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

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

Matthew F Glasser  1 , Timothy S Coalson  2 , Janine D Bijsterbosch  3 , Samuel J Harrison  4 , Michael P Harms  5 , Alan Anticevic  6 , David C Van Essen  2 , Stephen M Smith  3
Affiliations

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

Matthew F Glasser et al. Neuroimage. .

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.

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Figures

Figure 1.
Figure 1.
Temporal ICA cleaned greyplot from task subject 2 from Power 2019 compared with the task ‘on’ versus ‘off’ design convolved with the hemodynamic response function. Power’s regions of concern are highlighted with |_ _ _ _| (and also accompanied by a series of vertical tick marks). Task positive regions are located below row ~1000 in the greyplot (see original Figure 13 bottom row blue regions).
See this image and copyright information in PMC

References

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