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Abstract #2102

Resting State Network Detection with Searchlight on Functional MRI

Shiyang Chen 1,2 , Hasan Ertan Cetingul 2 , Xiaoping Hu 1,3 , and Mariappan S. Nadar 2

1 The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States, 2 Imaging and Computer Vision, Siemens Corporation, Corporate Technology, Princeton, NJ, United States, 3 Biomedical Imaging Technology Center, Emory University, Atlanta, GA, United States

Resting state networks (RSNs) are important biomarkers for disease diagnosis, (e.g. Alzheimers disease). The conventional methods to detect RSNs require a spatial smoothing step to compensate the low SNR of fMRI, and they assume the brain connectivity is voxel-to-voxel. These steps and assumptions are still controversial. We propose a searchlight plus multivariate regression method to detect the RSNs, which is able to detect region-to-region brain connection and can be performed on unsmoothed or slightly smoothed fMRI data. We validate this method by detecting the conventional RSNs, and we also found additional brain regions that are connected in the RSNs.

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