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Updated: May 27, 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

Cross-correlation: an fMRI signal-processing strategy.

James S Hyde¹, Andrzej Jesmanowicz

¹Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA. jshyde@mcw.edu

|November 5, 2011

Summary

This summary is machine-generated.

Functional MRI (fMRI) data analysis evolved with signal processing strategies like cross-correlation. This technique enabled the discovery of functional connectivity MRI (fcMRI) by analyzing resting-state brain activity.

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Area of Science:

Neuroimaging
Signal Processing

Background:

The advent of functional MRI (fMRI) in 1992 necessitated novel signal-processing techniques.
Early fMRI analysis involved processing image time courses and pixel time courses.

Observation:

Pixel time-course waveforms were influenced by task sequences and hemodynamic response functions.
Cross-correlation of reference waveforms with pixel time courses generated coefficient arrays.
Colorized images were created from these coefficients and overlaid onto anatomical images.

Findings:

Vector algebra and cross-correlation were foundational to early fMRI data analysis.
Cross-correlation methodology was pivotal in discovering functional connectivity MRI (fcMRI).
fcMRI studies brain connectivity by cross-correlating pixel time courses from resting-state fMRI data.

Implications:

Developed signal-processing strategies advanced fMRI data analysis.
Established cross-correlation as a key method for neuroimaging research.
Enabled the study of functional brain networks using fcMRI.