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

Matthew F. Glasser; Timothy S. Coalson; Janine D. Bijsterbosch; Samuel J. Harrison; Michael P. Harms; Alan Anticevic; David C. Van Essen; Stephen M. Smith

doi:10.1016/j.neuroimage.2019.04.046

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

Matthew F. Glasser
, Timothy S. Coalson
, Janine D. Bijsterbosch
, Samuel J. Harrison
, Michael P. Harms
, Alan Anticevic
, David C. Van Essen
, Stephen M. Smith

Research output: Contribution to journal › Article › peer-review

32 Scopus citations

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.

Original language	English
Pages (from-to)	435-438
Number of pages	4
Journal	NeuroImage
Volume	197
DOIs	https://doi.org/10.1016/j.neuroimage.2019.04.046
State	Published - Aug 15 2019

Access to Document

10.1016/j.neuroimage.2019.04.046

Cite this

@article{a63bd7ec957149448d91f2b60a937ee8,

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

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.",

author = "Glasser, \{Matthew F.\} and Coalson, \{Timothy S.\} and Bijsterbosch, \{Janine D.\} and Harrison, \{Samuel J.\} and Harms, \{Michael P.\} and Alan Anticevic and \{Van Essen\}, \{David C.\} and Smith, \{Stephen M.\}",

note = "Publisher Copyright: {\textcopyright} 2019 Elsevier Inc.",

year = "2019",

month = aug,

day = "15",

doi = "10.1016/j.neuroimage.2019.04.046",

language = "English",

volume = "197",

pages = "435--438",

journal = "NeuroImage",

issn = "1053-8119",

}

TY - JOUR

T1 - Classification of temporal ICA components for separating global noise from fMRI data

T2 - Reply to Power

AU - Glasser, Matthew F.

AU - Coalson, Timothy S.

AU - Bijsterbosch, Janine D.

AU - Harrison, Samuel J.

AU - Harms, Michael P.

AU - Anticevic, Alan

AU - Van Essen, David C.

AU - Smith, Stephen M.

PY - 2019/8/15

Y1 - 2019/8/15

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85065452790

U2 - 10.1016/j.neuroimage.2019.04.046

DO - 10.1016/j.neuroimage.2019.04.046

M3 - Article

C2 - 31026516

AN - SCOPUS:85065452790

SN - 1053-8119

VL - 197

SP - 435

EP - 438

JO - NeuroImage

JF - NeuroImage

ER -

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

Abstract

Access to Document

Other files and links

Fingerprint

Cite this