Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks

By Vince D. Calhoun, Kent A. Kiehl, Godfrey D. Pearlson. 2008

584 citations recorded by the source corpus.

Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called “resting state networks”); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer's disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail. Hum Brain Mapp, 2008. © 2008 Wiley‐Liss, Inc.

Subfields: Cognitive Neuroscience, Neuroscience

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Citations in the corpus

Learning deep representations by mutual information estimation and maximization (2018, background): “012; Bengio et al., 2013; Chen et al., 2018; Gonzalez-Garcia et al., 2018), or independently controllable (Thomas et al., 2017). As an example, independence is important in many neuroscience studies (Calhoun et al., 2008; Allen et al., 2012; Damaraju et al., 2014), where phrasing medical imaging dat”
Behavioral Interpretations of Intrinsic Connectivity Networks (2011, background): “Rather than a strict spatial decomposition for primary, secondary, or tertiary visual cortices, we instead observed a medial to lateral delineation of networks that has been replicated in numerous resting state analyses (Allen et al., 2011; Zuo et al., 2010; Smith et al., 2009; Calhoun et al., 2008;”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, result): “Patterns of FC observed at rest have also been shown to resemble those elicited by more traditional task-based paradigms or derived directly from task-data (Biswal et al., 1995; Calhoun et al., 2008; Fox et al., 2006; Laird et al., 2011; Smith et al., 2009; Vincent et al., 2007).”
Modular and Hierarchically Modular Organization of Brain Networks (2010, background): “…that functional neuroimaging data, recorded “at rest” or during performance of an experimentally controlled task, can generally be decomposed into sub-systems of functionally connected brain regions (Salvador et al., 2005; Calhoun et al., 2008; Van den Heuvel et al., 2008; Smith et al., 2009).”
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Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study (2012, methodology): “This approach has been utilized in numerous studies to describe intersubject differences in intrinsic networks in both health and disease (e.g., Calhoun et al., 2008; Jafri et al., 2008; Filippini et al., 2009; Beckmann et al., 2009; Biswal et al., 2010; Allen et al., 2011).”
Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies (2009, unknown): “…ICA analyses have identified additional RSNs connecting dorsal parietal with lateral prefrontal cortex, precuneus with prefrontal lobe, and inferior temporo-occipito-parietal with inferior prefrontal cortices, respectively (Calhoun et al., 2008; Damoiseaux et al., 2006; De Luca et al., 2006).”
Tracking whole-brain connectivity dynamics in the resting state. (2014, unknown): “2011), and also shows that these networks are not conditional upon a task-free resting state but are equally involved in task performance (Calhoun et al. 2008; Smith et al. 2009).”
CLASSIFICATION OF SCHIZOPHRENIA AND BIPOLAR PATIENTS USING STATIC AND DYNAMIC RESTING-STATE FMRI BRAIN CONNECTIVITY (2016, methodology): “FC can also be investigated at the network level using spatial independent component analysis (ICA), and connectivity among spatial components is referred to as functional network connectivity (FNC) (Jafri et al., 2008).”
Deep learning for neuroimaging: a validation study (2013, background): “…new details [3] (supporting their credibility), in recovering features that differentiate patients and controls [28] (assisting diagnosis and disease understanding), and starting a “resting state” revolution after revealing consistent patters in data from uncontrolled resting experiments [29, 35].”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, unknown): “Neuroimaging studies of the brain at rest quickly converged on a set of canonical FC networks that are consistently observed at rest and correspond with patterns of task-evoked activation and functional connectivity (Calhoun, Kiehl, & Pearlson, 2008; Damoiseaux et al., 2006; Smith et al., 2009).”
Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, unknown): “It is also important to note that INs comprise most of the variance of the fMRI data [62].”
Inscapes: a movie paradigm to improve compliance in functional magnetic resonance imaging (2015, background): “A number of researchers have studied the modulation of intrinsic functional connectivity by task-related activity (Fransson, 2006; Calhoun et al., 2008; Lv et al., 2013; Mennes et al., 2013; Li et al., 2015a, b).”
A Baseline for the Multivariate Comparison of Resting-State Networks (2011, methodology): “The spectral properties of network activation in schizophrenia have also been explored, revealing a signature of reduced low frequency power and increased high frequency power in the DMN as well as many other RSNs (Garrity et al., 2007; Calhoun et al., 2008b, 2009).”