Redefining Autism Subtypes: a machine learning approach leveraging topological data analysis, network measures and hemispheric lateralization
Year: 2025
Type: preprint
Study design: Observational Study
Abstract: Autism subtypes, including general Autism Spectrum Disorder (ASD) and Asperger Syndrome (AS), exhibit distinct neural connectivity patterns. This study is the first to systematically integrate Topological Data Analysis (TDA) with complex network measures and machine learning (ML) to investigate brain lateralization and connectivity differences among these subtypes. Using fMRI-derived connectivity matrices, TDA metrics—such as persistence entropy and fractal dimension—revealed that AS networks are highly integrated and hierar-chically complex, distinguishing them from both ASD and typically developing (TD) groups. Shapley Additive Explanations (SHAP) analysis identified the left primary motor cortex as a key feature across all subtypes, and highlighted its subtype-specific correlations with other brain regions. ML models trained on these features achieved high classification accuracy, with an AUC of 0.983. This fMRI-based analysis supports the classification of AS as a distinct group alongside ASD due to its unique neurobiological characteristics.
Source: medRxiv
Authors: Caroline L. Alves, Loriz Francisco Sallum, Patrícia Maria de Carvalho Aguiar, Joel Augusto Moura Porto, Francisco Aparecido Rodrigues, Thaise G. L. de O. Toutain, Michael Moeckel
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4409487129