Journal article

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.

Publication status:: Published

Peer review status:: Peer reviewed

Actions

Email

Email this record

Send the bibliographic details of this record to your email address.

Your Email
Please enter the email address that the record information will be sent to.

-
Your message (optional)
Please add any additional information to be included within the email.
Cite

Cite this record

APA Style

Griffanti, L., Douaud, G., Bijsterbosh, J., Evangelisti, S., Alfaro-Almagro, F., Glasser, M., Duff, E., Fitzgibbon, S., Westphal, R., Carone, D., Beckmann, C., & Smith, S. (2016). Hand classification of fMRI ICA noise components. NeuroImage, 154, 188–205.

MLA Style

Griffanti, L., et al. “Hand Classification of FMRI ICA Noise Components.” NeuroImage, vol. 154, Elsevier, 2016, pp. 188–205.

Chicago Style

Griffanti, L, G Douaud, J Bijsterbosh, S Evangelisti, F Alfaro-Almagro, M Glasser, E Duff, et al. 2016. “Hand Classification of FMRI ICA Noise Components.” NeuroImage 154: 188–205.
Share
Print

Access Document

Files:: Griffanti, L, Douaud, G, Bijsterbosh, J et al., (2016) NeuroIm...

(Preview, Version of record, pdf, 3.0MB, Terms of use)

Publisher copy:: 10.1016/j.neuroimage.2016.12.036

Authors

+ Griffanti, L More by this author

Institution:: University of Oxford
Division:: MSD
Department:: Clinical Neurosciences
Role:: Author

+ Douaud, G More by this author

Institution:: University of Oxford
Division:: MSD
Department:: Clinical Neurosciences
Role:: Author

+ Bijsterbosh, J More by this author

Role:: Author

+ Evangelisti, S More by this author

Role:: Author

+ Alfaro-Almagro, F More by this author

Institution:: University of Oxford
Division:: MSD
Department:: Clinical Neurosciences
Role:: Author

More authors...

+ Medical Research Council More from this funder

Funding agency for:: Douaud, G
Grant:: MR/K006673/1

+ Wellcome Trust More from this funder

Grant:: Strategic Award [098369/Z/12/Z]

+ National Institute for Health Research Oxford Biomedical Research Centre More from this funder

Funding agency for:: Griffanti, L

Publisher:: Elsevier
Journal:: NeuroImage More from this journal
Volume:: 154
Pages:: 188-205
Publication date:: 2016-12-16
DOI:: 10.1016/j.neuroimage.2016.12.036
EISSN:: 1095-9572
ISSN:: 1053-8119

Language:: English
Pubs id:: pubs:667162
UUID:: uuid:e5ec1c1c-dacf-463e-81f3-60ff8c6c3aaa
Local pid:: pubs:667162
Deposit date:: 2017-01-13

Terms of use

Copyright holder:: Griffanti et al
Copyright date:: 2016
Notes:: Copyright © 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

Licence:: CC Attribution (CC BY)

Views and Downloads

About views and downloads

If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP