fMRI Activation in a Visual-Perception Task: Network of Areas Detected Using the General Linear Model and Independent Components Analysis

By Vince D. Calhoun, T. Adalı, V. McGinty, James J. Pekar, G. Pearlson. NeuroImage, 2001

324 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/73a7fade254d0eba37c819037c0efa84d9d27aca

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In Cognitive Neuroscience

Citations in the corpus

Comparison of multi-subject ICA methods for analysis of fMRI data (2010, unknown): “Keywords: ICA; independent component analysis; fMRI; functional; brain; group ICA; backreconstruction; spatio-temporal regression; dual regression r r”
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…”
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).”
Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, unknown): “The first approaches for applying ICA to multisubject data were presented in 2001 [4], [5] and were quickly followed by a series of publications describing various methodological issues as well as applications to a number of challenging problems such as the analysis of naturalistic paradigms [6], [7”
Default-Mode Activity during a Passive Sensory Task: Uncoupled from Deactivation but Impacting Activation (2004, background): “There is no consensus, as yet, on how to choose the optimal number of components, although methods to do so are in development (Calhoun et al., 2001).”
Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data (2010, methodology): “…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…”
Group information guided ICA for fMRI data analysis (2013, unknown): “…perform ICA computation for each subject separately, and then establish correspondence of ICs across subjects using methods such as subjective identification (Calhoun, 2001; McKeown et al., 1998), clustering (De Martino et al., 2007; Esposito et al., 2005; ttern Recognition, Institute of , China.”
Independent component analysis of fMRI group studies by self-organizing clustering (2005, methodology): “…considered: the variant called spatial ICA (sICA) refers to the statistical distribution of signals across the sampled hemodynamic locations, while the variant called temporal ICA (tICA) refers to the statistical distribution of source signals across the sampled time-points (Calhoun et al., 2001a).”
A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data (2008, unknown): “The GIFT software additionally implements a back-reconstruction step which produces subject specific images (Calhoun et al., 2001b).”