Group comparison of resting-state FMRI data using multi-subject ICA and dual regression

By Christian F. Beckmann, Clare E. Mackay, Nicola Filippini, Stephen M. Smith. 2009

777 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/25fd7d29fc3c1b2fd07fb0d4390ded585ede3978

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Citations in the corpus

The future of FMRI connectivity (2012, background): “A similar point can be made with respect to the dual-regression approach (Beckmann et al., 2009); here, a set of group spatial maps (e.g., from a low-dimensional group- 2 In the case of having more than 3 nodes, all the other N-2 nodes are regressed out of the two under consideration.”
Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks (2012, neutral): “The two most common analytical approaches toward analyzing resting state functional connectivity (RSFC) data are ICA (e.g., Beckmann et al., 2005, 2009; Greicius et al., 2007; Stevens et al., 2009) and seed-driven RSFC (e.g., Biswal et al., 1995; Castellanos et al., 2008; Greicius et al., 2003; Fox…”
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).”
The Neuro Bureau ADHD-200 Preprocessed Repository (2016, methodology): “Based on these template ICNs, we applied a modified dual-regression approach (Beckmann et al., 2009) to the unfiltered preprocessed data.”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, methodology): “, 2001), spatiotemporal (dual)-regression (Beckmann et al., 2009; Erhardt et al., 2011) and group information guided ICA (GIG-ICA) (Du and Fan, 2013; Du et al.”
Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, neutral): “3) that with as few as five subjects out of 100 (large group size) that the estimated SMs and TCs are quite good, improving slightly with more than five subjects.”
Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks (2017, methodology): “To overcome the drawbacks of single subject ICA, group ICA using the dual regression approach was initiated.(31,32) In dual regression analysis, the regression technique is used for analysing the rs-fMRI data from a group of subjects.”
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).”
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, methodology): “For each participant we derived spatial maps of well-validated and commonly replicated resting-state networks (RSNs) using dual regression (Beckmann et al., 2009; Filippini et al., 2009).”
Comparison of multi-subject ICA methods for analysis of fMRI data (2010, methodology): “…we compare are three variations on PCA-based direct back-reconstruction approach group ICA (GICA) [Calhoun et al., 2001b, 2002] and a least squares-based indirect back-reconstruction approach spatio-temporal regression (STR, or dual regression) [Beckmann et al., 2009; Calhoun et al., 2004b].”
Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data (2010, methodology): “Alternatively, the subject-specific components can be derived using dual regression (Beckmann et al., 2009), which involves regressing the individual subject fMRI datasets (output from Step 5) against the group component spatial maps to obtain subject-specific component time courses; followed by…”
Discovering dynamic brain networks from big data in rest and task (2017, methodology): “Dual regression analysis (Beckmann et al., 2009) is performed to obtain subject-specific node timeseries associated with the group-level parcels, for feeding into the HMM analyses.”
Building a Science of Individual Differences from fMRI. (2016, methodology): “Projecting it into individual brains is typically done using dual regression in the case of IC components [176], and different approaches have been proposed for other parcellation schemes – for example a template-matching procedure which assigns each voxel or vertex from an individual subject to a p”
Reliable Intrinsic Connectivity Networks: Test-Retest Evaluation Using ICA and Dual Regression Approach (2009, positive): “In order to assess the TRT reliability of each group-level component, we used the dual regression approach to build individual-level DR components (Filippini et al., 2009; Beckmann et al., 2009).”
Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses (2017, methodology): “…the human brain (Beckmann et al., 2005; Smith et al., 2009), multivariate regression approaches that analyse the behavior of all networks simultaneously, for example, dual regression [DR; Beckmann et al., 2009; Filippini et al., 2009; implemented in FMRIB Software Library (FSL)], are most suitable.”
Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest (2013, methodology): “These weights are similar to the time-dependent profiles obtained for the “eigenimages” in Friston et al. (1993), and conceptually related to the subject-dependent time courses calculated in independent component analysis (ICA) by dual regression (Beckmann et al., 2009).”
A Correspondence between Individual Differences in the Brain's Intrinsic Functional Architecture and the Content and Form of Self-Generated Thoughts (2014, methodology): “It is worth noting that in contrast to seed-based functional connectivity [3] and dual-regression methods [101], the analyses we performed did not require a priori assumptions about the specific brain regions or networks involved.”