467 citations · 891 across the 5 of their papers we have counts for
6 papers
Parametric dynamic causal modelling
Amirhossein Jafarian, Peter Zeidman, Rob. C Wykes +2
This technical note introduces parametric dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states.…
Bayesian fusion and multimodal DCM for EEG and fMRI
Huilin Wei, Amirhossein Jafarian, Peter Zeidman +4
This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rest…
Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling
Amirhossein Jafarian, Peter Zeidman, Vladimir Litvak +1
Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill po…
Neurovascular coupling: insights from multi-modal dynamic causal modelling of fMRI and MEG
Amirhossein Jafarian, Vladimir Litvak, Hayriye Cagnan +2
This technical note presents a framework for investigating the underlying mechanisms of neurovascular coupling in the human brain using multi-modal magnetoencephalography (MEG) and…
A tutorial on group effective connectivity analysis, part 2: second level analysis with PEB
Peter Zeidman, Amirhossein Jafarian, Mohamed L. Seghier +4
This tutorial provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (…
A tutorial on group effective connectivity analysis, part 1: first level analysis with DCM for fMRI
Peter Zeidman, Amirhossein Jafarian, Nadège Corbin +4
Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and…