Comparing hemodynamic models with DCM
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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, epsilon, 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 epsilon is treated as a free parameter.
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DCM for fMRI rests on a generative model that has two components: a neuronal model describing interactions in a distributed network of neuronal populations, and a component mapping neuronal activity to observed hemodynamic responses.
“DCM for fMRI rests on a generative model that has two components.”
From Network discovery with DCM (2011) · cites this paper as Stephan et al., 2007
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Self-citation
- Authors
- Stephan, K. E., Weiskopf, N., Drysdale, P. M., Robinson, P. A., & Friston, K. J.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- The University of Sydney (AU)
- Corresponding author
- Stephan, Klaas Enno
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2007-11
- Volume
- 38
- Issue
- 3
- Pages
- 387-401
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2636182
- OpenAlex
- W1974105735
- Cited by
- 441
- References
- 58
- Citations by year
- 2026: 10 · 2025: 27 · 2024: 16 · 2023: 17 · 2022: 28 · 2021: 23 · 2020: 28 · 2019: 24 · 2018: 31 · 2017: 23 · 2016: 25 · 2015: 40 · 2014: 20 · 2013: 23 · 2012: 19
- FWCI
- 15.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Wellcome Trust (056750)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- BOLD signal, hemodynamic modeling, balloon model, dynamic causal modeling, model comparison, Bayesian model selection, effective connectivity
- MeSH terms
- Animals, Hemodynamics, Magnetic Resonance Imaging, Models, Biological, Nervous System Physiological Phenomena, Oxygen, Oxygen Consumption
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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