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Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder

Abstract

The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation. Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample. By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms.

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Fig. 1: Three brain–behavior dimensions explain individual differences in autism spectrum disorder.
Fig. 2: Functional connectivity correlates of autism spectrum disorder symptoms.
Fig. 3: Hierarchical clustering on brain–behavior dimension scores reveals four autism spectrum disorder subgroups.
Fig. 4: Autism spectrum disorder subgroups have distinct atypical connectivity patterns in dimension-related RSFC features.
Fig. 5: Transcriptomic correlates of atypical connectivity patterns in autism spectrum disorder subgroups.
Fig. 6: Protein–protein interaction networks reveal distinct connectivity-related genes with textual associations to autism spectrum disorder-related behaviors.

Data availability

The data that support the findings of this study are publicly available. The neuroimaging datasets are available from ABIDE I and ABIDE II (https://fcon_1000.projects.nitrc.org/indi/abide/) and the the NDAR database (https://nda.nih.gov/). Users must register with the NITRC and 1000 Functional Connectomes Project to gain access to ABIDE I and ABIDE II. Users must be affiliated with a National Institutes of Health (NIH)-recognized research institution that maintains active Federalwide Assurance, be registered on NIH’s eRA Commons and complete and submit a Data Use Certification that is reviewed by the Data Access Committee to gain access to NDAR. The gene expression datasets are available from the AHBA (https://human.brain-map.org/static/download) and BrainSpan (https://www.brainspan.org/static/download.html).

Code availability

Code packages used are indicated in the Methods. Custom code for the RCCA is included in the Supplementary Information.

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Acknowledgements

This work was supported by grants from the NIMH (MH118388, MH114976, MH123154, MH118451, MH109685 and MH109685-04S1), the National Institute on Drug Abuse (DA047851), the Hope for Depression Research Foundation, the Pritzker Neuropsychiatric Disorders Research Consortium, the Klingenstein–Simons Foundation Fund, the One Mind Institute, the Rita Allen Foundation, the Dana Foundation, the Foundation for OCD Research, the Hartwell Foundation and the Brain and Behavior Research Foundation (NARSAD).

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Contributions

A.M.B. and C.L. developed the concept for the study. A.M.B., L.G. and C.L. designed the analyses, which were implemented by A.M.B. S.H.K. provided consultation on interpreting the results, and P.E.V. and J.S. advised on the implementation of the transcriptomic and bioinformatic analyses. All authors contributed to writing the manuscript.

Corresponding authors

Correspondence to Logan Grosenick or Conor Liston.

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Competing interests

C.L. is listed as an inventor for Cornell University patent applications on neuroimaging biomarkers for depression that are pending or in preparation. C.L. has served as a scientific advisor or consultant to Compass Pathways, Delix Therapeutics, Magnus Medical and Brainify.AI. The authors declare no other competing interests.

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Nature Neuroscience thanks Jingyu Liu, Lucina Uddin and Aristotle Voineskos for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Connectivity score loadings on RSFC and atypical RSFC in 247 × 247 heatmaps.

Heatmaps of 247 × 247 regions of interest (ROIs) corresponding to panels in Fig. 2 sorted and labeled by functional network. (a) Correlation between verbal IQ-related dimension (dimension 1) and RSFC (FDR < 0.05; see Fig. 2a). (b) Correlation between social affect-related dimension (dimension 2) and RSFC (FDR < 0.05; see Fig. 2b). (c) Correlation between RRB-related dimension (dimension 3) and RSFC (FDR < 0.05; see Fig. 2c). (d) Atypical connectivity in ASD subjects versus controls (Welch’s t-test; FDR < 0.05; see Fig. 2d). Abbreviations described previously in Figs. 12.

Extended Data Fig. 2 Autism spectrum disorder subgroups replicate when using different clustering methods.

(a, b) K-means clustering with cosine distance, (c, d) spectral clustering with cosine distance, and (e, f) hierarchical clustering with Euclidean distance and Ward linkage across 1,000 training set replicates (N = 284). In (g, h) we show the original analysis using hierarchical clustering with cosine distance and average linkage (see Methods for more details). Boxplots show distribution of clinical symptom z-scores (superimposed bar graphs depict the median) for social affect, repetitive, restrictive behaviors and interests (RRB), verbal IQ, and total severity (color indicates subgroup). Plots include 284 subjects x 1,000 training sets to indicate distribution of clinical behaviors across all 1,000 training set cluster assignments. Box bounds: [25th,75th percentile]; center: median; whiskers: 99.3% data in + /–2.7 σ; outliers: circles). Heatmaps show patterns of mean atypical connectivity across replicates in each subgroup across brain regions (rows) and functional networks (columns), and were thresholded for significant atypical connectivity (two-sided Welch’s t-test, mean FDR < 0.05), evaluated relative to N = 907 neurotypical controls. See additional comparisons in Supplementary Fig. 9.

Extended Data Fig. 3 Functional connectivity differences reveal subgroup-specific atypical connectivity.

Subgroups were defined as the modal subgroup assignment over the 1,000 training set replicates, which is used in the main text for Figs. 36. (a-d) Heatmaps show patterns of atypical connectivity in each subgroup across brain regions (rows) and functional networks (columns). Thresholded for significant atypical connectivity (two-sided Welch’s t-test, FDR < 0.05), evaluated in N = 69 ASD subjects in subgroup 1, N = 87 ASD subjects in subgroup 2, N = 67 ASD subjects in subgroup 3, N = 76 ASD subjects in subgroup 4, relative to N = 907 neurotypical controls.

Extended Data Fig. 4 Cross-validation of the clinical symptom and atypical connectivity differences between subgroups.

To cross-validate the clinical symptom and atypical connectivity differences between subgroups in Figs. 34 and Extended Data Fig. 3, we first subsampled 95% of the data in 1,000 replicates. Second, we calculated canonical variates (connectivity score and clinical score for each brain–behavior dimension) in each replicate. Third, in each replicate, we hierarchically clustered on connectivity scores using cosine similarity distance and average linkage and identified four subgroups. Fourth, we used the Hungarian method to match clusters between replicates (numerical assignment of subgroups can change without changing subject composition in cluster). Fifth, we calculated the distribution of clinical symptom z-scores for each subgroup across replicates. Sixth, in each replicate, we calculated atypical connectivity per subgroup versus N = 907 neurotypical controls (two-sided Welch’s t-test). Seventh, we calculated the mean and standard deviation (σ) of atypical connectivity (t) on RSFC over 1,000 subsampled replicates. (a-d) Note similarity to Fig. 3b-e: Subgroups differ with respect to clinical symptoms, similar to subgroup differences identified when subgroups were calculated as modal cluster assignment across 1,000 training sets (mode analysis) shown in Fig. 3b-e. Plots include 284 subjects x 1,000 training sets to indicate distribution of clinical behaviors across all 1,000 training set cluster assignments. Box bounds: [25th,75th percentile]; center: median; whiskers: 99.3% data in + /–2.7 σ; outliers: circles). (e-h) Heatmaps show patterns of mean atypical connectivity across replicates in each subgroup across brain regions (rows) and functional networks (columns), and were thresholded for significant atypical connectivity (two-sided Welch’s t-test, mean FDR < 0.05). (i-l) Heatmaps show patterns of the standard deviation of atypical connectivity across replicates in each subgroup across brain regions (rows) and functional networks (columns).

Extended Data Fig. 5 RCCA and clustering analysis using narrower age range (ages 8–18) yields ASD subgroups with clinical symptoms and atypical connectivity consistent with main analysis.

We repeated all the main analyses (shown in box, i-p) using a smaller age range, including only ASD and neurotypical individuals of ages 8–18 (shown in a-d and i-l). This reduced our ASD sample from N = 299 ages 5–35 to N = 243 ages 8–18 and reduced our neurotypical sample from N = 907 to N = 573. In this secondary analysis, we found similar clinical symptom profiles associated with each subgroup (a-d vs. i-l). Boxplots of the distribution of clinical symptom z-scores (superimposed bar graphs depict the median) for (a,e) social affect, (b,f) repetitive, restrictive behaviors and interests (RRB), (c,g) verbal IQ, and (d,h) total severity (color indicates subgroup). Note that higher social affect, RRB, and total severity scores and lower verbal IQ indicate greater impairment. Box bounds: [25th,75th percentile]; center: median; whiskers: 99.3% data in + /–2.7 σ; outliers: circles). Next, we found similar atypical connectivity associated with each subtype (e-h vs. m-p). (e-h) Atypical connections that were significant (P < 0.05) in the narrower age range, thresholded for significant atypical connectivity (two-sided Welch’s t-test, FDR < 0.05). (m-p) Atypical connections that were significant (P < 0.05) in the full age range, thresholded for connections that were significant in the main analysis (two-sided Welch’s t-test, FDR < 0.05). Heatmaps show patterns of atypical connectivity in each subgroup across brain regions (rows) and functional networks (columns). Thresholded for significant atypical connectivity (two-sided Welch’s t-test, FDR < 0.05), evaluated relative to N = 907 neurotypical controls. For additional results, see Supplementary Figs. 13, 14 and 1720.

Extended Data Fig. 6 RCCA and clustering analysis using the Craddock 200 atlas yields ASD subgroups with clinical symptoms and atypical connectivity consistent when analyzed using the Power atlas.

We reparcellated the brains using the Craddock 200 atlas69, recalculated functional connectivity for each subject, and repeated the full analysis following the original pipeline (feature selection, RCCA, clustering, and PLS). Key findings from the primary analysis using the Power parcellation replicate in this secondary analysis using the Craddock atlas. Here we plot the clinical symptom scores (boxplots as in Extended Data Fig. 5) for each subgroup when (a-d) we used the Craddock 200 parcellation for functional connectivity versus (i-l) the Power parcellation for functional connectivity (main text analysis). Next, we measured atypical connectivity using the Craddock parcellation and mapped it onto the Power atlas for visual comparison between the two parcellations. We plot the atypical connectivity for each subgroup for (e-h) the analysis in the Craddock 200 parcellation thresholded the significant connections from the Power parcellation, and (m-p) the analysis in the Power atlas. Heatmaps show patterns of atypical connectivity in each subgroup across brain regions (rows) and functional networks (columns). Thresholded for significant atypical connectivity (two-sided Welch’s t-test, FDR < 0.05), each evaluated separately relative to N = 907 neurotypical controls. For additional results, see Supplementary Figs. 15, 16.

Extended Data Fig. 7 Out-of-sample replication of ASD subgroup clinical symptoms and atypical connectivity in NDA dataset (NNDA = 85 ASD subjects).

We repeated the main analyses to define ASD subgroups using the NDA dataset (RCCA and clustering). This analysis replicated key results from ABIDE, such that the four NDA subgroups (NNDA_1 = 20, NNDA_2 = 21; NNDA_3 = 27; NNDA_4 = 17) exhibited clinical symptom / behavior profiles and atypical connectivity patterns that were highly similar to those observed in the ABIDE subgroups (NABIDE_1 = 69, NABIDE_2 = 87; NABIDE_3 = 67; NABIDE_4 = 76). In this summary figure, we plot the clinical symptom scores (NDA: a-d, ABIDE: i-l; boxplots as in Extended Data Fig. 5) and atypical connectivity patterns for each subgroup (NDA: e-h, ABIDE: m-p). As expected, statistical power to detect significant atypical connectivity was reduced due to the smaller sample size of NDA. Here, the heatmaps show atypical functional connectivity in NDA and ABIDE subgroups, with the NDA subgroups thresholded by significance from ABIDE for comparison (that is, we set elements in the NDA heatmaps with FDR < 0.05 from a connectivity (two-sided Welch’s t-test in ABIDE heatmaps to 0). However, we confirmed that compared to an empirical null (100 shuffles, see Methods for details), atypical connectivity patterns in the NDA ASD subgroups were more correlated with ABIDE ASD subgroups than expected by chance (P1 = 0.0099, P2 = 0.0297, P3 = 0.0099, P4 = 0.0198). Note that the P values correspond to the probability of obtaining the observed sum of ranks statistic (sum of observed ranks across a range of FDR thresholds, FDR in {1, 0.05, 0.01, 0.005, 0.001, 0.0005, 0.0001, 0.00005}) under the empirical null. For additional results, see Supplementary Fig. 21.

Extended Data Fig. 8 Replication of transcriptomic correlates of subgroup atypical connectivity using BrainSpan gene expression.

We mapped data from the BrainSpan gene expression atlas to the Power atlas, and repeated the PLS and gene set enrichment analyses described in the main text. We found similar results to the original analysis in which we had used the AHBA gene expression dataset, including highly similar transcriptomic correlates of subgroup atypical connectivity. For the PLS analysis, we first calculated gene expression at each brain region (ROI) and atypical connectivity (RSFC) summed over ROIs for each subgroup. Second, we performed PLS regression for each subgroup. Third, we ranked genes by PLS gene weights in each model. The results were highly similar to those observed in the original analysis using the AHBA gene expression atlas. Heatmaps of gene set enrichment for each subgroup’s ranked gene weights for (a vs. b) ASD-related gene sets, (c vs. d) nonpsychiatric disease-related gene sets, (e vs. f) psychiatric disorder-related gene sets, (g vs. h) synaptic signaling gene sets, (i vs. j) immune signaling gene sets, and (k vs. l) protein translation gene sets. All subgroups were enriched for ASD-related gene sets, but not for unrelated diseases. Color indicates strength of negative log transformed FDR for normalized enrichment score multiplied by sign of gene weight (+1 or −1). The P values were calculated and FDR-corrected as in Fig. 5.

Extended Data Fig. 9 Transcriptomic correlates of atypical connectivity patterns associated with ASD-related behaviors.

To further assess relationships between gene expression with atypical connectivity and behavior in larger useable samples (that is, now including subjects with usable fMRI data who were excluded from primary analyses due to incomplete behavioral assessments) we started with the N = 782 subjects with usable scan data, and split the NVIQ = 590 subjects with VIQ into VIQ bins (ASD subjects with [NVIQ>120 = 127] VIQ > = 120, [N85≤VIQ≤120 = 383] VIQ 85–120, or [NVIQ<85 = 80] VIQ < = 85). We also split the NADOS-2 = 353 subjects with ADOS-2 assessment into bins by calculating social affect divided by RRB. The social affect > RRB bin (social affect / RRB > 1) had NSA>RRB = 113 ASD subjects and the RRB > social affect bin (social affect / RRB > 1) had NSA<RRB = 171 ASD subjects; the NSA=RRB = 69 ASD subjects with SA/RRB = 1 were not included in either ADOS-2 bin. The overlap of subjects between the NVIQ = 590 subjects with VIQ and NADOS-2 = 353 subjects with ADOS-2 was the NVIQ;ADOS-2 = 299 ASD subjects in the main analysis. We used the same PLS and gene set enrichment procedure as in Fig. 5 (see b,d,f,h,j,l in box) to assess the relationship of these binned subjects’ atypical connectivity with gene expression. Heatmaps of gene set enrichment for each subgroup’s ranked gene weights for (a-b) ASD-related gene sets, (c-d) nonpsychiatric disease-related gene sets, (e-f) psychiatric disorder-related gene sets, (g-h) synaptic signaling gene sets, (i-j) immune signaling gene sets, and (k-l) protein translation gene sets. Color indicates strength of negative log transformed FDR for normalized enrichment score multiplied by sign of gene weight (+1 or −1). The results were consistent with our predictions: gene set enrichments for the low-VIQ bin resembled those for subgroup 2 (featured low Verbal IQ) and enrichments for the high-VIQ bin resembled those for subgroup 1 (featured above-average VIQ). See further description of results in Supplementary Discussion. The P values were calculated and FDR-corrected as in Fig. 5.

Extended Data Fig. 10 Zero-order protein-protein interaction (PPI) networks for genes associated with multiple subgroups.

Zero-order protein-protein interaction (PPI) networks for (a) genes associated with all four subgroups and (b) genes associated with at least 3 subgroups (STRING database; see Methods). Blue genes are known to be transcriptionally regulated in ASD while red genes are genes not known to be transcriptionally regulated but that have been associated with ASD in the SFARI database. The significance of each PPI module is the two-sample Wilcoxon rank sum test (unpaired, two-sided) of within-module degrees versus cross-module degrees (no adjustments for multiple comparisons of modules). For each gene in the module, the within-module degree is the number of connected genes within the module and the cross-module degree is the number of connected genes outside of the module.

Supplementary information

Supplementary Information

Supplementary Discussion, Tables 1–3 and 6 and Figs. 1–25

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Supplementary Table

Supplementary Tables 4 and 5

Supplementary Code

Custom code for the RCCA with an example.

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Buch, A.M., Vértes, P.E., Seidlitz, J. et al. Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder. Nat Neurosci 26, 650–663 (2023). https://doi.org/10.1038/s41593-023-01259-x

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