Redefining Autism Subtypes: a machine learning approach leveraging topological data analysis, network measures and hemispheric lateralization
Alves, C. L.; Sallum, L. F.; Aguiar, P. M. d. C.; Porto, J. A. M.; Rodrigues, F. A.; Toutain, T. G. L. d. O.; Moeckel, M.
Show 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.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Multiclass Classification of Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, and Typically Developed Individuals Using fMRI Functional Connectivity Analysis 98%
- Identifying Neuroanatomical and Behavioral Features for Autism Spectrum Disorder Diagnosis in Children using Machine Learning 97%
- Neural Correlates of Eye Contact and Social Function in Autism Spectrum Disorder 93%
Similar papers in this journal
Similar papers in this journal
- Unraveling the Neural Landscape of Mental Disorders using Double Functional Independent Primitives (dFIPs) 94%
- Transdiagnostic Neurobiology of Social Cognition and Individual Variability as Measured by Fractional Amplitude of Low-Frequency Fluctuation in Schizophrenia and Autism Spectrum Disorders 93%
- Autism is associated with inter-individual variations of gray and white matter morphology 92%
Similar papers in this journal
- Cortico-Cerebellar Neurodynamics during Social Interaction in Autism Spectrum Disorder 94%
- Brain structural correlates of autistic traits across the diagnostic divide: A grey matter and white matter microstructure study 93%
- Examining the relationship between social cognition and neural synchrony during movies in children with and without autism 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.