The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX) Version 2.0: Modelling in vitro electrical stimulation of brain tissue

Thornton, Christopher; Hutchings, Frances; Kaiser, Marcus

doi:10.12688/wellcomeopenres.15058.1

Cited by 3 publications

(7 citation statements)

References 43 publications

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“…Three more (BioNet [51, 52], NetPyNE [53], and LFPy [57, 58]) include some of the features we needed, but as front-ends to the NEURON simulator [59] they are oriented towards biophysically detailed, expensive-to-simulate neuron models. The same can be said of VERTEX [49, 50], which is a tool for use in MATLAB. See Table 1 for details.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The increasing complexity of both experiments and models requires specialized software to meet this goal, creating barriers to access. While multiple existing tools facilitate some degree of stimulation and recording of high-level population simulations [48, 49, 50, 51, 52, 53], existing approaches have crucial limitations. For example, many are oriented towards detailed, multi-compartment neuron models that can be prohibitively expensive for the simulation of large populations, and none offer ready-to-use light and opsin models for optogenetics.…”

Section: Introductionmentioning

confidence: 99%

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Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed

Cruzado

Willats

et al. 2023

Preprint

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Recent advances in neurotechnology enable exciting new experiments, including novel paradigms such as closed-loop optogenetic control that achieve powerful causal interventions. At the same time, improved computational models are capable of reproducing behavior and neural activity with increasing fidelity. However, the complexities of these advances can make it difficult to reap the benefits of bridging model and experiment, such asin-silicoexperiment prototyping or direct comparison of model output to experimental data. We can bridge this gap more effectively by incorporating the simulation of the experimental interface into our models, but no existing tool integrates optogenetics, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed the Closed-Loop, Electrophysiology, and Optogenetics experiment simulation testbed (Cleo). Cleo is a Python package built on the Brian 2 simulator enabling injection of recording and stimulation devices as well as closed-loop control with realistic latency into a spiking neural network model. Here we describe the design, features, and use of Cleo, including validation of the individual system components. We further demonstrate its utility in three case studies using a variety of existing models and discuss potential applications for advancing causal neuroscience.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed

Cruzado

Willats

et al. 2023

Preprint

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“…The VERTEX model manages to be computationally tractable for large neuron populations over long run times using the simplified neuron morphologies first presented by Tomsett [19], and both Fig. 2 and further work presented in [30] show that these morphologies can also capture the impact of an electric field on a given neuron type. Electric fields have the largest impact on longer morphologies orientated in parallel with the field direction, as has previously been reported [16].…”

Section: Capturing Orientation Impact In the Cortical Tissue Modelmentioning

confidence: 95%

“…The electric field values from the finite element model are calculated at the midpoint of each compartment and then the field values are applied to update the axial currents of the neurons. The recent paper by Thornton et al [30] goes into more details on the stimulation implementation.…”

Section: Applying Electric Fieldsmentioning

confidence: 99%

Predicting the Impact of Electric Field Stimulation in a Detailed Computational Model of Cortical Tissue

Hutchings,

Thornton,

Zhang

et al. 2020

Preprint

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Background Neurostimulation using weak electric fields has generated excitement in recent years due to its potential as a medical intervention. However, study of this stimulation modality has been hampered by inconsistent results and large variability within and between studies. In order to begin addressing this variability we need to properly characterise the impact of the current on the underlying neuron populations.Objective To develop and test a computational model capable of capturing the impact of electric field stimulation on networks of neurons.Methods We construct a cortical tissue model with distinct layers and explicit neuron morphologies. We then apply a model of electrical stimulation and carry out multiple test case simulations. ResultsThe cortical slice model is compared to experimental literature and shown to capture the main features of the electrophysiological response to stimulation. Namely, the model showed 1) a similar level of depolarisation in individual pyramidal neurons, 2) acceleration of intrinsic oscillations, and 3) retention of the spatial profile of oscillations in different layers. We then apply alternative electric fields to demonstrate how the model can capture differences in neuronal responses to the electric field. We demonstrate that the tissue response is dependent on layer depth, the angle of the apical dendrite relative to the field, and stimulation strength. ConclusionsWe present publicly available computational modelling software that predicts the neuron network population response to electric field stimulation.

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Flexible modeling of large-scale neural network stimulation: electrical and optical extensions to The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX)

Pierce,

Shupe,

Fetz

et al. 2024

Preprint

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Computational models that predict effects of neural stimulation can be used as a preliminary tool to informin-vivoresearch, reducing the costs, time, and ethical considerations involved. However, current models do not support the diverse neural stimulation techniques usedin-vivo, including the expanding selection of electrodes, stimulation modalities, and stimulation paradigms. To develop a more comprehensive software, we created several extensions to The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX), the MATLAB-based neural stimulation tool from Newcastle University. VERTEX simulates input currents in a large population of multi-compartment neurons within a small cortical slice to model electric field stimulation, while recording local field potentials (LFPs) and spiking activity. Our extensions to its existing electric field stimulation framework include multiple pairs of parametrically defined electrodes and biphasic, bipolar stimulation delivered at programmable delays. To support the growing use of optogenetic approaches for targeted neural stimulation, we introduced a feature that models optogenetic stimulation through an additional VERTEX input function that converts irradiance to currents at optogenetically responsive neurons. Finally, we added extensions to allow complex stimulation protocols including paired-pulse, spatiotemporal patterned, and closed-loop stimulation. We demonstrated our novel features using VERTEX’s built-in functionalities, illustrating how these extensions can be used to efficiently and systematically test diverse, targeted, and individualized stimulation patterns.

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The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX) Version 2.0: Modelling in vitro electrical stimulation of brain tissue

Cited by 3 publications

References 43 publications

Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed

Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed

Predicting the Impact of Electric Field Stimulation in a Detailed Computational Model of Cortical Tissue

Flexible modeling of large-scale neural network stimulation: electrical and optical extensions to The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX)

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