A DCM for resting state fMRI
By Karl J. Friston, Joshua Kahan, Bharat B. Biswal, Adeel Razi. NeuroImage, 2014
560 citations recorded by the source corpus.
This technical note introduces a dynamic causal model (DCM) for resting state fMRI time series based upon observed functional connectivity—as measured by the cross spectra among different brain regions. This DCM is based upon a deterministic model that generates predicted crossed spectra from a biophysically plausible model of coupled neuronal fluctuations in a distributed neuronal network or graph. Effectively, the resulting scheme finds the best effective connectivity among hidden neuronal states that explains the observed functional connectivity among haemodynamic responses. This is because the cross spectra contain all the information about (second order) statistical dependencies among regional dynamics. In this note, we focus on describing the model, its relationship to existing measures of directed and undirected functional connectivity and establishing its face validity using simulations. In subsequent papers, we will evaluate its construct validity in relation to stochastic DCM and its predictive validity in Parkinson's and Huntington's disease.
Subfields: Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/b156a77276a4fffe7b17e32cdde05d1319374c29
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In Cognitive Neuroscience
- Karl J. Friston
- Mohamed L. Seghier
- Adeel Razi
- Dimitris A. Pinotsis
- Cathy J. Price
- Peter Zeidman
- Daniele Marinazzo
- Emmanuel Procyk
Citations in the corpus
- A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI (2019, methodology): “This means that stochastic DCM can be used to model resting state (e.g. Bastos-Leite et al., 2014; Goulden et al., 2014; Dirkx et al., 2016) as well as task-based fMRI studies where endogenous fluctuations are important (e.g. Bernal-Casas et al., 2013; Jung et al., 2018).”
- Frontal effective connectivity increases with task demands and time on task: a Dynamic Causal Model of electrocorticogram in macaque monkeys (2022, methodology): “Since we were interested in (1) which connections were modulated by task demands and time on task, (2) which modulation effects are consistent across sessions, and (3) which modulation effects are consistent between monkeys, we conducted a hierarchical PEB analysis based with three levels: a hierarc”