Dynamic Causal Modeling and subspace identification methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00184438" target="_blank" >RIV/68407700:21230/12:00184438 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11110/12:12009 RIV/00064165:_____/12:12009

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.bspc.2011.07.002" target="_blank" >http://dx.doi.org/10.1016/j.bspc.2011.07.002</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bspc.2011.07.002" target="_blank" >10.1016/j.bspc.2011.07.002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dynamic Causal Modeling and subspace identification methods

Popis výsledku v původním jazyce

The main contribution of the paper is in formulating the problem of detection of brain regions structure within the framework of dynamic system theory. The motivation is to see if the mature domain of experimental identification of dynamic systems can provide a methodology alternative to Dynamic Causal Modeling (DCM) which is currently used as an exclusive tool to estimate the structure of interconnections among a given set of brain regions using the measured data from functional magnetic resonance imaging (fMRI). The key tool proposed for modeling the structure of brain interconnections in this paper is subspace identification methods which produce linear state-space model, thus neglecting the bilinear term from DCM. The procedure is illustrated usinga simple two-region model with maximally simplified linearized hemodynamics. We assume that the underlying system can be modeled by a set of linear differential equations, and identify the parameters (in terms of state space matrices).

Název v anglickém jazyce

Dynamic Causal Modeling and subspace identification methods

Popis výsledku anglicky

The main contribution of the paper is in formulating the problem of detection of brain regions structure within the framework of dynamic system theory. The motivation is to see if the mature domain of experimental identification of dynamic systems can provide a methodology alternative to Dynamic Causal Modeling (DCM) which is currently used as an exclusive tool to estimate the structure of interconnections among a given set of brain regions using the measured data from functional magnetic resonance imaging (fMRI). The key tool proposed for modeling the structure of brain interconnections in this paper is subspace identification methods which produce linear state-space model, thus neglecting the bilinear term from DCM. The procedure is illustrated usinga simple two-region model with maximally simplified linearized hemodynamics. We assume that the underlying system can be modeled by a set of linear differential equations, and identify the parameters (in terms of state space matrices).

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Biomedical Signal Processing and Control

ISSN

1746-8094

e-ISSN

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Svazek periodika

7

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

6

Strana od-do

365-370

Kód UT WoS článku

000304843400007

EID výsledku v databázi Scopus

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