Cited by 2,994Blind Source Separation TechniquesFunctional Brain Connectivity StudiesTensor decomposition and applications

A method for making group inferences from functional MRI data using independent component analysis

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Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J.·2001·Human Brain Mapping

Abstract

Independent component analysis (ICA) is a promising analysis method that is being increasingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent component analysis has been successfully utilized to analyze single‐subject fMRI data sets, and an extension of this work would be to provide for group inferences. However, unlike univariate methods (e.g., regression analysis, Kolmogorov–Smirnov statistics), ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. We introduce a novel approach for drawing group inferences using ICA of fMRI data, and present its application to a simple visual paradigm that alternately stimulates the left or right visual field. Our group ICA analysis revealed task‐related components in left and right visual cortex, a transiently task‐related component in bilateral occipital/parietal cortex, and a non‐task‐related component in bilateral visual association cortex. We address issues involved in the use of ICA as an fMRI analysis method such as: (1) How many components should be calculated? (2) How are these components to be combined across subjects? (3) How should the final results be thresholded and/or presented? We show that the methodology we present provides answers to these questions and lay out a process for making group inferences from fMRI data using independent component analysis. Hum. Brain Mapping 14:140–151, 2001. © 2001 Wiley‐Liss, Inc.

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Crossref · 10.1002/hbm.1048

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  • The independent components which resembled the default mode network most were spatially sorted using the DMN template built-in in GIFT and then further identified by visual inspection by two different raters for resting state and meditation state separately.

    “The ICs which resembled the DMN most were firstly spatially sorted by using the DMN template built-in in GIFT [57] and then further identified by visual inspection by two different raters for RS and MS separately.”
  • The adult language network is lateralized and includes the inferior frontal gyrus and superior temporal gyrus; these regions begin to show asymmetry in functional connectivity with the contralateral homotopic region at about age two.

    “The language network, in adults is lateralized and encompasses the inferior frontal gyrus (IFG) and the superior temporal gyrus (STG); these regions begin to exhibit asymmetry in functional connectivity with the homotopic region on the contralateral hemisphere at about age 2 years 102.”

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Authors
Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J.
Affiliations
  • Johns Hopkins University (US)
  • Kennedy Krieger Institute (US)
Venue
Human Brain Mapping
Publisher
Wiley
Published
2001-08-27
Volume
14
Issue
3
Pages
140-151
ISSN
  • 1065-9471 (ISSN-L)
  • 1097-0193
OpenAlex
W1985327120
Cited by
2,994
References
18
Citations by year
2026: 96 · 2025: 158 · 2024: 128 · 2023: 158 · 2022: 190 · 2021: 223 · 2020: 201 · 2019: 217 · 2018: 186 · 2017: 195 · 2016: 174 · 2015: 143 · 2014: 181 · 2013: 173 · 2012: 153
FWCI
15.08
Type
journal article
Language
en
Open access
Locations
  • Human Brain Mapping (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
Johns Hopkins University
Topics
  • Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Physical Sciences › Mathematics › Computational Mathematics › Tensor decomposition and applications
Keywords
independent component analysis, visual cortex, visual field stimulation, component selection, statistical thresholding, visual association cortex
MeSH terms
Algorithms, Brain Mapping, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Signal Processing, Computer-Assisted, Visual Cortex

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