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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