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Abstract #3471

Improving the robustness of independent component analysis for denoising multi-echo fMRI data

Bahman Tahayori1, Robert E. Smith1, David N. Vaughan1, Chris Tailby1, Eric Y. Pierre1, Graeme D. Jackson1,2, and David F. Abbott1,2
1The Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia, 2Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia

Synopsis

Keywords: Data Analysis, fMRI (task based)Multi-Echo fMRI data acquisition has multiple advantages over single-echo acquisition. Principal amongst them is that multiple echoes can distinguish neural activity from artefacts. TE Dependent ANAlysis (TEDANA) is an existing software tool designed to denoise multi-echo fMRI datasets. We evaluated the performance of TEDANA to denoise fMRI data of 120 subjects. Our results demonstrated that TEDANA improved the activation detection at a group level. However, for a subset of subjects TEDANA degraded their individual result substantially. We identified potential causes and proposed a modified framework for multi-echo data analysis that provides reasonable results at an individual subject level.

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Keywords