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Abstract #2539

Bayesian Longitudinal Tensor Response Regression to model task-fMRI based neuroplasticity in post-stroke aphasia

Venkatagiri Krishnamurthy1,2, Alec Reinhardt3,4, Serena Song2, Joo Han2, M. Lawson Meadows2, Bruce Crosson2, and Suprateek Kundu3,4
1Dept. of Medicine, Emory University, Atlanta, GA, United States, 2Atlanta VA Medical Center, Decatur, GA, United States, 3Dept. of Biostatistics, Emory University, Atlanta, GA, United States, 4Dept. of Biostatistics, UT MD Anderson Cancer Center, Houston, TX, United States

Synopsis

Keywords: Data Analysis, fMRI (task based), NeuroplasticityStroke is inherently complex due to the heterogeneity in lesion location, size in addition to other clinical comorbidities. Further, longitudinal three-dimensional fMRI datasets add more complexity towards estimating sensitive and robust biomarkers of neuroplasticity. In this study we propose an innovative extension of Bayesian Tensor Response Regression (BTRR) approach to estimate neuroplasticity that is more sensitive, accurate and reliable compared to traditional voxel-wise approach. Results from our longitudinal aphasia-treatment study not only show that the BTRR approach is more superior but is also able to derive plasticity estimates that are sensitive to treatment differences that are subject and time-point specific.

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