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  fMRI at High Spatial Resolution: Implications for BOLD-Models

Goense, J., Bohraus, Y., & Logothetis, N. (2016). fMRI at High Spatial Resolution: Implications for BOLD-Models. Frontiers in Computational Neuroscience, 10: 66, pp. 1-13. doi:10.3389/fncom.2016.00066.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-79BC-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-962A-7
Genre: Journal Article

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Goense, J1, Author              
Bohraus, Y1, 2, Author              
Logothetis, NK1, 2, Author              
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1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: As high-resolution functional magnetic resonance imaging (fMRI) and fMRI of cortical layers become more widely used, the question how well high-resolution fMRI signals reflect the underlying neural processing, and how to interpret laminar fMRI data becomes more and more relevant. High-resolution fMRI has shown laminar differences in cerebral blood flow (CBF), volume (CBV), and neurovascular coupling. Features and processes that were previously lumped into a single voxel become spatially distinct at high resolution. These features can be vascular compartments such as veins, arteries, and capillaries, or cortical layers and columns, which can have differences in metabolism. Mesoscopic models of the blood oxygenation level dependent (BOLD) response therefore need to be expanded, for instance, to incorporate laminar differences in the coupling between neural activity, metabolism and the hemodynamic response. Here we discuss biological and methodological factors that affect the modeling and interpretation of high-resolution fMRI data. We also illustrate with examples from neuropharmacology and the negative BOLD response how combining BOLD with CBF- and CBV-based fMRI methods can provide additional information about neurovascular coupling, and can aid modeling and interpretation of high-resolution fMRI.

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 Dates: 2016-06
 Publication Status: Published online
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 Identifiers: DOI: 10.3389/fncom.2016.00066
BibTex Citekey: GoenseBL2016
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Title: Frontiers in Computational Neuroscience
  Abbreviation : Front Comput Neurosci
Source Genre: Journal
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Publ. Info: Lausanne : Frontiers Research Foundation
Pages: - Volume / Issue: 10 Sequence Number: 66 Start / End Page: 1 - 13 Identifier: Other: 1662-5188
CoNE: /journals/resource/1662-5188