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

By Vince D. Calhoun, T. Adalı, Godfrey D. Pearlson, James J. Pekar. Human Brain Mapping, 2001

3,068 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c2cae15f8335f2c4d28c78a6e145e1665a70e0b7

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Citations in the corpus

Abnormal intrinsic brain functional network dynamics in Parkinson’s disease (2017, methodology): “*P5 0.05. maps and time courses for each independent component, group independent components were back-projected using GICA back reconstruction algorithm (Calhoun et al., 2001).”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “Note that a violation of component independence would reduce the eﬃciency of ICA (Calhoun et al., 2001).”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, methodology): “…subject separately, and then establish correspondence of ICs across subjects using methods such as subjective identification (McKeown et al., 1998; Calhoun, 2001), clustering (Moritz et al., 2003; Esposito et al., 2005; De Martino et al., 2007), and automated matching based on reproducibility…”
A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data (2008, neutral): “The GIFT software additionally implements a back-reconstruction step which produces subject specific images (Calhoun et al., 2001b).”
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, methodology): “In joint ICA [173] and group ICA [174], [175], several ICA problems share a mixing matrix or source subspace, respectively, by concatenating the observation matrices in rows or columns.”
Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks (2008, neutral): “All but three patients and one control were right handed.”
Estimating the number of independent components for functional magnetic resonance imaging data (2007, methodology): “Wax and Kailath’s formulations are directly applicable to the multivariate order selection problem and have been used in estimating the number of latent sources in blind source separation (Karhunen et al., 1997) as well as in ICA of fMRI data (Calhoun et al., 2001; Beckman and Smith, 2004).”
Functional segmentation of the brain cortex using high model order group PICA (2009, background): “…cued task-activations, background activity within functional brain (i.e. resting state) networks (RSN), and various physiological noise and artifact sources [Beckmann and Smith, 2004; Beck-mann et al., 2005; Calhoun et al., 2001; Kiviniemi et al., 2003; McKeown et al., 1998; Van de Ven, 2004].”
Tracking whole-brain connectivity dynamics in the resting state. (2014, positive): “Subject-specific SMs (Si) and time courses (TCs, Ri) were estimated using the GICA1 backreconstruction method based on PCA compression and projection (Calhoun et al. 2001; Erhardt et al. 2011), which is equivalent to least-squares based dual regression (Filippini et al.”
Independent component analysis of fMRI group studies by self-organizing clustering (2005, methodology): “Following the typical matrix notation, they can be referred as column-wise (or subject-wise) (Calhoun et al., 2001c), and row-wise (across time-courses) concatenation (Svensen et al., 2002).”
Prediction of human errors by maladaptive changes in event-related brain networks (2008, methodology): “Group ICA requires that all subjects are analyzed at once, and principal component analysis (PCA) was used for compression to allow the datasets to be processed together (50).”
Reliable Intrinsic Connectivity Networks: Test-Retest Evaluation Using ICA and Dual Regression Approach (2009, positive): “This can be accomplished in a relatively efficient manner by carrying out an ICA on a single large dataset by temporally concatenating all individual datasets (Calhoun et al., 2001; Beckmann et al., 2005).”
Group comparison of resting-state FMRI data using multi-subject ICA and dual regression (2009, methodology): “Following back-projection as in [4] and dual regression as described here we can illustrate the difference in terms of:”
Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data (2010, background): “…Greicius et al., 2007; Perlbarg et al., 2007; Stevens et al., 2007; Tohka et al., 2008; Sui et al., 2009) or are "manual" (McKeown et al., 1998; Calhoun et al., 2001a; Moritz et al., 2003; Zou et al., 2009), requiring some element of visual inspection and human decision making; 3) whether the…”
Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study (2012, methodology): “Group ICA GICA is performed with the GIFT toolbox (http://icatb.sourceforge. net/) which uses a temporal concatenation approach to combine data across subjects (Calhoun et al., 2001b, 2002).”