Investigations into resting-state connectivity using independent component analysis

By Christian F. Beckmann, Marilena DeLuca, Joseph T. Devlin, Stephen M. Smith. Philosophical Transactions of the Royal Society B: Biological Sciences, 2005

3,227 citations recorded by the source corpus.

Subfields: Aerospace Engineering

View Paper: https://www.semanticscholar.org/paper/f26171ae3b0941a6ee3b3fe84985c0953b4f142d

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Citations in the corpus

Spatiotemporal dynamics of the brain at rest - Exploring EEG microstates as electrophysiological signatures of BOLD resting state networks (2012, neutral): “…correlation mapping to activity measured over spatial loci selected a priori (Biswal et al., 1995), or can be delineated using data-driven approaches, such as independent component analysis (ICA) (Beckmann et al., 2005; Calhoun et al., 2001; Damoiseaux et al., 2006; Mantini et al., 2007).”
Changes in structural and functional connectivity among resting-state networks across the human lifespan (2014, methodology): “RSNs can be extracted using differentmethodologies, including independent component analysis (ICA; Beckmann et al., 2005) and clustering approaches applied to whole-brain FC networks (e.g., Bellec et al., 2010; Power et al., 2011; Yeo et al., 2011).”
Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks (2008, positive): “Eight of the 11 TCNs were similar to those identified in previous work [Beckmann et al., 2005].”
Does the brain have a baseline? Why we should be resisting a rest (2007, background): “More recent observations by Beckmann et al. (2005) are particularly striking, and like those of Grecius et al. (2003), imply that activity in the resting task does not represent some unitary ‘default mode’, but rather is likely to fractionate.”
Functional segmentation of the brain cortex using high model order group PICA (2009, methodology): “It has been suggested that 10 to 12 RSNs can be detected from the brain cortex from resting state BOLD data, using ICA with a component dimensionality, i.e. a model order, around 25–40 components [Beckmann et al., 2005; De Luca et al., 2006; Damoiseaux et al., 2006].”
Investigating the electrophysiological basis of resting state networks using magnetoencephalography (2011, methodology): “To date, it has not been shown that MEG (or EEG) can independently measure the spatial pattern of RSNs in the manner that has been demonstrated in fMRI (13).”
Mapping the functional connectivity of anterior cingulate cortex (2007, background): “This analytic approach has demonstrated that functionally relevant patterns of activity, commonly observed during task performance, are intrinsically represented in spontaneous brain activity (Beckmann et al., 2005; Damoiseaux et al., 2006; De Luca et al., 2006; Fransson, 2005; Greicius et al., 2003”
Selective aberrant functional connectivity of resting state networks in social anxiety disorder (2010, methodology): “…was largely consistent with those mapped with the specific task-induced activation (Corbetta and Shulman, 2002) or with seed-based functional connectivity (Fox et al., 2006) or ICA (Beckmann et al., 2005; Damoiseaux et al., 2006; De Luca et al., 2006; Liao et al., 2010; Mantini et al., 2007 ).”
Toward discovery science of human brain function (2010, methodology): “Building on the term “connectome,” coined to describe the comprehensive map of structural connections in the human brain (7), we use “functional connectome” to describe the collective set of functional connections in the human brain.”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “Third, whole-brain independent component analysis (ICA; Beckmann et al., 2005) of single-subject time series has increasingly been used for denoising, with noise components selected either by a trained classifier (ICA-FIX; Griffanti et al., 2014; Salimi-Khorshidi et al., 2014) or using a priori…”
Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks (2019, positive): “ICA is a model-free approach that decomposes neuroimaging datasets into a set of independent one-dimensional time series and associated three-dimensional spatial maps that describe the temporal and spatial characteristics of the underlying signals (Beckmann et al. 2005).”
Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity (2009, background): “Among these independent components are several readily identified functional brain networks, but also artifacts related to movement and physiological noise (Beckmann et al. 2005).”
The role of the posterior cingulate cortex in cognition and disease. (2014, background): “Although it covers a relatively large area of cortex it is often discussed as having a unitary function.”
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, background): “ICA is a data-driven approach that decomposes fMRI data into signal (e.g. functional networks) and structured noise (e.g. motion-induced variance and cardiac pulsation; Beckmann et al. (2005)) components.”
Default mode network activity in schizophrenia studied at resting state using probabilistic ICA. (2012, methodology): “Hence, the pICA approach makes it feasible to separate uninteresting physiological noise from other effects such as resting-state maps even in cases where the physiological noise fluctuations become aliased in the temporal domain (Beckmann et al., 2005).”
Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses (2017, methodology): “Beckmann et al., 2005; Salimi-Khorshidi et al., 2014; Du et al., 2016).”
Impact of In-Scanner Head Motion on Multiple Measures of Functional Connectivity: Relevance for Studies of Neurodevelopment in Youth (2012, methodology): “Temporal concatenation group independent component analysis (TC-GICA) was conducted on the age/motion related sample using MELODIC (Beckmann et al., 2005).”