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 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…

• WT
  Wellcome Trust

  Awards: 1U54MH091657, MR/K006673/1, 098369/Z/12/Z
• NI
  National Institute for Health and Care Research

  Award: MR/K006673/1
• UO
  University of Oxford
• NO
  Nederlandse Organisatie voor Wetenschappelijk Onderzoek

  Awards: 864-12-003, NWO-Vidi 864-12-003, 1U54MH091657, NWO-Vidi 864-12
• NI
  National Institutes of Health

  Award: 1U54MH091657
• MC
  McDonnell Center for Systems Neuroscience

  Award: 1U54MH091657
• MR
  Medical Research Council

  Awards: MR/K006673/1, MR/M024962/1, 098369/Z/12/Z, 1U54MH091657, MR/K006673/1, MC_EX_MR/N50192X/1, MR/L023784/1
• NB
  NIH Blueprint for Neuroscience Research

  Award: 1U54MH091657