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Virtual NEURON: a strategy for merged biochemical and electrophysiological modeling.

ABSTRACT: Because of its highly branched dendrite, the Purkinje neuron requires significant computational resources if coupled electrical and biochemical activity are to be simulated. To address this challenge, we developed a scheme for reducing the geometric complexity; while preserving the essential features of activity in both the soma and a remote dendritic spine. We merged our previously published biochemical model of calcium dynamics and lipid signaling in the Purkinje neuron, developed in the Virtual Cell modeling and simulation environment, with an electrophysiological model based on a Purkinje neuron model available in NEURON. A novel reduction method was applied to the Purkinje neuron geometry to obtain a model with fewer compartments that is tractable in Virtual Cell. Most of the dendritic tree was subject to reduction, but we retained the neuron's explicit electrical and geometric features along a specified path from spine to soma. Further, unlike previous simplification methods, the dendrites that branch off along the preserved explicit path are retained as reduced branches. We conserved axial resistivity and adjusted passive properties and active channel conductances for the reduction in surface area, and cytosolic calcium for the reduction in volume. Rallpacks are used to validate the reduction algorithm and show that it can be generalized to other complex neuronal geometries. For the Purkinje cell, we found that current injections at the soma were able to produce similar trains of action potentials and membrane potential propagation in the full and reduced models in NEURON; the reduced model produces identical spiking patterns in NEURON and Virtual Cell. Importantly, our reduced model can simulate communication between the soma and a distal spine; an alpha function applied at the spine to represent synaptic stimulation gave similar results in the full and reduced models for potential changes associated with both the spine and the soma. Finally, we combined phosphoinositol signaling and electrophysiology in the reduced model in Virtual Cell. Thus, a strategy has been developed to combine electrophysiology and biochemistry as a step toward merging neuronal and systems biology modeling.

SUBMITTER: Brown SA

PROVIDER: S-EPMC3221887 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Virtual NEURON: a strategy for merged biochemical and electrophysiological modeling.

Brown Sherry-Ann SA Moraru Ion I II Schaff James C JC Loew Leslie M LM

Journal of computational neuroscience 20110222 2

Because of its highly branched dendrite, the Purkinje neuron requires significant computational resources if coupled electrical and biochemical activity are to be simulated. To address this challenge, we developed a scheme for reducing the geometric complexity; while preserving the essential features of activity in both the soma and a remote dendritic spine. We merged our previously published biochemical model of calcium dynamics and lipid signaling in the Purkinje neuron, developed in the Virtu ...[more]

PMID: 21340454

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Project description:BackgroundThe COVID-19 pandemic challenges our ability to provide surgical education, as our ability to gather and train together has been restricted due to safety concerns. However, the importance of quality surgical education has remained. High-fidelity simulation platforms have been developed that merge virtual reality video streams to allow for remote instruction and collaboration. This study sought to validate the use of a merged virtual reality (MVR) platform for the instruction and assessment of the fundamentals of laparoscopic surgery (FLS) skills.MethodsThis was a prospective randomized controlled non-inferiority study. Thirty participants were randomized between three groups: The standard group received in-person instruction and expert feedback, the experimental group received identical training via the MVR platform, and the control group practiced on their own, but received no feedback. All participants were pre-tested for baseline performance at the beginning of the study. Change in performance was evaluated immediately after training and one month later for retention. Ordinary one-way analysis of variance was used to evaluate the effects of time, group, and time-on-group.ResultsThe pre-test confirmed baseline homogeneity between the groups. MVR was non-inferior to standard in-person training for total FLS times on either the post-test (p = 0.632) or the retention test (p = 0.829). Performance was also identical between MVR and standard training groups for each of the individual FLS tasks. Each group improved significantly in nearly all tasks after practice; however, the standard and MVR training groups both improved significantly more than controls for the ligating loop, extracorporeal suturing, intracorporeal suturing, and total FLS task training but did not reach statistical significance for peg transfer and pattern cut tasks.ConclusionThis randomized, controlled trial has demonstrated the use of an MVR platform as non-inferior to in-person instruction for the FLS program, forming the foundation for future work on remote instruction and collaboration.

Dataset Information

Virtual NEURON: a strategy for merged biochemical and electrophysiological modeling.

Publications

Virtual NEURON: a strategy for merged biochemical and electrophysiological modeling.

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