Graphical modelling for brain connectivity via partial coherence

T. Medkour, A.T. Walden, Adrian P. Burgess

Research output: Contribution to journalArticlepeer-review


Spectral and coherence methodologies are ubiquitous for the analysis of multiple time series. Partial coherence analysis may be used to try to determine graphical models for brain functional connectivity. The outcome of such an analysis may be considerably influenced by factors such as the degree of spectral smoothing, line and interference removal, matrix inversion stabilization and the suppression of effects caused by side-lobe leakage, the combination of results from different epochs and people, and multiple hypothesis testing. This paper examines each of these steps in turn and provides a possible path which produces relatively ‘clean’ connectivity plots. In particular we show how spectral matrix diagonal up-weighting can simultaneously stabilize spectral matrix inversion and reduce effects caused by side-lobe leakage, and use the stepdown multiple hypothesis test procedure to help formulate an interaction strength.
Original languageEnglish
Pages (from-to)374-383
Number of pages10
JournalJournal of Neuroscience Methods
Issue number2
Publication statusPublished - 15 Jun 2009


  • brain connectivity
  • graphical models
  • partial spectral coherence


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