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Brain network analysis : separating cost from topology using cost-integration
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Ginestet, Cedric E., Nichols, Thomas E., Bullmore, Edward T. and Simmons, Andrew (2011) Brain network analysis : separating cost from topology using cost-integration. PLoS ONE, Vol.6 (No.7). Article: e21570. doi:10.1371/journal.pone.0021570 ISSN 1932-6203.
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Official URL: http://dx.doi.org/10.1371/journal.pone.0021570
Abstract
A statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of
brain networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of
edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated
topological metrics. Our focus is on comparing populations of weighted undirected graphs that differ in mean association
weight, using global efficiency. Our key result shows that integrating over cost is equivalent to controlling for any
monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the
differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any
unweighted topological measure, and for any choice of distribution over cost levels. Cost-integration is therefore helpful in
disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of
a topological metric is generally not a valid approach to this problem. Indeed, we prove that, under weak conditions, the
use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the
reporting of (i) differences in weighted costs and (ii) differences in cost-integrated topological measures with respect to
different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI
working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures.
Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero,
when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences,
which could be masked by cost-integration.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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Library of Congress Subject Headings (LCSH): | Graph theory, System analysis, Brain -- Mathematical models | ||||
Journal or Publication Title: | PLoS ONE | ||||
Publisher: | Public Library of Science | ||||
ISSN: | 1932-6203 | ||||
Official Date: | 28 July 2011 | ||||
Dates: |
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Volume: | Vol.6 | ||||
Number: | No.7 | ||||
Number of Pages: | 17 | ||||
Page Range: | Article: e21570 | ||||
DOI: | 10.1371/journal.pone.0021570 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 17 December 2015 | ||||
Date of first compliant Open Access: | 17 December 2015 | ||||
Funder: | National Institute for Health Research (Great Britain) (NIHR), Guy's & St. Thomas' Hospital Trust. Charitable Foundation, South London and Maudsley NHS Trust |
Data sourced from Thomson Reuters' Web of Knowledge
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