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Detecting network modules in fMRI time series: a weighted network analysis approach.

Jeanette A Mumford¹, Steve Horvath, Michael C Oldham

¹Department of Psychology, University of Texas at Austin, Austin, TX 78712-0187, USA. mumford@mail.utexas.edu

|June 18, 2010

Summary

We introduce weighted voxel coactivation network analysis (WVCNA), a new unsupervised method for identifying brain modules in fMRI data. WVCNA offers a more nuanced approach to network analysis, improving the definition of brain networks.

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Area of Science:

Neuroimaging
Network Neuroscience
Computational Neuroscience

Background:

Network analysis of fMRI data typically involves defining regions, extracting signals, and analyzing correlations.
A key challenge is determining optimal methods for defining network neighborhoods for signal combination.

Purpose of the Study:

To present a novel unsupervised method, weighted voxel coactivation network analysis (WVCNA), for identifying tightly interconnected modules of voxels in fMRI data.
To address the need for improved methods in defining brain network neighborhoods for fMRI analysis.

Main Methods:

WVCNA adapts a gene network module-finding approach for fMRI data.
It uses a continuous measure of voxel connections based on topological overlap, unlike traditional hard thresholding.

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The method assesses voxel coactivation and shared correlations with other voxels.

Main Results:

WVCNA reliably parcellates the brain into modules detectable within and across subjects.
WVCNA modules exhibit similar structure to Independent Component Analysis (ICA) components but are more spatially focused.
Network metrics were demonstrated for assessing voxel-module membership and inter-module relationships.

Conclusions:

WVCNA provides a robust and spatially focused method for brain parcellation and network analysis in fMRI.
The method offers a continuous, topological overlap-based approach to defining voxel interactions.
WVCNA modules can be effectively used to identify brain region connections, yielding reasonable results.