Hand classification of fMRI ICA noise components

Ludovica Griffanti; Gwenaëlle Douaud; Janine Bijsterbosch; Stefania Evangelisti; Fidel Alfaro-Almagro; Matthew F. Glasser; Eugene P. Duff; Sean Fitzgibbon; Robert Westphal; Davide Carone; Christian F. Beckmann; Stephen M. Smith

doi:10.1016/j.neuroimage.2016.12.036

Hand classification of fMRI ICA noise components

Ludovica Griffanti, Gwenaëlle Douaud, Janine Bijsterbosch, Stefania Evangelisti, Fidel Alfaro-Almagro, Matthew F. Glasser, Eugene P. Duff, Sean Fitzgibbon, Robert Westphal, Davide Carone, Christian F. Beckmann, Stephen M. Smith

Research output: Contribution to journal › Article › peer-review

359 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	188-205
Number of pages	18
Journal	NeuroImage
Volume	154
DOIs	https://doi.org/10.1016/j.neuroimage.2016.12.036
State	Published - Jul 1 2017

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10.1016/j.neuroimage.2016.12.036

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title = "Hand classification of fMRI ICA noise components",

abstract = "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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AU - Douaud, Gwenaëlle

AU - Bijsterbosch, Janine

AU - Evangelisti, Stefania

AU - Alfaro-Almagro, Fidel

AU - Glasser, Matthew F.

AU - Duff, Eugene P.

AU - Fitzgibbon, Sean

AU - Westphal, Robert

AU - Carone, Davide

AU - Beckmann, Christian F.

AU - Smith, Stephen M.

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Hand classification of fMRI ICA noise components

Abstract

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