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Related Experiment Video

Updated: Jan 30, 2026

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

An average sliding window correlation method for dynamic functional connectivity.

Victor M Vergara¹, Anees Abrol^1,2, Vince D Calhoun^1,2

¹The Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico.

Human Brain Mapping

|January 20, 2019

Summary

This summary is machine-generated.

Average sliding window correlation (ASWC) reduces artifacts and improves temporal resolution for analyzing brain connectivity dynamics. This new method offers a more accurate estimation of functional connectivity compared to standard sliding window correlation (SWC).

Keywords:

dynamic functional connectivity functional MRI sliding window correlation

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Last Updated: Jan 30, 2026

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Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Published on: May 27, 2020

Area of Science:

Neuroscience
Signal Processing
Computational Biology

Background:

Sliding window correlation (SWC) is widely used for analyzing temporal brain connectivity.
SWC can produce spurious artifacts, particularly with large window lengths.
Recent recommendations suggest a 100s window for SWC, potentially sacrificing temporal resolution.

Purpose of the Study:

To introduce and evaluate a novel Average Sliding Window Correlation (ASWC) method.
To address the limitations of SWC, specifically artifact generation and temporal resolution.
To provide a more accurate estimation of transient functional connectivity dynamics.

Main Methods:

Development of the Average Sliding Window Correlation (ASWC) approach.
Utilizing simulated signals with controlled dynamic connectivity for testing.
Analytical modeling to explain ASWC and SWC behavior in different dynamic scenarios.
Application of ASWC to real neuroimaging data.

Main Results:

ASWC requires smaller window lengths, enhancing temporal resolution.
ASWC functions as a tunable high-pass filter.
ASWC demonstrates fewer artifacts and better detection of transient connectivity fluctuations than SWC.
ASWC achieves a lower mean square error compared to SWC.

Conclusions:

ASWC offers significant advantages over traditional SWC for brain connectivity analysis.
The ASWC method provides a more accurate and reliable assessment of dynamic functional connectivity.
ASWC is a promising technique for neuroimaging studies requiring high temporal resolution and artifact reduction.