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Dynamic spatiotemporal brain analyses using high-performance electrical neuroimaging, Part II: A step-by-step

Stephanie Cacioppo¹, John T Cacioppo²

¹High-Performance Electrical Neuroimaging Laboratory, Biological Science Division, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA.

Journal of Neuroscience Methods

|September 13, 2015

Summary

This summary is machine-generated.

This tutorial introduces the Chicago Electrical Neuroimaging Analytics (CENA) toolbox for analyzing brain microstate dynamics and global field power. CENA offers advanced tools for statistical analysis of electrical brain activity, enhancing neuroimaging research.

Keywords:

Biomarkers Bootstrapping Brain electrodynamics Chicago Electrical Neuroimaging Analytics (CENA)Cosine distance metric EEG/ERP Electrical neuroimaging Freeware Global field power High-performance microsegmentation suite (HPMS)Root mean square error Tutorial

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Last Updated: Apr 4, 2026

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

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Cortical Source Analysis of High-Density EEG Recordings in Children

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

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Published on: June 30, 2018

Area of Science:

Neuroscience
Computational Neuroscience
Brain Imaging Analysis

Background:

Existing analytical toolboxes for electroencephalography (EEG) and magnetoencephalography (MEG) analysis have limitations in detecting event-related changes in brain activity patterns.
Automatic detection of brain microstates and global field power requires robust statistical frameworks and validated analytical methods.

Purpose of the Study:

To provide a comprehensive, step-by-step tutorial for the Chicago Electrical Neuroimaging Analytics (CENA) toolbox.
To detail analytical plans for statistical analysis of brain microstate configuration and global field power.
To facilitate the use of CENA for within- and between-subject designs in neuroimaging research.

Main Methods:

The tutorial details the application of the CENA toolbox, which includes functions for difference wave analysis.
It elaborates on the high-performance microsegmentation suite (HPMS) with tools for root mean square error (RMSE) and cosine similarity metrics.
It describes bootstrapping for assessing solution robustness and procedures for a priori contrasts in data analysis.

Main Results:

The CENA toolbox provides a validated framework for automatic detection of event-related changes in electrical brain activity.
The HPMS suite effectively identifies stable and transition states in brain microstates and quantifies changes in global field power.
Bootstrapping and contrast procedures enhance the reliability and interpretability of neuroimaging data analysis.

Conclusions:

The CENA toolbox offers a powerful and accessible resource for advanced spatiotemporal analysis of brain electrical activity.
This tutorial and associated resources empower researchers to conduct rigorous statistical analyses of brain microstate dynamics.
CENA is freely available, promoting wider adoption and advancement in the field of neuroimaging analysis.