Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

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 publication

DOI

10.1016/j.neuroimage.2016.12.036

Type

Journal article

Journal

NeuroImage

Publication Date

07/2017

Volume

154

Pages

188 - 205

Addresses

Centre for the functional MRI of the Brain (FMRIB), University of Oxford, United Kingdom. Electronic address: ludovica.griffanti@ndcn.ox.ac.uk.

Keywords

Brain, Humans, Magnetic Resonance Imaging, Image Processing, Computer-Assisted, Adult, Child, Functional Neuroimaging