A Lightweight, End-to-End Explainable, and Generalized attention-based graph neural network to Classify Autism Spectrum Disorder using Meta-Connectivity

Abstract

1AbstractRecent technological advancement in Graph Neural Networks (GNNs) have been extensively used to diagnose brain disorders such as autism (ASD), which is associated with deficits in social communication, interaction, and restricted/repetitive behaviors. However, the existing machine-learning/deep-learning (ML/DL) models suffer from low accuracy and explainability due to their internal architecture and feature extraction techniques, which also predominantly focus on node-centric features. As a result, performance is moderate on unseen data due to ignorance of edge-centric features. Here, we argue that meaningful features and information can be extracted by focusing on meta connectivity between large-scale brain networks which is an edge-centric higher order dynamic correlation in time. In the current study, we have proposed a novel explainable and generalized node-edge connectivity-based graph attention neural network(Ex-NEGAT) model to classify ASD subjects from neuro-typicals (TD) on unseen data using a node edge-centric feature set for the first time and predicted their symptom severity scores. We used ABIDE (I and II) dataset with a large sample size (Total no. of samples = 1500). The framework employs meta-connectivity derived from Theory-of-Mind (ToM), Default-mode Network (DMN), Central executive (CEN), and Salience network (SN) that measure the dynamic functional connectivity (dFC) as a flow across morphing connectivity configurations. To generalize the Ex-NEGAT model, we trained the proposed model on ABIDE I(No. of samples =840) and performed testing on the ABIDE II(no. of samples =660) dataset and achieved 88% accuracy with an F1-score of 0.89. Additionally, we identified symptom severity scores for each individual subjects using their meta-connectivity links between relevant brain networks and passing that to Connectome-based Prediction Modelling (CPM) pipeline to identify the specific large-scale brain networks whose edge connectivity contributed positively and negatively to the prediction. Our approach accurately predicted ADOS-Total, ADOS-Social, ADOS-Communication, ADOS-Module, ADOS-STEREO, and FIQ scores.