Everything about Dynamic causal modeling
Statistical modeling framework
Dynamic causal modeling (DCM) is a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. It uses nonlinear state-space models in continuous time, specified using stochastic or ordinary differential equations. DCM was initially developed for testing hypotheses about neural dynamics. In this setting, differential equations describe the interaction of neural populations, which directly or indirectly give rise to functional neuroimaging data e.g., functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) or electroencephalography (EEG). Parameters in these models quantify the directed influences or effective connectivity among neuronal populations, which are estimated from the data using Bayesian statistical methods. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Dynamic causal modelingA 5-minute orientation
- Skim a recent paper: Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivityInfluential work since 2022
- Pick up a classic book: Maximising Ghana's debt reliefThe most-reprinted book on the subject
- Read the landmark paper: Ten simple rules for dynamic causal modelingCited 794 times
- Go sideways: Bayesian model reductionA closely related topic
- Go sideways: Functional integration (neurobiology)A closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Ten simple rules for dynamic causal modeling
Dynamic causal modeling of evoked responses in EEG and MEG
Neural masses and fields in dynamic causal modeling
Dynamic Causal Modeling of the Response to Frequency Deviants
Dynamic causal modeling for EEG and MEG
Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI
Physiologically informed dynamic causal modeling of fMRI data
Dynamic causal modeling with neural fields
🆕 Influential since 2022
Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivity
Test-retest reliability of regression dynamic causal modeling
Dynamic causal modeling for nonstationary industrial process performance degradation analysis and fault prognosis
Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study
Brain dynamics of mental workload in a multitasking context: Evidence from dynamic causal modeling
đź“š Books worth reading
- 📕Maximising Ghana's debt reliefJohn Asafu-Adjaye · 2006
Foundational Papers in Complexity ScienceDavid Krakauer · 2024
Advances in dynamics and controlS. Sivasundaram · 2003- 📕Perception, realism and the problem of referenceAthanassios Raftopoulos, Peter K. Machamer · 2012
Economics, information systems, and electronic commerceRobert J. Kauffman, Paul P. Tallon · 2009- 📕CausalityCarlo Berzuini · 2012
- 📕Regional DynamicsGordon L. Clark, John E. M. Whiteman · 2017
Making better decisionsItzhak Gilboa · 2010
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
đź”— Related topics
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