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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
Published on: October 20, 2023
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Jointly estimating individual and group networks from fMRI data.
Don van den Bergh1, Linda Douw2, Zarah van der Pal3
1Department of Psychological Methods, University of Amsterdam.
Network Neuroscience (Cambridge, Mass.)
|October 24, 2025
Summary
This study introduces a multilevel approach for analyzing functional magnetic resonance imaging (fMRI) networks, improving the understanding of brain connectivity by accounting for individual differences within groups. The method enhances the generalizability of fMRI findings.
Area of Science:
- Neuroscience
- Cognitive Science
- Biostatistics
Background:
- Functional magnetic resonance imaging (fMRI) research often uses graphical models to map brain region relationships.
- Traditional fMRI analyses frequently overlook the nested structure of data (observations within participants, within populations), potentially leading to inaccurate or paradoxical results.
- Ignoring within-participant and between-participant variance compromises the generalizability of fMRI findings.
Purpose of the Study:
- To propose and evaluate a novel multilevel approach for modeling fMRI networks that accounts for nested data structures.
- To address the limitations of individual or aggregate analysis in fMRI connectivity studies.
- To investigate both commonalities and individual differences in resting-state brain networks.
Main Methods:
- Developed a multilevel modeling framework using Gaussian graphical models at the individual level and Curie-Weiss graphical models at the group level.
- Conducted simulations to compare the proposed method's performance against individual and aggregate analysis techniques for edge retrieval.
- Applied the multilevel approach to resting-state fMRI data from 724 healthy participants.
Main Results:
- Simulations demonstrated that the multilevel approach outperforms traditional methods in accurately identifying connections (edge retrieval).
- The group-level analysis successfully recovered the seven known resting-state networks.
- Significant heterogeneity was observed in individual-level brain networks, highlighting substantial differences among participants.
Conclusions:
- A multilevel approach is essential for accurate fMRI network analysis, effectively capturing both group-level patterns and individual variations.
- The proposed method enhances the reliability and generalizability of fMRI findings by respecting data hierarchy.
- Future research should explore additional challenges and extensions of this multilevel modeling technique for complex brain network analysis.
