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Literature DB >> 31026516

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

Matthew F Glasser¹, Timothy S Coalson², Janine D Bijsterbosch³, Samuel J Harrison⁴, Michael P Harms⁵, Alan Anticevic⁶, David C Van Essen², Stephen M Smith³.

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.

Entities: CellLine Chemical Disease Gene Species

Mesh：

Year: 2019 PMID： 31026516 PMCID： PMC6591096 DOI： 10.1016/j.neuroimage.2019.04.046

Source DB: PubMed Journal: Neuroimage ISSN： 1053-8119 Impact factor: 6.556

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5. Impact of Independent Component Analysis Dimensionality on the Test-Retest Reliability of Resting-State Functional Connectivity.

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