"5"^^ . "Dynamick\u00E9 kauz\u00E1ln\u00ED modelov\u00E1n\u00ED (DCM), jako jedna z metod pro anal\u00FDzu efektivn\u00ED mozkov\u00E9 konektivity n\u00E1m umo\u017E\u0148uje vyvozovat z\u00E1v\u011Bry o neur\u00E1ln\u00EDch procesech na z\u00E1klad\u011B nam\u011B\u0159en\u00FDch dat z funk\u010Dn\u00ED magnetick\u00E9 rezonance (fMRI). Hlavn\u00EDm c\u00EDlem je odhadnout parametry modelu neuron\u00E1ln\u00EDho syst\u00E9mu, jeho\u017E v\u00FDstupy co nejp\u0159esn\u011Bji odpov\u00EDdaj\u00ED pozorovan\u00E9 hemodynamick\u00E9 odezv\u011B. Jeliko\u017E DCM nepat\u0159\u00ED mezi explorativn\u00ED techniky, je v\u017Edy nutn\u00E9 definovat hypot\u00E9zu, kter\u00E1 obsahuje informace o vstupech, vazb\u00E1ch a oblastech mozku. Tato pr\u00E1ce se zab\u00FDv\u00E1 zp\u016Fsobem extrakce hemodynamick\u00FDch sign\u00E1l\u016F z definovan\u00FDch mozkov\u00FDch oblast\u00ED a vlivem nep\u0159esnosti z\u00EDsk\u00E1n\u00ED vhodn\u00E9ho reprezentanta oblasti na v\u00FDsledek odhadu modelu DCM. Pro kvantitativn\u00ED vyhodnocen\u00ED vlivu nep\u0159esn\u00E9 extrakce jsme navrhli a implementovali simul\u00E1tor dat zalo\u017Een\u00FD na DCM modelu. Hlavn\u00ED z\u00E1jem spo\u010D\u00EDv\u00E1 v po\u010Dtu spr\u00E1vn\u011B odhadnut\u00FDch vazeb mezi vybran\u00FDmi \u010D\u00E1stmi mozku definovan\u00E9ho modelu, jeho\u017E chov\u00E1n\u00ED je" . "Dynamick\u00E9 kauz\u00E1ln\u00ED modelov\u00E1n\u00ED: extrakce vhodn\u00FDch hemodynamick\u00FDch sign\u00E1l\u016F z fMRI dat" . . . "RIV/00216305:26220/13:PU106474!RIV14-GA0-26220___" . . "Dynamic Causal Modelling: extraction of propher hemodynamic signals from fMRI data"@en . . "Neuveden" . "2013-11-20+01:00"^^ . . . "Dynamic Causal Modelling (DCM), as one of methods for effective brain connectivity analysis allows us making inferences about neural processes that underlie measured functional magnetic resonance imaging (fMRI) data. The main goal is to estimate parameters of the neuronal system model, whose outputs correspond most precisely to observed blood oxygenation level dependent (BOLD) response. As DCM is not exploratory technique, we have to define a hypothesis, which contains information about inputs, connections and brain regions. This contribution deals with a method of signal extraction from defined brain areas and with the effect of inaccurately extracted representative signal from specific brain area on the result of DCM estimation. For quantitative evaluation of the inaccuracy extraction effect we designed and implemented a data simulator based on the DCM model. We are interested in the amount of correctly estimated connections between selected regions of the specific model. Monte Carlo simulations are"@en . . "Sborn\u00EDk p\u0159\u00EDsp\u011Bvk\u016F workshopu Nov\u00E9 sm\u011Bry v biomedic\u00EDnsk\u00E9m in\u017Een\u00FDrstv\u00ED" . "Gajdo\u0161, Martin" . "26220" . . . "Brno" . . "Neuveden" . . "Dynamick\u00E9 kauz\u00E1ln\u00ED modelov\u00E1n\u00ED: extrakce vhodn\u00FDch hemodynamick\u00FDch sign\u00E1l\u016F z fMRI dat"@cs . "Dynamick\u00E9 kauz\u00E1ln\u00ED modelov\u00E1n\u00ED (DCM), jako jedna z metod pro anal\u00FDzu efektivn\u00ED mozkov\u00E9 konektivity n\u00E1m umo\u017E\u0148uje vyvozovat z\u00E1v\u011Bry o neur\u00E1ln\u00EDch procesech na z\u00E1klad\u011B nam\u011B\u0159en\u00FDch dat z funk\u010Dn\u00ED magnetick\u00E9 rezonance (fMRI). Hlavn\u00EDm c\u00EDlem je odhadnout parametry modelu neuron\u00E1ln\u00EDho syst\u00E9mu, jeho\u017E v\u00FDstupy co nejp\u0159esn\u011Bji odpov\u00EDdaj\u00ED pozorovan\u00E9 hemodynamick\u00E9 odezv\u011B. Jeliko\u017E DCM nepat\u0159\u00ED mezi explorativn\u00ED techniky, je v\u017Edy nutn\u00E9 definovat hypot\u00E9zu, kter\u00E1 obsahuje informace o vstupech, vazb\u00E1ch a oblastech mozku. Tato pr\u00E1ce se zab\u00FDv\u00E1 zp\u016Fsobem extrakce hemodynamick\u00FDch sign\u00E1l\u016F z definovan\u00FDch mozkov\u00FDch oblast\u00ED a vlivem nep\u0159esnosti z\u00EDsk\u00E1n\u00ED vhodn\u00E9ho reprezentanta oblasti na v\u00FDsledek odhadu modelu DCM. Pro kvantitativn\u00ED vyhodnocen\u00ED vlivu nep\u0159esn\u00E9 extrakce jsme navrhli a implementovali simul\u00E1tor dat zalo\u017Een\u00FD na DCM modelu. Hlavn\u00ED z\u00E1jem spo\u010D\u00EDv\u00E1 v po\u010Dtu spr\u00E1vn\u011B odhadnut\u00FDch vazeb mezi vybran\u00FDmi \u010D\u00E1stmi mozku definovan\u00E9ho modelu, jeho\u017E chov\u00E1n\u00ED je"@cs . . "Lamo\u0161, Martin" . . . . "Dynamick\u00E9 kauz\u00E1ln\u00ED modelov\u00E1n\u00ED: extrakce vhodn\u00FDch hemodynamick\u00FDch sign\u00E1l\u016F z fMRI dat" . "10"^^ . "RIV/00216305:26220/13:PU106474" . . "Effective brain connectivity, dynamic causal modelling, representative signal extraction, simulation, visual oddball experiment, fMRI, MATLAB"@en . "P(GAP103/12/0552), S" . "Jan, Ji\u0159\u00ED" . . "4"^^ . . . . . "Mikl, Michal" . "[7D7F6C915A06]" . "70934" . "Dynamic Causal Modelling: extraction of propher hemodynamic signals from fMRI data"@en . . "978-80-214-4814-8" . "Kl\u00EDmov\u00E1, Jana" . "Dynamick\u00E9 kauz\u00E1ln\u00ED modelov\u00E1n\u00ED: extrakce vhodn\u00FDch hemodynamick\u00FDch sign\u00E1l\u016F z fMRI dat"@cs . . . .