Results 11 to 20 of about 5,911,579 (291)
Test-retest reliability of regression dynamic causal modeling [PDF]
Harvard Data Science Review, 2022 Regression dynamic causal modeling (rDCM) is a novel and computationally highly efficient method for inferring effective connectivity at the whole-brain level. While face and construct validity of rDCM have already been demonstrated, here we assessed its
Stefan Frässle, Klaas E. Stephan
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Comparing families of dynamic causal models [PDF]
PLoS Computational Biology, 2010 Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and ...
Stephan, Klaas E +46 more
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Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling [PDF]
Frontiers in Human Neuroscience, 2020 Motor imagery-based brain–computer interfaces (MI-BCIs) send commands to a computer using the brain activity registered when a subject imagines—but does not perform—a given movement.
Minji Lee, Jae-Geun Yoon, Seong-Whan Lee
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Dynamic Causal Modeling of the Cortical Responses to Wrist Perturbations [PDF]
Frontiers in Neuroscience, 2017 Mechanical perturbations applied to the wrist joint typically evoke a stereotypical sequence of cortical and muscle responses. The early cortical responses (<100 ms) are thought be involved in the “rapid” transcortical reaction to the perturbation ...
Yuan Yang +4 more
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A Functional Data Method for Causal Dynamic Network Modeling of Task-Related fMRI [PDF]
Frontiers in Neuroscience, 2019 Functional MRI (fMRI) is a popular approach to investigate brain connections and activations when human subjects perform tasks. Because fMRI measures the indirect and convoluted signals of brain activities at a lower temporal resolution, complex ...
Xuefei Cao, Björn Sandstede, Xi Luo
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Causal Models for Dynamical Systems [PDF]
, 2022 A probabilistic model describes a system in its observational state. In many situations, however, we are interested in the system's response under interventions. The class of structural causal models provides a language that allows us to model the behaviour under interventions.
Peters, J., Bauer, S., Pfister, N.
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Adiabatic dynamic causal modelling
NeuroImage, 2021 This technical note introduces adiabatic dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions in
Amirhossein Jafarian +4 more
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Dynamic Causal Effects in Econometrics with a Focus on the Nonparametric Method: A Review Paper [PDF]
Iranian Journal of Economic Studies, 2023 The understanding of applied modeling in causal effects is of particular importance in econometrics, according to recent developments and research in causal inference applications. We also provide an outline of econometrics’ use of causal inference.
Pegah Mahdavi, Mohammad Ali Ehsani
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Dynamic causal modelling of immune heterogeneity [PDF]
Scientific Reports, 2021 AbstractAn interesting inference drawn by some COVID-19 epidemiological models is that there exists a proportion of the population who are not susceptible to infection—even at the start of the current pandemic. This paper introduces a model of the immune response to a virus.
Parr, Thomas +6 more
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Dynamic Causal Modelling of Active Vision [PDF]
The Journal of Neuroscience, 2019 In this paper, we draw from recent theoretical work on active perception, which suggests that the brain makes use of an internal (i.e., generative) model to make inferences about the causes of sensations. This view treats visual sensations as consequent on action (i.e., saccades) and implies that visual percepts must be actively constructed via a ...
Parr, T +3 more
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