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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F14%3APU109412" target="_blank" >RIV/00216305:26220/14:PU109412 - isvavai.cz</a>

Výsledek na webu

<a href="http://ww4.aievolution.com/hbm1401/index.cfm?do=abs.viewAbs&abs=3359" target="_blank" >http://ww4.aievolution.com/hbm1401/index.cfm?do=abs.viewAbs&abs=3359</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

FH - Neurologie, neurochirurgie, neurovědy

OECD FORD obor

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Projekt

<a href="/cs/project/GAP103%2F12%2F0552" target="_blank" >GAP103/12/0552: Srovnání a inference metod hodnocení funkční a efektivní konektivity ve fMRI</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů