Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction
This work aims to improve the consistency and generalizability of brain functional network construction for researchers and clinicians studying brain activity in heterogeneous scenarios, offering an incremental improvement over existing methods.
The paper addresses the challenge of constructing personalized brain functional networks, which is difficult due to variations in neural activity patterns across individuals and scenarios. The authors propose a neural dynamics-informed pre-trained framework that extracts personalized representations of neural activity to guide brain parcellation and correlation estimation, demonstrating superior performance across 18 datasets in tasks like virtual neural modulation and abnormal neural circuit identification.
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.