Publication detail

Effects of imprecise signal extraction on posterior DCM parameters.

SLAVÍČEK, T. LAMOŠ, M. GAJDOŠ, M. MIKL, M. JAN, J.

Original Title

Effects of imprecise signal extraction on posterior DCM parameters.

Czech Title

Vliv nepřesné extrakce signálu na posteriorní parametry DCM modelů.

English Title

Effects of imprecise signal extraction on posterior DCM parameters.

Type

abstract

Language

en

Original Abstract

Dynamic causal modeling (DCM) is a method for analyzing effective connectivity in functional magnetic resonance imaging (fMRI) data. Specific parameters describing the generative model (involved regions, connections, modulatory effects, inputs, etc.) represent input to the DCM method. By inverting the forward model, DCM infers (hidden) neuronal processes using fitting to the experimentally measured signal (Kahan and Foltynie 2013). Then, correct localization and extraction of the brain signals from regions of interest (ROIs) directly influences the result. In our study, we compared two approaches for ROIs position specification (common vs. individual) and evaluated their sensitivity to random shifts of ROI position.

Czech abstract

Dynamic kauzální modelování (DCM) je metoda pro analýzu efektivní konektivity z dat funkční magnetické rezonance (fMRI). Specifické parametry popisující generativní model (zapojené regiony, spojení, modulační efekty, vstupy, atd.) představují vstup do metody DCM. Inverzí dopředného modelu, DCM odvozuje (skryté) neuronální parametry pomocí fitování na experimentálně naměřený signál (Kahan a Foltynie 2013). Správná lokalizace a extrakce mozkových signálů z oblastí zájmu (ROI) má přímý vliv na výsledek. V naší studii jsme porovnávali dva přístupy pro specifikaci polohy ROI (společná a individuálně přizpůsobená) a hodnotili jejich citlivost vůči náhodným posunům.

English abstract

Dynamic causal modeling (DCM) is a method for analyzing effective connectivity in functional magnetic resonance imaging (fMRI) data. Specific parameters describing the generative model (involved regions, connections, modulatory effects, inputs, etc.) represent input to the DCM method. By inverting the forward model, DCM infers (hidden) neuronal processes using fitting to the experimentally measured signal (Kahan and Foltynie 2013). Then, correct localization and extraction of the brain signals from regions of interest (ROIs) directly influences the result. In our study, we compared two approaches for ROIs position specification (common vs. individual) and evaluated their sensitivity to random shifts of ROI position.

Keywords

fMRI, DCM, effective connectivity, signal extraction, group statistics

Released

12.06.2014

Pages count

3

URL

BibTex


@misc{BUT107932,
  author="Tomáš {Slavíček} and Martin {Lamoš} and Martin {Gajdoš} and Michal {Mikl} and Jiří {Jan}",
  title="Effects of imprecise signal extraction on posterior DCM parameters.",
  annote="Dynamic causal modeling (DCM) is a method for analyzing effective connectivity in functional magnetic resonance imaging (fMRI) data. Specific parameters describing the generative model (involved regions, connections, modulatory effects, inputs, etc.) represent input to the DCM method. By inverting the forward model, DCM infers (hidden) neuronal processes using fitting to the experimentally measured signal (Kahan and Foltynie 2013). Then, correct localization and extraction of the brain signals
from regions of interest (ROIs) directly influences the result. In our study, we compared two approaches for ROIs position specification (common vs. individual) and evaluated their sensitivity to random shifts of ROI position.",
  booktitle="20th Annual Meeting of the Organization for Human Brain Mapping (OHBM), 2014.",
  chapter="107932",
  howpublished="online",
  year="2014",
  month="june",
  type="abstract"
}