A mathematical model for persistent post-CSD vasoconstriction.

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2020-07-15

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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.

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Cerebral Cortex, Endoplasmic 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

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https://hdl.handle.net/10161/23460

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10.1371/journal.pcbi.1007996

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Xu, Shixin, Joshua C Chang, Joshua C Chang, Carson C Chow, KC Brennan and Huaxiong Huang (2020). A mathematical model for persistent post-CSD vasoconstriction. PLoS computational biology, 16(7). p. e1007996. 10.1371/journal.pcbi.1007996 Retrieved from https://hdl.handle.net/10161/23460.

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Xu

Shixin Xu

Assistant Professor of Mathematics at Duke Kunshan University

Shixin Xu is an Assistant Professor of Mathematics whose research spans several dynamic and interconnected fields. His primary interests include machine learning and data-driven models for disease prediction, multiscale modeling of complex fluids, neurovascular coupling, homogenization theory, and numerical analysis. His current projects reflect a diverse and impactful portfolio:

  • Developing predictive models based on image data to identify hemorrhagic transformation in acute ischemic stroke.
  • Conducting electrodynamics modeling of saltatory conduction along myelinated axons to understand nerve impulse transmission.
  • Engaging in electrochemical modeling to explore the interactions between electric fields and chemical processes.
  • Investigating fluid-structure interactions with mass transport and reactions, crucial for understanding physiological and engineering systems.

These projects demonstrate his commitment to addressing complex problems through interdisciplinary approaches that bridge mathematics with biological and physical sciences.

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