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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.

Original publication

DOI

10.1016/j.neuroimage.2019.04.046

Type

Journal article

Journal

Neuroimage

Publication Date

15/08/2019

Volume

197

Pages

435 - 438

Keywords

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