Browsing by Subject "Computational Model"

Impulse propagation in cardiac muscle is determined not only by the excitable properties of the myocyte membrane, but also by the gross and fine structure of cardiac muscle. Ionic diffusion pathways are defined by the muscle's interconnected myocytes and interweaving interstitial spaces. Resistive variations arising from spatial changes in tissue structure, including geometry, composition and electrical properties have a significant impact on the success or failure of impulse propagation. Although much as been learned about the impact of discrete resistive architecture of the intracellular space, the role of the interstitial space in the spread of electrical activity is less well understood or appreciated at the microscopic scale.

The interstitial space, or interstitium, occupies from 20-25% of the total heart volume.

The structural and material composition of the interstitial space is both complex and

heterogeneous, encompassing non-myocyte cell structures and a conglomeration of

extracellular matrix proteins. The spatial distribution of the interstitium can vary from confined spaces between abutting myocytes and tightly packed cardiac fibers to large gaps between cardiac bundles and sheets

This work presents a discrete multidomain formulation that describes the three-dimensional ionic diffusion pathways between connected myocytes within a variable interstitial physiology and morphology. Unlike classically used continuous and discontinuous models of impulse propagation, the intracellular and extracellular spaces are represented as spatially distinct volumes with dynamic and static boundary conditions that electrically couple neighboring spaces to form the electrically cooperative tissue model. The discrete multidomain model provides a flexible platform to simulate impulse propagation at the microscopic scale within a three-dimensional context. The three-dimensional description of the interstitial space that

encompasses a single cell improves the capability of the model to realistically investigate the impact of the discontinuous and electrotonic inhomogeneities of the myocardium's interstitium.

Under the discrete multidomain representation, a non-uniformly described interstitium

capturing the passive properties of the intravascular space or variable distribution and

composition of the extracellular space that encompasses a cardiac fiber creates an

electrotonic load perpendicular to the direction of the propagating wavefront. During

longitudinal propagation along a cardiac fiber, results demonstrate waveshape

alterations due to variations in loads experienced radially that would have been otherwise masked in traditional model descriptions. Findings present a mechanism for eliminating myocyte membrane participation in impulse propagation, as the result of decreased loading experienced radially from a non-uniformly resistive extracellular space. Ultimately, conduction velocity increases by decreasing the "effective" surface-to-volume ratio, as theoretically hypothesized to occur in the conducting Purkinje tissue.

Spinal cord stimulation (SCS) is a surgically implanted therapy for chronic pain that delivers electrical stimulation to the spinal cord. Despite significant technological and clinical improvements, the therapeutic success of SCS has plateaued (North et al., 1993; Taylor et al., 2014), in part due to incomplete understanding of how changing stimulation parameters (i.e. amplitude, pulse duration, and timing) affect the neuronal circuitry that modulates pain perception. The work in this dissertation uses computational modeling and in vivo neural recordings to understand how dorsal horn circuitry changes in response to neuropathic pain and predict optimal stimulation parameters for SCS. Computational models are important tools for studying and predicting neural circuit responses and the first part of this dissertation concerns the development of novel computational models. The models are subsequently used to predict responses to neuropathic pain, and the models quantified distinct shifts in responses observed in experimental recordings, demonstrating their validity as a tool for understanding mechanisms of action of SCS. Multiple single unit recordings in the dorsal horn replicated the predictions made using the computational models and predicted that correlations between neurons could be used as a biomarker of neuropathic pain and stimulation efficacy. Model-based design uncovered multifrequency stimulation parameters and temporal patterns of stimulation that optimized neural responses and present promising avenues for improving clinical efficacy. We also used the computational model to predict the mechanism of action of a novel modality of SCS and validated our predicted mechanism of action through experimental recordings. Overall, this thesis work improved our understanding of dorsal horn circuits and the mechanisms of action underlying multiple modalities of SCS, developed new strategies for optimizing stimulation parameters, and demonstrated the effectiveness of optimized stimulation parameters in preclinical models.