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Abstract #4126

Decoding functional MRI data using sPFM and temporal ICA: a validation study

Francisca Marie Tan 1,2 , Karen Mullinger 1 , Csar Caballero Gaudes 3 , Yaping Zhang 2 , David Siu-Yeung Cho 2 , Yihui Liu 4 , Susan Francis 1 , and Penny Gowland 1

1 Sir Peter Mansfield Magnetic Resonance Centre, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom, 2 Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo, Zhejiang, China, 3 Basque Center on Cognition, Brain and Language, Donostia, Spain, 4 School of Information Science, Qilu University of Technology, Jinan, Shandong, China

Decoding mental activity at rest is a challenge because spontaneous events occur in the brain without any attributed task or prior stimulus timing. In this study, we validate the use of Sparse Paradigm Free Mapping prior to Temporal Independent Component Analysis (tICA) on a movement task to detect discrete motor events. The tICA components are assessed against EMG and classified using a meta-analysis, with 78 % of task-driven events identified by tICA. Results suggest that this method can be used in future studies of resting data to detect events and map these to functional areas using a meta-analysis.

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