Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery

By Vince D. Calhoun, T. Adalı. IEEE Reviews in Biomedical Engineering, 2012

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Since the discovery of functional connectivity in fMRI data (i.e., temporal correlations between spatially distinct regions of the brain) there has been a considerable amount of work in this field. One important focus has been on the analysis of brain connectivity using the concept of networks instead of regions. Approximately ten years ago, two important research areas grew out of this concept. First, a network proposed to be “a default mode of brain function” since dubbed the default mode network was proposed by Raichle. Secondly, multisubject or group independent component analysis (ICA) provided a data-driven approach to study properties of brain networks, including the default mode network. In this paper, we provide a focused review of how ICA has contributed to the study of intrinsic networks. We discuss some methodological considerations for group ICA and highlight multiple analytic approaches for studying brain networks. We also show examples of some of the differences observed in the default mode and resting networks in the diseased brain. In summary, we are in exciting times and still just beginning to reap the benefits of the richness of functional brain networks as well as available analytic approaches.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/55b1b66def831438767e9a23d3be6881ef07cb37

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Citations in the corpus

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest (2013, background): “Calhoun and Adali (2012) and Starck et al. (2012) recently reported changes in dynamic FC variability in patients with schizophrenia and autism.”
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).”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, methodology): “Since the late 1990s, spatial ICA (sICA) has been applied to fMRI as a data-driven approach that estimates networks from the entire spatiotemporal dataset at once (Beckmann and Smith, 2004; Calhoun and Adali, 2012; Calhoun et al., 2001; McKeown et al., 1998).”
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia (2015, unknown): “Classification performance using FC patterns of functionally parcellated brain regions from GICA The 145 brain regions were functionally defined using the 145 ICs from the GICA (Fig.”
CLASSIFICATION OF SCHIZOPHRENIA AND BIPOLAR PATIENTS USING STATIC AND DYNAMIC RESTING-STATE FMRI BRAIN CONNECTIVITY (2016, methodology): “Twowidely used FC approaches are (i) seed-based analysis (Biswal et al., 1995; Greicius et al., 2003) and (ii) purely data-driven methods, such as ICA (Calhoun and Adali, 2012; Calhoun et al., 2001a; Calhoun et al., 2009; Damoiseaux et al., 2006; Fox and Raichle, 2007; Hyvärinen and Oja, 2000).”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, methodology): “These popular approaches include spatial independent component analysis (ICA) (Calhoun et al., 2001; Calhoun and Adali, 2012; Du and Fan, 2013; Du et al., 2016; Calhoun and De Lacy, 2017), principle component analysis (PCA), and clustering methods (Van Den Heuvel et al.”