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

TL;DR

The paper tackles the challenge of comparing brain networks across task and resting-state fMRI by addressing noise and multivariate dependencies that limit traditional causal methods. It introduces BOLDSimNet, which builds $MTE$-based directed brain networks from ROI time series and aligns networks via a functionally informed node substitution/insertion/deletion strategy using $D_{fs}$, yielding a similarity score of $Similarity\_score = \frac{1}{1+NC+ED}$. The approach demonstrates developmental differences: children show higher task-rest similarity than adolescents, who exhibit more pronounced reconfiguration in the DAN and DMN. This framework provides a quantitative tool to study brain network reconfiguration and attentional fluctuations across cognitive states and developmental stages, with implications for developmental neuroscience and potential clinical applications.

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.

Figures (3)

• Figure 1: BOLDSimNet calculates the node matching cost and edge difference using set of algorithms to find the similarity score between task and resting state fMRI.
• Figure 2: Brain networks evaluated using MTE during the task and rest. The directed edge values represent averaged MTE within each age group. The method effectively demonstrates greater activation in adolescents than children in DAN during task and in DMN during rest.
• Figure 3: (a) BOLDSimNet similarity score showing brain network similarity between the task and resting states. (b) Mean BOLD signal for DMN and DAN in task and resting states for each age group. Adolescents suppressed DMN and activated DAN when switched from resting to task, whereas children did not. DMN is the average of Default Mode A, B, C, and Temporal Parietal. DAN is the average of Dorsal Attention A and B.