Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction

TL;DR

This paper introduces a neural dynamics-informed pre-trained framework that enhances personalized brain functional network construction by capturing neural activity variations across diverse scenarios, outperforming traditional methods.

Contribution

It presents a novel framework that leverages neural dynamics to improve the accuracy and personalization of brain network construction in heterogeneous conditions.

Findings

Outperforms traditional methods in diverse scenarios

Effective in neural modulation and abnormal circuit detection

Validated on 18 datasets across multiple tasks

Abstract

Brain activity is intrinsically a neural dynamic process constrained by anatomical space. This leads to significant variations in spatial distribution patterns and correlation patterns of neural activity across variable and heterogeneous scenarios. However, dominant brain functional network construction methods, which relies on pre-defined brain atlases and linear assumptions, fails to precisely capture varying neural activity patterns in heterogeneous scenarios. This limits the consistency and generalizability of the brain functional networks constructed by dominant methods. Here, a neural dynamics-informed pre-trained framework is proposed for personalized brain functional network construction. The proposed framework extracts personalized representations of neural activity patterns in heterogeneous scenarios. Personalized brain functional networks are obtained by utilizing these representations to guide brain parcellation and neural activity correlation estimation. Systematic evaluations were employed on 18 datasets across tasks, such as virtual neural modulation and abnormal neural circuit identification. Experimental results demonstrate that the proposed framework attains superior performance in heterogeneous scenarios. Overall, the proposed framework challenges the dominant brain functional network construction method.