Hand classification of fMRI ICA noise components

• 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
• DOI: 10.1016/j.neuroimage.2016.12.036

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.