A Mathematical Network Model of Ischemic Stroke

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Date

2017-05

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The Ohio State University

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Abstract

Stroke is a major cause of morbidity and mortality in the United States, with one stoke occurring every forty seconds. About eighty-seven percent of all stokes are ischemic, in which a blood vessel to the brain is blocked, resulting in an energy crisis and subsequent disruption of ion gradients. As a result, waves of recurrent spreading depolarizations (RSDs) start to spread throughout the penumbra. Eventually, extracellular glutamate builds up and leads to excitotoxicity, swelling and death of brain cells. The purpose of this research is to better understand the dynamics of the RSDs that have been associated with glutamate accumulation and growth of brain lesions. We have constructed a functioning model of 25 astrocytes and 25 neurons that incorporates membrane potentials, ion currents, cell receptors, sodium-potassium pumps and glial sodium-glutamate transporters. The network model is then reduced to 15 neuron-astrocyte pairs, so that the effect of parameters, such as neuron Na+-K+ ATPase strength and astrocytic Na+-K+ ATPase strength, on RSDs can be determined. Gap junctions between astrocytes are also included in the reduced model to understand how the number of gap junction connections and strength of gap junction impact RSDs.

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To work on this thesis, I received a $5,000 undergraduate research scholarship from Arts and Sciences (ASC) Honors

Keywords

mathematical neuroscience, model, ischemia, stroke, computational

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