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Related Experiment Video

Updated: Aug 31, 2025

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Brain Network Analysis: A Review on Multivariate Analytical Methods.

Mohsen Bahrami^1,2, Paul J Laurienti^1,2, Heather M Shappell^1,3

¹Laboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.

Brain Connectivity

|August 25, 2022

Summary

This summary is machine-generated.

This review introduces multivariate methods for brain network analysis, addressing limitations of current univariate and graph-based approaches. It guides researchers in selecting appropriate methods for connectivity and topology analysis in complex brain data.

Keywords:

brain networks connectivity data-driven model-based multivariate

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Last Updated: Aug 31, 2025

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Published on: October 13, 2023

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Published on: June 26, 2013

Area of Science:

Neuroscience
Complex Systems Analysis
Data Science

Background:

Neuroimaging studies face methodological gaps in analyzing the brain as a complex system.
Current brain network analysis tools are often univariate and ill-suited for big, complex brain data.
Existing graph-based methods have limitations, and principled multivariate models for brain network analysis are underdeveloped.

Purpose of the Study:

To review and categorize important multivariate methods for brain network analysis.
To address the challenge of selecting the most appropriate multivariate method for specific research questions.
To aid investigators in choosing methods based on network type, data characteristics, and analytical goals (connectivity vs. topology).

Main Methods:

Categorization of multivariate methods into data-driven and model-based approaches.
Discussion of method suitability for analyzing connectivity (edge-level) and topology (system-level).
Consideration of factors influencing method selection, including network size, number of subjects, and brain regions.

Main Results:

Multivariate methods offer a promising avenue to overcome limitations of univariate and graph-based approaches.
A framework is presented to help researchers navigate the selection of appropriate multivariate techniques.
The review highlights the need for accessible guidelines in this multidisciplinary field.

Conclusions:

Choosing the right multivariate method is crucial for advancing brain network analysis.
This review provides a valuable resource for neuroimaging researchers to select appropriate analytical tools.
Dissemination of advanced analytical tools is key to improving our understanding of human health through biomedical data.