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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