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Artificial Intelligence Networks Combining Histopathology and Machine Learning Can Extract Axon Pathology in Autism

Arash Yazdanbakhsh^1,2,3, Kim T M Dang¹, Kelvin Kuang¹

¹Computational Neuroscience and Vision Laboratory, Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA.

Autism Research : Official Journal of the International Society for Autism Research

|November 3, 2025

Summary

Machine learning accurately classifies autism spectrum disorder (ASD) brain tissue, distinguishing white matter types. This automated analysis aids in studying neurodevelopmental differences in autism.

Keywords:

anterior cingulate cortex convolutional neural network deep neural network long‐range pathways short‐range pathways white matter

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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Area of Science:

Neuroscience
Computational Biology
Pathology

Background:

Autism Spectrum Disorder (ASD) is characterized by distinct axon organization in cortical pathways compared to neurotypical controls (CTR).
Traditional analysis of postmortem human brain tissue is labor-intensive and time-consuming, limiting systematic study of large brain regions.

Purpose of the Study:

To develop a machine learning approach for automated classification of white matter in ASD and CTR brains.
To differentiate between superficial white matter (SWM) and deep white matter (DWM) pathways.

Main Methods:

A deep neural network was trained to classify microscopic sections of white matter from ASD and CTR individuals.
The model considered different white matter regions: SWM (short-range connections) and DWM (long-range pathways).
Sensitivity maps and multidimensional scaling were used for analysis of classification and pathological markers.

Main Results:

The deep neural network achieved 98% accuracy in classifying ASD vs. CTR white matter below the anterior cingulate cortex (ACC).
The model distinguished between SWM and DWM pathway composition with up to 80% average accuracy.
Analysis identified key pathological markers in ASD and highlighted the heterogeneity within the ASD spectrum.

Conclusions:

Machine learning offers an automated solution for high-resolution microscopic analysis of postmortem brain tissue.
This approach can systematically study white matter in health and disease, advancing our understanding of neurodevelopmental disorders.
The findings contribute to understanding ASD heterogeneity and overlaps with neurotypical brain characteristics.