NeuroPathNet: Dynamic Path Trajectory Learning for Brain Functional Connectivity Analysis
This work addresses the challenge of analyzing dynamic brain networks for cognitive and neurological disease diagnosis, representing an incremental improvement in domain-specific methods.
The paper tackled the problem of capturing temporal evolution in brain functional connectivity by proposing NeuroPathNet, a path-level trajectory modeling framework, and demonstrated that it outperforms existing methods on three fMRI datasets.
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.