Volume 10 Supplement 1
Modelling the activation of neuronal populations during deep brain stimulation
© Purswani et al; licensee BioMed Central Ltd. 2009
Published: 13 July 2009
Deep brain stimulation (DBS) has become an increasingly used clinical therapy for several neurological conditions . However, it is currently impossible to directly measure the distribution of the stimulus in the patient's brain surrounding the implanted electrode, and as a result computational models in the form of finite element models coupled to axon cable models for predicting the volume of tissue activated have proved useful for visualizing stimulation effects. The use of such unconnected axon models, however, relies on the debatable assumption that the excitation of efferent fibres is the functional effect of DBS. quantifying the VTA using anatomically accurate neuronal models as an alternative can provide further clues about the mechanism of DBS.
For this study, we focused in projection neurons in the subthalamic nucleus (STN), a common target for the treatment of Parkinson's disease. used results from a finite element electric field model [2–4] which provided the extracellular potential as a stimulus for a range of multicompartment models, starting with myelinated axons  and followed by the subthalamic projection neurons of increasing complexity .
Our results show that the estimation of the volume of tissue activated is strongly dependent on the type of neuronal model coupled to the FEM results. In response to a 1 V amplitude extracellular pulse, unconnected myelinated axon models up to 4.5 mm away from the electrode fire action potentials at the same frequency as the stimulus. However, for the same stimulus, none of the model STN cells fired at the stimulus frequency, and only cells within 1 mm of the electrode fired at all.
In agreement with previous work , the prediction of the VTA using cable models of unconnected axons and defining activation as generating action potentials in a 1:1 ratio with the stimulus frequency, provided a convenient way of comparing stimulation parameters. However, the responses of the subthalamic projection neurons, with more complex morphologies and membrane dynamics provide more detailed information about the possible mechanisms underlying the observed improvement in patients' symptoms.
This study was supported by a grant from the Medical Research Council of the UK (Grant ID: 78512).
- Dostrovsky JO, Lozano AM: Mechanisms of deep brain stimulation. Mov Disord. 2003, 17: S63-S68. 10.1002/mds.10143.View ArticleGoogle Scholar
- Yousif N, Bayford R, Bain PG, Liu X: The perielectrode space is a significant element of the electrode-brain interface in deep brain stimulation: A computational study. Brain Res Bull. 2007, 74: 361-368. 10.1016/j.brainresbull.2007.07.007.PubMed CentralPubMedView ArticleGoogle Scholar
- Yousif N, Bayford R, Wang S, Liu X: Quantifying the effects of the electrode-brain interface on the crossing electric currents in deep brain recording and stimulation. Neuroscience. 2008, 152: 683-91. 10.1016/j.neuroscience.2008.01.023.PubMed CentralPubMedView ArticleGoogle Scholar
- Yousif N, Bayford R, Liu X: The influence of reactivity of the electrode-brain interface on the crossing electric current in therapeutic deep brain stimulation. Neuroscience. 2008, 156: 597-606. 10.1016/j.neuroscience.2008.07.051.PubMed CentralPubMedView ArticleGoogle Scholar
- Butson CR, McIntyre CC: Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation. Clin Neurophysiol. 2005, 116: 1-11. 10.1016/j.clinph.2005.06.023.View ArticleGoogle Scholar
- Gillies A, Walshaw A: Membrane channel interactions underlying rat subthalamic projection neuron rhythmic and bursting activity. J Neurophysiol. 2006, 95: 2352-2365. 10.1152/jn.00525.2005.PubMedView ArticleGoogle Scholar
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