Statistical model for dynamically-changing correlation matrices with application to brain connectivity.

Shih-Gu Huang, S Balqis Samdin, Chee-Ming Ting, Hernando Ombao, Moo K Chung

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


BACKGROUND:Recent studies have indicated that functional connectivity is dynamic even during rest. A common approach to modeling the dynamic functional connectivity in whole-brain resting-state fMRI is to compute the correlation between anatomical regions via sliding time windows. However, the direct use of the sample correlation matrices is not reliable due to the image acquisition and processing noises in resting-sate fMRI. NEW METHOD:To overcome these limitations, we propose a new statistical model that smooths out the noise by exploiting the geometric structure of correlation matrices. The dynamic correlation matrix is modeled as a linear combination of symmetric positive-definite matrices combined with cosine series representation. The resulting smoothed dynamic correlation matrices are clustered into disjoint brain connectivity states using the k-means clustering algorithm. RESULTS:The proposed model preserves the geometric structure of underlying physiological dynamic correlation, eliminates unwanted noise in connectivity and obtains more accurate state spaces. The difference in the estimated dynamic connectivity states between males and females is identified. COMPARISON WITH EXISTING METHODS:We demonstrate that the proposed statistical model has less rapid state changes caused by noise and improves the accuracy in identifying and discriminating different states. CONCLUSIONS:We propose a new regression model on dynamically changing correlation matrices that provides better performance over existing windowed correlation and is more reliable for the modeling of dynamic connectivity.
Original languageEnglish (US)
Pages (from-to)108480
JournalJournal of neuroscience methods
StatePublished - Nov 21 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This study was supported by NIH Brain Initiative grant EB022856, NIH grant R01-MH11569 and KAUST. We would like to thank Andrey
Gritsenko, Gregory Kirk and Rasmus M. Birn of University of Wisconsin Madison and Martin Lindquist of Johns Hopkins University for valuable discussions and logistic supports.


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