Detail publikace

Sigmoid function parameter stability in anatomically informed priors for dynamic causal models

Originální název

Sigmoid function parameter stability in anatomically informed priors for dynamic causal models

Anglický název

Sigmoid function parameter stability in anatomically informed priors for dynamic causal models

Jazyk

en

Originální abstrakt

Stephan et al. (2009b) introduced and showed that the anatomical connectivity (AC) can improve effective connectivity estimation via dynamic causal modelling (DCM) (Friston et al. 2003). They proved it on the model with 4 regions of interest (ROIs) in visual cortex. But it can not be expected that the sigmoid parameters which affects the variance of shrinkage priors based on AC will be stable and same for different brain areas or especially for different number of ROIs. Additionally, usage of the fully-connected model could be more beneficial in this application. The aim of this work was comparison of optimal sigmoid function parameters in approach introduced by Stephan et al. (2009b) on 6 ROIs fully-connected model of motor task data with optimal parameters estimated by the authors in 2009.

Anglický abstrakt

Stephan et al. (2009b) introduced and showed that the anatomical connectivity (AC) can improve effective connectivity estimation via dynamic causal modelling (DCM) (Friston et al. 2003). They proved it on the model with 4 regions of interest (ROIs) in visual cortex. But it can not be expected that the sigmoid parameters which affects the variance of shrinkage priors based on AC will be stable and same for different brain areas or especially for different number of ROIs. Additionally, usage of the fully-connected model could be more beneficial in this application. The aim of this work was comparison of optimal sigmoid function parameters in approach introduced by Stephan et al. (2009b) on 6 ROIs fully-connected model of motor task data with optimal parameters estimated by the authors in 2009.

BibTex


@misc{BUT108043,
  author="René {Labounek} and Martin {Gajdoš} and Jan {Fousek} and Michal {Mikl} and Martin {Havlíček} and Milan {Brázdil} and Jiří {Jan}",
  title="Sigmoid function parameter stability in anatomically informed priors for dynamic causal models",
  annote="Stephan et al. (2009b) introduced and showed that the anatomical connectivity (AC) can improve effective connectivity estimation via dynamic causal modelling (DCM) (Friston et al. 2003). They proved it on the model with 4 regions of interest (ROIs) in visual cortex. But it can not be expected that the sigmoid parameters which affects the variance of shrinkage priors based on AC will be stable and same for different brain areas or especially for different number of ROIs. Additionally, usage of the fully-connected model could be more beneficial in this application. The aim of this work was comparison of optimal sigmoid function parameters in approach introduced by Stephan et al. (2009b) on 6 ROIs fully-connected model of motor task data with optimal parameters estimated by the authors in 2009.",
  address="OHBM",
  booktitle="In Proceedinds of 20th Annual Meeting of the Organization for Human Brain Mapping",
  chapter="108043",
  howpublished="online",
  institution="OHBM",
  year="2014",
  month="june",
  pages="1--3",
  publisher="OHBM",
  type="abstract"
}