Deep Generative Modeling with Spatial and Network Images: An Explainable AI (XAI) Approach

TL;DR

This paper introduces a scalable deep generative model for neuroimaging data that captures complex spatial and network relationships, providing explainability, uncertainty quantification, and strong empirical performance.

Contribution

It presents one of the first XAI frameworks for heterogeneous imaging data, integrating spatial and network features with deep neural networks for neuroimaging analysis.

Findings

Strong performance compared to existing methods

Superior uncertainty quantification and faster computation

Revealed brain-cortical feature associations

Abstract

This article addresses the challenge of modeling the amplitude of spatially indexed low frequency fluctuations (ALFF) in resting state functional MRI as a function of cortical structural features and a multi-task coactivation network in the Adolescent Brain Cognitive Development (ABCD) Study. It proposes a generative model that integrates effects of spatially-varying inputs and a network-valued input using deep neural networks to capture complex non-linear and spatial associations with the output. The method models spatial smoothness, accounts for subject heterogeneity and complex associations between network and spatial images at different scales, enables accurate inference of each images effect on the output image, and allows prediction with uncertainty quantification via Monte Carlo dropout, contributing to one of the first Explainable AI (XAI) frameworks for heterogeneous imaging data. The model is highly scalable to high-resolution data without the heavy pre-processing or summarization often required by Bayesian methods. Empirical results demonstrate its strong performance compared to existing statistical and deep learning methods. We applied the XAI model to the ABCD data which revealed associations between cortical features and ALFF throughout the entire brain. Our model performed comparably to existing methods in predictive accuracy but provided superior uncertainty quantification and faster computation, demonstrating its effectiveness for large-scale neuroimaging analysis. Open-source software in Python for XAI is available.