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Abstract #0439

Investigating the neural basis of the default mode network using blind hemodynamic deconvolution of resting state fMRI data

Sreenath Pruthviraj Kyathanahally 1,2 , Karthik R Sreenivasan 1 , Daniele Marinazzo 3 , Guorong Wu 3,4 , and Gopikrishna Deshpande 1,5

1 AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, United States, 2 Department of Clinical Research, Unit for MR Spectroscopy and Methodology, University of Bern, Bern, Switzerland, 3 Department of Data Analysis, Ghent University, Ghent, Belgium, 4 School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China, 5 Department of Psychology, Auburn University, Auburn, Alabama, United States

Since the fMRI time series at each voxel is the convolution of an underlying neural signal with the hemodynamic response, there is a debate on whether the Default mode network(DMN) has a neural origin or is at least in part (or at most fully) a consequence of hemodynamic processes and physiological noise arising due to cardiac pulsation and respiration. In order to investigate this, we performed blind hemodynamic deconvolution of resting state fMRI data that was acquired with different TR and magnetic field strength. Subsequently functional connectivity maps were found using seed based correlation analysis on latent neuronal signals with a posterior cingulate seed in order to identify the DMN.

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