A mathematical model for persistent post-CSD vasoconstriction.
Abstract
Cortical spreading depression (CSD) is the propagation of a relatively slow wave in
cortical brain tissue that is linked to a number of pathological conditions such as
stroke and migraine. Most of the existing literature investigates the dynamics of
short term phenomena such as the depolarization and repolarization of membrane potentials
or large ion shifts. Here, we focus on the clinically-relevant hour-long state of
neurovascular malfunction in the wake of CSDs. This dysfunctional state involves widespread
vasoconstriction and a general disruption of neurovascular coupling. We demonstrate,
using a mathematical model, that dissolution of calcium that has aggregated within
the mitochondria of vascular smooth muscle cells can drive an hour-long disruption.
We model the rate of calcium clearance as well as the dynamical implications on overall
blood flow. Based on reaction stoichiometry, we quantify a possible impact of calcium
phosphate dissolution on the maintenance of F0F1-ATP synthase activity.
Type
Journal articleSubject
Cerebral CortexEndoplasmic Reticulum
Mitochondria
Cytosol
Humans
Calcium Phosphates
Oxygen
Calcium
Proton-Translocating ATPases
Adenosine Triphosphate
Oscillometry
Membrane Potentials
Phosphorylation
Cerebrovascular Circulation
Vasoconstriction
Models, Theoretical
Stroke
Cortical Spreading Depression
Gray Matter
Neurovascular Coupling
Permalink
https://hdl.handle.net/10161/23460Published Version (Please cite this version)
10.1371/journal.pcbi.1007996Publication Info
Xu, Shixin; Chang, Joshua C; Chang, Joshua C; Chow, Carson C; Brennan, KC; & Huang,
Huaxiong (2020). A mathematical model for persistent post-CSD vasoconstriction. PLoS computational biology, 16(7). pp. e1007996. 10.1371/journal.pcbi.1007996. Retrieved from https://hdl.handle.net/10161/23460.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
Collections
More Info
Show full item recordScholars@Duke
Shixin Xu
Assistant Professor of Mathematics at Duke Kunshan University
Shixin Xu is an Assistant Professor of Mathematics. His research interests are machine
learning and data-driven model for diseases, multiscale modeling of complex fluids,
Neurovascular coupling, homogenization theory, and numerical analysis. The current
projects he is working on are
image data-based for the prediction of hemorrhagic transformation in acute ischemic
stroke,
electrodynamics modeling of saltatory conduction along myelina

Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy
Rights for Collection: Scholarly Articles
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info