Robust identification of partial-correlation based networks with applications to cortical thickness data

D. Wheland, A. Joshi, K. McMahon, N. Hansell, N. Martin, M. Wright, P. Thompson, D. Shattuck, R. Leahy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Insight into brain development and organization can be gained by computing correlations between structural and functional measures in parcellated cortex. Partial correlations can often reduce ambiguity in correlation data by identifying those pairs of regions whose similarity cannot be explained by the influence of other regions with which they may both interact. Consequently a graph with edges indicating non-zero partial correlations may reveal important subnetworks obscured in the correlation data. Here we describe and investigate PC*, a graph pruning algorithm for identification of the partial correlation network in comparison to direct calculation of partial correlations from the inverse of the sample correlation matrix. We show that PC* is far more robust and illustrate its use in the study of covariation in cortical thickness in ROIs defined on a parcellated cortex.

Original languageEnglish
Title of host publication2012 9th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2012 - Proceedings
Pages1551-1554
Number of pages4
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain
Duration: 2 May 20125 May 2012

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
Country/TerritorySpain
CityBarcelona
Period2/05/125/05/12

Keywords

  • PC algorithm
  • brain networks
  • graphical Gaussian model
  • human connectome
  • partial correlation

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