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A DCM for resting state fMRI
- Source :
- Neuroimage
- Publication Year :
- 2014
- Publisher :
- Academic Press, 2014.
-
Abstract
- 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.<br />Highlights • This paper describes an efficient estimation of resting state connectivity. • The scheme is based on fitting observed complex fMRI cross spectra. • This spectral DCM grandfathers functional connectivity and Granger causality.
- Subjects :
- Brain Mapping
Models, Statistical
Quantitative Biology::Neurons and Cognition
Cognitive Neuroscience
Rest
fMRI
Motion Perception
Reproducibility of Results
Bayesian
Magnetic Resonance Imaging
Sensitivity and Specificity
Graph
Functional connectivity
Neurology
Data Interpretation, Statistical
Image Interpretation, Computer-Assisted
Technical Note
Humans
Attention
Computer Simulation
Dynamic causal modelling
Effective connectivity
Resting state
Algorithms
Visual Cortex
Subjects
Details
- Language :
- English
- ISSN :
- 10959572 and 10538119
- Volume :
- 94
- Issue :
- 100
- Database :
- OpenAIRE
- Journal :
- Neuroimage
- Accession number :
- edsair.pmid.dedup....75a29be13b48e0fb24101e8a48562e07