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Symptom dimensions of resting-state electroencephalographic functional connectivity in autism


Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social and communication deficits (SCDs), restricted and repetitive behaviors (RRBs) and fixated interests. Despite its prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To comprehensively explore these heterogeneities, we developed a new analytical framework combining contrastive learning and sparse canonical correlation analysis that identifies symptom-linked resting-state electroencephalographic connectivity dimensions within 392 ASD samples. We present two dimensions with multivariate connectivity basis exhibiting significant correlations with SCD and RRB, confirm their robustness through cross-validation and demonstrate their conceptual generalizability using an independent dataset (n = 222). Specifically, the right inferior parietal lobe is the core region for RRB, while connectivity between the left angular gyrus and the right middle temporal gyrus show key contribution to SCD. These findings provide a promising avenue to parse ASD heterogeneity with high clinical translatability, paving the way for ASD treatment development and precision medicine.

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Fig. 1: Flowchart of data-driven dissection of symptom-linked neurophysiological dimensions in individuals with ASD based on contrastive rsEEG FC features.
Fig. 2: Linked dimensions between contrastive rsEEG features and symptom traits in individuals with ASD.
Fig. 3: Signature alterations in rsEEG FC for ASD symptoms.
Fig. 4: Association between symptom-linked FC dimensions and behavioral traits in the independent HBN dataset.

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Data availability

The data supporting the results in this study are available within this paper and its supplementary materials. The ABC-CT dataset is publicly available via the National Institute of Mental Health Data Archive at The HBN dataset was obtained from the Child Mind Institute Biobank. The EEG data of the HBN dataset are publicly available through the International Neuroimaging Data-sharing Initiative at The phenotypical data of the HBN dataset can be accessed under a Data Usage Agreement with the Child Mind Institute Biobank, from the Longitudinal Online Research and Imaging System located at The manuscript reflects the views of the authors and does not necessarily reflect the opinions or views of the Child Mind Institute.

Code availability

The cPCA step was implemented in Python (v.3.8.8) with a publicly available package ( The sCCA step was implemented in MATLAB (v.R2022a) using custom code. The statistical analyses were conducted in MATLAB with built-in functions. The code used in this study is available at


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This work was supported by National Institutes of Health grant nos. R21MH130956, R01MH129694 and R21AG080425 to Y.Z., grant R01MH110512 to H.X., grants K23MH114023 and R01MH125886 to G.A.F., grants. R37MH125829, R01EB022573, R01MH112847 and R01MH120482 to T.D.S., as well as Lehigh University FIG (FIGAWD35), CORE and Accelerator grants to Y.Z. Portions of this research were conducted on the Lehigh University’s Research Computing infrastructure, which is partially supported by a National Science Foundation award 2019035. This work was also supported in part by philanthropic funding and grants from the One Mind-Baszucki Brain Research Fund, the SEAL Future Foundation and the Brain and Behavior Research Foundation to G.A.F. The funders had no role in the design and conduct of the study, and the collection, management, analysis and interpretation of the data, nor were they involved in the decision to submit the manuscript for publication.

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Authors and Affiliations



X.T. conceptualized and designed the work, wrote the code, analyzed and interpreted the data, and drafted and revised the manuscript. H.X., G.A.F. and K.Z. interpreted the data and refined the design of the work. T.D.S. interpreted the data and revised the manuscript. N.B.C. conceptualized the work and revised the manuscript. Y.Z. conceptualized and designed the work, oversaw the analysis and interpretation of the data, and revised the manuscript.

Corresponding author

Correspondence to Yu Zhang.

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G.A.F. received monetary compensation for consulting work for SynapseBio AI and owns equity in Alto Neuroscience. None of the other authors declare any competing interests.

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Nature Mental Health thanks Janine Bijsterbosch, Guillaume Dumas and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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Tong, X., Xie, H., Fonzo, G.A. et al. Symptom dimensions of resting-state electroencephalographic functional connectivity in autism. Nat. Mental Health 2, 287–298 (2024).

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