Disease-Specific Brain Function Representation Generation for Diagnosis Using Large Language Models

Motivation: Functional connectivity (FC) based on rs-fMRI is a classical brain-connectomic measure. However, FC is handcraft feature that insufficently describes complex brain activity and neglects disease specificity. Goal(s): We aim to learn function representation directly from rs-fMRI and integrate disease-specific brain function abnormalities in the learning process assisted by large language model (LLM). Approach: We employ an encoder-decoder architecture and introduce neuroscience knowledge from existing literature through an LLM to guide the generation of function representation. The resulting function representations are used for disease diagnosis to verify their effectiveness. Results: The generated function representations improve the diagnostic performance, verifying the effectiveness of our method. . Impact: Our findings indicate that general and disease-specific brain function representations guided with LLM improve diagnostic accuracy. Additionally, the framework's adaptability across different diseases positions it as a versatile tool in neuroimaging research, with potential applications in studying various disorders.