Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction

TL;DR

This paper introduces an interpretable multi-modal graph convolutional network that effectively predicts cognitive ability from multi-modal fMRI data, leveraging manifold regularization and interpretability techniques to identify key brain biomarkers.

Contribution

It presents a novel multi-modal GCN model with manifold regularization and interpretability methods for cognitive prediction from fMRI data, outperforming existing approaches.

Findings

Superior prediction of WRAT scores using MGCN

Effective identification of cognition-related brain biomarkers

Enhanced interpretability through Grad-RAM and edge mask learning

Abstract

Objective: Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity networks. Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating the fMRI time series and the functional connectivity (FC) between each pair of brain regions. Specifically, our model learns a graph embedding from individual brain networks derived from multi-modal data. A manifold-based regularization term is then enforced to consider the relationships of subjects both within and between modalities. Furthermore, we propose the gradient-weighted regression activation mapping (Grad-RAM) and the edge mask learning to interpret the model, which is used to identify significant cognition-related biomarkers. Results: We validate our MGCN model on the Philadelphia Neurodevelopmental Cohort to predict individual wide range achievement test (WRAT) score. Our model obtains superior predictive performance over GCN with a single modality and other competing approaches. The identified biomarkers are cross-validated from different approaches. Conclusion and Significance: This paper develops a new interpretable graph deep learning framework for cognitive ability prediction, with the potential to overcome the limitations of several current data-fusion models. The results demonstrate the power of MGCN in analyzing multi-modal fMRI and discovering significant biomarkers for human brain studies.