BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification
Journal:
arXiv
Published Date:
Apr 12, 2025
Abstract
Neurological conditions, such as Alzheimer's Disease, are challenging to
diagnose, particularly in the early stages where symptoms closely resemble
healthy controls. Existing brain network analysis methods primarily focus on
graph-based models that rely solely on imaging data, which may overlook
important non-imaging factors and limit the model's predictive power and
interpretability. In this paper, we present BrainPrompt, an innovative
framework that enhances Graph Neural Networks (GNNs) by integrating Large
Language Models (LLMs) with knowledge-driven prompts, enabling more effective
capture of complex, non-imaging information and external knowledge for
neurological disease identification. BrainPrompt integrates three types of
knowledge-driven prompts: (1) ROI-level prompts to encode the identity and
function of each brain region, (2) subject-level prompts that incorporate
demographic information, and (3) disease-level prompts to capture the temporal
progression of disease. By leveraging these multi-level prompts, BrainPrompt
effectively harnesses knowledge-enhanced multi-modal information from LLMs,
enhancing the model's capability to predict neurological disease stages and
meanwhile offers more interpretable results. We evaluate BrainPrompt on two
resting-state functional Magnetic Resonance Imaging (fMRI) datasets from
neurological disorders, showing its superiority over state-of-the-art methods.
Additionally, a biomarker study demonstrates the framework's ability to extract
valuable and interpretable information aligned with domain knowledge in
neuroscience. The code is available at
https://github.com/AngusMonroe/BrainPrompt
