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Updated: Jun 21, 2025

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

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

Published on: October 6, 2023

Gradient synchronization for multivariate functional data, with application to brain connectivity.

Yaqing Chen¹, Shu-Chin Lin², Yang Zhou²

¹Department of Statistics, Rutgers University, New Brunswick, New Jersey, USA.

Journal of the Royal Statistical Society. Series B, Statistical Methodology

|July 15, 2024

Summary

This summary is machine-generated.

We introduce new gradient synchronization measures to quantify dynamic functional connectivity in brain imaging. These methods improve the discrimination of disease status in Alzheimer's patients using resting-state fMRI data.

Keywords:

Alzheimer’s disease Pearson correlation concordance fMRI functional connectivity functional data analysis

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

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Published on: July 1, 2014

Area of Science:

Statistics
Neuroimaging
Functional Data Analysis

Background:

Functional connectivity analysis in neuroimaging, particularly using functional magnetic resonance imaging (fMRI), is crucial for understanding brain function.
Traditional methods like static temporal Pearson correlation have limitations in capturing the dynamic nature of brain connectivity.
Recent studies highlight the importance of dynamic functional connectivity, necessitating advanced similarity measures.

Purpose of the Study:

To develop novel similarity measures for multivariate random curves that capture dynamic functional features.
To introduce gradient synchronization measures for assessing dynamic functional connectivity.
To evaluate the proposed measures in simulations and real-world neuroimaging data.

Main Methods:

Development of gradient synchronization measures based on the concordance and discordance of gradients between paired smooth random functions.
Theoretical analysis to obtain asymptotic normality of the proposed estimates under regularity conditions.
Application and validation using simulated data and resting-state fMRI signals from the Alzheimer's Disease Neuroimaging Initiative.

Main Results:

The proposed gradient synchronization measures effectively quantify dynamic functional similarity between random curves.
Asymptotic normality of the estimators was established, providing a theoretical foundation for the methods.
Application to Alzheimer's Disease Neuroimaging Initiative data demonstrated improved discrimination between subjects with different disease statuses.

Conclusions:

Gradient synchronization offers a powerful new approach for analyzing dynamic functional connectivity.
These measures enhance the ability to differentiate between healthy and diseased states in neuroimaging studies.
The findings have significant implications for advancing functional brain connectivity research and clinical applications.