Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power

Neuroimage. 2019 Aug 15:197:435-438. doi: 10.1016/j.neuroimage.2019.04.046. Epub 2019 Apr 24.

Abstract

We respond to a critique of our temporal Independent Components Analysis (ICA) method for separating global noise from global signal in fMRI data that focuses on the signal versus noise classification of several components. While we agree with several of Power's comments, we provide evidence and analysis to rebut his major criticisms and to reassure readers that temporal ICA remains a powerful and promising denoising approach.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artifacts*
  • Brain / physiology*
  • Brain Mapping / methods*
  • Data Interpretation, Statistical
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Magnetic Resonance Imaging*
  • Principal Component Analysis
  • Signal Processing, Computer-Assisted