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Effects of spike-time dependent plasticity on deep brain stimulation of the basal ganglia for treatment of Parkinson's disease
BMC Neurosciencevolume 16, Article number: P83 (2015)
Deep brain stimulation (DBS) of the basal ganglia is a widely used and effective treatment for patients with medication-refractory Parkinson's disease (PD). However, tuning the stimulation parameters to maximize therapy while minimizing side effects is performed mostly through a trial and error approach, consuming time and energy of both clinician and patient . As such, there is a need for a systematic, engineering based approach to improve patient outcomes. Current theories of DBS mechanisms propose that DBS suppresses pathological oscillations (15-35 Hz) that dominate the basal ganglia by suppressing information flow [2, 3]. However, effects of DBS are not instant, often taking minutes or more before an effect is seen . This time scale suggests DBS may induce a change in network architecture through synaptic plasticity, destabilizing oscillations in the network. Recently, a new approach to DBS called "Coordinated Reset" appears to take advantage of this phenomenon, resulting in therapeutic benefits that last from hours to days [5, 6]. We hypothesize that if beta oscillations are indeed responsible for Parkinsonian signs, then the dissipation and return of these oscillations should follow a time course similar to that of the symptoms themselves. We use a computational network model of PD with emergent pathological 34 Hz oscillation developed by Hahn & McIntyre  to test the effects of DBS on the basal ganglia, implemented with spike-time dependent plasticity (STDP) as described by Badoual et al. . Preliminary results show that with the introduction of STDP, pathological beta oscillations dissipate over time after the onset of DBS stimulation in computational models. This work suggests that it may be possible to tune stimulation settings to take advantage of long term plasticity effects in DBS to improve patient outcomes.
Volkmann J, Moro E, Pahwa R: Basic algorithms for the programming of deep brain stimulation in Parkinson's disease. Mov Disord. 2006, 21 (Suppl 1): S284-S289.
Johnson MD, Miocinovic S, Mcintyre CC, Vitek JL: Mechanisms and Targets of Deep Brain Stimulation in Movement Disorders. 2008, 5 (April): 294-308.
Agnesi F, Connolly AT, Baker KB, Vitek JL, Johnson MD: Deep brain stimulation imposes complex informational lesions. PLoS One. 2013, 8: e74462-
Cooper SE, Noecker AM, Abboud H, Vitek JL, McIntyre CC: Return of bradykinesia after subthalamic stimulation ceases: relationship to electrode location. Exp Neurol. 2011, 231: 207-13.
Tass P a, Qin L, Hauptmann C, Dovero S, Bezard E, Boraud T, Meissner WG: Coordinated reset has sustained aftereffects in Parkinsonian monkeys. Ann Neurol. 2012, 72: 816-20.
Adamchic I, Hauptmann C, Barnikol UB, Pawelczyk N, Popovych O, Barnikol TT, Silchenko A, Volkmann J, Deuschl G, Meissner WG, Maarouf M, Sturm V, Freund H-J, Tass PA: Coordinated reset neuromodulation for Parkinson's disease: Proof-of-concept study. Mov Disord. 2014, 29: 1679-84.
Hahn PJ, McIntyre CC: Modeling shifts in the rate and pattern of subthalamopallidal network activity during deep brain stimulation. J Comput Neurosci. 2010, 28: 425-41.
Badoual M, Zou Q, Davison AP, Rudolph M, Bal T, Frégnac Y, Destexhe A: Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity. Int J Neural Syst. 2006, 16: 79-97.
Research supported by Neuroengineering NSF IGERT under DGE-1069104