Understanding DCM: Ten simple rules for the clinician
By Joshua Kahan, Tom Foltynie. NeuroImage, 2013
1,534 citations recorded by the source corpus.
Despite almost a decade since the introduction of Dynamic Causal Modelling (DCM), there remains some confusion within the wider neuroimaging, neuroscience and clinical communities as to what DCM studies are probing, and what all the jargon means. We provide ten simple rules, and a theoretical example to gently introduce the reader to the rationale behind DCM analyses, and how one should consider neuroimaging data and experiments that use DCM. It is deliberately written as a primer or orientation for non-technical imaging neuroscientists or clinicians who have had to contend with the technical intricacies of understanding DCM.
Subfields: Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/002f7a181982323b6c8cb432c296e5f78c3d01f4
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In Cognitive Neuroscience
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Citations in the corpus
- Neurophysiological studies of auditory verbal hallucinations. (2012, background): “They are closely related to specific resting-state networks, as measured by the blood oxygen level–dependent response in fMRI.(8)”
- Experimental and theoretical approaches to conscious processing. (2011, background): “These microstates have recently been related to some of the fMRI resting-state networks (Britz et al., 2010).”
- Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “While the neural underpinnings and functional role of spontaneous fluctuations and correlations remain unresolved (reviewed in Leopold and Maier, 2012), evidence suggests that ICNs relate to underlying neural activity (Britz et al., 2010; Brookes et al., 2011a,b; de Pasquale et al., 2010, 2012; Fox ”
- Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques (2013, methodology): “The periods of metastable RSN synchronization observed in this study could represent the hemodynamic correlate of ‘‘microstates’’ [Britz et al., 2010; Musso et al., 2010], a fingerprint of specific electrophysiological processes [Lehmann et al.”
- EEG microstates as a tool for studying the temporal dynamics of whole‐brain neuronal networks: A review (2017, methodology): “Britz and colleagues (Britz et al., 2010) utilized a different approach more closely related to the conventional method for EEG microstate analysis.”
- Parameterizing neural power spectra into periodic and aperiodic components (2020, neutral): “MEG data were preprocessed following best-practice guidelines 65 , using the Brainstorm software toolbox 66 .”
- MEG and EEG data analysis with MNE-Python (2013, neutral): “Noise reduction strategies generally fall into two broad categories: exclusion of contaminated data segments and attenuation of artifacts by use of signal-processing techniques (Gross et al., 2013).”
- Spatiotemporal Analysis of Multichannel EEG: CARTOOL (2011, background): “We have shown that these different microstates are correlated with well-known fMRI resting states [35].”
- Fast transient networks in spontaneous human brain activity (2014, background): “Segmentation of EEG scalp maps into microstates is based on finding repeating distributions of power across multiple recording sites and therefore captures similar interactions to those that drive Neuroscience Baker et al. eLife 2014;3:e01867.”
- A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI (2019, positive): “We hope this worked example and tutorial-style overview of the theory will complement and expand on previous reviews and tutorials on DCM (Stephan, 2004; Seghier et al., 2010; Stephan et al., 2010; Kahan and Foltynie, 2013).”
- Electroencephalographic Resting-State Networks: Source Localization of Microstates (2017, positive): “Nevertheless, in our study, the EEG networks include additional areas in comparison to the four fMRI-based networks described in Britz et al. (2010).”
- MNE software for processing MEG and EEG data (2013, neutral): “Artifact reduction strategies generally fall in two broad categories: exclusion of contaminated data segments and attenuation of artifacts by the use of signal-processing techniques (Gross et al., 2013).”
- The PREP pipeline: standardized preprocessing for large-scale EEG analysis (2015, neutral): “Gross et al. (2013) lay out extensive guidelines for handling MEG data, including recommendations for processing approaches, caveats, and reporting requirements for a variety of applications.”
- Towards the utilization of EEG as a brain imaging tool (2012, background): “dominate the spontaneous EEG in awake, healthy adults (Strik and Lehmann, 1993; Britz et al., 2010; Wackerman et al., 1993).”