| Paper: | SAM-L2.4 | ||
| Session: | Blind Source Estimation and Channel Identification | ||
| Time: | Wednesday, May 19, 10:30 - 10:50 | ||
| Presentation: | Lecture | ||
| Topic: | Sensor Array and Multichannel Signal Processing: Multi-channel biological and medical modeling and processing | ||
| Title: | MOTION-CORRECTED INDEPENDENT COMPONENT ANALYSIS FOR ROBUST FUNCTIONAL MAGNETIC RESONANCE IMAGING | ||
| Authors: | Rui Liao; Duke University | ||
| Martin McKeown; University of British Columbia | |||
| Jeffrey Krolik; Duke University | |||
| Abstract: | Patient head movement during fMRI data collection can result in confusing artifacts when estimating task-related brain activations. In this paper, we propose a method, named Motion-Corrected Independent Component Analysis (MCICA), for motion correction of fMRI timeseries by maximizing the entropy difference between the observed fMRI data and a nonlinear function of the derived ICA components. Simulations demonstrate that MCICA was robust to activation level and the resultswere more accurate than cubic interpolation even when the displacement was known. Moreover, in a real data from a motor fMRI experiment, preprocessing the data with MCICA resulted in the emergence of activation in the primary motor and supplementary motor cortices, and the Mutual Information (MI) between all subsequent volumes and the first one was consistently increased. | ||
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