Deep Representations for Time-varying Brain Datasets

TL;DR

This paper introduces a graph neural network model that effectively captures and interprets the dynamic brain activities from fMRI and DWI data, enabling identification of task-related and individual-specific brain subnetworks.

Contribution

It presents a novel GNN approach with adaptive adjacency matrices and multi-resolution smoothing for modeling latent brain dynamics, along with interpretability via integrated gradients.

Findings

Superior performance in modeling spatial-temporal brain signals.

Effective identification of task-specific and individual-specific subnetworks.

Enhanced interpretability of brain dynamics and connections.

Abstract

Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications. Due to its highly dynamic nature, temporally averaged fMRI (functional magnetic resonance imaging) can only provide a narrow view of underlying brain activities. Previous works lack the ability to learn and interpret the latent dynamics in brain architectures. This paper builds an efficient graph neural network model that incorporates both region-mapped fMRI sequences and structural connectivities obtained from DWI (diffusion-weighted imaging) as inputs. We find good representations of the latent brain dynamics through learning sample-level adaptive adjacency matrices and performing a novel multi-resolution inner cluster smoothing. We also attribute inputs with integrated gradients, which enables us to infer (1) highly involved brain connections and subnetworks for each task, (2) temporal keyframes of imaging sequences that characterize tasks, and (3) subnetworks that discriminate between individual subjects. This ability to identify critical subnetworks that characterize signal states across heterogeneous tasks and individuals is of great importance to neuroscience and other scientific domains. Extensive experiments and ablation studies demonstrate our proposed method's superiority and efficiency in spatial-temporal graph signal modeling with insightful interpretations of brain dynamics.