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Related Experiment Video

Updated: Apr 17, 2026

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics.

Jingyuan E Chen¹, Catie Chang², Michael D Greicius³

¹Department of Radiology, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.

|February 10, 2015

Summary

This summary is machine-generated.

New metrics quantify brain network dynamics during cognitive tasks. Co-activation pattern (CAP) analysis reveals reduced variability in brain networks during working memory tasks compared to rest.

Keywords:

Brain dynamics Co-activation patterns Point process analysis Resting state networks Working memory

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Last Updated: Apr 17, 2026

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Published on: October 30, 2018

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Published on: July 21, 2021

Area of Science:

Neuroscience
Cognitive Neuroscience
Brain Imaging

Background:

Brain's intrinsic network patterns change during resting state (RS) scans.
Quantifying brain network variability during cognitive tasks is challenging.
Existing metrics for brain dynamics are limited.

Purpose of the Study:

Introduce novel quantification metrics for brain network dynamics based on co-activation pattern (CAP) analysis.
Apply these metrics to assess changes in brain dynamics during a working memory (WM) task compared to rest.
Focus on functional connectivity of the default-mode network (DMN) and executive control network (ECN).

Main Methods:

Utilized an extension of co-activation pattern (CAP) analysis, a point-process method.
Applied sliding-window analysis to assess functional connectivity.
Quantified changes in brain network variability during a 2-back WM task versus rest.

Main Results:

Demonstrated reduced variability in global Pearson correlations for DMN and ECN during WM task compared to rest.
Showed that decreased correlation variability during WM is linked to fewer dominant CAPs, increased spatial consistency, and concentrated fractional contributions.
CAP metrics effectively characterized macroscopic decreases in correlation variations.

Conclusions:

Co-activation pattern (CAP) metrics offer a straightforward quantitative method for characterizing brain network dynamics.
These metrics provide an alternative to traditional time-windowed correlation analyses for studying brain states.
The findings highlight dynamic changes in brain network organization during cognitive tasks.