1
|
Duffley G, Szabo A, Lutz BJ, Mahoney-Rafferty EC, Hess CW, Ramirez-Zamora A, Zeilman P, Foote KD, Chiu S, Pourfar MH, Goas Cnp C, Wood JL, Haq IU, Siddiqui MS, Afshari M, Heiry M, Choi J, Volz M, Ostrem JL, San Luciano M, Niemann N, Billnitzer A, Savitt D, Tarakad A, Jimenez-Shahed J, Aquino CC, Okun MS, Butson CR. Interactive mobile application for Parkinson's disease deep brain stimulation (MAP DBS): An open-label, multicenter, randomized, controlled clinical trial. Parkinsonism Relat Disord 2023; 109:105346. [PMID: 36966051 DOI: 10.1016/j.parkreldis.2023.105346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/20/2023] [Accepted: 02/23/2023] [Indexed: 03/17/2023]
Abstract
INTRODUCTION Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD), but its efficacy is tied to DBS programming, which is often time consuming and burdensome for patients, caregivers, and clinicians. Our aim is to test whether the Mobile Application for PD DBS (MAP DBS), a clinical decision support system, can improve programming. METHODS We conducted an open-label, 1:1 randomized, controlled, multicenter clinical trial comparing six months of SOC standard of care (SOC) to six months of MAP DBS-aided programming. We enrolled patients between 30 and 80 years old who received DBS to treat idiopathic PD at six expert centers across the United States. The primary outcome was time spent DBS programming and secondary outcomes measured changes in motor symptoms, caregiver strain and medication requirements. RESULTS We found a significant reduction in initial visit time (SOC: 43.8 ± 28.9 min n = 37, MAP DBS: 27.4 ± 13.0 min n = 35, p = 0.001). We did not find a significant difference in total programming time between the groups over the 6-month study duration. MAP DBS-aided patients experienced a significantly larger reduction in UPDRS III on-medication scores (-7.0 ± 7.9) compared to SOC (-2.7 ± 6.9, p = 0.01) at six months. CONCLUSION MAP DBS was well tolerated and improves key aspects of DBS programming time and clinical efficacy.
Collapse
Affiliation(s)
- Gordon Duffley
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Aniko Szabo
- Division of Biostatistics, Institute for Health & Equity, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Barbara J Lutz
- School of Nursing, University of North Carolina-Wilmington, Wilmington, NC, USA
| | - Emily C Mahoney-Rafferty
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Christopher W Hess
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Adolfo Ramirez-Zamora
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Pamela Zeilman
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Shannon Chiu
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Michael H Pourfar
- Center for Neuromodulation, New York University Langone Medical Center, New York, NY, USA
| | - Clarisse Goas Cnp
- Department of Neurology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Jennifer L Wood
- Department of Neurology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Ihtsham U Haq
- Department of Neurology, University of Miami, Miami, FL, USA
| | - Mustafa S Siddiqui
- Department of Neurology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Mitra Afshari
- Department of Neurological Sciences, Section of Movement Disorders, Rush University, Chicago, IL, USA
| | - Melissa Heiry
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Jennifer Choi
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Monica Volz
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Jill L Ostrem
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Marta San Luciano
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Nicki Niemann
- Department of Neurology, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Andrew Billnitzer
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Daniel Savitt
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Arjun Tarakad
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Joohi Jimenez-Shahed
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Camila C Aquino
- Department of Neurology, University of Utah, Salt Lake City, UT, USA; Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Christopher R Butson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA; Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA; Department of Neurology, University of Utah, Salt Lake City, UT, USA; Departments of Neurosurgery, and Psychiatry, University of Utah, Salt Lake City, UT, USA.
| |
Collapse
|
2
|
Duffley G, Lutz BJ, Szabo A, Wright A, Hess CW, Ramirez-Zamora A, Zeilman P, Chiu S, Foote KD, Okun MS, Butson CR. Home Health Management of Parkinson Disease Deep Brain Stimulation: A Randomized Clinical Trial. JAMA Neurol 2021; 78:972-981. [PMID: 34180949 DOI: 10.1001/jamaneurol.2021.1910] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Importance The travel required to receive deep brain stimulation (DBS) programming causes substantial burden on patients and limits who can access DBS therapy. Objective To evaluate the efficacy of home health DBS postoperative management in an effort to reduce travel burden and improve access. Design, Settings, and Participants This open-label randomized clinical trial was conducted at University of Florida Health from November 2017 to April 2020. Eligible participants had a diagnosis of Parkinson disease (PD) and were scheduled to receive DBS independently of the study. Consenting participants were randomized 1:1 to receive either standard of care or home health postoperative DBS management for 6 months after surgery. Primary caregivers, usually spouses, were also enrolled to assess caregiver strain. Interventions The home health postoperative management was conducted by a home health nurse who chose DBS settings with the aid of the iPad-based Mobile Application for PD DBS system. Prior to the study, the home health nurse had no experience providing DBS care. Main Outcomes and Measures The primary outcome was the number of times each patient traveled to the movement disorders clinic during the study period. Secondary outcomes included changes from baseline on the Unified Parkinson's Disease Rating Scale part III. Results Approximately 75 patients per year were scheduled for DBS. Of the patients who met inclusion criteria over the entire study duration, 45 either declined or were excluded for various reasons. Of the 44 patients enrolled, 19 of 21 randomized patients receiving the standard of care (mean [SD] age, 64.1 [10.0] years; 11 men) and 23 of 23 randomized patients receiving home health who underwent a minimum of 1 postoperative management visit (mean [SD] age, 65.0 [10.9] years; 13 men) were included in analysis. The primary outcome revealed that patients randomized to home health had significantly fewer clinic visits than the patients in the standard of care arm (mean [SD], 0.4 [0.8] visits vs 4.8 [0.4] visits; P < .001). We found no significant differences between the groups in the secondary outcomes measuring the efficacy of DBS. No adverse events occurred in association with the study procedure or devices. Conclusions and Relevance This study provides evidence supporting the safety and feasibility of postoperative home health DBS management. Trial Registration ClinicalTrials.gov Identifier: NCT02474459.
Collapse
Affiliation(s)
- Gordon Duffley
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City.,Department of Biomedical Engineering, University of Utah, Salt Lake City
| | - Barbara J Lutz
- School of Nursing, University of North Carolina-Wilmington, Wilmington
| | - Aniko Szabo
- Division of Biostatistics, Institute for Health & Equity, Medical College of Wisconsin, Milwaukee
| | - Adrienne Wright
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Christopher W Hess
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Adolfo Ramirez-Zamora
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Pamela Zeilman
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Shannon Chiu
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Christopher R Butson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City.,Department of Biomedical Engineering, University of Utah, Salt Lake City.,Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville.,Departments of Neurology, Neurosurgery, and Psychiatry, University of Utah, Salt Lake City
| |
Collapse
|
3
|
Johnson KA, Duffley G, Anderson DN, Ostrem JL, Welter ML, Baldermann JC, Kuhn J, Huys D, Visser-Vandewalle V, Foltynie T, Zrinzo L, Hariz M, Leentjens AFG, Mogilner AY, Pourfar MH, Almeida L, Gunduz A, Foote KD, Okun MS, Butson CR. Structural connectivity predicts clinical outcomes of deep brain stimulation for Tourette syndrome. Brain 2020; 143:2607-2623. [PMID: 32653920 DOI: 10.1093/brain/awaa188] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 03/12/2020] [Accepted: 04/20/2020] [Indexed: 12/11/2022] Open
Abstract
Deep brain stimulation may be an effective therapy for select cases of severe, treatment-refractory Tourette syndrome; however, patient responses are variable, and there are no reliable methods to predict clinical outcomes. The objectives of this retrospective study were to identify the stimulation-dependent structural networks associated with improvements in tics and comorbid obsessive-compulsive behaviour, compare the networks across surgical targets, and determine if connectivity could be used to predict clinical outcomes. Volumes of tissue activated for a large multisite cohort of patients (n = 66) implanted bilaterally in globus pallidus internus (n = 34) or centromedial thalamus (n = 32) were used to generate probabilistic tractography to form a normative structural connectome. The tractography maps were used to identify networks that were correlated with improvement in tics or comorbid obsessive-compulsive behaviour and to predict clinical outcomes across the cohort. The correlated networks were then used to generate 'reverse' tractography to parcellate the total volume of stimulation across all patients to identify local regions to target or avoid. The results showed that for globus pallidus internus, connectivity to limbic networks, associative networks, caudate, thalamus, and cerebellum was positively correlated with improvement in tics; the model predicted clinical improvement scores (P = 0.003) and was robust to cross-validation. Regions near the anteromedial pallidum exhibited higher connectivity to the positively correlated networks than posteroventral pallidum, and volume of tissue activated overlap with this map was significantly correlated with tic improvement (P < 0.017). For centromedial thalamus, connectivity to sensorimotor networks, parietal-temporal-occipital networks, putamen, and cerebellum was positively correlated with tic improvement; the model predicted clinical improvement scores (P = 0.012) and was robust to cross-validation. Regions in the anterior/lateral centromedial thalamus exhibited higher connectivity to the positively correlated networks, but volume of tissue activated overlap with this map did not predict improvement (P > 0.23). For obsessive-compulsive behaviour, both targets showed that connectivity to the prefrontal cortex, orbitofrontal cortex, and cingulate cortex was positively correlated with improvement; however, only the centromedial thalamus maps predicted clinical outcomes across the cohort (P = 0.034), but the model was not robust to cross-validation. Collectively, the results demonstrate that the structural connectivity of the site of stimulation are likely important for mediating symptom improvement, and the networks involved in tic improvement may differ across surgical targets. These networks provide important insight on potential mechanisms and could be used to guide lead placement and stimulation parameter selection, as well as refine targets for neuromodulation therapies for Tourette syndrome.
Collapse
Affiliation(s)
- Kara A Johnson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA.,Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - Gordon Duffley
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA.,Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - Daria Nesterovich Anderson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA.,Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA.,Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Jill L Ostrem
- Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Marie-Laure Welter
- Institut du Cerveau et de la Moelle Epiniere, Sorbonne Universités, University of Pierre and Marie Curie University of Paris, the French National Institute of Health and Medical Research U 1127, the National Center for Scientific Research 7225, Paris, France
| | - Juan Carlos Baldermann
- Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany.,Department of Neurology, University of Cologne, Cologne, Germany
| | - Jens Kuhn
- Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany.,Department of Psychiatry, Psychotherapy, and Psychosomatic Medicine, Johanniter Hospital Oberhausen, EVKLN, Oberhausen, Germany
| | - Daniel Huys
- Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotaxy and Functional Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Thomas Foltynie
- Functional Neurosurgery Unit, Department of Clinical and Movement Neurosciences, University College London, Queen Square Institute of Neurology, London, UK
| | - Ludvic Zrinzo
- Functional Neurosurgery Unit, Department of Clinical and Movement Neurosciences, University College London, Queen Square Institute of Neurology, London, UK
| | - Marwan Hariz
- Functional Neurosurgery Unit, Department of Clinical and Movement Neurosciences, University College London, Queen Square Institute of Neurology, London, UK.,Department of Clinical Neuroscience, Umea University, Umea, Sweden
| | - Albert F G Leentjens
- Department of Psychiatry and Neuropsychology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Alon Y Mogilner
- Center for Neuromodulation, New York University Langone Medical Center, New York, New York, USA
| | - Michael H Pourfar
- Center for Neuromodulation, New York University Langone Medical Center, New York, New York, USA
| | - Leonardo Almeida
- Norman Fixel Institute for Neurological Diseases , Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, Florida, USA
| | - Aysegul Gunduz
- Norman Fixel Institute for Neurological Diseases , Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, Florida, USA.,J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases , Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, Florida, USA
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases , Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, Florida, USA
| | - Christopher R Butson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA.,Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA.,Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA.,Departments of Neurology and Psychiatry, University of Utah, Salt Lake City, Utah, USA
| |
Collapse
|
4
|
Johnson KA, Duffley G, Foltynie T, Hariz M, Zrinzo L, Joyce EM, Akram H, Servello D, Galbiati TF, Bona A, Porta M, Meng FG, Leentjens AFG, Gunduz A, Hu W, Foote KD, Okun MS, Butson CR. Basal Ganglia Pathways Associated With Therapeutic Pallidal Deep Brain Stimulation for Tourette Syndrome. Biol Psychiatry Cogn Neurosci Neuroimaging 2020; 6:961-972. [PMID: 33536144 DOI: 10.1016/j.bpsc.2020.11.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 10/28/2020] [Accepted: 11/14/2020] [Indexed: 12/27/2022]
Abstract
BACKGROUND Deep brain stimulation (DBS) targeting the globus pallidus internus (GPi) can improve tics and comorbid obsessive-compulsive behavior (OCB) in patients with treatment-refractory Tourette syndrome (TS). However, some patients' symptoms remain unresponsive, the stimulation applied across patients is variable, and the mechanisms underlying improvement are unclear. Identifying the fiber pathways surrounding the GPi that are associated with improvement could provide mechanistic insight and refine targeting strategies to improve outcomes. METHODS Retrospective data were collected for 35 patients who underwent bilateral GPi DBS for TS. Computational models of fiber tract activation were constructed using patient-specific lead locations and stimulation settings to evaluate the effects of DBS on basal ganglia pathways and the internal capsule. We first evaluated the relationship between activation of individual pathways and symptom improvement. Next, linear mixed-effects models with combinations of pathways and clinical variables were compared in order to identify the best-fit predictive models of tic and OCB improvement. RESULTS The best-fit model of tic improvement included baseline severity and the associative pallido-subthalamic pathway. The best-fit model of OCB improvement included baseline severity and the sensorimotor pallido-subthalamic pathway, with substantial evidence also supporting the involvement of the prefrontal, motor, and premotor internal capsule pathways. The best-fit models of tic and OCB improvement predicted outcomes across the cohort and in cross-validation. CONCLUSIONS Differences in fiber pathway activation likely contribute to variable outcomes of DBS for TS. Computational models of pathway activation could be used to develop novel approaches for preoperative targeting and selecting stimulation parameters to improve patient outcomes.
Collapse
Affiliation(s)
- Kara A Johnson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah; Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah
| | - Gordon Duffley
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah; Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah
| | - Thomas Foltynie
- Functional Neurosurgery Unit, Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Marwan Hariz
- Functional Neurosurgery Unit, Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, United Kingdom; Department of Clinical Neuroscience, Umea University, Umea, Sweden
| | - Ludvic Zrinzo
- Functional Neurosurgery Unit, Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Eileen M Joyce
- Functional Neurosurgery Unit, Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Harith Akram
- Functional Neurosurgery Unit, Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Domenico Servello
- Neurosurgical Department, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Tommaso F Galbiati
- Neurosurgical Department, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Alberto Bona
- Neurosurgical Department, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Mauro Porta
- Tourette's Syndrome and Movement Disorders Center, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Fan-Gang Meng
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Albert F G Leentjens
- Department of Psychiatry and Neuropsychology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Aysegul Gunduz
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, Florida; J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida
| | - Wei Hu
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, Florida
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, Florida
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, Florida
| | - Christopher R Butson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah; Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah; Department of Neurology, University of Utah, Salt Lake City, Utah; Department of Neurosurgery, University of Utah, Salt Lake City, Utah; Department of Psychiatry, University of Utah, Salt Lake City, Utah.
| |
Collapse
|
5
|
Aquino CC, Duffley G, Hedges DM, Vorwerk J, House PA, Ferraz HB, Rolston JD, Butson CR, Schrock LE. Interleaved deep brain stimulation for dyskinesia management in Parkinson's disease. Mov Disord 2019; 34:1722-1727. [PMID: 31483534 PMCID: PMC10957149 DOI: 10.1002/mds.27839] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 07/09/2019] [Accepted: 07/25/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In patients with Parkinson's disease, stimulation above the subthalamic nucleus (STN) may engage the pallidofugal fibers and directly suppress dyskinesia. OBJECTIVES The objective of this study was to evaluate the effect of interleaving stimulation through a dorsal deep brain stimulation contact above the STN in a cohort of PD patients and to define the volume of tissue activated with antidyskinesia effects. METHODS We analyzed the Core Assessment Program for Surgical Interventional Therapies dyskinesia scale, Unified Parkinson's Disease Rating Scale parts III and IV, and other endpoints in 20 patients with interleaving stimulation for management of dyskinesia. Individual models of volume of tissue activated and heat maps were used to identify stimulation sites with antidyskinesia effects. RESULTS The Core Assessment Program for Surgical Interventional Therapies dyskinesia score in the on medication phase improved 70.9 ± 20.6% from baseline with noninterleaved settings (P < 0.003). With interleaved settings, dyskinesia improved 82.0 ± 27.3% from baseline (P < 0.001) and 61.6 ± 39.3% from the noninterleaved phase (P = 0.006). The heat map showed a concentration of volume of tissue activated dorsally to the STN during the interleaved setting with an antidyskinesia effect. CONCLUSION Interleaved deep brain stimulation using the dorsal contacts can directly suppress dyskinesia, probably because of the involvement of the pallidofugal tract, allowing more conservative medication reduction. © 2019 International Parkinson and Movement Disorder Society.
Collapse
Affiliation(s)
- Camila C Aquino
- Sleep and Movement Disorder Division, University of Utah, Salt Lake City, Utah, USA
- Department of Neurology and Neurosurgery, Universidade Federal de Sao Paulo, Sao Paulo, Brazil
- Department of Health, Evidence and Impact, McMaster University, Hamilton, Minnesota, Canada
| | - Gordon Duffley
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA
| | - David M Hedges
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA
| | - Johannes Vorwerk
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA
| | | | - Henrique B Ferraz
- Department of Neurology and Neurosurgery, Universidade Federal de Sao Paulo, Sao Paulo, Brazil
| | - John D Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - Christopher R Butson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
- Department of Psychiatry, University of Utah, Salt Lake City, Utah, USA
| | - Lauren E Schrock
- Department of Neurology, University of Minnesota, Minneapolis, Minnesota, USA
| |
Collapse
|
6
|
Duffley G, Anderson DN, Vorwerk J, Dorval AD, Butson CR. Evaluation of methodologies for computing the deep brain stimulation volume of tissue activated. J Neural Eng 2019; 16:066024. [PMID: 31426036 PMCID: PMC7187771 DOI: 10.1088/1741-2552/ab3c95] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Objective. Computational models are a popular tool for predicting the effects of deep brain stimulation (DBS) on neural tissue. One commonly used model, the volume of tissue activated (VTA), is computed using multiple methodologies. We quantified differences in the VTAs generated by five methodologies: the traditional axon model method, the electric field norm, and three activating function based approaches—the activating function at each grid point in the tangential direction (AF-Tan) or in the maximally activating direction (AF-3D), and the maximum activating function along the entire length of a tangential fiber (AF-Max). Approach. We computed the VTA using each method across multiple stimulation settings. The resulting volumes were compared for similarity, and the methodologies were analyzed for their differences in behavior. Main results. Activation threshold values for both the electric field norm and the activating function varied with regards to electrode configuration, pulse width, and frequency. All methods produced highly similar volumes for monopolar stimulation. For bipolar electrode configurations, only the maximum activating function along the tangential axon method, AF-Max, produced similar volumes to those produced by the axon model method. Further analysis revealed that both of these methods are biased by their exclusive use of tangential fiber orientations. In contrast, the activating function in the maximally activating direction method, AF-3D, produces a VTA that is free of axon orientation and projection bias. Significance. Simulating tangentially oriented axons, the standard approach of computing the VTA, is too computationally expensive for widespread implementation and yields results biased by the assumption of tangential fiber orientation. In this work, we show that a computationally efficient method based on the activating function, AF-Max, reliably reproduces the VTAs generated by direct axon modeling. Further, we propose another method, AF-3D as a potentially superior model for representing generic neural tissue activation.
Collapse
Affiliation(s)
- Gordon Duffley
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States of America. Scientific Computing & Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States of America
| | | | | | | | | |
Collapse
|
7
|
Anderson DN, Duffley G, Vorwerk J, Dorval AD, Butson CR. Anodic stimulation misunderstood: preferential activation of fiber orientations with anodic waveforms in deep brain stimulation. J Neural Eng 2018; 16:016026. [PMID: 30275348 DOI: 10.1088/1741-2552/aae590] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE During deep brain stimulation (DBS), it is well understood that extracellular cathodic stimulation can cause activation of passing axons. Activation can be predicted from the second derivative of the electric potential along an axon, which depends on axonal orientation with respect to the stimulation source. We hypothesize that fiber orientation influences activation thresholds and that fiber orientations can be selectively targeted with DBS waveforms. APPROACH We used bioelectric field and multicompartment NEURON models to explore preferential activation based on fiber orientation during monopolar or bipolar stimulation. Preferential fiber orientation was extracted from the principal eigenvectors and eigenvalues of the Hessian matrix of the electric potential. We tested cathodic, anodic, and charge-balanced pulses to target neurons based on fiber orientation in general and clinical scenarios. MAIN RESULTS Axons passing the DBS lead have positive second derivatives around a cathode, whereas orthogonal axons have positive second derivatives around an anode, as indicated by the Hessian. Multicompartment NEURON models confirm that passing fibers are activated by cathodic stimulation, and orthogonal fibers are activated by anodic stimulation. Additionally, orthogonal axons have lower thresholds compared to passing axons. In a clinical scenario, fiber pathways associated with therapeutic benefit can be targeted with anodic stimulation at 50% lower stimulation amplitudes. SIGNIFICANCE Fiber orientations can be selectively targeted with simple changes to the stimulus waveform. Anodic stimulation preferentially activates orthogonal fibers, approaching or leaving the electrode, at lower thresholds for similar therapeutic benefit in DBS with decreased power consumption.
Collapse
Affiliation(s)
- Daria Nesterovich Anderson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States of America. Scientific Computing & Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States of America
| | | | | | | | | |
Collapse
|
8
|
Hollister BE, Duffley G, Butson C, Johnson C, Rosen P. Visualization for Understanding Uncertainty in Activation Volumes for Deep Brain Stimulation. Eurograph IEEE VGTC Symp Vis 2016; 2016:37-41. [PMID: 28217766 PMCID: PMC5312974 DOI: 10.2312/eurovisshort.20161158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
We have created the Neurostimulation Uncertainty Viewer (nuView or νView) tool for exploring data arising from deep brain stimulation (DBS). Simulated volume of tissue activated (VTA), using clinical electrode placements, are recorded along with patient outcomes in the Unified Parkinson's disease rating scale (UPDRS). The data is volumetric and sparse, with multi-value patient results for each activated voxel in the simulation. νView provides a collection of visual methods to explore the activated tissue to enhance understanding of electrode usage for improved therapy with DBS.
Collapse
Affiliation(s)
| | - Gordon Duffley
- Scientific Computing and Imaging Institute, University of Utah
| | - Chris Butson
- Scientific Computing and Imaging Institute, University of Utah
| | - Chris Johnson
- Scientific Computing and Imaging Institute, University of Utah
| | | |
Collapse
|