Controlling the false discovery rate in modeling brain functional connectivity

TitleControlling the false discovery rate in modeling brain functional connectivity
Publication TypeConference Paper
Year of Publication2008
AuthorsLi, J., Z. J. Wang, and M. J. McKeown
Conference NameAcoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Pagination2105 -2108
Date Publishedmar.
Keywordsbiomedical MRI, brain, brain functional connectivity modeling, conditional-dependence relationships, diseases, error-rate-control method, false discovery rate control, fMRI study, functional magnetic resonance imaging, graph theory, graphical models, group analysis, Parkinson's disease

Graphical models of brain functional connectivity have matured from confirming a priori hypotheses to an exploratory tool for discovering unknown connectivity. However, exploratory methods must control the error rate of "discovered" connectivity networks. Here we explore an error-rate-control method for graphical models which controls the false-discovery-rate (FDR) of the conditional-dependence relationships that a graphical model encodes. The application of this method to a group analysis of fMRI study on Parkinson's disease shows that it effectively controls the errors introduced by randomness, and yields meaningful and consistent results. The proposed approach appears promising for functional-connectivity modeling and deserves further investigation.


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