Independent component analysis of fMRI group studies by self-organizing clustering

By Fabrizio Esposito, T. Scarabino, Aapo Hyvärinen, Johan Himberg, Elia Formisano, S. Comani, G. Tedeschi, Rainer Goebel, E. Seifritz, F. Salle. NeuroImage, 2005

387 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d1acc26c40bb75e233ad710d3b67e0e451555a52

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Signal Processing

Citations in the corpus

Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions (2016, neutral): “onality or statistical independence constraints, the problem in (40) can be related to models of group PCA/ICA through suitable pre-processing, dimensionality reduction and post-processing procedures [94, 110, 240]. The terms ”group component analysis” and ”joint multi-block data analysis” are used ”
Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks (2017, background): “As a discussion on the different methods of group analysis technique in detail is beyond the scope of this review, interested readers could refer to the previous articles by Calhoun et al.,33 Guo and Pagnoni,34 Esposito et al.35 and Schmithorst and Holland.36”
EEG-vigilance and BOLD effect during simultaneous EEG/fMRI measurement (2009, neutral): “…of the white and grey matter boundary of the transformed anatomical MR datasets (Kriegeskorte and Goebel, 2001 sorted upon their spatial similarity with the self-organizing group ICA (sogICA) method provided by the BrainVoyager Software " plugin " (Esposito et al., 2005; Van de Ven et al., 2008).”
Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, neutral): “A number of approaches first perform single-subject ICA on each subject and then attempt to combine the output into a group post hoc by spatial correlation [4], [5], self-organized clustering [46], or retrospective matching [47] of the components [4], [46].”
Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study (2012, methodology): “To avoid these problems, algorithms have recently been developed to automate the identification of common components without templates using self-organized clustering (Esposito et al., 2005) and cross-correlation (Schöpf et al.”
A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks (2008, methodology): “During the performance of cognitively demanding tasks, the CEN and SN typically show increases in activation whereas the DMN shows decreases in activation (1, 2, 6).”
A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data (2008, neutral): “Despite this, several ICA multi-subject analysis approaches have been proposed (Beckmann and Smith, 2005; Calhoun et al., 2001a; Calhoun et al., 2001b; Calhoun et al., 2004a; Esposito et al., 2005; Guo and Giuseppe, In Press; Lukic et al., 2002; Schmithorst and Holland, 2004; Svensen et al., 2002).”
A method for functional network connectivity among spatially independent resting-state components in schizophrenia (2008, methodology): “Another useful method to examine functional connectivity is independent component analysis (ICA) (Calhoun et al., 2001b; Esposito et al., 2005; Garrity et al., 2007; McKeown et al., 1998), particularly as applied to “resting state” scans, which are relatively easy to obtain and do not suffer from…”
Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data (2010, neutral): “…efforts advocated running single-session ICA, separately for each subject, then attempting to fi nd the ‘best-fi t’ component to an a priori RSN template at the individual level, to carry forward to group comparison stages (e.g., Greicius et al., 2003; Esposito et al., 2005; De Luca et al., 2006).”
Independent component analysis: recent advances (2013, background): “In the case of neuroimaging, for example, one typically measures brain activities of many subjects, and tries to ﬁnd components which the subjects have in common (Esposito et al., 2005).”
Estimating the number of independent components for functional magnetic resonance imaging data (2007, methodology): “ICA estimation on a group of subjects can be addressed, e.g., by performing ICA on the aggregated fMRI data (Calhoun et al., 2004), or clustering on the IC estimates from each individual subject ( Esposito et al., 2005 ).”
Group information guided ICA for fMRI data analysis (2013, neutral): “…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.”
Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: From single‐subject to cortically aligned group general linear model analysis and self‐organizing group independent component analysis (2006, methodology): “Single-subject ICA [Formisano et al., 2002a,b, 2004] and Group ICA [Esposito et al., 2005] were applied to the ﬁrst run of the block design experimental time-series.”
Toward discovery science of human brain function (2010, background): “Buttressed by moderate to high test–retest reliability (8–10) and replicability (11, 12), as well as widespread access, R-fMRI has overcome initial skepticism (13) regarding the validity of examining such an apparently unconstrained state (5, 8, 14).”
Multi-level bootstrap analysis of stable clusters in resting-state fMRI (2009, methodology): “Esposito et al. (2005) and De Luca et al. (2006) applied component matching to study the stability of RSNs across different subjects.”
BOLD correlates of EEG alpha phase-locking and the fMRI default mode network (2009, methodology): “These resting state networks (RSN) are defined by common low-frequency fluctuations of the BOLD signal that can be separated by ICA (Damoiseaux et al., 2006; De Luca et al., 2006; Formisano et al., 2002; Fox et al., 2005; Hyvarinen and Oja, 2000; Esposito et al., 2005, 2008).”