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  Comparing hemodynamic models with DCM

Stephan, K. E., Weiskopf, N., Drysdale, P. M., Robinson, P. A., & Friston, K. J. (2007). Comparing hemodynamic models with DCM. NeuroImage, 38(3), 387-401. doi:10.1016/j.neuroimage.2007.07.040.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0028-65BD-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-4D41-F
Genre: Journal Article

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 Creators:
Stephan, Klaas Enno1, Author
Weiskopf, Nikolaus1, Author              
Drysdale, Peter M.2, 3, Author
Robinson, Peter A.2, 3, 4, Author
Friston, Karl J.1, Author
Affiliations:
1Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom, ou_persistent22              
2School of Physics, University of Sydney, Australia, ou_persistent22              
3Brain Dynamics Center, Westmead Clinical School, University of Sydney, Australia, ou_persistent22              
4Faculty of Medicine, University of Sydney, Australia, ou_persistent22              

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Free keywords: Dynamic causal modeling; Balloon model; Effective connectivity; BOLD signal; Bayesian model selection; System identification
 Abstract: The classical model of blood oxygen level-dependent (BOLD) responses by Buxton et al. [Buxton, R.B., Wong, E.C., Frank, L.R., 1998. Dynamics of blood flow and oxygenation changes during brain activation: the Balloon model. Magn. Reson. Med. 39, 855–864] has been very important in providing a biophysically plausible framework for explaining different aspects of hemodynamic responses. It also plays an important role in the hemodynamic forward model for dynamic causal modeling (DCM) of fMRI data. A recent study by Obata et al. [Obata, T., Liu, T.T., Miller, K.L., Luh, W.M., Wong, E.C., Frank, L.R., Buxton, R.B., 2004. Discrepancies between BOLD and flow dynamics in primary and supplementary motor areas: application of the Balloon model to the interpretation of BOLD transients. NeuroImage 21, 144–153] linearized the BOLD signal equation and suggested a revised form for the model coefficients. In this paper, we show that the classical and revised models are special cases of a generalized model. The BOLD signal equation of this generalized model can be reduced to that of the classical Buxton model by simplifying the coefficients or can be linearized to give the Obata model. Given the importance of hemodynamic models for investigating BOLD responses and analyses of effective connectivity with DCM, the question arises which formulation is the best model for empirically measured BOLD responses. In this article, we address this question by embedding different variants of the BOLD signal equation in a well-established DCM of functional interactions among visual areas. This allows us to compare the ensuing models using Bayesian model selection. Our model comparison approach had a factorial structure, comparing eight different hemodynamic models based on (i) classical vs. revised forms for the coefficients, (ii) linear vs. non-linear output equations, and (iii) fixed vs. free parameters, ε, for region-specific ratios of intra- and extravascular signals. Using fMRI data from a group of twelve subjects, we demonstrate that the best model is a non-linear model with a revised form for the coefficients, in which ε is treated as a free parameter.

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Language(s): eng - English
 Dates: 2007-08-112007-11-15
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1016/j.neuroimage.2007.07.040
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Title: NeuroImage
Source Genre: Journal
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Pages: - Volume / Issue: 38 (3) Sequence Number: - Start / End Page: 387 - 401 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166