A hemodynamic model for layered BOLD signals
Open accessAbstract
High-resolution blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) at the sub-millimeter scale has become feasible with recent advances in MR technology. In principle, this would enable the study of layered cortical circuits, one of the fundaments of cortical computation. However, the spatial layout of cortical blood supply may become an important confound at such high resolution. In particular, venous blood draining back to the cortical surface perpendicularly to the layered structure is expected to influence the measured responses in different layers. Here, we present an extension of a hemodynamic model commonly used for analyzing fMRI data (in dynamic causal models or biophysical network models) that accounts for such blood draining effects by coupling local hemodynamics across layers. We illustrate the properties of the model and its inversion by a series of simulations and show that it successfully captures layered fMRI data obtained during a simple visual experiment. We conclude that for future studies of the dynamics of layered neuronal circuits with high-resolution fMRI, it will be pivotal to include effects of blood draining, particularly when trying to infer on the layer-specific connections in cortex--a theme of key relevance for brain disorders like schizophrenia and for theories of brain function such as predictive coding.
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Signals in upper cortical layers are contaminated by blood draining effects from lower layers, confounding the identification of layer-specific activity and connections and requiring adaptation of generative models of fMRI data.
“However, signals in upper cortical layers are contaminated by blood draining effects from lower layers. This confounds the identification of layer-specific activity and connections and requires adapting generative models of fMRI data.”
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- Authors
- Heinzle, J., Koopmans, P. J., den Ouden, H. E. M., Raman, S., & Stephan, K. E.
- Affiliations
- University of Zurich (CH)
- University of Oxford (GB)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Heinzle, Jakob
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 125
- Pages
- 556-570
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1937872394
- Cited by
- 144
- References
- 73
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 14 · 2023: 12 · 2022: 12 · 2021: 21 · 2020: 20 · 2019: 13 · 2018: 7 · 2017: 15 · 2016: 8
- FWCI
- 8.12
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Wellcome Trust (091593, WT100092MA)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Universität Zürich
- René und Susanne Braginsky Stiftung
- 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 fMRI, hemodynamic modeling, dynamic causal modeling, model inversion, visual stimulation, predictive coding, schizophrenia
- MeSH terms
- Algorithms, Brain, Brain Mapping, Hemodynamics, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Models, Theoretical, Oxygen
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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