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Related Experiment Video

Updated: Nov 26, 2025

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Robust brain network identification from multi-subject asynchronous fMRI data.

Jian Li¹, Jessica L Wisnowski², Anand A Joshi¹

¹Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089, United States.

|December 10, 2020

Summary

This summary is machine-generated.

This study introduces a new tensor decomposition method to identify brain networks and their activity over time from fMRI data. This robust approach enhances the analysis of common brain networks across multiple subjects.

Keywords:

Brain network identification Functional MRI Optimization Tensor decomposition

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Last Updated: Nov 26, 2025

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

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Published on: March 21, 2019

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Published on: July 1, 2014

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Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Area of Science:

Neuroimaging
Computational Neuroscience
Data Science

Background:

Analyzing functional Magnetic Resonance Imaging (fMRI) data across subjects presents challenges due to asynchronicity.
Identifying common brain networks and their temporal dynamics is crucial for understanding brain function.

Purpose of the Study:

To develop a novel, robust method for identifying common brain networks and their temporal dynamics from asynchronous fMRI data across subjects.
To compare the proposed method with existing group Independent Component Analysis (ICA) techniques.

Main Methods:

Temporally aligning asynchronous fMRI data using the orthogonal BrainSync transform.
Mapping synchronized fMRI data into a 3D tensor (vertices × time × subject/session).
Applying Nesterov-accelerated adaptive moment estimation (Nadam) within a sequential Canonical Polyadic (CP) decomposition framework.

Main Results:

Successfully identified twelve known brain networks and their temporal dynamics from 40 subjects' fMRI data without prior task information.
Seven networks showed distinct subject-specific responses to a language task.
Bootstrap analysis indicated increased robustness of the CP tensor decomposition method compared to ICA-based methods.

Conclusions:

The proposed CP tensor decomposition method offers a robust and scalable approach for identifying common brain networks and their temporal dynamics from asynchronous fMRI data.
This method provides a powerful tool for cross-subject brain network analysis, outperforming traditional ICA in robustness.