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Abstract #5381

Reproducibility of graphical measures and dynamic network features in resting state fMRI

Sue-Jin Lin1,2, Tobias R. Baumeister2,3, Alex MacKay4,5, Irene Vavasour5, David K.B. Li5,6, and Martin J McKeown1,2,6

1Graduate Program in Neuroscience, University of British Columbia, Vancouver, BC, Canada, 2Pacific Parkinson’s Research Centre, UBC Hospital, Vancouver, BC, Canada, 3Department of Biomedical Engineering Program, University of British Columbia, 4Department of Physics and Astronomy, University of British Columbia, 5Department of Radiology, UBC Hospital, 6Neurology, Faculty of Medicine, University of British Columbia

Resting state fMRI (rsfMRI) has been widely used to study brain function. Numerous informative features derived from rsfMRI data have been proposed, such as graphical metrics and dynamic connectivity, but their robustness is uncertain. In order to verify their reproducibility, we acquired rsfMRI three times for 11 subjects and calculated 7 graphical measures and 7 dynamic network features. None of the measures showed significant differences among the three rsfMRI sessions. Therefore, we concluded that graphical measures and dynamic network features in rsfMRI are at least robust to inter-trial variability, which should ameliorate uncertainties when applying them to clinical research.

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