Literature DB >> 12169248

A model of the hemodynamic response and oxygen delivery to brain.

Ying Zheng1, John Martindale, David Johnston, Myles Jones, Jason Berwick, John Mayhew.   

Abstract

A recent nonlinear system by Friston et al. (2000. NeuroImage 12: 466-477) links the changes in BOLD response to changes in neural activity. The system consists of five subsystems, linking: (1) neural activity to flow changes; (2) flow changes to oxygen delivery to tissue; (3) flow changes to changes in blood volume and venous outflow; (4) changes in flow, volume, and oxygen extraction fraction to deoxyhemoglobin changes; and finally (5) volume and deoxyhemoglobin changes to the BOLD response. Friston et al. exploit, in subsystem 2, a model by Buxton and Frank coupling flow changes to changes in oxygen metabolism which assumes tissue oxygen concentration to be close to zero. We describe below a model of the coupling between flow and oxygen delivery which takes into account the modulatory effect of changes in tissue oxygen concentration. The major development has been to extend the original Buxton and Frank model for oxygen transport to a full dynamic capillary model making the model applicable to both transient and steady state conditions. Furthermore our modification enables us to determine the time series of CMRO(2) changes under different conditions, including CO(2) challenges. We compare the differences in the performance of the "Friston system" using the original model of Buxton and Frank and that of our model. We also compare the data predicted by our model (with appropriate parameters) to data from a series of OIS studies. The qualitative differences in the behaviour of the models are exposed by different experimental simulations and by comparison with the results of OIS data from brief and extended stimulation protocols and from experiments using hypercapnia.

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Year:  2002        PMID: 12169248     DOI: 10.1006/nimg.2002.1078

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  46 in total

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2.  Nonlinear local electrovascular coupling. I: A theoretical model.

Authors:  Jorge J Riera; Xiaohong Wan; Juan Carlos Jimenez; Ryuta Kawashima
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3.  A multicompartment vascular model for inferring baseline and functional changes in cerebral oxygen metabolism and arterial dilation.

Authors:  Theodore J Huppert; Monica S Allen; Heval Benav; Phill B Jones; David A Boas
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4.  Nonlinear local electrovascular coupling. II: From data to neuronal masses.

Authors:  J J Riera; J C Jimenez; X Wan; R Kawashima; T Ozaki
Journal:  Hum Brain Mapp       Date:  2007-04       Impact factor: 5.038

5.  A vascular anatomical network model of the spatio-temporal response to brain activation.

Authors:  David A Boas; Stephanie R Jones; Anna Devor; Theodore J Huppert; Anders M Dale
Journal:  Neuroimage       Date:  2008-01-15       Impact factor: 6.556

6.  Coupling between neuronal activity and microcirculation: implications for functional brain imaging.

Authors:  Ivo Vanzetta; Amiram Grinvald
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7.  Independent components in stimulus-related BOLD signals and estimation of the underlying neural responses.

Authors:  C W Tyler; L L Kontsevich; T C Ferree
Journal:  Brain Res       Date:  2008-06-24       Impact factor: 3.252

Review 8.  Optical brain imaging in vivo: techniques and applications from animal to man.

Authors:  Elizabeth M C Hillman
Journal:  J Biomed Opt       Date:  2007 Sep-Oct       Impact factor: 3.170

9.  Spatial and Temporal Heterogeneities of Capillary Hemodynamics and Its Functional Coupling During Neural Activation.

Authors:  Wei Wei; Yuandong Li; Zhiying Xie; Anthony J Deegan; Ruikang K Wang
Journal:  IEEE Trans Med Imaging       Date:  2018-11-26       Impact factor: 10.048

10.  Dynamic model for the tissue concentration and oxygen saturation of hemoglobin in relation to blood volume, flow velocity, and oxygen consumption: Implications for functional neuroimaging and coherent hemodynamics spectroscopy (CHS).

Authors:  Sergio Fantini
Journal:  Neuroimage       Date:  2013-04-10       Impact factor: 6.556

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