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Related Experiment Video

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Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

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Bridging functional and anatomical neural connectivity through cluster synchronization.

Valentina Baruzzi¹, Matteo Lodi¹, Francesco Sorrentino²

¹DITEN, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy.

Scientific Reports

|December 16, 2023

Summary

This summary is machine-generated.

This study integrates brain structure and function using neuroimaging data to create accurate brain models. The novel approach refines structural connectivity to match observed brain activity patterns, even with complex variations.

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Last Updated: Jul 8, 2025

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Published on: October 6, 2023

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How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

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Published on: September 8, 2021

Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Brain dynamics arise from complex interactions between neural populations.
Modeling the brain requires integrating structural connectivity and functional activity.
Existing models face challenges in reconciling diverse experimental data.

Purpose of the Study:

To develop a data-driven model integrating structural and functional brain data.
To refine structural connectivity matrices using cluster synchronization.
To validate the model's robustness against biological complexities.

Main Methods:

Combined diffusion MRI (dMRI) for structural connectomes and functional MRI (fMRI) for activity patterns.
Employed cluster synchronization to reconcile structural and dynamic information.
Utilized data-driven and model-based approaches to refine the structural connectivity matrix.

Main Results:

Successfully integrated dMRI and fMRI data into a coherent brain model.
Refined structural connectivity to align with experimentally observed clusters of coherent activity.
Demonstrated model robustness with heterogeneous brain areas, including noise, parameter mismatches, and delays.

Conclusions:

The cluster synchronization approach effectively reconciles structural and functional brain data.
The developed model provides a robust framework for understanding brain dynamics.
This method offers a proof of concept for applying integrated modeling to real-world MRI data.