Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging

TL;DR

This paper introduces a brain-aware readout layer for GNNs that enhances early Alzheimer's detection by improving interpretability and predictive accuracy using neuroimaging data.

Contribution

The study proposes a novel brain-aware readout layer for GNNs that captures complex brain network features and improves early AD diagnosis accuracy.

Findings

GNNs with BA readout outperform traditional models in predicting PACC scores

The approach enhances interpretability by identifying critical brain regions

Results demonstrate higher robustness and stability in predictions

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive memory and cognitive decline, affecting millions worldwide. Diagnosing AD is challenging due to its heterogeneous nature and variable progression. This study introduces a novel brain-aware readout layer (BA readout layer) for Graph Neural Networks (GNNs), designed to improve interpretability and predictive accuracy in neuroimaging for early AD diagnosis. By clustering brain regions based on functional connectivity and node embedding, this layer improves the GNN's capability to capture complex brain network characteristics. We analyzed neuroimaging data from 383 participants, including both cognitively normal and preclinical AD individuals, using T1-weighted MRI, resting-state fMRI, and FBB-PET to construct brain graphs. Our results show that GNNs with the BA readout layer significantly outperform traditional models in predicting the Preclinical Alzheimer's Cognitive Composite (PACC) score, demonstrating higher robustness and stability. The adaptive BA readout layer also offers enhanced interpretability by highlighting task-specific brain regions critical to cognitive functions impacted by AD. These findings suggest that our approach provides a valuable tool for the early diagnosis and analysis of Alzheimer's disease.