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

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    Type
    Article
    Authors
    Huang, Shih-Gu
    Samdin, S Balqis
    Ting, Chee-Ming
    Ombao, Hernando cc
    Chung, Moo K
    KAUST Department
    Statistics Program, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
    Statistics Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-11-25
    Online Publication Date
    2019-11-21
    Print Publication Date
    2020-02
    Embargo End Date
    2020-12-06
    Permanent link to this record
    http://hdl.handle.net/10754/660465
    
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    Abstract
    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.
    Citation
    Huang, S.-G., Samdin, S. B., Ting, C.-M., Ombao, H., & Chung, M. K. (2019). Statistical model for dynamically-changing correlation matrices with application to brain connectivity. Journal of Neuroscience Methods, 108480. doi:10.1016/j.jneumeth.2019.108480
    Sponsors
    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.
    Publisher
    Elsevier BV
    Journal
    Journal of neuroscience methods
    DOI
    10.1016/j.jneumeth.2019.108480
    arXiv
    1812.10050
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0165027019303371
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7739896
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jneumeth.2019.108480
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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