Visual Explanation for Identification of the Brain Bases for Dyslexia on fMRI Data

TL;DR

This paper demonstrates that deep learning models can accurately classify developmental dyslexia from fMRI data and provide meaningful visual explanations that align with neuroscientific knowledge, aiding understanding of the disorder's neural basis.

Contribution

It introduces a visualization approach for CNNs applied to fMRI data, enabling interpretability and neuroscientific insights into dyslexia classification.

Findings

High accuracy in classifying dyslexia from fMRI data

Visual explanations align with neuroscientific understanding

Method provides interpretable neural features for diagnosis

Abstract

Brain imaging of mental health, neurodevelopmental and learning disorders has coupled with machine learning to identify patients based only on their brain activation, and ultimately identify features that generalize from smaller samples of data to larger ones. However, the success of machine learning classification algorithms on neurofunctional data has been limited to more homogeneous data sets of dozens of participants. More recently, larger brain imaging data sets have allowed for the application of deep learning techniques to classify brain states and clinical groups solely from neurofunctional features. Deep learning techniques provide helpful tools for classification in healthcare applications, including classification of structural 3D brain images. Recent approaches improved classification performance of larger functional brain imaging data sets, but they fail to provide diagnostic insights about the underlying conditions or provide an explanation from the neural features that informed the classification. We address this challenge by leveraging a number of network visualization techniques to show that, using such techniques in convolutional neural network layers responsible for learning high-level features, we are able to provide meaningful images for expert-backed insights into the condition being classified. Our results show not only accurate classification of developmental dyslexia from the brain imaging alone, but also provide automatic visualizations of the features involved that match contemporary neuroscientific knowledge, indicating that the visual explanations do help in unveiling the neurological bases of the disorder being classified.