Hand classification of fMRI ICA noise components.
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ABSTRACT:
SUBMITTER: Griffanti L
PROVIDER: S-EPMC5489418 | biostudies-literature | 2017 Jul
REPOSITORIES: biostudies-literature
Publications
Hand classification of fMRI ICA noise components.
Griffanti Ludovica L Douaud Gwenaëlle G Bijsterbosch Janine J Evangelisti Stefania S Alfaro-Almagro Fidel F Glasser Matthew F MF Duff Eugene P EP Fitzgibbon Sean S Westphal Robert R Carone Davide D Beckmann Christian F CF Smith Stephen M SM
NeuroImage 20161216
We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Example ...[more]
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