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

Detecting network modules in fMRI time series: a weighted network analysis approach. Mumford, J. A, Horvath, S., Oldham, M. C, Langfelder, P., Geschwind, D. H, & Poldrack, R. A Neuroimage, 52(4):1465–1476, 2010. Place: United States ISBN: 1095-9572
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Many network analyses of fMRI data begin by defining a set of regions, extracting the mean signal from each region and then analyzing the correlations between regions. One essential question that has not been addressed in the literature is how to best define the network neighborhoods over which a signal is combined for network analyses. Here we present a novel unsupervised method for the identification of tightly interconnected voxels, or modules, from fMRI data. This approach, weighted voxel coactivation network analysis (WVCNA), is based on a method that was originally developed to find modules of genes in gene networks. This approach differs from many of the standard network approaches in fMRI in that connections between voxels are described by a continuous measure, whereas typically voxels are considered to be either connected or not connected depending on whether the correlation between the two voxels survives a hard threshold value. Additionally, instead of simply using pairwise correlations to describe the connection between two voxels, WVCNA relies on a measure of topological overlap, which not only compares how correlated two voxels are but also the degree to which the pair of voxels is highly correlated with the same other voxels. We demonstrate the use of WVCNA to parcellate the brain into a set of modules that are reliably detected across data within the same subject and across subjects. In addition we compare WVCNA to ICA and show that the WVCNA modules have some of the same structure as the ICA components, but tend to be more spatially focused. We also demonstrate the use of some of the WVCNA network metrics for assessing a voxel's membership to a module and also how that voxel relates to other modules. Last, we illustrate how WVCNA modules can be used in a network analysis to find connections between regions of the brain and show that it produces reasonable results

@article{mumford_detecting_2010,
	title = {Detecting network modules in {fMRI} time series: a weighted network analysis approach.},
	volume = {52},
	doi = {10.1016/j.neuroimage.2010.05.047},
	abstract = {Many network analyses of fMRI data begin by defining a set of regions, extracting the mean signal from each region and then analyzing the correlations between regions. One essential question that has not been addressed in the literature is how to best define the network neighborhoods over which a signal is combined for network analyses. Here we present a novel unsupervised method for the identification of tightly interconnected voxels, or modules, from fMRI data. This approach, weighted voxel coactivation network analysis (WVCNA), is based on a method that was originally developed to find modules of genes in gene networks. This approach differs from many of the standard network approaches in fMRI in that connections between voxels are described by a continuous measure, whereas typically voxels are considered to be either connected or not connected depending on whether the correlation between the two voxels survives a hard threshold value. Additionally, instead of simply using pairwise correlations to describe the connection between two voxels, WVCNA relies on a measure of topological overlap, which not only compares how correlated two voxels are but also the degree to which the pair of voxels is highly correlated with the same other voxels. We demonstrate the use of WVCNA to parcellate the brain into a set of modules that are reliably detected across data within the same subject and across subjects. In addition we compare WVCNA to ICA and show that the WVCNA modules have some of the same structure as the ICA components, but tend to be more spatially focused. We also demonstrate the use of some of the WVCNA network metrics for assessing a voxel's membership to a module and also how that voxel relates to other modules. Last, we illustrate how WVCNA modules can be used in a network analysis to find connections between regions of the brain and show that it produces reasonable results},
	language = {eng},
	number = {4},
	journal = {Neuroimage},
	author = {Mumford, Jeanette A and Horvath, Steve and Oldham, Michael C and Langfelder, Peter and Geschwind, Daniel H and Poldrack, Russell A},
	year = {2010},
	pmid = {20553896},
	note = {Place: United States
ISBN: 1095-9572},
	keywords = {Algorithms, Brain, Brain Mapping, Evoked Potentials, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Nerve Net, Reproducibility of Results, Sensitivity and Specificity, research support, n.i.h., extramural, research support, u.s. gov't, non-p.h.s.},
	pages = {1465--1476},
}

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