BOLDSimNet: Examining Brain Network Similarity between Task and Resting-State fMRI

2025-04-02

AI Summary

Traditional fMRI causal connectivity methods are highly sensitive to noise and struggle to model multivariate dependencies, rendering directed information flow in task-based and resting-state brain networks incomparable and impeding cross-state network reconfiguration analysis. To address this, we propose BOLDSimNet—a novel framework for cross-state causal network modeling that integrates multivariate transfer entropy (MTE) with function-driven ROI clustering, explicitly abandoning the spatial proximity assumption. Instead, ROIs are grouped and networks aligned based on functional similarity. Validated on 40 healthy participants, BOLDSimNet reveals significantly higher task–rest causal network similarity in children than in adolescents. Moreover, adolescents exhibit stronger causal differentiation between the dorsal attention network (DAN) and the default mode network (DMN), uncovering a dual developmental mechanism: declining stability of attentional regulation alongside enhanced network adaptability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Causal Learning
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Social Networks and Social Media: Computational social science
• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Traditional causal connectivity methods in task-based and resting-state functional magnetic resonance imaging (fMRI) face challenges in accurately capturing directed information flow due to their sensitivity to noise and inability to model multivariate dependencies. These limitations hinder the effective comparison of brain networks between cognitive states, making it difficult to analyze network reconfiguration during task and resting states. To address these issues, we propose BOLDSimNet, a novel framework utilizing Multivariate Transfer Entropy (MTE) to measure causal connectivity and network similarity across different cognitive states. Our method groups functionally similar regions of interest (ROIs) rather than spatially adjacent nodes, improving accuracy in network alignment. We applied BOLDSimNet to fMRI data from 40 healthy controls and found that children exhibited higher similarity scores between task and resting states compared to adolescents, indicating reduced variability in attention shifts. In contrast, adolescents showed more differences between task and resting states in the Dorsal Attention Network (DAN) and the Default Mode Network (DMN), reflecting enhanced network adaptability. These findings emphasize developmental variations in the reconfiguration of the causal brain network, showcasing BOLDSimNet's ability to quantify network similarity and identify attentional fluctuations between different cognitive states.

Problem

• Measures causal connectivity and network similarity across cognitive states
• Improves accuracy in brain network alignment via functional ROI grouping
• Identifies developmental differences in task-resting state network adaptability

Innovation

• Uses Multivariate Transfer Entropy (MTE) for connectivity
• Groups functionally similar ROIs for alignment
• Quantifies network similarity across cognitive states