An interaction graph approach to gain new insights into mechanisms that modulate cerebrovascular tone.

Mechanisms to modulate cerebrovascular tone are numerous, interconnected, and spatially dependent, increasing the complexity of experimental study design, interpretation of action-effect pathways, and mechanistic modelling. This difficulty is exacerbated when there is an incomplete understanding of these pathways. We propose interaction graphs to break down this complexity, while still maintaining a holistic view of mechanisms to modulate cerebrovascular tone. These graphs highlight the competing processes of neurovascular coupling, cerebral autoregulation, and cerebral reactivity. Subsequent analysis of these interaction graphs provides new insights and suggest potential directions for research on neurovascular coupling, modelling, and dementia.

Automated model calibration with parallel MCMC: Applications for a cardiovascular system model.

Computational physiological models continue to increase in complexity, however, the task of efficiently calibrating the model to available clinical data remains a significant challenge. One part of this challenge is associated with long calibration times, which present a barrier for the routine application of model-based prediction in clinical practice. Another aspect of this challenge is the limited available data for the unique calibration of complex models. Therefore, to calibrate a patient-specific model, it may be beneficial to verify that task-specific model predictions have acceptable uncertainty, rather than requiring all parameters to be uniquely identified. We have developed a pipeline that reduces the set of fitting parameters to make them structurally identifiable and to improve the efficiency of a subsequent Markov Chain Monte Carlo (MCMC) analysis. MCMC was used to find the optimal parameter values and to determine the confidence interval of a task-specific prediction. This approach was demonstrated on numerical experiments where a lumped parameter model of the cardiovascular system was calibrated to brachial artery cuff pressure, echocardiogram volume measurements, and synthetic cerebral blood flow data that approximates what can be obtained from 4D-flow MRI data. This pipeline provides a cerebral arterial pressure prediction that may be useful for determining the risk of hemorrhagic stroke. For a set of three patients, this pipeline successfully reduced the parameter set of a cardiovascular system model from 12 parameters to 8-10 structurally identifiable parameters. This enabled a significant ( > 4 × ) efficiency improvement in determining confidence intervals on predictions of pressure compared to performing a naive MCMC analysis with the full parameter set. This demonstrates the potential that the proposed pipeline has in helping address one of the key challenges preventing clinical application of such models. Additionally, for each patient, the MCMC approach yielded a 95% confidence interval on systolic blood pressure prediction in the middle cerebral artery smaller than ±10 mmHg (±1.3 kPa). The proposed pipeline exploits available high-performance computing parallelism to allow straightforward automation for general models and arbitrary data sets, enabling automated calibration of a parameter set that is specific to the available clinical data with minimal user interaction.

Electroporation of tissue and cells: A three-equation model of drug delivery.

The exposure of the cell membrane to electric pulses of sufficient intensity is known to result in an increased permeability due to the formation of microscopic pores. This is electroporation, and it has been implemented to increase the efficacy of targeted drug delivery. In this study we introduce a novel three-equation model of transport that is able to distinguish the drug uptake in reversibly electroporated cells from that in irreversibly electroporated cells. In order to relate the permeability increases and the cell survival to the local electric field, sigmoidal functions are fit to published experimental data. The resealing of reversibly electroporated cells is also considered. A numerical study is presented that considers two different electrode configurations with different initial drug distributions. This model is able to capture the existence of an optimal applied voltage, above which any increases in voltage act to decrease the total drug delivery to the surviving cells, illustrating the competing influences of increased cell permeability and decreased cell survival.