Abstract #5381

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

Sue-Jin Lin^1,2, Tobias R. Baumeister^2,3, Alex MacKay^4,5, Irene Vavasour⁵, David K.B. Li^5,6, and Martin J McKeown^1,2,6

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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