Reversing cognitive-motor impairments in Parkinson's disease patients using a computational modelling approach to deep brain stimulation programming

Anneke M.M. Frankemolle, Jennifer Wu, Angela M. Noecker, Claudia Voelcker-Rehage, Jason C. Ho, Jerrold L. Vitek, Cameron C. McIntyre, Jay L. Alberts

Research output: Contribution to journalArticlepeer-review

157 Scopus citations

Abstract

Deep brain stimulation in the subthalamic nucleus is an effective and safe surgical procedure that has been shown to reduce the motor dysfunction of patients with advanced Parkinson's disease. Bilateral subthalamic nucleus deep brain stimulation, however, has been associated with declines in cognitive and cognitive-motor functioning. It has been hypothesized that spread of current to nonmotor areas of the subthalamic nucleus may be responsible for declines in cognitive and cognitive-motor functioning. The aim of this study was to assess the cognitive-motor performance in advanced Parkinson's disease patients with subthalamic nucleus deep brain stimulation parameters determined clinically (Clinical) to settings derived from a patient-specific computational model (Model). Data were collected from 10 patients with advanced Parkinson's disease bilaterally implanted with subthalamic nucleus deep brain stimulation systems. These patients were assessed off medication and under three deep brain stimulation conditions: Off, Clinical or Model based stimulation. Clinical stimulation parameters had been determined based on clinical evaluations and were stable for at least 6 months prior to study participation. Model-based parameters were selected to minimize the spread of current to nonmotor portions of the subthalamic nucleus using Cicerone Deep Brain Stimulation software. For each stimulation condition, participants performed a working memory (n-back task) and motor task (force tracking) under single-and dual-task settings. During the dual-task, participants performed the n-back and force-tracking tasks simultaneously. Clinical and Model parameters were equally effective in improving the Unified Parkinson's disease Rating Scale III scores relative to Off deep brain stimulation scores. Single-task working memory declines, in the 2-back condition, were significantly less under Model compared with Clinical deep brain stimulation settings. Under dual-task conditions, force tracking was significantly better with Model compared with Clinical deep brain stimulation. In addition to better overall cognitive-motor performance associated with Model parameters, the amount of power consumed was on average less than half that used with the Clinical settings. These results indicate that the cognitive and cognitive-motor declines associated with bilateral subthalamic nucleus deep brain stimulation may be reversed, without compromising motor benefits, by using model-based stimulation parameters that minimize current spread into nonmotor regions of the subthalamic nucleus.

Original languageEnglish (US)
Pages (from-to)746-761
Number of pages16
JournalBrain
Volume133
Issue number3
DOIs
StatePublished - Mar 2010

Bibliographical note

Funding Information:
National Institutes of Health (R01 NS058706, R01 NS059736); Sigma Beta, W.H. Coulter Foundation.

Keywords

  • Cognitive function
  • Computational modelling
  • Deep brain stimulation
  • Dual-task
  • Force control
  • Parkinson's disease

Fingerprint Dive into the research topics of 'Reversing cognitive-motor impairments in Parkinson's disease patients using a computational modelling approach to deep brain stimulation programming'. Together they form a unique fingerprint.

Cite this