Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms

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

734 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/11b7ac66b8068f44ba5daf6f7a0a99186076b909

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Citations in the corpus

Independent Component Analysis as a Tool to Eliminate Artifacts in EEG: A Quantitative Study (2003, methodology): “It has also been used for MRI, positron emission tomograms, audiograms, and others (Arfanakis et al., 2000; Calhoun et al., 2001; Hoyer and Hyvarinen, 2000; Lee et al., 2001; Suzuki et al., 2002).”
Whole-brain activity mapping onto a zebrafish brain atlas (2015, background): “…activity indicator, which will limit the temporal separation of signals, 2) the complex nature of the stimuli and experiences of the individual fish, which may not yield strictly temporally independent signals, and 3) the number of voxels dwarfs the number of observations (fish) in the dataset 76 .”
Spatiotemporal Independent Component Analysis of Event-Related fMRI Data Using Skewed Probability Density Functions (2002, background): “…assumption that data can be decomposed into underlying sources that are independent over space (McKeown et al., 1998) or time (Makeig et al., 1997; Calhoun et al., 2001) and that the probability density functions (pdf) of these sources are highly kurtotic and symmetric (McKeown et al., 1998;…”
Regional homogeneity approach to fMRI data analysis (2004, methodology): “Some typical examples include clustering analysis (Baumgartner et al., 1997; Filzmoser et al., 1999), principal component analysis (PCA) (Hansen et al., 1999; Lai and Fang, 1999), independent component analysis (ICA) (Calhoun et al., 2001; Duann et al., 2002; McKeown et al., 1998), and self-organizi”
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).”
A method for making group inferences from functional MRI data using independent component analysis (2001, positive): “…component analysis as applied to fMRI data can be used to separate either spatially [McKe-own et al., 1998a] or temporally [Biswal and Ulmer, 1999] independent sources and works well in both situations when appropriate assumptions are met [Calhoun et al., 2001b; McKeown and Sejnowski, 1998].”
Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, neutral): “EEG and MEG most frequently use temporal ICA (tICA), which assumes that a time course is a composed of a linear combination of time courses with associated spatial maps [36].”
Learning brain connectivity of Alzheimer’s disease by sparse inverse covariance estimation (2010, methodology): “…have been used, such as principle component analysis (PCA) (Friston, 1994), PCA-based scaled sub-pro ﬁ le model (Alexander and Moeller, 1994), independent component analysis (Calhoun et al., 2001; Calhoun et al., 2003), and partial least squares (McIntosh et al., 1996; Worsley et al., 1997).”
Validating the independent components of neuroimaging time series via clustering and visualization (2004, methodology): “ICA can be performed on fMRI data is two different ways, called spatial and temporal ICA (Calhoun et al., 2001b); here we used spatial ICA.”
Group information guided ICA for fMRI data analysis (2013, positive): “The temporal concatenation method concatenates the multi-subject fMRI data along the temporal dimension with an assumption that all subjects have common spatial ICs (Beckmann et al., 2009; Calhoun et al., 2001a, 2009).”
Temporally-independent functional modes of spontaneous brain activity (2012, background): “It was shown that both spatial and temporal ICA could identify a small number of strongly distinct task activation and artifact processes (5).”
Impact of In-Scanner Head Motion on Multiple Measures of Functional Connectivity: Relevance for Studies of Neurodevelopment in Youth (2012, methodology): “In ICA of fcMRI data, the most common approach is TC-GICA, where all subjects' timeseries data are first temporally concatenated prior to performing a group-level ICA (Beckmann et al., 2005; Calhoun et al., 2001).”
Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty (2011, neutral): “In recent years, several methods have been developed to characterize functional brain networks and connectivity (Beckmann and Smith, 2004; Calhoun et al., 2001, 2003); (Bullmore and Sporns, 2009; Cole et al., 2010; Deco et al., 2011; Friston et al., 1997; Greicius et al., 2003; McIntosh, 2000;…”
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).”