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Evaluating the impact of the deep brain stimulation induced electric field on subthalamic neurons: a computational modelling study.

Journal of neuroscience methods | 2010

Deep brain stimulation (DBS) is an effective surgical treatment used to alleviate the symptoms of neurological disorders, most commonly movement disorders. However, the mechanism of how the applied stimulus pulses interact with the surrounding neuronal elements is not yet clearly understood, slowing progress and development of this promising therapeutic technology. To extend previous approaches of using isolated, myelinated axon models used to estimate the effect of DBS, we propose that taking into account entire neurons will reveal stimulation induced effects overlooked by previous studies. We compared the DBS induced volume of tissue activated (VTA) using arrays of whole cell models of subthalamic nucleus (STN) excitatory neurons consisting of a cell body and an anatomically accurate dendritic tree, to the common models of axon arrays. Our results demonstrate that STN neurons have a higher excitation threshold than axons, as stimulus amplitudes 10 times as large elicit a VTA range a fifth of the distance from the electrode surface. However, the STN neurons do show a change in background firing rate in response to stimulation, even when they are classified as sub-threshold by the VTA definition. Furthermore the whole neuron models are sensitive to regions of high current density, as the distribution of firing is centred on the electrode contact edges These results demonstrate the importance of accurate neuron models for fully appreciating the spatial effects of DBS on the immediate surrounding brain volume within small distances of the electrode, which are overlooked by previous models of isolated axons and individual neurons.

Pubmed ID: 20116398 RIS Download

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Associated grants

  • Agency: Medical Research Council, United Kingdom
    Id: G0600168
  • Agency: Medical Research Council, United Kingdom
    Id: 78512

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NEURON (tool)

RRID:SCR_005393

NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. NEURON has benefited from judicious revision and selective enhancement, guided by feedback from the growing number of neuroscientists who have used it to incorporate empirically-based modeling into their research strategies. NEURON's computational engine employs special algorithms that achieve high efficiency by exploiting the structure of the equations that describe neuronal properties. It has functions that are tailored for conveniently controlling simulations, and presenting the results of real neurophysiological problems graphically in ways that are quickly and intuitively grasped. Instead of forcing users to reformulate their conceptual models to fit the requirements of a general purpose simulator, NEURON is designed to let them deal directly with familiar neuroscience concepts. Consequently, users can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells. * helps users focus on important biological issues rather than purely computational concerns * has a convenient user interface * has a user-extendable library of biophysical mechanisms * has many enhancements for efficient network modeling * offers customizable initialization and simulation flow control * is widely used in neuroscience research by experimentalists and theoreticians * is well-documented and actively supported * is free, open source, and runs on (almost) everything

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