NeuroPathNet: Dynamic Path Trajectory Learning for Brain Functional Connectivity Analysis

2025-10-27

AI Summary

Existing methods struggle to characterize the temporal dynamics of functional connectivity pathways among brain communities, hindering our understanding of cognitive and neuropathological mechanisms. To address this, we propose NeuroPathNet—the first path-level dynamic network framework that models functional connectivity pathways as time-varying trajectories. Integrating neuroanatomically grounded parcellations (e.g., Yeo and Smith atlases), it extracts inter-regional connectivity strength sequences and employs temporal neural networks to learn their evolution. This design significantly enhances model interpretability and biological plausibility. Evaluated on three public fMRI datasets, NeuroPathNet outperforms state-of-the-art methods across key metrics—including dynamic connectivity reconstruction accuracy, brain state identification, and neurological disorder classification—demonstrating its potential for uncovering dynamic functional brain mechanisms and supporting clinical diagnosis.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Social Networks and Social Media: Computational social science

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.

Problem

• Captures temporal evolution of brain functional network connections
• Models dynamic pathways between brain functional partitions
• Improves neurological disease diagnosis through trajectory learning

Innovation

• Dynamic path trajectory modeling for brain connectivity
• Temporal neural network modeling connection strength evolution
• Validated on fMRI datasets outperforming existing methods