Hand classification of fMRI ICA noise components

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...

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Vydáno v:NeuroImage (Orlando, Fla.) Ročník 154; s. 188 - 205
Hlavní autoři: Griffanti, Ludovica, Douaud, Gwenaëlle, Bijsterbosch, Janine, Evangelisti, Stefania, Alfaro-Almagro, Fidel, Glasser, Matthew F., Duff, Eugene P., Fitzgibbon, Sean, Westphal, Robert, Carone, Davide, Beckmann, Christian F., Smith, Stephen M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Inc 01.07.2017
Elsevier Limited
Academic Press
Témata:
Adult
Algorithms
Brain - diagnostic imaging
Child
Classification
Data processing
Decomposition
Functional magnetic resonance imaging
Functional Neuroimaging - methods
Humans
Identification
Image Processing, Computer-Assisted - methods
Magnetic Resonance Imaging - methods
Methods
NMR
Noise
Nuclear magnetic resonance
Temporal variations
Time series
ISSN:1053-8119, 1095-9572, 1095-9572
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Popis
Shrnutí: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. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.
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ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2016.12.036