Parsing Autism Heterogeneity: Transcriptomic Subgrouping of Imaging-Derived Phenotypes in Autism

Johanna Leyhausen, Caroline Gurr, Lisa M. Berg, Hanna Seelemeyer, Bassem Hermila, Tim Schäfer, Andreas G. Chiocchetti, Charlotte M. Pretzsch, Eva Loth, Bethany Oakley, Jan K. Buitelaar, Christian F. Beckmann, Tony Charman, Thomas Bourgeron, Eli Barthome, Tobias Banaschewski, Emily JH. Jones, on behalf of The EU-AIMS LEAP Group, Declan Murphy, Christine Ecker

PII: S2451-9022(25)00204-6

DOI: https://doi.org/10.1016/j.bpsc.2025.07.001

Reference: BPSC 1455

To appear in: *Biological Psychiatry: Cognitive Neuroscience and Neuroimaging*

Received Date: 2 June 2025

Revised Date: 6 June 2025

Accepted Date: 3 July 2025

Please cite this article as: Leyhausen J., Gurr C., Berg L.M., Seelemeyer H., Hermila B., Schäfer T., Chiocchetti A.G., Pretzsch C.M., Loth E., Oakley B., Buitelaar J.K., Beckmann C.F., Charman T., Bourgeron T., Barthome E., Banaschewski T., Jones E.J., on behalf of The EU-AIMS LEAP Group, Murphy D. & Ecker C., Parsing Autism Heterogeneity: Transcriptomic Subgrouping of Imaging-Derived Phenotypes in Autism, *Biological Psychiatry: Cognitive Neuroscience and Neuroimaging* (2025), doi: https://doi.org/10.1016/j.bpsc.2025.07.001.

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1 **Title**

2 Parsing Autism Heterogeneity: Transcriptomic Subgrouping of Imaging-Derived Phenotypes in Autism

3

4 **Short title**

5 Subgrouping Imaging-Derived Phenotypes in Autism

6

7 **Authors**

8 Johanna Leyhausen<sup>a,b,c</sup>, Caroline Gurr<sup>a,b</sup>, Lisa M. Berg<sup>a,b,c</sup>, Hanna Seelemeyer<sup>a,b</sup>, Bassem Hermila<sup>a,b</sup>, Tim

9 Schäfer<sup>a,b</sup>, Andreas G. Chiocchetti<sup>a</sup>, Charlotte M. Pretzsch<sup>d</sup>, Eva Loth<sup>d</sup>, Bethany Oakley<sup>d</sup>, Jan K.

10 Buitelaar<sup>e</sup>, Christian F. Beckmann<sup>e</sup>, Tony Charman<sup>g</sup>, Thomas Bourgeron<sup>h</sup>, Eli Barthome<sup>h</sup>, Tobias

11 Banaschewski<sup>i</sup>, Emily JH Jones<sup>j</sup>, on behalf of The EU-AIMS LEAP Group, Declan Murphy<sup>d</sup>, Christine

12 Ecker<sup>a,b,d</sup>

13

14 **Affiliations**

15 a Department of Child and Adolescent Psychiatry, University Hospital, Goethe University, 60528 Frankfurt am Main, Germany.

17 b Brain Imaging Center, Goethe-University, 60528 Frankfurt am Main, Germany.

18 c Department of Biosciences, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany.

19 d Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London SE5 8AF, UK.

21 e Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Center, 6525 EN Nijmegen, The Netherlands.

23 f Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK.

25 h Institut Pasteur, Human Genetics and Cognitive Functions Unit, Paris Cedex 15, France.

26 i Child and Adolescent Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, 68159 Mannheim, Germany.

28 j Centre for Brain and Cognitive Development, Birkbeck, University of London, London WC1E 7JL, UK.

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31 **Corresponding author:** Johanna Leyhausen; Department of Child and Adolescent Psychiatry, University

32 Hospital, Goethe University, Deutschordenstrasse 50, 60528 Frankfurt am Main; Tel: +49 (0)69 6301

33 85832; E-Mail: [Leyhausen@med.uni-frankfurt.de](mailto:Leyhausen@med.uni-frankfurt.de)

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35 **Key words**

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37 Autism spectrum disorder, structural MRI, neuroimaging, Imaging-Transcriptomics, Stratification,

38 brain structure
39 Abstract

40 Background

41 Neurodevelopmental conditions, such as autism, are highly heterogeneous both at the
42 mechanistic and phenotypic level. Parsing heterogeneity is therefore vital for uncovering
43 underlying processes that could inform the development of targeted, personalized support.
44 The study aimed to parse heterogeneity in autism by identifying subgroups that converge at
45 both phenotypic and molecular levels.

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47 Methods

48 An imaging-transcriptomics approach was used to link neuroanatomical imaging-derived
49 phenotypes in autism to whole-brain gene expression signatures provided by the Allen Human
50 Brain Atlas. Neuroimaging and clinical data of N=359 autistic participants aged 6-30 years were
51 provided by the EU-AIMS Longitudinal European Autism Project. Individuals were stratified
52 using data-driven clustering techniques based on the correlation between brain phenotypes
53 and transcriptomic profiles. The resulting subgroups were characterized on the clinical,
54 neuroanatomical, and molecular level.

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56 Results

57 We identified three subgroups of autistic individuals based on the correlation between
58 imaging-derived phenotypes and transcriptomic profiles which showed different clinical
59 phenotypes. The individuals with the strongest transcriptomic associations to imaging-derived
60 phenotypes showed the lowest level of symptom severity. The genesets most characteristic
61 for each subgroup were significantly enriched for genes previously implicated in autism
62 etiology, including processes like synaptic transmission and neuronal communication, and
63 mapped onto different gene ontology categories.

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65 Conclusion
Autistic individuals can be sub-grouped based on the transcriptomic signatures associated with their neuroanatomical fingerprints, revealing subgroups that show differences in clinical measures. The study presents an analytical framework for linking neurodevelopmental and clinical diversity in autism to underlying molecular mechanisms, thus highlighting the need for personalized support strategies.

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72 **Introduction**

73 There is increasing recognition that most neurodevelopmental conditions are highly
74 heterogeneous both at the mechanistic and phenotypic level (1). Understanding heterogeneity
75 is therefore vital for uncovering underlying mechanisms that could pave the way for targeted,
76 personalized support (2,3). This applies to autism, a neurodevelopmental condition
77 characterized by (i) differences in social communication and interaction, (ii) the presence of
78 repetitive and stereotyped behaviors, and (iii) altered sensory processing (4). These traits
79 typically emerge during early childhood and are accompanied by differences in brain anatomy
80 (5). While the neuroanatomy of autism is highly diverse, imaging-transcriptomics studies,
81 linking brain imaging data to gene expression patterns, suggest that imaging-derived
82 phenotypes (IDPs) in autism may be linked to molecular pathways implicated in autism etiology
83 (6,7). This approach enables subgrouping of autistic individuals both on the neuroanatomical
84 and transcriptomic level, and to provide biologically-informed subgroups that may be linked to
85 underlying mechanisms and therefore inform future approaches for targeted support.

86 Previous studies have explored the link between in vivo neuroanatomy and cortical gene
87 expression signatures in autism. Romero-Garcia et al. (8) reported that cortical thickness (CT)
88 differences in autistic children are linked to genes involved in synaptic transmission pathways
89 (9,10). These findings were replicated and extended in an independent cohort of children,
90 adolescents, and adults (11). The authors demonstrated that cortical patterns reflecting
91 differences in CT are transcriptomically enriched for genes known to be involved in autism
92 etiology which map onto synaptic transmission and cell adhesion pathways (11). Different gene
93 enrichment patterns were observed across clinical autistic subgroups defined by their sensory
94 profile (12). This suggests that specific autism phenotypes may have a distinctive
95 neuroanatomical signature or 'fingerprint', which may relate to different molecular
96 mechanisms.

97 So far, however, gene expression decoding, i.e., the analysis of spatial correlations between
98 imaging and gene expression signatures, was mostly restricted to IDPs representing group-
99 level effects. Due to the high variability within the autistic population, group-level analyses
100 might overlook individual differences (13). More recent approaches have therefore been
101 developed to shift the analysis of statistical effects from the group level to the individual level.
102 This includes normative modelling approaches, where each individual is positioned relative to
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the neurotypical range of phenotypic diversity in brain structure (14,15). These studies show that autistic individuals may be sub-grouped based on patterns of neuroanatomical deviations, with each subgroup showing different clinical profiles (16,17). The examination of these neuroanatomical 'fingerprints', rather than group differences, may therefore hold the key to subgrouping autism.

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In this study, we employed an imaging-transcriptomics approach to subgroup autistic individuals based on the transcriptomic signature associated with their IDPs, characterized by CT. This analytical framework facilitates linking neuroanatomical and clinical diversity in autism to underlying mechanisms. We aimed to identify subgroups that differ in their neurobiology and symptomatology and might be linked to putative underlying mechanisms. Therefore, we examined a large and clinically diverse cohort of autistic individuals and controls recruited within the EU-AIMS Longitudinal European Autism Project (LEAP 18; www.aims-2-trials.eu). This study offers comprehensive phenotypic assessments of over 700 individuals, including both males and females aged 6-30 years, with varying degrees of autistic traits (19). Therefore, the LEAP cohort is particularly well-suited for subgrouping purposes. To link IDPs with gene expression signatures, a spatially-dense (i.e., vertex-level) representation of the Allen Human Brain Atlas (AHBA) was generated characterizing each gene's expression signature across the cortical surface. This allowed us to assess the spatial correlation between each brain phenotype and the cortical expression signatures. Subsequently, we stratified autistic individuals based on similarities in their IDP-associated transcriptomic profiles and compared the resulting subgroups regarding clinical characteristics. Last, we tested for an enrichment of the genesets most strongly associated with the subgroups. This allowed us to (i) link neuroanatomical variability in autism to specific transcriptomic correlates, and (ii) determine the extent to which these correlates converge within and across subgroups.

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130 Methods and Materials

131 Participants

132 This study used data provided by the multicentered EU-AIMS Longitudinal European Autism
133 Project. A comprehensive description of the sample has been published elsewhere (18,19). In
134 brief, a total of N=359 (N=258 male, N=101 female) autistic individuals aged 6-30 years with
135 structural MRI data were included (Table1). A detailed description of inclusion/exclusion
136 criteria, clinical assessments, and medication status has been provided elsewhere (Supplement
137 and 11). Independent ethics committees approved the study, and written informed consent
138 was obtained for all participants.

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140 MRI data acquisition

141 All participants underwent MR-imaging at 3-T-scanners located at six sites: University of
142 Cambridge, U.K., King's College London, U.K., Central Institute of Mental Health, Mannheim,
143 Germany, Radboud University Medical Centre, Netherlands, University Medical Centre
144 Utrecht, Netherlands, and Rome University, Italy. High-resolution structural T1-weighted
145 volumetric images were acquired with full head coverage at 1.2mm thickness with 1.2x1.2mm²
146 in-plane resolution (see supplement).

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148 Cortical surface reconstruction using FreeSurfer

149 FreeSurfer version6.0.0 was used to obtain cortical surface representations for each T1-
150 weighted image of N=708 autistic and non-autistic individuals within the LEAP sample. These
151 fully automated processes have been described in detail elsewhere (20,21). All surface
152 reconstructions underwent quality assessments as outlined in Ecker *et al.* (11). In brief, surface
153 reconstructions were rated by three independent raters leading to the inclusion of N=638
154 individuals (N=359 autistic, N=279 non-autistic) (SuppTable2). We examined measures of
155 cortical thickness (CT), defined as the shortest distance from the outer to the inner boundary
156 at each vertex (22), smoothed using a 15mm kernel to enhance the signal-to-noise ratio and
157 align with previous research (11,23,24). For each participant, we also computed the average
158 CT across the cortex (CT₀).

159 To make individuals comparable, IDPs were standardized within the neurotypical range of
160 control subjects by means of a General Linear Model (GLM) that included both linear and
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quadratic age, sex, full-scale IQ (FSIQ), acquisition site, and $CT_0$ as predictors ($X$). The model coefficients ($\beta_{TD}$) were subsequently used to predict $CT$ across the cortex for all individuals in our cohort ($\hat{Y}=X\beta_{TD}$). The resulting residuals ($res=\hat{Y}-Y$) were centered and scaled based on the neurotypical $CT$ distribution, thus expressing all data in unit standard deviations of the predicted neurotypical mean ($z_{res}$). Thus, instead of using absolute $CT$ metrics, all datasets were normalized to unit standard deviations relative to the canonical trajectory (Figure1-Step1).

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Transcriptomic decoding of imaging-derived phenotypes

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To link IDPs to gene expression patterns, we initially generated a spatially-dense (i.e., vertex-level) representation of the AHBA human brain transcriptome as described by Gryglewski et al. (25). The AHBA data was quality assessed using the *abagen* toolbox (26), and mapped onto the FreeSurfer *fsaverage6* template. mRNA expression values for vertices without AHBA representation were predicted via spatial interpolation using Gaussian Process Regression (i.e., ordinary Kriging). To account for the complex pattern of cortical folding, spatial interpolation was performed based on existing AHBA samples located within a geodesic neighborhood of 40 millimeters (see Supplement section 3 for details). This resulted in spatially smooth gene expression maps for>15,600 genes measured in cortical brain tissue. Subsequently, we assessed the spatial correlations between each of the 359 standardized IDPs and the expression profiles of 15,633 genes (Figure1-Step2).

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The statistical assessment of spatial correlations was conducted within the framework of spatial-autocorrelation ($\alpha$) preserving null modeling (27), using the variogram matching approach developed by Burt et al. (28). Given the high computational demands of this approach, we employed Principal Component analysis to decompose the matrix of normalized gene expression maps into a set of nine co-expression gradients with Eigenvalues>1, that captured 41% of total variability in gene expression across the cortex. For each gradient, a total of 1,000 $\alpha$-preserving null models were then precomputed to characterize the empirical distribution of spatial correlations under the null hypothesis, following *knn*-parameter optimization (28). This allowed us to derive a non-parametric $\alpha$-corrected *p*-value estimate for each gene based on the null distribution of spatial correlations with its respective gradient pattern. Gene-level *p*-values were subsequently adjusted for multiple comparisons using the
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empirical cumulative density function of the extreme value distribution of spatial correlations
across gradient nulls (Ecker et al., 2025) (see Supplement section 4 for details) (Figure1-Step3).

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## Clustering of IDPs based on transcriptomic profiles

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The matrix of absolute spatial correlations between IDPs and cortical gene expression patterns (359 IDPs x 15,633 genes) served as input to a k-means clustering algorithm to identify subgroups of participants (and genes) with a similar IDP-related transcriptomic profile. Here, the optimal number of clusters was identified using the R-package 'NbClust' (version 3.0.1) using the 'complete' aggregation method, which evaluates clustering solutions for different numbers of clusters across multiple validity indices (Supplement and 29). The stability of the clusters was assessed using the 'clusterboot' function implemented in the R-package 'fpc' (version 2.2.9 30), which performs bootstrap resampling of the data and evaluates the stability of each cluster by calculating Jaccard coefficients representing the similarity between the original clustering and the bootstrapped clustering. A Jaccard similarity value of > 0.75 generally indicating a stable clustering solution (31) (Figure1-Step4).

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## Subgroup differences in clinical measures

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For further characterization of the subgroups with regards to the clinical measures, we used ANCOVAs and post-hoc t-tests to compare clinical measures between subgroups. We covaried for age, IQ, site, and biological sex to assess subgroup differences independent from demographic factors. All subgroup differences between clinical measures were corrected for multiple comparisons using Bonferroni correction (32). Autistic traits were assessed using Autism Diagnostic Observation Schedule (ADOS, 33), Autism Diagnostic Interview-Revised (ADI-R, 34), Repetitive Behavior Scale-Revised (RBS-R, 35), Social Responsiveness Scale (SRS-2, 36), and the Short Sensory Profile (SSP, 37). Co-occurring Attention Deficit Hyperactivity Disorder (ADHD) was assessed using the parent- and self-reported DSM-5 ADHD rating scale (4). The Beck's Anxiety Inventory (BAI, 38), adult and youth version self-reported, was used to assess levels of anxiety. The Beck's Depression Inventory (BDI-II) adult and youth version self-reported was used (39,40) to assess the severity of depressive symptoms (Figure1-Step5). For all clinical scales, missing data were imputed wherever possible to achieve a maximum sample size (see 41 for details).
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223 Surface-Based statistical analyses of CT

224 The vertex-level statistical analyses were conducted using the SurfStat toolbox for MATLAB (42) and R (43). Vertex-wise between-subgroup differences in CT (Y) were examined with a general linear model (GLM) incorporating subgroup, sex, and acquisition site as fixed effect factors, and age, quadratic age, FSIQ, and CT₀ as coresntinuous covariates, while εᵢ is the residual error at vertex i.

229 $Y_i = \beta_0 + \beta_1 \text{Subgroup} + \beta_2 \text{Sex} + \beta_3 \text{Age} + \beta_4 \text{Age}^2 + \beta_5 \text{FSIQ} + \beta_6 \text{Site} + \beta_7 \text{CT} + \varepsilon_i$

230 Between-subgroup differences were estimated from the coefficient β₁, normalized by the standard error. All continuous covariates were mean-centered. Corrections for multiple comparisons were performed using random-field-theory-based cluster analysis for non-isotropic images with a cluster-based significance threshold (p<clust) of 0.05 (two-tailed, 44).

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235 Molecular characterization of subgroups using gene set enrichment analysis

236 To link subgroups to mechanisms, geneset enrichment analyses were performed to annotate each subgroup based on the genesets most representative of each subgroup. Therefore, we used the genesets with the highest average correlations obtained through clustering. These subsets contained the most significant genes based on the absolute correlations, thresholded at p<adj<0.01. Initially, we tested these genesets for enrichment of genes known to be involved in the etiology of autism including genes with rare and *de novo* variants (45), differentially expressed genes (46), and co-expression modules mediating typical brain development in autism (47). Additionally, we tested for an enrichment of GO terms. For details about the gene set enrichment analyses see Supplementary section 7.

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249 **Results**

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251 **Subgrouping of IDPs based on similarities in transcriptomic signatures**

252 Gene expression decoding of standardized IDPs resulted in a subject-by-gene (359x15633) matrix of spatial correlations (Figure2A) representing the absolute strength of the transcriptomic associations between IDPs and cortically-expressed genes and was subsequently utilized for data-driven, unsupervised k-means clustering. We discerned an optimal bifurcated clustering solution with a mean bootstrapped Jaccard similarity index of 0.95 and 0.98, subdividing participants into two subgroups: one group of individuals (Subgroup 1) with an average moderate degree of transcriptomic associations (N=101,72 males, 27 females, mean absolute r<sub>spatial</sub>=0.13), and one group (Subgroup 2) with a low average degree of transcriptomic correlations (N=258 individuals, 184 males, 74 females, r&#773;<sub>spatial</sub>=0.07) (Figure2A). Clustering across genes revealed four *Genesets* (Genesets 1 to 4), with Jaccard indices of 0.96, 0.95, 0.93, 0.95, respectively.

263 To further investigate the putative underlying mechanisms in individuals with a moderate degree of transcriptomic correlations, a second-level clustering was performed across Subgroup 1, delineating two subgroups, Subgroup 1A (N=75,50 males, 25 females) and Subgroup 1B (N=26,24 males, 2 females), with a mean bootstrapped Jaccard similarity index of 0.96 and 0.99, respectively. Each of the three subgroups was linked with genesets, i.e., Subgroup 1A showed low to moderate transcriptomic associations with Geneset 1 (r&#773;<sub>spatial</sub>=0.16) (Figure2A). Individuals in Subgroup 1B exhibited the highest spatial correlations with genes in Geneset 1 (r&#773;<sub>spatial</sub>=0.26) and Geneset 4 (r&#773;<sub>spatial</sub>=0.18) (Figure2A, Figure4A). In contrast, individuals in Subgroup 2 exhibited low correlations across all four genesets (Figure2A). There were no subgroup differences in terms of age (F=0.06, p=0.94), FSIQ (F=0.67, p=0.51), and mean CT (F=1.88, p=0.16). However, the male-to-female ratio was significantly lower in Subgroup 1B (X<sup>2</sup>=6.41, p=0.04) (Table 1). We therefore covaried for these measures (age, FSIQ, sex, site and mean CT) in all subsequent analyses. Overall, we found three participant subgroups that are differentially associated with the four genesets.

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278 **Subgroup differences in clinical phenotypes**
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Next, we examined differences in clinical measures between subgroups. There was a significant main effect of subgroup in the communication domain of the ADI-R (F=3.9, p=0.03), and in the RBS-R (F=3.14, p=0.04) (Figure3A). Individuals in *Subgroup 1B* had lower symptom scores compared to individuals in *Subgroups 1A* or 2. There were significant subgroup differences in sensory symptoms, particularly in the ‘taste’ subscale of the SSP (F=4.22, p=0.02) (Figure3B).

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Thus, individuals in *Subgroup 1B*, exhibiting the strongest transcriptomic associations, showed significantly lower symptom scores compared to other subgroups confirmed by post-hoc t-tests (Figure3F). There were, however, no significant differences in co-occurring symptoms and other autistic traits (SuppTable4).

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## Neuroanatomical differences between subgroups

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Subgroups also differed significantly in CT. There was a significant main effect of subgroup in the left temporal pole (approximately Brodman area [BA] 36/38) (SuppFigure4). Furthermore, individuals in *Subgroup 1A* had significantly increased CT in the left isthmus-cingulate cortex and the left posterior-cingulate cortex (BA 23/24) compared to individuals in *Subgroup 1B* and 2. Additionally, individuals in *Subgroup 1A* showed significantly increased CT in the right middle frontal gyrus (BA 9/44/45) and decreased CT in the left posterior-cingulate cortex (BA 23/31), the left temporal pole (BA 36/38), and the right isthmus-cingulate cortex (BA 24) compared to *Subgroup 2* (Figure3F-H, and SuppTable3).

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## Molecular characterization of subgroups

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We tested the genesets with the highest absolute transcriptomic associations with the subgroups using geneset enrichment analysis. The three resulting subsets consisted of significant genes only ($p_{adj}<0.01$). *Gene Subset A* contained N=1,221 genes significantly associated with *Subgroup 1A*. *Gene Subsets B* and *C* were associated with *Subgroup 1B* and consisted of N=1,340 and N=65 genes, respectively (Figure4A). Out of these subsets, only *Subsets A* and *B* were significantly enriched for autism candidate genes and differentially expressed genes (DEGs) in autism (Figure4B). *Gene Subset A* was significantly enriched for up-and downregulated genes in autism, and that have previously been associated with GO-terms representing synaptic functioning and neuronal signals (9,46). *Gene Subset B* was enriched for DEGs linked to synaptic functioning and transmembrane activity, and for SFARI autism genes (46) (Figure4B).

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Next, we tested the enrichment of genes in co-expression modules mediating typical brain development (47). Notably, all gene subsets showed a significant enrichment for co-expression module M2, associated with 'synaptic transmission', which exhibits peak expression during early childhood and adolescence (Figure4C). The same applies to synaptic transmission module M15, which was significantly enriched in Gene Subset A and B. We observed a significant enrichment of module M20, which is linked to zing-finger (ZNF) proteins and transcription factors (TFs) (47). Unlike synaptic modules, co-expression modules representing TFs exhibit peak expression during prenatal brain development and the lowest expression levels during childhood (Figure4D). Last, in Gene Subset B linked to Subgroup 1B, we observed an enrichment of modules M8 ('Neuronal development TFs') and M16 ('Cell adhesion signaling'), with a peak expression in prenatal stage and in childhood/adolescence. Overall, IDPs in autism converge onto the co-expression signatures of specific transcriptional programs, that impact on brain development at different developmental stages.

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Last, we annotated *Subgroup 1A* and *Subgroup 1B* by testing for an enrichment of general GO-terms. Therefore, we extracted the genes from *Gene Subset A* that are exclusively associated with *Subgroup 1A* (*Gene Subset 1A<sub>only</sub>*) and *Subset B* genes that are only associated with *Subgroup 1B* (*Gene Subset 1B<sub>only</sub>*). This resulted in *N*=657 genes for *Gene Subset 1A<sub>only</sub>* and *N*=776 genes for *Gene Subset 1B<sub>only</sub>*. Both subgroups differed in terms of enrichment profiles with *Subset 1A<sub>only</sub>* showing a stronger enrichment of biological processes, and *Gene Subset 1B<sub>only</sub>* showing a stronger enrichment of cellular component terms (Figure4E&F, SuppTable7).

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333 **Discussion**

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In this study, we used an imaging-transcriptomics approach to dissect autism heterogeneity based on the transcriptomic signatures associated with individual IDPs. Additionally, the derived subgroups were characterized both in terms of clinical, neuroanatomical, and molecular measures. We established that (i) subgroups differed on the clinical, neuroanatomical and molecular level, and that (ii) subgroups converged onto different putative molecular mechanisms. These findings highlight mechanistic and phenotypic differences between subgroups of autistic individuals providing a framework to further elucidate the variability within autism.

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Within this framework, we initially transcriptomically decoded the structural IDPs from N=359 autistic individuals, using spatially-dense representations of the AHBA transcriptome. The individual's neuro-phenotypes were characterized based on measures of CT, a morphometric feature known to be influenced by both genetic variation, and differences in gene expression (48,49). Differences in CT in autism are well documented and have been linked to its molecular etiology (8,11). Our study indicates that the transcriptomic profiles associated with IDPs in autism vary across individuals. This finding is in line with previous reports suggesting that each person's neuroanatomy is marked by individualized patterns of neuroanatomical deviations from the typical trajectory of brain development, i.e., a neuroanatomical 'fingerprint' (16). Thus, rather than analyzing absolute measures of CT, we standardized IDPs within the neurotypical range prior to decoding using an age- and sex-matched sample of controls acquired at the same acquisition sites. This standardization of IDPs is an important step, as it makes it possible to perform comparative analyses across demographically diverse phenotypes and focus on interindividual differences rather than on group mean differences. This approach allows to account for interindividual variability due to demographic and technical confounds. It thus places all data within the broader context of neurodiversity (50), and aligns with previous approaches (14,15,51) emphasizing the conceptual relevance for elucidating neurodiversity in autism. This approach is particularly well suited for subgrouping purposes.

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Using unsupervised clustering approaches, we subdivided individuals based on similarities in their IDP-related transcriptomic profiles. We discerned three subgroups that were differentially associated with four different genesets. The strongest transcriptomic
associations were observed in individuals within *Subgroup 1B*. Most individuals exhibited small spatial associations between IDPs and gene expression signatures, as characteristic for *Subgroup 2*. Autistic individuals therefore differ in both the magnitude and nature of the transcriptomic associations, further substantiating the growing evidence of significant heterogeneity in autism on the phenotypic and mechanistic level (13,52). These differences cannot be accounted for by variations in participant demographics such as age and IQ. However, there was a significant difference in the male-to-female ratio, with a higher proportion of males in *Subgroup 1B*. Therefore, we controlled for biological sex to reduce potential demographic confounding and observed that participants in *subgroup 1B* exhibited lower levels of symptom severity in social communication, repetitive behaviors, and sensory symptoms but not in co-occurring symptoms (e.g., anxiety, depression, ADHD). Previous research suggests sex differences to be a factor to the heterogeneity in autism (53). Thus, our findings need to be interpreted within this context (54) and future research on the interaction between sex, transcriptomic profiles and clinical measures is needed. Our findings suggest that autistic individuals with a higher association with gene expression patterns exhibit less severe symptoms overall. This might be due to the fact that typical brain development is governed by various transcriptional programs - temporally and spatially coordinated patterns of gene expression guiding the formation of the nervous system, i.e., processes such as neurogenesis, axon guidance, and synapse formation (47). Changes in these programs are therefore likely to impact the formation of the brain's neurocircuitry and have been implicated in the etiology of autism through genetic and transcriptomic investigations (46,55).

Co-expression networks were also implicated by our study. We find that the IDPs of individuals in *Subgroup 1* (i.e., the subgroup with the highest transcriptomic associations) were significantly associated with the gene expression signatures of genes previously reported to be up- and down-regulated in the autism cortex (9,46). These included module CTX.M20, which contains genes implicated in the development and regulation of cell differentiation (9). Moreover, we found an enrichment of the gene co-expression modules that mediate typical brain development (47). This includes modules M2 and M15, which map onto synaptic transmission pathways, as well as modules M8 and M20, which are enriched for GO categories related to transcription factors (9). Notably, M2 and M20 follow opposite developmental trajectories across brain regions, with M20 being most active during prenatal brain
development, and M2 being most expressed during childhood. Taken together our results suggest that different co-expression networks influence brain maturation across developmental states in a region-specific manner, and that autistic individual with neuroanatomical characteristics, spatially more closely aligned with these large-scale transcriptomic gradients, may exhibit greater resilience.

Our study's strengths include the combination of imaging and transcriptomics data, the large, heterogenous, and deep-phenotyped dataset, and our analytical framework for parsing autism heterogeneity. However, there are limitations to this study. So far, we have not examined genomic variation as yet that could underpin differential gene expression. It thus remains to be established how genetic variation influences gene expression in autism. We utilized the AHBA (56) data, which is the most comprehensive gene expression atlas to date. Nevertheless, the AHBA is based on adult donors while we examined IDPs of children, adolescents, and adults. We therefore acknowledge the importance of repeating our analyses in age-matched samples, to corroborate the relationship between genetic variation, gene expression, and neurodiversity in autism.

Taken together, we identified subgroups within a sample of autistic participants based on the link between brain phenotypes and gene expression profiles. These subgroups showed differences in clinical measures and neuroanatomy. They also varied in their underlying genetic profiles highlighting the biological heterogeneity in autism and therefore the need to develop more targeted support strategies.

415
416 Acknowledgments

417 We gratefully acknowledge the contributions of the EU-AIMS LEAP Group: Jumana Ahmad, Sara
418 Ambrosino, Bonnie Auyeung, Tobias Banaschewski, Simon Baron-Cohen, Sarah Baumeister, Christian F.
419 Beckmann, Sven Bölte, Thomas Bourgeron, Carsten Bours, Michael Brammer, Daniel Brandeis, Claudia
420 Brogna, Yvette de Bruijn, Jan K. Buitelaar, Bhismadev Chakrabarti, Tony Charman, Ineke Cornelissen,
421 Daisy Crawley, Flavio Dell'Acqua, Guillaume Dumas, Sarah Durston, Christine Ecker, Jessica Faulkner,
422 Vincent Frouin, Pilar Garcés, David Goyard, Lindsay Ham, Hannah Hayward, Joerg Hipp, Rosemary Holt,
423 Mark H. Johnson, Emily J.H. Jones, Prantik Kundu, Meng-Chuan Lai, Xavier Liogier D'ardhuy, Michael V.
424 Lombardo, Eva Loth, David J. Lythgoe, René Mandl, Andre Marquand, Luke Mason, Maarten Mennes,
425 Andreas Meyer-Lindenberg, Carolin Moessnang, Nico Bast, Declan G.M. Murphy, Bethany Oakley,
426 Laurence O'Dwyer, Marianne Oldehinkel, Bob Oranje, Gahan Pandina, Antonio M. Persico, Barbara
427 Ruggeri, Amber Ruigrok, Jessica Sabet, Roberto Sacco, Antonia San José Cáceres, Emily Simonoff, Will
428 Spooren, Julian Tillmann, Roberto Toro, Heike Tost, Jack Waldman, Steve C.R. Williams, Caroline
429 Wooldridge, and Marcel P. Zwiers. This project has received funding from the Innovative Medicines
430 Initiative 2 Joint Undertaking under grant agreement No 115300 for the project EU-AUIMS and No
431 777394 for the project AIMS-2- TRIALS. This Joint Undertaking receives support from the European
432 Union's Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica,
433 SFARI. The primary contact for the EU-AIMS LEAP Group is Declan G. Murphy (Email: pa-
434 dmurphy@kcl.ac.uk). CE gratefully acknowledges support from the German Research Foundation (DFG)
435 (Project Nr. 551579379), the Rolf M. Schwiete Stiftung (Project Nr. 2024-022), and the DYNAMIC center
436 funded by the LOEWE program of the Hessian Ministry of Science and Arts (Grant Number:
437 LOEWE1/16/519/03/09.001(0009)/98). Moreover, this work was supported by the DFG-funded
438 Transregio Aggression (TRR 379) under Project Nr. 512007073. DGM also acknowledges support from
439 the NIHR Maudsley Biomedical Research Centre. Furthermore, this work has received funding from
440 Horizon Europe [grant agreement no. 101057385] and from UK Research and Innovation (UKRI) under
441 the UK government's Horizon Europe funding guarantee [grant no.10039383] (R2D2-MH).
442

442
443 Disclosure

444 The IHI-JU2 had no role in the design of the study; in the collection, analyses, or interpretation of data;
445 in the writing of the manuscript, or in the decision to publish the results. Any views expressed are those
446 of the author(s) and not necessarily those of the IHI-JU2. Jan K. Buitelaar has been in the past 3 years a
447 consultant to/member of advisory board of/and/or speaker for Janssen Cilag BV, Takeda/Shire, Roche,
448 Novartis, Medice, Angelini, and Servier. He is not an employee of any of these companies and not a
449 stock shareholder of any of these companies. Tobias Banaschewski has served in an advisory or
450 consultancy role for eye level, Infectopharm, Medice, Neurim Pharmaceuticals, Oberberg GmbH and
451 Takeda. He received conference support or speaker's fee by Janssen-Cilag, Medice and Takeda. He
452 received royalties from Hogrefe, Kohlhammer, CIP Medien, and Oxford University Press. The present
453 work is unrelated to the above grants and relationships. Tony Charman has received research grant
454 support from the Medical Research Council (UK), the National Institute for Health Research, Horizon
455 2020 and the Innovative Medicines Initiative (European Commission), MQ, Autistica, FP7 (European
456 Commission), the Charles Hawkins Fund, and the Waterloo Foundation. He has served as a consultant
457 to F. Hoffmann-La Roche Ltd and Servier. He has received royalties from Sage Publications and Guilford
458 Publications. Declan Murphy has received consultancy fees from Roche and Servier, and grant support
459 from the Medical Research Council (UK), the National Institute for Health Research, Horizon 2020 and
460 the Innovative Medicines Initiative (European Commission). Emily Jones has received funding from
461 Action Medical Research, EU Horizon 2020, the Innovative Medicines Initiative, the Medical Research
462 Council (UK), MQ: Transforming Mental Health, and the Economic and Social Research Council. All other
463 authors report no biomedical financial interests or potential conflicts of interest.

464

465

466 Supplement Description:

467 Supplement Methods, Figures S1-S5, Tables S1-S8

468
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625 Tables

626 Table 1. Participant demographics.

<table><thead><tr><th rowspan="2"></th><th colspan="2">Sample total<br>(N = 359)</th><th colspan="2">Subgroup 1A<br>(N = 75)</th><th colspan="2">Subgroup 1B<br>(N = 26)</th><th colspan="2">Subgroup 2<br>(N = 258)</th><th colspan="3">Statistics</th></tr><tr><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>F</th><th>df</th><th>P</th></tr></thead><tbody><tr><td>Age</td><td>17.49</td><td>5.52</td><td>17.69</td><td>5.85</td><td>17.42</td><td>5.71</td><td>17.44</td><td>5.42</td><td>0.06</td><td>2</td><td>0.94</td></tr><tr><td>IQ</td><td>98.92</td><td>19.76</td><td>97.71</td><td>19.66</td><td>102.92</td><td>22.7</td><td>98.87</td><td>19.51</td><td>0.67</td><td>2</td><td>0.51</td></tr><tr><td>mean CT<br>[mm]</td><td>2.28</td><td>0.13</td><td>2.66</td><td>0.12</td><td>2.72</td><td>0.16</td><td>2.68</td><td>0.13</td><td>1.88</td><td>2</td><td>0.16</td></tr><tr><th></th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th><th>X2</th><th>df</th><th>P</th></tr><tr><td>Sex</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>6.41</td><td>2</td><td>0.04</td></tr><tr><td>Male</td><td>258</td><td>71.9</td><td>50</td><td>66.7</td><td>24</td><td>92.3</td><td>184</td><td>71.3</td><td></td><td></td><td></td></tr><tr><td>Female</td><td>101</td><td>28.1</td><td>25</td><td>33.3</td><td>2</td><td>7.7</td><td>74</td><td>28.7</td><td></td><td></td><td></td></tr><tr><td>Site</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>8.25</td><td>10</td><td>0.6</td></tr></tbody></table>

627 Note: CT = cortical thickness, SD = standard deviation

628

629 Figure legends

630

631 **Figure 1.** Schematic overview of the analytical framework. a| Schematic overview over the background of the study. b| Schematic overview over the methods of the study consisting of: Transcriptomic alignment between standardized imaging-derived phenotypes (IDPs) (Step 1) and gene expression data provided by the Allen Human Brain Atlas (AHBA) (Step 2) via spatial correlation (Step 3). Clustering of IDPs based on similarities in transcriptomic associations (Step 4), with subsequent clinical and molecular characterization (Step 5).

637

638 **Figure 2: Subgrouping results.** a| Heatmap of absolute spatial correlations ($r_{spatial}$) between each standardized IDP and the cortical expression signatures of N=15,633 protein-coding genes. Subfields result from a two-step clustering solution (k-means) that initially subdivided participants into a group with medium to high transcriptomic associations (Subgroup 1), and a group with low to medium transcriptomic associations (Subgroup 2). Second-level clustering further subdivided individuals in Subgroup 1 into Subgroup 1A and 1B with differential transcriptomic associations with Geneset 1 and Geneset 4, respectively. b| Average standardized IDPs within subgroups (IDPz). Note. z: normalized deviations from the typical developmental trajectory of cortical thickness.

646
**Figure 3: Clinical characterization of transcriptomic subgroups.** a| Subgroup differences in autism symptoms as measured by the Social Responsiveness Scale -2 (SRS-2), Repetitive Behavior Scale-Revised (RBS-R), Autism Diagnostic Interview-Revised (ADI-R), Autism Diagnostic Observation Schedule (ADOS), and Short Sensory Profile (SSP). Note. For all clinical scales, significant differences are marked by an asterisk (*), based on *p*<0.05. b| Subgroup scores on the Short Sensory Profile (SSP). c| Differences in co-occurring traits measured using the ADHD rating scale, Beck' Anxiety Inventory (BAI), Beck's Anxiety Inventory for Youth (BYI-II), Beck's Depression Inventory (BDI-II), and Beck's Depression Inventory for Youth (BDI-Y). d| Subgroup differences in the Repetitive Behavior Scale -Revised (RBS-R), using a post-hoc t-test. e| Subgroup differences in the communication domain of the ADI-R, using a post-hoc t-test. f| Significant subgroup differences on the 'Taste' item of the SSP, using a post-hoc t-test. g| Subgroup differences in cortical thickness (CT) between Subgroup 1A and Subgroup 1B. h| Subgroup differences in CT between Subgroup 1A and Subgroup 2. i| Subgroup differences in CT between Subgroup 1B and Subgroup 2. For all surface-based statistical patterns, the upper panels show the t-test statistic (unthresholded) associated with each subgroup contrast. The lower panels indicate clusters with significantly increased (orange) and decreased (blue) CT for the respective subgroup comparison, (random-field-theory-based cluster corrected *p*<0.05, two-tailed). L = left hemisphere, R = right hemisphere.

**Figure 4. Geneset enrichment analysis.** a| Average absolute spatial correlations (Fisher's Z transformed) within subgroups, resulting in three Gene Subsets labelled A (yellow), B (green), and C (red). b| Odds ratios at a false discovery rate (FDR) corrected *p*<sub>adj</sub> threshold of 0.01 resulting from the gene set enrichment analyses for genes expressed in the Gene Subsets A, B, and C, respectively. Genesets were subdivided into sets with differential gene expression in autism and sets representing autism likelihood genes. Genesets are annotated and labeled based on their original publication. CTX = cortex; DEG = differentially expressed gene; down = down-regulated expression in autism; up = upregulated expression in autism. c| Enrichment of genes mediating typical brain development as reported in the spatiotemporal transcriptome data set provided by Kang et al. {ref}. Set names contain their respective co-expression module label (e.g., M1), followed by their functional description based on their Gene Ontology term enrichment. d| Spatiotemporal expression profiles of brain gene modules significantly enriched in the gene subsets for module 2 ("synaptic transmission") and module 20 ("ZNFSOX Transcription Factor"). The x-axis shows the developmental time frame (pcw/postconception weeks) and the y-axis shows the different brain regions: OFC=orbital prefrontal cortex; DFC=dorsolateral prefrontal cortex; VFC=ventrolateral prefrontal cortex; MFC=medial prefrontal cortex; M1C=primary motor (M1) cortex; S1C=primary somatosensory (S1) cortex; IPC=posterior inferior parietal cortex;
681 A1C=primary auditory (A1) cortex; STC=superior temporal cortex; ITC=inferior temporal cortex;
682 V1C=primary visual (V1) cortex; HIP=hippocampus; AMY=amygdala; STR=striatum; MD=mediodorsal
683 nucleus of the thalamus; CBC=cerebellar cortex. e|Venn diagram showing the number of shared and
684 distinct genes from Subsets A and B, resulting in Gene Subset 1A<sub>only</sub> (significantly associated with
685 Subgroup 1A exclusively) and Gene Subset 1B<sub>only</sub> (significantly associated with Subgroup 1B exclusively)
686 (left). Middle and right panels indicate the functional enrichment of Gene Ontology (GO) terms for Gene
687 Subset 1A<sub>only</sub> and Gene Subset 1B<sub>only</sub>, respectively. Summary statistics and details for all enriched terms
688 are listed in Supplementary Table 6 and 7. GO:MF= Gene Ontology category 'Molecular Function',
689 GO:CC= Gene Ontology category 'Cell Compartment', GO:BP= Gene Ontology category 'Biological
690 Process'.

691

692
<figure><img src="image_1.png" /></figure>

## B | Methods overview

### Step 1: Standardization of imaging phenotypes

<figure><img src="image_2.png" /></figure>

### Step 2: Preparation of gene expression data from the AHBA

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

### Step 4: Transcriptomic subgrouping of IDPs

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

### Step 5: Characterization of subgroups
A| Unsupervised clustering of IDPs

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

B| Mean subgroup IDPs

<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /></figure>

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

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>
A| Gene Subsets within Groups

<figure><img src="image_24.png" /></figure>

B| Autism gene set enrichment

<figure><img src="image_25.png" /></figure>

C| Co-expression module enrichment (Kang et al. 2011)

<figure><img src="image_26.png" /></figure>

D| Spatiotemporal transcriptome patterns (Kang et al. 2011)

<figure><img src="image_27.png" /></figure>

E| GO term enrichment

<figure><img src="image_28.png" /></figure>