Dynamic causal modelling revisited

Article English OPEN
Friston, K.J.; Preller, Katrin H.; Mathys, Chris; Cagnan, Hayriye; Heinzle, Jakob; Razi, Adeel; Zeidman, Peter;
(2019)
  • Publisher: Elsevier
  • Journal: volume 199issn: 1053-8119, eissn: 1095-9572
  • Publisher copyright policies & self-archiving
  • Related identifiers: doi: 10.3929/ethz-b-000357785, doi: 10.5167/uzh-147223, doi: 10.1016/j.neuroimage.2017.02.045
  • Subject: Dynamic causal modelling; Haemodynamic models; Neural mass models; Effective connectivity; Bayesian | Bayesian, dynamic causal modelling, effective connectivity, haemodynamic models, neural mass models | 610 Medicine & health | Bayesian; Dynamic causal modelling; Effective connectivity; Haemodynamic models; Neural mass models | Clinic for Psychiatry, Psychotherapy, and Psychosomatics | M-PSI/02 - Psychobiology and Physiologic Psychology | Cognitive Neuroscience | Neurology

This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific response... View more
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