Explainable AI for Autism Diagnosis: Identifying Critical Brain Regions Using fMRI Data

TL;DR

This paper develops an explainable deep learning model for ASD diagnosis using fMRI data, achieving high accuracy and identifying key brain regions, thus enhancing interpretability and potential clinical utility.

Contribution

It introduces a novel deep learning approach that not only classifies ASD from fMRI data but also provides explainable insights into brain regions involved, improving interpretability over prior models.

Findings

Accurately classifies ASD with high performance

Identifies critical brain regions associated with ASD

Validated findings across multiple datasets

Abstract

Early diagnosis and intervention for Autism Spectrum Disorder (ASD) has been shown to significantly improve the quality of life of autistic individuals. However, diagnostics methods for ASD rely on assessments based on clinical presentation that are prone to bias and can be challenging to arrive at an early diagnosis. There is a need for objective biomarkers of ASD which can help improve diagnostic accuracy. Deep learning (DL) has achieved outstanding performance in diagnosing diseases and conditions from medical imaging data. Extensive research has been conducted on creating models that classify ASD using resting-state functional Magnetic Resonance Imaging (fMRI) data. However, existing models lack interpretability. This research aims to improve the accuracy and interpretability of ASD diagnosis by creating a DL model that can not only accurately classify ASD but also provide explainable insights into its working. The dataset used is a preprocessed version of the Autism Brain Imaging Data Exchange (ABIDE) with 884 samples. Our findings show a model that can accurately classify ASD and highlight critical brain regions differing between ASD and typical controls, with potential implications for early diagnosis and understanding of the neural basis of ASD. These findings are validated by studies in the literature that use different datasets and modalities, confirming that the model actually learned characteristics of ASD and not just the dataset. This study advances the field of explainable AI in medical imaging by providing a robust and interpretable model, thereby contributing to a future with objective and reliable ASD diagnostics.