Abstract #4707

ICA-based overt speech artifact removal leads to improved estimation of deconvolution-based hemodynamic response function in aphasics

Venkatagiri Krishnamurthy^1,2, Lisa C. Krishnamurthy^2,3, Michelle L. Benjamin⁴, Kaundinya Gopinath⁵, and Bruce A. Crosson^1,2,6

¹Dept. of Neurology, Emory University, Atlanta, GA, United States, ²Center for Visual and Neurocognitive Rehabilitation, Atlanta VAMC, Decatur, GA, United States, ³Dept. of Physics & Astronomy, Georgia State University, Atlanta, GA, United States, ⁴University of Florida, Gainesville, FL, United States, ⁵Dept. of Radiology & Imaging Sciences, Emory University, Atlanta, GA, United States, ⁶Dept. of Psychology, Georgia State University, Atlanta, United States

Overt speech task functional Magnetic Resonance Imaging (fMRI) paradigms are very attractive to study aphasic patients, but are also plagued by task-correlated motion (TCM). Speech involves movements of the mouth and soft palate, and causes a change in air volume around these areas leading to localized motion and susceptibility artifacts. These artifacts become more severe in patients with Aphasia. The goal of this study is to utilize existing FSL-based semi-automated ICA tools, and optimize them to go beyond removing standard fMRI artifacts by also mitigating TCM artifacts to obtain meaningful hemodynamic response function (HRF) in aphasic patients. Our preliminary results to utilize ICA for TCM-based artifact removal is promising as evidenced by the improved sensitivity and specificity, but needs further optimization. Optimal denoising of overt speech task fMRI in aphasic patients will also help us to delineate their task-based networks in an effort to monitor plastic changes due to language behavior interventions.

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