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Reduced and Modified Neuronal Activity in the Subthalamic Nucleus of Parkinson’s Disease Patients with Prior Pallidotomy

  • Adam Zaidel
  • Hagai Bergman
  • Zvi Israel
Conference paper
Part of the Advances in Behavioral Biology book series (ABBI, volume 58)

Abstract

Parkinson’s disease (PD) patients with prior pallidotomy [radio-frequency lesions in the internal segment of the globus pallidus (GPi)], whose symptoms have deteriorated, may be candidates for subthalamic nucleus (STN) deep brain stimulation (STN-DBS). In this study we analyzed the microelectrode recordings (MER, used intra-operatively for target verification) from 7 patients with prior pallidotomy (6 unilaterally and 1 bilaterally) and MERs from 12 matched PD patients without prior pallidotomy who underwent bilateral STN-DBS. The MERs were divided into three groups for comparison: (a) ipsilateral and (b) contralateral to prior pallidotomy and (c) from patients with no prior pallidotomy. For each MER trajectory, average, variance and mean successive difference [(MSD), a measure of irregularity] of the root mean square (RMS) of the STN-MER were calculated. The RMS in trajectories ipsilateral to pallidotomy demonstrated significant reduction of the mean average and MSD of STN activity when compared with trajectories from patients without prior pallidotomy, while RMS parameters contralateral to pallidotomy tended to be in between the two. The average power spectral density of 10–20 Hz oscillatory activity in the somatosensory STN was notably lower ipsilateral to pallidotomy, compared with contralateral or without prior pallidotomy. These findings highlight the critical role of direct projections from the basal ganglia to brainstem structures and suggest a possible GPi-STN reciprocal positive-feedback mechanism.

Keywords

Root Mean Square Deep Brain Stimulation Oscillatory Activity Average Power Spectral Density Lower Root Mean Square 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank Anan Moran for help in data acquisition, and Ya’acov Ritov and Israel Nelken for help with the statistical analysis. This research was supported in part by the “Fighting Against Parkinson” Foundation of the Hebrew University Netherlands Association (HUNA).

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Physiology and the Interdisciplinary Center for Neural ComputationThe Hebrew University-Hadassah Medical SchoolJerusalemIsrael

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