initiative (European Commission). All other authors report no biomedical financial interests or potential conflicts of interest.

ARTICLE INFORMATION

From the Department of Child and Adolescent Psychiatry, University Hospital, Goethe University Frankfurt, Frankfurt am Main, Germany (HS, CG, JL, LMB, BH, CMF, CE); Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany (HS, CG, JL, LMB, BH, CE); Department of Biosciences, Goethe University Frankfurt, Frankfurt am Main, Germany (JL, LMB); Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom (CMP, EL, BO, LM, DM, CE); Fries Lab, Ernst Strüngmann Institut for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany (TS); Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands (JKB, CFB, DLF); Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland (DLF); Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom (TC); Child and Adolescent Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany (TBa); German Center for Mental Health, partner site Mannheim-Heidelberg-Ulm, Mannheim, Germany (TBa); Centre for Brain and Cognitive Development, Birkbeck, University of London, London, United Kingdom (EJ); and Institut Pasteur, Human Genetics and Cognitive Functions Unit, Paris, France (TBo).

Address correspondence to Hanna Seelemeyer, M.Sc., at seelemeyer@med.uni-frankfurt.de.

Received Oct 25, 2024; revised Nov 14, 2024; accepted Dec 7, 2024.

Supplementary material cited in this article is available online at https://
doi.org/10.1016/j.bpsc.2024.12.003.

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