Results 21 to 30 of about 5,911,579 (291)

Dynamic causal modelling for EEG and MEG [PDF]

Cognitive Neurodynamics, 2008
Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnetic resonance imaging (fMRI) to quantify effective connectivity between brain areas. Recently, this framework has been extended and established in the magneto/encephalography (M/EEG) domain.
Kiebel, Stefan J., Garrido, Marta I., Moran, Rosalyn J., Friston, Karl J.   +3 more
openaire   +6 more sources

Hypothalamic interaction with reward-related regions during subjective evaluation of foods

NeuroImage, 2022
The reward system implemented in the midbrain, ventral striatum, orbitofrontal cortex, and ventromedial prefrontal cortex evaluates and compares various types of rewards given to the organisms.
Akitoshi Ogawa, Takahiro Osada, Masaki Tanaka, Akimitsu Suda, Koji Nakajima, Satoshi Oka, Koji Kamagata, Shigeki Aoki, Yasushi Oshima, Sakae Tanaka, Nobutaka Hattori, Seiki Konishi   +11 more
doaj   +1 more source

Qualitative dynamics and causality in a Keynesian model [PDF]

Journal of Economic Dynamics and Control, 1989
Abstract In this paper we present a formalism to describe economic dynamics in a qualitative way. This formalism is a modification of an existing algorithm for qualitative simulation as proposed by Kuipers. It is demonstrated that the framework of qualitative dynamics can clarify economic reasoning without using any quantitive data. Especially causal
Berndsen, R.J., Daniels, H.A.M.
openaire   +3 more sources

Comparing dynamic causal models

NeuroImage, 2004
This article describes the use of Bayes factors for comparing dynamic causal models (DCMs). DCMs are used to make inferences about effective connectivity from functional magnetic resonance imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, the connectivity pattern between the regions included
William D. Penny, Klaas E. Stephan, Andrea Mechelli, Karl J. Friston   +3 more
openaire   +5 more sources

Modeling Early Phases of COVID-19 Pandemic in Northern Italy and Its Implication for Outbreak Diffusion

Frontiers in Public Health, 2021
The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. Several computational models have been proposed to inform effective social and healthcare strategies. Crucially, the predictive validity
Daniela Gandolfi, Giuseppe Pagnoni, Giuseppe Pagnoni, Tommaso Filippini, Alessia Goffi, Marco Vinceti, Marco Vinceti, Egidio D'Angelo, Egidio D'Angelo, Jonathan Mapelli, Jonathan Mapelli   +10 more
doaj   +1 more source

Dynamic causal modeling for EEG and MEG [PDF]

Human Brain Mapping, 2009
AbstractWe present a review of dynamic causal modeling (DCM) for magneto‐ and electroencephalography (M/EEG) data. DCM is based on a spatiotemporal model, where the temporal component is formulated in terms of neurobiologically plausible dynamics. Following an intuitive description of the model, we discuss six recent studies, which use DCM to analyze M/
Kiebel, Stefan J, Garrido, Marta I, Moran, Rosalyn, Chen, Chun-Chuan, Friston, Karl J   +4 more
openaire   +6 more sources

Interdisciplinary Research on Healthy Aging: Introduction

Demographic Research, 2018
Background: This is an introduction to a Special Collection of Demographic Research on Interdisciplinary Research on Healthy Aging. The collection is an outcome of an international conference in China on biodemography and multistate modeling in healthy ...
Frans Willekens, James R. Carey, Qiang Li   +2 more
doaj   +1 more source

Dynamic causal modelling

NeuroImage, 2003
In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters ...
Karl J. Friston, Lee M. Harrison, William D. Penny   +2 more
openaire   +3 more sources

Analysing connectivity with Granger causality and dynamic causal modelling [PDF]

Current Opinion in Neurobiology, 2013
This review considers state-of-the-art analyses of functional integration in neuronal macrocircuits. We focus on detecting and estimating directed connectivity in neuronal networks using Granger causality (GC) and dynamic causal modelling (DCM). These approaches are considered in the context of functional segregation and integration and--within ...
Friston, Karl, Moran, Rosalyn, Seth, Anil K   +2 more
openaire   +3 more sources

Using rDCM Method in the Mixed Model in Order to Inference Effective Connectivity in Emotions

Frontiers in Biomedical Technologies, 2019
Purpose: Recently, data from functional magnetic resonance imaging in the field of neuroscience have been strongly considered for the modeling of cognitive activities.
Naemeh Farahani, Emad Fatemizadeh, Ali Motie Nasrabadi   +2 more
doaj   +1 more source