A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data

By Vince D. Calhoun, Jingyu Liu, T. Adalı. NeuroImage, 2008

1,216 citations recorded by the source corpus.

Independent component analysis (ICA) has become an increasingly utilized approach for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the data (e.g. the brain's response to stimuli), ICA, by relying upon a general assumption of independence, allows the user to be agnostic regarding the exact form of the response. In addition, ICA is intrinsically a multivariate approach, and hence each component provides a grouping of brain activity into regions that share the same response pattern thus providing a natural measure of functional connectivity. There are a wide variety of ICA approaches that have been proposed, in this paper we focus upon two distinct methods. The first part of this paper reviews the use of ICA for making group inferences from fMRI data. We provide an overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software. In the next part of this paper, we provide an overview of the use of ICA to combine or fuse multimodal data. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism (SNP) data or event-related potentials. As demonstrated by a number of examples in this paper, ICA is a powerful and versatile data-driven approach for studying the brain.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ff3671cfc841b26dd44240ec270e6c3db6c12656

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

Impact of In-Scanner Head Motion on Multiple Measures of Functional Connectivity: Relevance for Studies of Neurodevelopment in Youth (2012, background): “While seed-based connectivity analyses remain the most common way of approaching rsfMRI data, alternative methods have seen wide use (Damoiseaux et al., 2006; Calhoun et al., 2009).”
The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data (2014, neutral): “Multimodality approaches Exploit joint information in several imaging modalities or other biomarkers at the same time Multimodal data fusion using ICA (Calhoun et al. 2009) Seemingly Unrelated Regression to pool information across simultaneous models (SUR; Jahanshad et al.”
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014, background): “and (ii) the corresponding latent variables are statistically independent [107], [108], both quite stringent and limiting assumptions.”
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion (2017, methodology): “The second approach is “YeoBackProject”, which is analogous to the ICA back-projection algorithm (Calhoun et al., 2009; Beckmann et al., 2009; Filippini et al., 2009; Zuo et al., 2010; Calhoun and Adali 2012).”
Reliable Intrinsic Connectivity Networks: Test-Retest Evaluation Using ICA and Dual Regression Approach (2009, neutral): “Accordingly, somehave advocated carrying out a single ICA analysis across an entire group of participants (see (Guo andPagnoni, 2008) and (Calhoun et al., 2009) for reviews on various group ICA methods).”
Fast transient networks in spontaneous human brain activity (2014, methodology): “One good example of this is that, when using temporal ICA on fMRI data, no single DMN is found (Smith et al., 2012).”
Multi-level bootstrap analysis of stable clusters in resting-state fMRI (2009, methodology): “Some group-level ICA techniques have the potential to establish correspondences between individual and group-level analysis , i.e. back-reconstruction in temporally-concatenated group ICA (Calhoun et al., 2009) or dual-regression (Filippini et al., 2009).”
The Nuisance of Nuisance Regression: Spectral Misspecification in a Common Approach to Resting-State fMRI Preprocessing Reintroduces Noise and Obscures Functional Connectivity (2013, methodology): “In addition, because our study focused on the use of bandpass filtering and nuisance regression in seed-based correlation analyses, we categorized other popular analytic methods for RS-fcMRI data, such as independent components analysis (ICA; Calhoun et al., 2009), as “Other,” and did not report pre”
Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development? (2016, methodology): “A variety of methods, such as independent component analysis (ICA) (Calhoun et al., 2009), has also been proposed to facilitate the combination of pupillometry with fMRI, EEG, genetic, or other data.”
Comparison of multi-subject ICA methods for analysis of fMRI data (2010, methodology): “As we discuss in a recent review article, group ICA approaches can be organized into five categories [Calhoun et al., 2009].”
Handbook of Functional MRI Data Analysis: Index (2011, methodology): “In the GIFT software (Calhoun et al., 2009), a separate mixing matrix is estimated for each individual.”
Group information guided ICA for fMRI data analysis (2013, neutral): “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).”
Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, neutral): “The application of ICA to multimodal data fusion [35] and genetics [96], [97] is another very important area which has been applied to study brain disease which should be considered though it is beyond the scope of this particular review paper.”
Independent component analysis: recent advances (2013, background): “Diﬀerent computational strategies are available to cope with this problem, as reviewed by Calhoun et al. (2009).”