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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Bibliographic Details
Published in:	NeuroImage (Orlando, Fla.) Vol. 154; pp. 188 - 205
Main Authors:	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.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.07.2017 Elsevier Limited Academic Press
Subjects:	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
Online Access:	Get full text
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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.
AbstractList	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. 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.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.
Author	Carone, Davide Griffanti, Ludovica Smith, Stephen M. Westphal, Robert Duff, Eugene P. Alfaro-Almagro, Fidel Evangelisti, Stefania Glasser, Matthew F. Beckmann, Christian F. Fitzgibbon, Sean Bijsterbosch, Janine Douaud, Gwenaëlle
AuthorAffiliation	f Laboratory of Experimental Stroke Research, Department of Surgery and Translational Medicine, University of Milano Bicocca, Milan Center of Neuroscience, Monza, Italy b Functional MR Unit, Policlinico S. Orsola - Malpighi, Bologna, Italy - Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy e Acute Stroke Programme, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom a Centre for the functional MRI of the Brain (FMRIB), University of Oxford, United Kingdom c Washington University School of Medicine, Washington University, St. Louis, MO, USA g Department of Cognitve Neuroscience, Radboudumc and Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands d Department of Psychiatry, University of Oxford, United Kingdom
AuthorAffiliation_xml	– name: a Centre for the functional MRI of the Brain (FMRIB), University of Oxford, United Kingdom – name: g Department of Cognitve Neuroscience, Radboudumc and Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands – name: e Acute Stroke Programme, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom – name: d Department of Psychiatry, University of Oxford, United Kingdom – name: c Washington University School of Medicine, Washington University, St. Louis, MO, USA – name: f Laboratory of Experimental Stroke Research, Department of Surgery and Translational Medicine, University of Milano Bicocca, Milan Center of Neuroscience, Monza, Italy – name: b Functional MR Unit, Policlinico S. Orsola - Malpighi, Bologna, Italy - Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
Author_xml	– sequence: 1 givenname: Ludovica surname: Griffanti fullname: Griffanti, Ludovica email: ludovica.griffanti@ndcn.ox.ac.uk organization: Centre for the functional MRI of the Brain (FMRIB), University of Oxford, United Kingdom – sequence: 2 givenname: Gwenaëlle surname: Douaud fullname: Douaud, Gwenaëlle organization: Centre for the functional MRI of the Brain (FMRIB), University of Oxford, United Kingdom – sequence: 3 givenname: Janine surname: Bijsterbosch fullname: Bijsterbosch, Janine organization: Centre for the functional MRI of the Brain (FMRIB), University of Oxford, United Kingdom – sequence: 4 givenname: Stefania surname: Evangelisti fullname: Evangelisti, Stefania organization: Centre for the functional MRI of the Brain (FMRIB), University of Oxford, United Kingdom – sequence: 5 givenname: Fidel surname: Alfaro-Almagro fullname: Alfaro-Almagro, Fidel organization: Centre for the functional MRI of the Brain (FMRIB), University of Oxford, United Kingdom – sequence: 6 givenname: Matthew F. surname: Glasser fullname: Glasser, Matthew F. organization: Washington University School of Medicine, Washington University, St. Louis, MO, USA – sequence: 7 givenname: Eugene P. surname: Duff fullname: Duff, Eugene P. organization: Centre for the functional MRI of the Brain (FMRIB), University of Oxford, United Kingdom – sequence: 8 givenname: Sean surname: Fitzgibbon fullname: Fitzgibbon, Sean organization: Centre for the functional MRI of the Brain (FMRIB), University of Oxford, United Kingdom – sequence: 9 givenname: Robert surname: Westphal fullname: Westphal, Robert organization: Department of Psychiatry, University of Oxford, United Kingdom – sequence: 10 givenname: Davide surname: Carone fullname: Carone, Davide organization: Acute Stroke Programme, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom – sequence: 11 givenname: Christian F. surname: Beckmann fullname: Beckmann, Christian F. organization: Centre for the functional MRI of the Brain (FMRIB), University of Oxford, United Kingdom – sequence: 12 givenname: Stephen M. surname: Smith fullname: Smith, Stephen M. organization: Centre for the functional MRI of the Brain (FMRIB), University of Oxford, United Kingdom
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/27989777$$D View this record in MEDLINE/PubMed
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Snippet	We present a practical “how-to” guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual... We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual...
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SubjectTerms	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
Title	Hand classification of fMRI ICA noise components
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