Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis

TL;DR

This paper introduces a fine-tuned self-supervised transformer-based brain network model that enhances brain disease diagnosis by expanding regional features across multiple dimensions, improving generalizability and diagnostic accuracy.

Contribution

It presents a novel multi-dimensional brain network model with an adapter module and a self-supervised transformer, advancing brain disease diagnosis methods.

Findings

Achieves superior diagnostic performance compared to existing models.

Effectively extracts inter-region associations from fMRI data.

Provides a compact latent representation for brain disease classification.

Abstract

Functional brain network analysis has become an indispensable tool for brain disease analysis. It is profoundly impacted by deep learning methods, which can characterize complex connections between ROIs. However, the research on foundation models of brain network is limited and constrained to a single dimension, which restricts their extensive application in neuroscience. In this study, we propose a fine-tuned brain network model for brain disease diagnosis. It expands brain region representations across multiple dimensions based on the original brain network model, thereby enhancing its generalizability. Our model consists of two key modules: (1)an adapter module that expands brain region features across different dimensions. (2)a fine-tuned foundation brain network model, based on self-supervised learning and pre-trained on fMRI data from thousands of participants. Specifically, its transformer block is able to effectively extract brain region features and compute the inter-region associations. Moreover, we derive a compact latent representation of the brain network for brain disease diagnosis. Our downstream experiments in this study demonstrate that the proposed model achieves superior performance in brain disease diagnosis, which potentially offers a promising approach in brain network analysis research.