BrainMAP: Multimodal Graph Learning For Efficient Brain Disease Localization

TL;DR

BrainMAP is a multimodal graph learning framework that efficiently localizes brain regions affected by neurodegenerative diseases by focusing on critical subgraphs and integrating multimodal data, achieving high accuracy with reduced computational cost.

Contribution

It introduces an atlas-guided subgraph extraction and a novel multimodal fusion method, significantly reducing computational complexity while improving localization accuracy.

Findings

Over 50% reduction in computational overhead.

Outperforms state-of-the-art methods in accuracy.

Effective multimodal data integration for disease localization.

Abstract

Recent years have seen a surge in research focused on leveraging graph learning techniques to detect neurodegenerative diseases. However, existing graph-based approaches typically lack the ability to localize and extract the specific brain regions driving neurodegenerative pathology within the full connectome. Additionally, recent works on multimodal brain graph models often suffer from high computational complexity, limiting their practical use in resource-constrained devices. In this study, we present BrainMAP, a novel multimodal graph learning framework designed for precise and computationally efficient identification of brain regions affected by neurodegenerative diseases. First, BrainMAP utilizes an atlas-driven filtering approach guided by the AAL atlas to pinpoint and extract critical brain subgraphs. Unlike recent state-of-the-art methods, which model the entire brain network, BrainMAP achieves more than 50% reduction in computational overhead by concentrating on disease-relevant subgraphs. Second, we employ an advanced multimodal fusion process comprising cross-node attention to align functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data, coupled with an adaptive gating mechanism to blend and integrate these modalities dynamically. Experimental results demonstrate that BrainMAP outperforms state-of-the-art methods in computational efficiency, without compromising predictive accuracy.