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Mechanistic Mathematical Modeling Tests Hypotheses of the Neurovascular Coupling in fMRI
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences.
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
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2016 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, no 6, e1004971Article in journal (Refereed) PublishedText
Abstract [en]

Functional magnetic resonance imaging (fMRI) measures brain activity by detecting the blood-oxygen-level dependent (BOLD) response to neural activity. The BOLD response depends on the neurovascular coupling, which connects cerebral blood flow, cerebral blood volume, and deoxyhemoglobin level to neuronal activity. The exact mechanisms behind this neurovascular coupling are not yet fully investigated. There are at least three different ways in which these mechanisms are being discussed. Firstly, mathematical models involving the so-called Balloon model describes the relation between oxygen metabolism, cerebral blood volume, and cerebral blood flow. However, the Balloon model does not describe cellular and biochemical mechanisms. Secondly, the metabolic feedback hypothesis, which is based on experimental findings on metabolism associated with brain activation, and thirdly, the neurotransmitter feed-forward hypothesis which describes intracellular pathways leading to vasoactive substance release. Both the metabolic feedback and the neurotransmitter feed-forward hypotheses have been extensively studied, but only experimentally. These two hypotheses have never been implemented as mathematical models. Here we investigate these two hypotheses by mechanistic mathematical modeling using a systems biology approach; these methods have been used in biological research for many years but never been applied to the BOLD response in fMRI. In the current work, model structures describing the metabolic feedback and the neurotransmitter feed-forward hypotheses were applied to measured BOLD responses in the visual cortex of 12 healthy volunteers. Evaluating each hypothesis separately shows that neither hypothesis alone can describe the data in a biologically plausible way. However, by adding metabolism to the neurotransmitter feed-forward model structure, we obtained a new model structure which is able to fit the estimation data and successfully predict new, independent validation data. These results open the door to a new type of fMRI analysis that more accurately reflects the true neuronal activity.

Place, publisher, year, edition, pages
PUBLIC LIBRARY SCIENCE , 2016. Vol. 12, no 6, e1004971
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Bioinformatics (Computational Biology)
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URN: urn:nbn:se:liu:diva-130437DOI: 10.1371/journal.pcbi.1004971ISI: 000379349700045PubMedID: 27310017OAI: oai:DiVA.org:liu-130437DiVA: diva2:951161
Note

Funding Agencies|Swedish Research council [2014-6249]; Knut and Alice Wallenbergs foundation, KAW [2013.0076]; Research council of Southeast Sweden [FORSS-481691]; Linkoping University

Available from: 2016-08-06 Created: 2016-08-05 Last updated: 2016-08-22

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Lundengård, KarinCedersund, GunnarSten, SebastianLeong, FelixSmedberg, AlexanderElinder, FredrikEngström, Maria
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Division of Radiological SciencesFaculty of Medicine and Health SciencesCenter for Medical Image Science and Visualization (CMIV)Department of Biomedical EngineeringFaculty of Science & EngineeringDepartment of Clinical and Experimental MedicineDepartment of Medical and Health SciencesDivision of Cell Biology
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