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Subtyping Autism Spectrum Disorder With a Population Graph-Based Dual Autoencoder: Revealing Two Distinct Biotypes.

Xinwei Li^1,2, Guomei Xu^1,3, Guohong Geng¹

¹Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Posts and Telecommunications, Chongqing, China.

CNS Neuroscience & Therapeutics

|December 4, 2025

Summary

This summary is machine-generated.

Researchers identified two distinct autism spectrum disorder (ASD) biotypes using a novel graph-based approach. These biotypes show unique brain connectivity patterns and symptom profiles, offering new insights into ASD heterogeneity.

Keywords:

autism spectrum disorder functional connectivity heterogeneity population graph resting‐state fMRI subtyping

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Area of Science:

Neuroscience
Genetics
Psychiatry

Background:

Autism spectrum disorder (ASD) presents significant heterogeneity in clinical symptoms and neurobiology.
Understanding this heterogeneity is crucial for developing targeted interventions.

Purpose of the Study:

To identify distinct ASD biotypes using a novel graph-based subtyping approach.
To uncover the neurobiological underpinnings of these identified ASD biotypes.

Main Methods:

Analysis of resting-state fMRI and clinical data from 443 males with ASD.
Utilized a population graph-based dual autoencoder for subtyping (PG-DAS) framework.
Compared clinical scores and functional connectivity patterns between biotypes; examined brain-behavior associations.

Main Results:

Identified two distinct ASD biotypes with differing clinical scores and network connectivity.
ASD1 showed reduced network integration linked to communication symptoms; ASD2 exhibited greater network segregation.
Biotype-specific brain-behavior associations were revealed, correlating with social and communication scores.

Conclusions:

Biotype-specific brain network patterns are critical for understanding ASD heterogeneity.
The PG-DAS framework is effective for ASD subtyping and applicable to other neuroheterogeneous disorders.