Back to Search
Start Over
Dynamic causal modelling: a critical review of the biophysical and statistical foundations
- Source :
- NeuroImage, NeuroImage, Elsevier, 2011, 58 (2), pp.312-22. ⟨10.1016/j.neuroimage.2009.11.062⟩
- Publication Year :
- 2011
- Publisher :
- HAL CCSD, 2011.
-
Abstract
- International audience; The goal of dynamic causal modelling (DCM) of neuroimaging data is to study experimentally induced changes in functional integration among brain regions. This requires (i) biophysically plausible and physiologically interpretable models of neuronal network dynamics that can predict distributed brain responses to experimental stimuli and (ii) efficient statistical methods for parameter estimation and model comparison. These two key components of DCM have been the focus of more than thirty methodological articles since the seminal work of Friston and colleagues published in 2003. In this paper, we provide a critical review of the current state-of-the-art of DCM. We inspect the properties of DCM in relation to the most common neuroimaging modalities (fMRI and EEG/MEG) and the specificity of inference on neural systems that can be made from these data. We then discuss both the plausibility of the underlying biophysical models and the robustness of the statistical inversion techniques. Finally, we discuss potential extensions of the current DCM framework, such as stochastic DCMs, plastic DCMs and field DCMs.
- Subjects :
- MESH: Magnetoencephalography
Computer science
Inference
computer.software_genre
MESH: Magnetic Resonance Imaging
170 Ethics
Bayes' theorem
0302 clinical medicine
SX00 SystemsX.ch
10007 Department of Economics
Image Processing, Computer-Assisted
MESH: Brain Mapping
Brain Mapping
medicine.diagnostic_test
Functional integration (neurobiology)
05 social sciences
Magnetoencephalography
Electroencephalography
Causality
Magnetic Resonance Imaging
MESH: Image Processing, Computer-Assisted
330 Economics
MESH: Reproducibility of Results
Neurology
Data Interpretation, Statistical
SX11 Neurochoice
2805 Cognitive Neuroscience
Cognitive Neuroscience
MESH: Bayes Theorem
Models, Neurological
Biophysics
U5 Foundations of Human Social Behavior: Altruism and Egoism
MESH: Causality
Machine learning
050105 experimental psychology
03 medical and health sciences
Neuroimaging
Robustness (computer science)
MESH: Models, Neurological
MESH: Electroencephalography
medicine
[SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology
Humans
0501 psychology and cognitive sciences
[SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology
MESH: Biophysics
Models, Statistical
MESH: Humans
business.industry
Dynamic causal modelling
Reproducibility of Results
Bayes Theorem
2808 Neurology
570 Life sciences
biology
Artificial intelligence
Functional magnetic resonance imaging
business
computer
MESH: Data Interpretation, Statistical
030217 neurology & neurosurgery
MESH: Models, Statistical
Subjects
Details
- Language :
- English
- ISSN :
- 10538119 and 10959572
- Database :
- OpenAIRE
- Journal :
- NeuroImage, NeuroImage, Elsevier, 2011, 58 (2), pp.312-22. ⟨10.1016/j.neuroimage.2009.11.062⟩
- Accession number :
- edsair.doi.dedup.....7355524a6b9aefdef6baf40fc6ca2b92
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2009.11.062⟩