A Network Model of Local Field Potential Activity in Essential Tremor and the Impact of Deep Brain Stimulation

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  • Nada Yousif, School of Engineering and Technology, University of Hertfordshire, Hatfield, United Kingdom.
  • ,
  • Michael Mace, Department of Bioengineering, Imperial College London, London, United Kingdom.
  • ,
  • Nicola Pavese
  • Roman Borisyuk, Institute of Mathematical Problems of Biology of RAS, The Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, Moscow, Russia.
  • ,
  • Dipankar Nandi, Division of Brain Sciences, Department of Medicine, Imperial College London, London, United Kingdom.
  • ,
  • Peter Bain, Division of Brain Sciences, Department of Medicine, Imperial College London, London, United Kingdom.

Essential tremor (ET), a movement disorder characterised by an uncontrollable shaking of the affected body part, is often professed to be the most common movement disorder, affecting up to one percent of adults over 40 years of age. The precise cause of ET is unknown, however pathological oscillations of a network of a number of brain regions are implicated in leading to the disorder. Deep brain stimulation (DBS) is a clinical therapy used to alleviate the symptoms of a number of movement disorders. DBS involves the surgical implantation of electrodes into specific nuclei in the brain. For ET the targeted region is the ventralis intermedius (Vim) nucleus of the thalamus. Though DBS is effective for treating ET, the mechanism through which the therapeutic effect is obtained is not understood. To elucidate the mechanism underlying the pathological network activity and the effect of DBS on such activity, we take a computational modelling approach combined with electrophysiological data. The pathological brain activity was recorded intra-operatively via implanted DBS electrodes, whilst simultaneously recording muscle activity of the affected limbs. We modelled the network hypothesised to underlie ET using the Wilson-Cowan approach. The modelled network exhibited oscillatory behaviour within the tremor frequency range, as did our electrophysiological data. By applying a DBS-like input we suppressed these oscillations. This study shows that the dynamics of the ET network support oscillations at the tremor frequency and the application of a DBS-like input disrupts this activity, which could be one mechanism underlying the therapeutic benefit.

Original languageEnglish
JournalPLOS Computational Biology
Pages (from-to)e1005326
Publication statusPublished - Jan 2017
Externally publishedYes

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  • Journal Article

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