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Xu SS, Sinclair NC, Bulluss KJ, Perera T, Lee WL, McDermott HJ, Thevathasan W. Towards guided and automated programming of subthalamic area stimulation in Parkinson’s disease. Brain Commun 2022; 4:fcac003. [PMID: 35169708 PMCID: PMC8833293 DOI: 10.1093/braincomms/fcac003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 11/25/2021] [Accepted: 01/10/2022] [Indexed: 11/16/2022] Open
Abstract
Selecting the ideal contact to apply subthalamic nucleus deep brain stimulation in Parkinson’s disease can be an arduous process, with outcomes highly dependent on clinician expertise. This study aims to assess whether neuronal signals recorded intraoperatively in awake patients, and the anatomical location of contacts, can assist programming. In a cohort of 14 patients with Parkinson’s disease, implanted with subthalamic nucleus deep brain stimulation, the four contacts on each lead in the 28 hemispheres were ranked according to proximity to a nominated ideal anatomical location and power of the following neuronal signals: evoked resonant neural activity, beta oscillations and high-frequency oscillations. We assessed how these rankings predicted, on each lead: (i) the motor benefit from deep brain stimulation applied through each contact and (ii) the ‘ideal’ contact to apply deep brain stimulation. The ranking of contacts according to each factor predicted motor benefit from subthalamic nucleus deep brain stimulation, as follows: evoked resonant neural activity; r2 = 0.50, Akaike information criterion 1039.9, beta; r2 = 0.50, Akaike information criterion 1041.6, high-frequency oscillations; r2 = 0.44, Akaike information criterion 1057.2 and anatomy; r2 = 0.49, Akaike information criterion 1048.0. Combining evoked resonant neural activity, beta and high-frequency oscillations ranking data yielded the strongest predictive model (r2 = 0.61, Akaike information criterion 1021.5). The ‘ideal’ contact (yielding maximal benefit) was ranked first according to each factor in the following proportion of hemispheres; evoked resonant neural activity 18/28, beta 17/28, anatomy 16/28, high-frequency oscillations 7/28. Across hemispheres, the maximal available deep brain stimulation benefit did not differ from that yielded by contacts chosen by clinicians for chronic therapy or contacts ranked first according to evoked resonant neural activity. Evoked resonant neural activity, beta oscillations and anatomy similarly predicted how motor benefit from subthalamic nucleus deep brain stimulation varied across contacts on each lead. This could assist programming by providing a probability ranking of contacts akin to a ‘monopolar survey’. However, these factors identified the ‘ideal’ contact in only a proportion of hemispheres. More advanced signal processing and anatomical techniques may be needed for the full automation of contact selection.
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Affiliation(s)
- San San Xu
- Bionics Institute, East Melbourne, Victoria, Australia
- Medical Bionics Department, The University of Melbourne, East Melbourne, Victoria, Australia
- Department of Neurology, Austin Hospital, Heidelberg, Victoria, Australia
| | - Nicholas C. Sinclair
- Bionics Institute, East Melbourne, Victoria, Australia
- Medical Bionics Department, The University of Melbourne, East Melbourne, Victoria, Australia
| | - Kristian J. Bulluss
- Bionics Institute, East Melbourne, Victoria, Australia
- Department of Neurosurgery, St Vincent’s Hospital Melbourne, Fitzroy, and Department of Neurosurgery, Austin Hospital, Heidelberg, Victoria, Australia
- Department of Surgery, The University of Melbourne, Parkville, Victoria, Australia
| | - Thushara Perera
- Bionics Institute, East Melbourne, Victoria, Australia
- Medical Bionics Department, The University of Melbourne, East Melbourne, Victoria, Australia
| | - Wee-Lih Lee
- Bionics Institute, East Melbourne, Victoria, Australia
| | - Hugh J. McDermott
- Bionics Institute, East Melbourne, Victoria, Australia
- Medical Bionics Department, The University of Melbourne, East Melbourne, Victoria, Australia
| | - Wesley Thevathasan
- Bionics Institute, East Melbourne, Victoria, Australia
- Department of Neurology, Austin Hospital, Heidelberg, Victoria, Australia
- Department of Medicine, The University of Melbourne, and Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
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Pearce P, Bulluss K, Xu SS, Kim B, Milicevic M, Perera T, Thevathasan W. How accurately are subthalamic nucleus electrodes implanted relative to the ideal stimulation location for Parkinson's disease? PLoS One 2021; 16:e0254504. [PMID: 34264988 PMCID: PMC8282046 DOI: 10.1371/journal.pone.0254504] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 06/27/2021] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION The efficacy of subthalamic nucleus (STN) deep brain stimulation (DBS) in Parkinson's disease (PD) depends on how closely electrodes are implanted relative to an individual's ideal stimulation location. Yet, previous studies have assessed how closely electrodes are implanted relative to the planned location, after homogenizing data to a reference. Thus here, we measured how accurately electrodes are implanted relative to an ideal, dorsal STN stimulation location, assessed on each individual's native imaging. This measure captures not only the technical error of stereotactic implantation but also constraints imposed by planning a suitable trajectory. METHODS This cross-sectional study assessed 226 electrodes in 113 consecutive PD patients implanted with bilateral STN-DBS by experienced clinicians utilizing awake, microelectrode guided, surgery. The error (Euclidean distance) between the actual electrode trajectory versus a nominated ideal, dorsal STN stimulation location was determined in each hemisphere on native imaging and predictive factors sought. RESULTS The median electrode location error was 1.62 mm (IQR = 1.23 mm). This error exceeded 3 mm in 28/226 electrodes (12.4%). Location error did not differ between hemispheres implanted first or second, suggesting brain shift was minimised. Location error did not differ between electrodes positioned with (48/226), or without, a preceding microelectrode trajectory shift (suggesting such shifts were beneficial). There was no relationship between location error and case order, arguing against a learning effect. DISCUSSION/CONCLUSION The proximity of STN-DBS electrodes to a nominated ideal, dorsal STN, stimulation location is highly variable, even when implanted by experienced clinicians with brain shift minimized, and without evidence of a learning effect. Using this measure, we found that assessments on awake patients (microelectrode recordings and clinical examination) likely yielded beneficial intraoperative decisions to improve positioning. In many patients the error is likely to have reduced therapeutic efficacy. More accurate methods to implant STN-DBS electrodes relative to the ideal stimulation location are needed.
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Affiliation(s)
- Patrick Pearce
- Bionics Institute, East Melbourne, Victoria, Australia
- Department of Neurosurgery, St Vincent’s Hospital Melbourne, Fitzroy, Victoria, Australia
| | - Kristian Bulluss
- Bionics Institute, East Melbourne, Victoria, Australia
- Department of Neurosurgery, St Vincent’s Hospital Melbourne, Fitzroy, Victoria, Australia
- Department of Neurosurgery, Austin Hospital, Heidelberg, Victoria, Australia
- Department of Surgery, The University of Melbourne, Parkville, Victoria, Australia
| | - San San Xu
- Bionics Institute, East Melbourne, Victoria, Australia
- Medical Bionics Department, The University of Melbourne, East Melbourne, Victoria, Australia
- Department of Neurology, Austin Hospital, Heidelberg, Victoria, Australia
| | - Boaz Kim
- Bionics Institute, East Melbourne, Victoria, Australia
- Department of Neurosurgery, St Vincent’s Hospital Melbourne, Fitzroy, Victoria, Australia
| | | | - Thushara Perera
- Bionics Institute, East Melbourne, Victoria, Australia
- Medical Bionics Department, The University of Melbourne, East Melbourne, Victoria, Australia
| | - Wesley Thevathasan
- Bionics Institute, East Melbourne, Victoria, Australia
- Department of Neurology, Austin Hospital, Heidelberg, Victoria, Australia
- Department of Medicine, The University of Melbourne, Parkville, Victoria, Australia
- Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
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