TY - JOUR
T1 - Hand classification of fMRI ICA noise components
AU - Griffanti, Ludovica
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.
N1 - Funding Information:
LG is supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre based at Oxford University Hospitals NHS Trust and University of Oxford. GD is supported by the UK Medical Research Council (MRC) MR/K006673/1. C.F.B. is supported by the Netherlands Organisation for Scientific Research (NWO-Vidi 864-12-003). Data for the HCP 3T dataset were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: D.C. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The authors gratefully acknowledge funding from the Wellcome Trust UK Strategic Award [098369/Z/12/Z]. For the 7T dataset we thank Stuart Clare and Samuel Hurley for data acquisition, and Jill Betts and Sirius Boessenkool for participant recruitment. We also thank Andreas Bartsch for the helpful discussion on cerebral vascular anatomy.
Publisher Copyright:
© 2016 The Authors
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85008185859&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2016.12.036
DO - 10.1016/j.neuroimage.2016.12.036
M3 - Article
C2 - 27989777
AN - SCOPUS:85008185859
SN - 1053-8119
VL - 154
SP - 188
EP - 205
JO - NeuroImage
JF - NeuroImage
ER -