NeuroPathNet: Dynamic Path Trajectory Learning for Brain Functional Connectivity Analysis

TL;DR

NeuroPathNet is a novel neural network framework that models the temporal evolution of brain functional connectivity pathways, aiding in understanding cognitive mechanisms and neurological disease diagnosis.

Contribution

It introduces a path-level trajectory modeling framework based on static partitioning schemes to capture dynamic brain network behaviors.

Findings

Outperforms existing methods on multiple indicators

Validated on three public fMRI datasets

Enhances dynamic graph learning for brain analysis

Abstract

Understanding the evolution of brain functional networks over time is of great significance for the analysis of cognitive mechanisms and the diagnosis of neurological diseases. Existing methods often have difficulty in capturing the temporal evolution characteristics of connections between specific functional communities. To this end, this paper proposes a new path-level trajectory modeling framework (NeuroPathNet) to characterize the dynamic behavior of connection pathways between brain functional partitions. Based on medically supported static partitioning schemes (such as Yeo and Smith ICA), we extract the time series of connection strengths between each pair of functional partitions and model them using a temporal neural network. We validate the model performance on three public functional Magnetic Resonance Imaging (fMRI) datasets, and the results show that it outperforms existing mainstream methods in multiple indicators. This study can promote the development of dynamic graph learning methods for brain network analysis, and provide possible clinical applications for the diagnosis of neurological diseases.