Publication detail

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

LABOUNEK, R. GAJDOŠ, M. FOUSEK, J. MIKL, M. HAVLÍČEK, M. BRÁZDIL, M. JAN, J.

Original Title

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

English Title

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

Type

abstract

Language

en

Original Abstract

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.

English abstract

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.

Keywords

anatomical and effective connectivity, fMRI, diffusion MRI, dynamic causal modelling

Released

08.06.2014

Publisher

OHBM

Location

Hamburg, Germany

Pages from

1

Pages to

3

Pages count

3

URL

Documents

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"
}