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Wu Y, Hu K, Liu S. Computational models advance deep brain stimulation for Parkinson's disease. NETWORK (BRISTOL, ENGLAND) 2024:1-32. [PMID: 38923890 DOI: 10.1080/0954898x.2024.2361799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 05/25/2024] [Indexed: 06/28/2024]
Abstract
Deep brain stimulation(DBS) has become an effective intervention for advanced Parkinson's disease(PD), but the exact mechanism of DBS is still unclear. In this review, we discuss the history of DBS, the anatomy and internal architecture of the basal ganglia (BG), the abnormal pathological changes of the BG in PD, and how computational models can help understand and advance DBS. We also describe two types of models: mathematical theoretical models and clinical predictive models. Mathematical theoretical models simulate neurons or neural networks of BG to shed light on the mechanistic principle underlying DBS, while clinical predictive models focus more on patients' outcomes, helping to adapt treatment plans for each patient and advance novel electrode designs. Finally, we provide insights and an outlook on future technologies.
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Affiliation(s)
- Yongtong Wu
- School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China
| | - Kejia Hu
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shenquan Liu
- School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China
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2
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Liu X, Chou KL, Patil PG, Malaga KA. Effect of Anisotropic Brain Conductivity on Patient-Specific Volume of Tissue Activation in Deep Brain Stimulation for Parkinson Disease. IEEE Trans Biomed Eng 2024; 71:1993-2000. [PMID: 38277250 DOI: 10.1109/tbme.2024.3359119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
OBJECTIVE Deep brain stimulation (DBS) modeling can improve surgical targeting by quantifying the spatial extent of stimulation relative to subcortical structures of interest. A certain degree of model complexity is required to obtain accurate predictions, particularly complexity regarding electrical properties of the tissue around DBS electrodes. In this study, the effect of anisotropy on the volume of tissue activation (VTA) was evaluated in an individualized manner. METHODS Tissue activation models incorporating patient-specific tissue conductivity were built for 40 Parkinson disease patients who had received bilateral subthalamic nucleus (STN) DBS. To assess the impact of local changes in tissue anisotropy, one VTA was computed at each electrode contact using identical stimulation parameters. For comparison, VTAs were also computed assuming isotropic tissue conductivity. Stimulation location was considered by classifying the anisotropic VTAs relative to the STN. VTAs were characterized based on volume, spread in three directions, sphericity, and Dice coefficient. RESULTS Incorporating anisotropy generated significantly larger and less spherical VTAs overall. However, its effect on VTA size and shape was variable and more nuanced at the individual patient and implantation levels. Dorsal VTAs had significantly higher sphericity than ventral VTAs, suggesting more isotropic behavior. Contrastingly, lateral and posterior VTAs had significantly larger and smaller lateral-medial spreads, respectively. Volume and spread correlated negatively with sphericity. CONCLUSION The influence of anisotropy on VTA predictions is important to consider, and varies across patients and stimulation location. SIGNIFICANCE This study highlights the importance of considering individualized factors in DBS modeling to accurately characterize the VTA.
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3
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Rajamani N, Friedrich H, Butenko K, Dembek T, Lange F, Navrátil P, Zvarova P, Hollunder B, de Bie RMA, Odekerken VJJ, Volkmann J, Xu X, Ling Z, Yao C, Ritter P, Neumann WJ, Skandalakis GP, Komaitis S, Kalyvas A, Koutsarnakis C, Stranjalis G, Barbe M, Milanese V, Fox MD, Kühn AA, Middlebrooks E, Li N, Reich M, Neudorfer C, Horn A. Deep brain stimulation of symptom-specific networks in Parkinson's disease. Nat Commun 2024; 15:4662. [PMID: 38821913 PMCID: PMC11143329 DOI: 10.1038/s41467-024-48731-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/13/2024] [Indexed: 06/02/2024] Open
Abstract
Deep Brain Stimulation can improve tremor, bradykinesia, rigidity, and axial symptoms in patients with Parkinson's disease. Potentially, improving each symptom may require stimulation of different white matter tracts. Here, we study a large cohort of patients (N = 237 from five centers) to identify tracts associated with improvements in each of the four symptom domains. Tremor improvements were associated with stimulation of tracts connected to primary motor cortex and cerebellum. In contrast, axial symptoms are associated with stimulation of tracts connected to the supplementary motor cortex and brainstem. Bradykinesia and rigidity improvements are associated with the stimulation of tracts connected to the supplementary motor and premotor cortices, respectively. We introduce an algorithm that uses these symptom-response tracts to suggest optimal stimulation parameters for DBS based on individual patient's symptom profiles. Application of the algorithm illustrates that our symptom-tract library may bear potential in personalizing stimulation treatment based on the symptoms that are most burdensome in an individual patient.
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Affiliation(s)
- Nanditha Rajamani
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
| | - Helen Friedrich
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- University of Würzburg, Faculty of Medicine, Josef-Schneider-Str. 2, 97080, Würzburg, Germany
| | - Konstantin Butenko
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Till Dembek
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, University of Cologne, Cologne, Germany
| | - Florian Lange
- Department of Neurology, University Clinic of Würzburg, Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Pavel Navrátil
- Department of Neurology, University Clinic of Würzburg, Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Patricia Zvarova
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, 10117, Germany
| | - Barbara Hollunder
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, 10117, Germany
- Brain Simulation Section, Department of Neurology, Charité University Medicine Berlin and Berlin Institute of Health, Berlin, 10117, Germany
| | - Rob M A de Bie
- Department of Neurology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Vincent J J Odekerken
- Department of Neurology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Jens Volkmann
- Department of Neurology, University Clinic of Würzburg, Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Xin Xu
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, 100853, China
| | - Zhipei Ling
- Department of Neurosurgery, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan, 572000, China
| | - Chen Yao
- Department of Neurosurgery, The National Key Clinic Specialty, Shenzhen Key Laboratory of Neurosurgery, the First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, 518035, China
| | - Petra Ritter
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, 10117, Germany
- Brain Simulation Section, Department of Neurology, Charité University Medicine Berlin and Berlin Institute of Health, Berlin, 10117, Germany
- Bernstein center for Computational Neuroscience Berlin, Berlin, 10117, Germany
| | - Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Georgios P Skandalakis
- Section of Neurosurgery, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03756, USA
- Department of Neurosurgery, National and Kapodistrian University of Athens Medical School, Evangelismos General Hospital, Athens, Greece
| | - Spyridon Komaitis
- Department of Neurosurgery, National and Kapodistrian University of Athens Medical School, Evangelismos General Hospital, Athens, Greece
- Centre for Spinal Studies and Surgery, Queen's Medical Centre, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Aristotelis Kalyvas
- Department of Neurosurgery, National and Kapodistrian University of Athens Medical School, Evangelismos General Hospital, Athens, Greece
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Christos Koutsarnakis
- Department of Neurosurgery, National and Kapodistrian University of Athens Medical School, Evangelismos General Hospital, Athens, Greece
| | - George Stranjalis
- Department of Neurosurgery, National and Kapodistrian University of Athens Medical School, Evangelismos General Hospital, Athens, Greece
| | - Michael Barbe
- Department of Neurology, University of Cologne, Cologne, Germany
| | - Vanessa Milanese
- Neurosurgical Division, Hospital Beneficência Portuguesa de São Paulo, São Paulo, Brazil
- Department of Neurosurgery, Mayo Clinic, Florida, USA
- Movement Disorders and Neuromodulation Unit, DOMMO Clinic, São Paulo, Brazil
| | - Michael D Fox
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, 02114, USA
- Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, 10117, Germany
- Brain Simulation Section, Department of Neurology, Charité University Medicine Berlin and Berlin Institute of Health, Berlin, 10117, Germany
| | | | - Ningfei Li
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Martin Reich
- Department of Neurology, University Clinic of Würzburg, Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Clemens Neudorfer
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, 02114, USA
- Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Andreas Horn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, 02114, USA
- Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02114, USA
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Butenko K, Neudorfer C, Dembek TA, Hollunder B, Meyer GM, Li N, Oxenford S, Bahners BH, Al-Fatly B, Lofredi R, Gordon EM, Dosenbach NUF, Ganos C, Hallett M, Starr PA, Ostrem JL, Wu Y, Zhang C, Fox MD, Horn A. Engaging dystonia networks with subthalamic stimulation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.24.24307896. [PMID: 38903109 PMCID: PMC11188120 DOI: 10.1101/2024.05.24.24307896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Deep brain stimulation is a viable and efficacious treatment option for dystonia. While the internal pallidum serves as the primary target, more recently, stimulation of the subthalamic nucleus (STN) has been investigated. However, optimal targeting within this structure and its complex surroundings have not been studied in depth. Indeed, multiple historical targets that have been used for surgical treatment of dystonia are directly adjacent to the STN. Further, multiple types of dystonia exist, and outcomes are variable, suggesting that not all types would profit maximally from the exact same target. Therefore, a thorough investigation of the neural substrates underlying effects on dystonia symptoms is warranted. Here, we analyze a multi-center cohort of isolated dystonia patients with subthalamic implantations (N = 58) and relate their stimulation sites to improvement of appendicular and cervical symptoms as well as blepharospasm. Stimulation of the ventral oral posterior nucleus of thalamus and surrounding regions was associated with improvement in cervical dystonia, while stimulation of the dorsolateral STN was associated with improvement in limb dystonia and blepharospasm. This dissociation was also evident for structural connectivity, where the cerebellothalamic, corticospinal and pallidosubthalamic tracts were associated with improvement of cervical dystonia, while hyperdirect and subthalamopallidal pathways were associated with alleviation of limb dystonia and blepharospasm. Importantly, a single well-placed electrode may reach the three optimal target sites. On the level of functional networks, improvement of limb dystonia was correlated with connectivity to the corresponding somatotopic regions in primary motor cortex, while alleviation of cervical dystonia was correlated with connectivity to the recently described 'action-mode' network that involves supplementary motor and premotor cortex. Our findings suggest that different types of dystonia symptoms are modulated via distinct networks. Namely, appendicular dystonia and blepharospasm are improved with modulation of the basal ganglia, and, in particular, the subthalamic circuitry, including projections from the primary motor cortex. In contrast, cervical dystonia was more responsive when engaging the cerebello-thalamo-cortical circuit, including direct stimulation of ventral thalamic nuclei. These findings may inform DBS targeting and image-based programming strategies for patient-specific treatment of dystonia.
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Affiliation(s)
- Konstantin Butenko
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Clemens Neudorfer
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Till A Dembek
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Barbara Hollunder
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Garance M Meyer
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ningfei Li
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Simón Oxenford
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Bahne H Bahners
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Bassam Al-Fatly
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Roxanne Lofredi
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Nico U F Dosenbach
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St Louis, MO, USA
| | - Christos Ganos
- Movement Disorder Clinic, Edmond J. Safra Program in Parkinson's Disease, Division of Neurology, University of Toronto, Toronto Western Hospital, Toronto, ON, Canada
| | - Mark Hallett
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Jill L Ostrem
- Movement Disorders and Neuromodulation Centre, Department of Neurology, University of California, San Francisco, CA, USA
| | - Yiwen Wu
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - ChenCheng Zhang
- Department of Neurosurgery, Rujin Hospital, Shanghai Jiaotong University Schools of Medicine, Shanghai, China
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Andreas Horn
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
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Patrick EE, Fleeting CR, Patel DR, Casauay JT, Patel A, Shepherd H, Wong JK. Modeling the volume of tissue activated in deep brain stimulation and its clinical influence: a review. Front Hum Neurosci 2024; 18:1333183. [PMID: 38660012 PMCID: PMC11039793 DOI: 10.3389/fnhum.2024.1333183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 03/26/2024] [Indexed: 04/26/2024] Open
Abstract
Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson's disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.
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Affiliation(s)
- Erin E. Patrick
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States
| | - Chance R. Fleeting
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Drashti R. Patel
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Jed T. Casauay
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Aashay Patel
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Hunter Shepherd
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Joshua K. Wong
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
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6
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Seas A, Noor MS, Choi KS, Veerakumar A, Obatusin M, Dahill-Fuchel J, Tiruvadi V, Xu E, Riva-Posse P, Rozell CJ, Mayberg HS, McIntyre CC, Waters AC, Howell B. Subcallosal cingulate deep brain stimulation evokes two distinct cortical responses via differential white matter activation. Proc Natl Acad Sci U S A 2024; 121:e2314918121. [PMID: 38527192 PMCID: PMC10998591 DOI: 10.1073/pnas.2314918121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 02/20/2024] [Indexed: 03/27/2024] Open
Abstract
Subcallosal cingulate (SCC) deep brain stimulation (DBS) is an emerging therapy for refractory depression. Good clinical outcomes are associated with the activation of white matter adjacent to the SCC. This activation produces a signature cortical evoked potential (EP), but it is unclear which of the many pathways in the vicinity of SCC is responsible for driving this response. Individualized biophysical models were built to achieve selective engagement of two target bundles: either the forceps minor (FM) or cingulum bundle (CB). Unilateral 2 Hz stimulation was performed in seven patients with treatment-resistant depression who responded to SCC DBS, and EPs were recorded using 256-sensor scalp electroencephalography. Two distinct EPs were observed: a 120 ms symmetric response spanning both hemispheres and a 60 ms asymmetrical EP. Activation of FM correlated with the symmetrical EPs, while activation of CB was correlated with the asymmetrical EPs. These results support prior model predictions that these two pathways are predominantly activated by clinical SCC DBS and provide first evidence of a link between cortical EPs and selective fiber bundle activation.
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Affiliation(s)
- Andreas Seas
- Department of Biomedical Engineering, Duke University, Durham, NC27708
- Department of Neurosurgery, Duke University, Durham, NC27708
| | - M. Sohail Noor
- Department of Biomedical Engineering, Duke University, Durham, NC27708
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH10900
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY10029
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA30329
| | - Ashan Veerakumar
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA30329
| | - Mosadoluwa Obatusin
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY10029
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA30329
| | - Jacob Dahill-Fuchel
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY10029
| | - Vineet Tiruvadi
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA30329
| | - Elisa Xu
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY10029
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA30329
| | - Christopher J. Rozell
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA30332
| | - Helen S. Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY10029
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA30329
| | - Cameron C. McIntyre
- Department of Biomedical Engineering, Duke University, Durham, NC27708
- Department of Neurosurgery, Duke University, Durham, NC27708
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH10900
| | - Allison C. Waters
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY10029
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA30329
| | - Bryan Howell
- Department of Biomedical Engineering, Duke University, Durham, NC27708
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH10900
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7
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Noetscher GM, Tang D, Nummenmaa AR, Bingham CS, McIntyre CC, Makaroff SN. Estimations of Charge Deposition Onto Convoluted Axon Surfaces Within Extracellular Electric Fields. IEEE Trans Biomed Eng 2024; 71:307-317. [PMID: 37535481 PMCID: PMC10837334 DOI: 10.1109/tbme.2023.3299734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
OBJECTIVE Biophysical models of neural stimulation are a valuable approach to explaining the mechanisms of neuronal recruitment via applied extracellular electric fields. Typically, the applied electric field is estimated via a macroscopic finite element method solution and then applied to cable models as an extracellular voltage source. However, the field resolution is limited by the finite element size (typically 10's-100's of times greater than average neuronal cross-section). As a result, induced charges deposited onto anatomically realistic curved membrane interfaces are not taken into consideration. However, these details may alter estimates of the applied electric field and predictions of neural tissue activation. METHODS To estimate microscopic variations of the electric field, data for intra-axonal space segmented from 3D scanning electron microscopy of the mouse brain genu of corpus callosum were used. The boundary element fast multipole method was applied to accurately compute the extracellular solution. Neuronal recruitment was then estimated via an activating function. RESULTS Taking the physical structure of the arbor into account generally predicts higher values of the activating function. The relative integral 2-norm difference is 90% on average when the entire axonal arbor is present. A large fraction of this difference might be due to the axonal body itself. When an isolated physical axon is considered with all other axons removed, the relative integral 2-norm difference between the single-axon solution and the complete solution is 25% on average. CONCLUSION Our result may provide an explanation as to why Deep Brain Stimulation experiments typically predict lower activation thresholds than commonly used FEM/Cable model approaches to predicting neuronal responses to extracellular electrical stimulation. SIGNIFICANCE These results may change methods for bi-domain neural modeling and neural excitation.
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Palopoli-Trojani K, Schmidt SL, Baringer KD, Slotkin TA, Peters JJ, Turner DA, Grill WM. Temporally non-regular patterns of deep brain stimulation (DBS) enhance assessment of evoked potentials while maintaining motor symptom management in Parkinson's disease (PD). Brain Stimul 2023; 16:1630-1642. [PMID: 37863388 PMCID: PMC10872419 DOI: 10.1016/j.brs.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 09/25/2023] [Accepted: 10/11/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Traditional deep brain stimulation (DBS) at fixed regular frequencies (>100 Hz) is effective in treating motor symptoms of Parkinson's disease (PD). Temporally non-regular patterns of DBS are a new parameter space that may help increase efficacy and efficiency. OBJECTIVE To compare the effects of temporally non-regular patterns of DBS to traditional regularly-spaced pulses. METHODS We simultaneously recorded local field potentials (LFP) and monitored motor symptoms (tremor and bradykinesia) in persons with PD during DBS in subthalamic nucleus (STN). We quantified both oscillatory activity and DBS local evoked potentials (DLEPs) from the LFP. RESULTS Temporally non-regular patterns were as effective as traditional pulse patterns in modulating motor symptoms, oscillatory activity, and DLEPs. Moreover, one of our novel patterns enabled recording of longer duration DLEPs during clinically effective stimulation. CONCLUSIONS Stimulation gaps of 50 ms can be used to increase efficiency and to enable regular assessment of long-duration DLEPs while maintaining effective symptom management. This may be a promising paradigm for closed-loop DBS with biomarker assessment during the gaps.
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Affiliation(s)
| | - Stephen L Schmidt
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Karley D Baringer
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Theodore A Slotkin
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, USA
| | - Jennifer J Peters
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Dennis A Turner
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurobiology and Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurobiology and Department of Neurosurgery, Duke University, Durham, NC, USA; Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
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9
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Vedaei F, Fayed I, Alizadeh M, Miller C, Zhang AB, Koa V, Khan S, Mohamed FB, Wu C. Effect of Enlarged Perivascular Spaces in Reliable Distinction of Prospective Targeting During Deep Brain Stimulation in Patients With Advanced Parkinson's Disease: A Study of Deterministic and Probabilistic Tractography. Neurosurgery 2023; 93:691-698. [PMID: 37010304 DOI: 10.1227/neu.0000000000002478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 02/06/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND Precise electrode position is vital for effective deep brain stimulation in treating motor symptoms in Parkinson's disease (PD). Enlarged perivascular spaces (PVSs) are associated with pathophysiology of neurodegenerative diseases including PD and may affect the microstructure of surrounding brain tissue. OBJECTIVE To quantify the clinical implications of enlarged PVS on tractography-based stereotactic targeting in patients with advanced PD selected to undergo deep brain stimulation. METHODS Twenty patients with PD underwent MRI scanning. The PVS areas were visualized and segmented. Based on the size of the PVS areas, the patient group was split into 2 categories of large vs small PVSs. Probabilistic and deterministic tractography methods were applied to a diffusion-weighted data set. Fiber assignment was performed using motor cortex as an initiation seed and the globus pallidus interna and subthalamic nucleus, separately, as inclusion masks. Two exclusion masks used consisted of cerebral peduncles and the PVS mask. The center of gravity of the tract density map was measured and compared between the tracts generated with and without consideration of the PVS mask. RESULTS The average differences between the center of gravity of the tracts made by excluding PVS and without excluding PVS using deterministic and probabilistic tractography methods were less than 1 mm. Statistical analysis showed nonsignificant differences between deterministic and probabilistic methods and differences between patients with large and small PVSs ( P > .05). CONCLUSION This study demonstrated that the presence of enlarged PVS is unlikely to affect targeting of basal ganglia nuclei based on tractography.
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Affiliation(s)
- Faezeh Vedaei
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia , Pennsylvania , USA
| | - Islam Fayed
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia , Pennsylvania , USA
| | - Mahdi Alizadeh
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia , Pennsylvania , USA
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia , Pennsylvania , USA
| | - Christopher Miller
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia , Pennsylvania , USA
| | - Ashley B Zhang
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia , Pennsylvania , USA
| | - Victoria Koa
- College of Medicine, Drexel University, Philadelphia , Pennsylvania , USA
| | - Suharto Khan
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia , Pennsylvania , USA
| | - Feroze B Mohamed
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia , Pennsylvania , USA
| | - Chengyuan Wu
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia , Pennsylvania , USA
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia , Pennsylvania , USA
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10
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Neumann WJ, Horn A, Kühn AA. Insights and opportunities for deep brain stimulation as a brain circuit intervention. Trends Neurosci 2023; 46:472-487. [PMID: 37105806 DOI: 10.1016/j.tins.2023.03.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/13/2023] [Accepted: 03/17/2023] [Indexed: 04/29/2023]
Abstract
Deep brain stimulation (DBS) is an effective treatment and has provided unique insights into the dynamic circuit architecture of brain disorders. This Review illustrates our current understanding of the pathophysiology of movement disorders and their underlying brain circuits that are modulated with DBS. It proposes principles of pathological network synchronization patterns like beta activity (13-35 Hz) in Parkinson's disease. We describe alterations from microscale including local synaptic activity via modulation of mesoscale hypersynchronization to changes in whole-brain macroscale connectivity. Finally, an outlook on advances for clinical innovations in next-generation neurotechnology is provided: from preoperative connectomic targeting to feedback controlled closed-loop adaptive DBS as individualized network-specific brain circuit interventions.
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Affiliation(s)
- Wolf-Julian Neumann
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Humboldt Universität zu Berlin, Berlin, Germany
| | - Andreas Horn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Humboldt Universität zu Berlin, Berlin, Germany; Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA; MGH Neurosurgery & Center for Neurotechnology and Neurorecovery at MGH Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrea A Kühn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Humboldt Universität zu Berlin, Berlin, Germany; NeuroCure Clinical Research Centre, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany; DZNE, German Center for Degenerative Diseases, Berlin, Germany.
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11
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Brinda A, Slopsema JP, Butler RD, Ikramuddin S, Beall T, Guo W, Chu C, Patriat R, Braun H, Goftari M, Palnitkar T, Aman J, Schrock L, Cooper SE, Matsumoto J, Vitek JL, Harel N, Johnson MD. Lateral cerebellothalamic tract activation underlies DBS therapy for Essential Tremor. Brain Stimul 2023; 16:445-455. [PMID: 36746367 PMCID: PMC10200026 DOI: 10.1016/j.brs.2023.02.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 01/17/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND While deep brain stimulation (DBS) therapy can be effective at suppressing tremor in individuals with medication-refractory Essential Tremor, patient outcome variability remains a significant challenge across centers. Proximity of active electrodes to the cerebellothalamic tract (CTT) is likely important in suppressing tremor, but how tremor control and side effects relate to targeting parcellations within the CTT and other pathways in and around the ventral intermediate (VIM) nucleus of thalamus remain unclear. METHODS Using ultra-high field (7T) MRI, we developed high-dimensional, subject-specific pathway activation models for 23 directional DBS leads. Modeled pathway activations were compared with post-hoc analysis of clinician-optimized DBS settings, paresthesia thresholds, and dysarthria thresholds. Mixed-effect models were utilized to determine how the six parcellated regions of the CTT and how six other pathways in and around the VIM contributed to tremor suppression and induction of side effects. RESULTS The lateral portion of the CTT had the highest activation at clinical settings (p < 0.05) and a significant effect on tremor suppression (p < 0.001). Activation of the medial lemniscus and posterior-medial CTT was significantly associated with severity of paresthesias (p < 0.001). Activation of the anterior-medial CTT had a significant association with dysarthria (p < 0.05). CONCLUSIONS This study provides a detailed understanding of the fiber pathways responsible for therapy and side effects of DBS for Essential Tremor, and suggests a model-based programming approach will enable more selective activation of lateral fibers within the CTT.
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Affiliation(s)
- AnneMarie Brinda
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Julia P Slopsema
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Rebecca D Butler
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Salman Ikramuddin
- Department of Neurology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Thomas Beall
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - William Guo
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Cong Chu
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Remi Patriat
- Department of Radiology, CMRR, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Henry Braun
- Department of Radiology, CMRR, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Mojgan Goftari
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Tara Palnitkar
- Department of Radiology, CMRR, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Joshua Aman
- Department of Neurology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Lauren Schrock
- Department of Neurology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Scott E Cooper
- Department of Neurology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Joseph Matsumoto
- Department of Neurology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Jerrold L Vitek
- Department of Neurology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Noam Harel
- Department of Radiology, CMRR, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Matthew D Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, 55455, USA; Institute for Translational Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA.
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12
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Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks. Neuroimage 2023; 268:119862. [PMID: 36610682 PMCID: PMC10144063 DOI: 10.1016/j.neuroimage.2023.119862] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 12/22/2022] [Accepted: 01/03/2023] [Indexed: 01/07/2023] Open
Abstract
Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.
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13
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Bingham CS, Petersen MV, Parent M, McIntyre CC. Evolving characterization of the human hyperdirect pathway. Brain Struct Funct 2023; 228:353-365. [PMID: 36708394 PMCID: PMC10716731 DOI: 10.1007/s00429-023-02610-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 01/11/2023] [Indexed: 01/29/2023]
Abstract
The hyperdirect pathway (HDP) represents the main glutamatergic input to the subthalamic nucleus (STN), through which the motor and prefrontal cerebral cortex can modulate basal ganglia activity. Further, direct activation of the motor HDP is thought to be an important component of therapeutic deep brain stimulation (DBS), mediating the disruption of pathological oscillations. Alternatively, unintended recruitment of the prefrontal HDP may partly explain some cognitive side effects of DBS therapy. Previous work describing the HDP has focused on non-human primate (NHP) histological pathway tracings, diffusion-weighted MRI analysis of human white matter, and electrophysiology studies involving paired cortical recordings with DBS. However, none of these approaches alone yields a complete understanding of the complexities of the HDP. As such, we propose that generative modeling methods hold promise to bridge anatomy and physiology results, from both NHPs and humans, into a more detailed representation of the human HDP. Nonetheless, numerous features of the HDP remain to be experimentally described before model-based methods can simulate corticosubthalamic activity with a high degree of scientific detail. Therefore, the goals of this review are to examine the experimental evidence for HDP projections from across the primate neocortex and discuss new data which are required to improve the utility of anatomical and biophysical models of the human corticosubthalamic system.
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Affiliation(s)
- Clayton S Bingham
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Martin Parent
- Department of Psychiatry and Neuroscience, Laval University, Quebec, Canada
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Department of Neurosurgery, Duke University, Durham, NC, USA.
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14
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Golabek J, Schiefer M, Wong JK, Saxena S, Patrick E. Artificial neural network-based rapid predictor of biological nerve fiber activation for DBS applications. J Neural Eng 2023; 20. [PMID: 36599158 DOI: 10.1088/1741-2552/acb016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 01/04/2023] [Indexed: 01/06/2023]
Abstract
Objective.Computational models are powerful tools that can enable the optimization of deep brain stimulation (DBS). To enhance the clinical practicality of these models, their computational expense and required technical expertise must be minimized. An important aspect of DBS models is the prediction of neural activation in response to electrical stimulation. Existing rapid predictors of activation simplify implementation and reduce prediction runtime, but at the expense of accuracy. We sought to address this issue by leveraging the speed and generalization abilities of artificial neural networks (ANNs) to create a novel predictor of neural fiber activation in response to DBS.Approach.We developed six variations of an ANN-based predictor to predict the response of individual, myelinated axons to extracellular electrical stimulation. ANNs were trained using datasets generated from a finite-element model of an implanted DBS system together with multi-compartment cable models of axons. We evaluated the ANN-based predictors using three white matter pathways derived from group-averaged connectome data within a patient-specific tissue conductivity field, comparing both predicted stimulus activation thresholds and pathway recruitment across a clinically relevant range of stimulus amplitudes and pulse widths.Main results.The top-performing ANN could predict the thresholds of axons with a mean absolute error (MAE) of 0.037 V, and pathway recruitment with an MAE of 0.079%, across all parameters. The ANNs reduced the time required to predict the thresholds of 288 axons by four to five orders of magnitude when compared to multi-compartment cable models.Significance.We demonstrated that ANNs can be fast, accurate, and robust predictors of neural activation in response to DBS.
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Affiliation(s)
- Justin Golabek
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States of America
| | - Matthew Schiefer
- Malcom Randall Department of Veterans Affairs Medical Center, Gainesville, FL, United States of America
| | - Joshua K Wong
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States of America
| | - Shreya Saxena
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States of America
| | - Erin Patrick
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States of America
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15
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Ríos AS, Oxenford S, Neudorfer C, Butenko K, Li N, Rajamani N, Boutet A, Elias GJB, Germann J, Loh A, Deeb W, Wang F, Setsompop K, Salvato B, Almeida LBD, Foote KD, Amaral R, Rosenberg PB, Tang-Wai DF, Wolk DA, Burke AD, Salloway S, Sabbagh MN, Chakravarty MM, Smith GS, Lyketsos CG, Okun MS, Anderson WS, Mari Z, Ponce FA, Lozano AM, Horn A. Optimal deep brain stimulation sites and networks for stimulation of the fornix in Alzheimer's disease. Nat Commun 2022; 13:7707. [PMID: 36517479 PMCID: PMC9751139 DOI: 10.1038/s41467-022-34510-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 10/27/2022] [Indexed: 12/15/2022] Open
Abstract
Deep brain stimulation (DBS) to the fornix is an investigational treatment for patients with mild Alzheimer's Disease. Outcomes from randomized clinical trials have shown that cognitive function improved in some patients but deteriorated in others. This could be explained by variance in electrode placement leading to differential engagement of neural circuits. To investigate this, we performed a post-hoc analysis on a multi-center cohort of 46 patients with DBS to the fornix (NCT00658125, NCT01608061). Using normative structural and functional connectivity data, we found that stimulation of the circuit of Papez and stria terminalis robustly associated with cognitive improvement (R = 0.53, p < 0.001). On a local level, the optimal stimulation site resided at the direct interface between these structures (R = 0.48, p < 0.001). Finally, modulating specific distributed brain networks related to memory accounted for optimal outcomes (R = 0.48, p < 0.001). Findings were robust to multiple cross-validation designs and may define an optimal network target that could refine DBS surgery and programming.
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Grants
- P30 AG066507 NIA NIH HHS
- R01 NS127892 NINDS NIH HHS
- R01 MH113929 NIMH NIH HHS
- R01 MH130666 NIMH NIH HHS
- P30 AG072979 NIA NIH HHS
- Deutsche Forschungsgemeinschaft (German Research Foundation)
- Received grants and personal fees from Medtronic and Boston Scientific, grants from Abbott/St. Jude, and Functional Neuromodulation outside the submitted work.
- Received grants from Functional Neuromodulation during conduct of this study, grants and personal fees from Avid/Lily, and Merck, personal fees from Jannsen, GE Healthcare, Biogen and Neuronix outside the submitted work.
- Receives personal fees from Elsai, Lilly, Roche Novartis and Biogen outside the submitted work.
- Received personal fees from Allergan, Biogen, Roche-Genentech, Cortexyme, Bracket, Sanofi, and other type of support from Brain Health Inc and uMethod Health outside of the submitted work.
- Received grants from Functional Neuromodulation Inc. during conduct of this study, from Avanir and Eli Lily and NFL Benefits Office outside of the submitted work.
- Received grants from NIH, Tourette Association of America Grant, Parkinson’s Alliance, Smallwood Foundation, and personal fees from Parkinson’s Foundation Medical Director, Books4Patients, American Academy of Neurology, Peerview, WebMD/Medscape, Mededicus, Movement Disorders Society, Taylor and Francis, Demos, Robert Rose and non-financial support from Medtronic outside of the submitted work.
- Received grants from Medtronic and Functional Neuromodulation during conduct of this study, personal fees from Medtronic, St. Jude, Boston Scientific, and Functional Neuromodulation outside of submitted work
- Deutsches Zentrum für Luft- und Raumfahrt (German Centre for Air and Space Travel)
- National Institutes of Health (R01 13478451, 1R01NS127892-01 & 2R01 MH113929) New Venture Fund (FFOR Seed Grant).
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Affiliation(s)
- Ana Sofía Ríos
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Simón Oxenford
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Clemens Neudorfer
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Konstantin Butenko
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ningfei Li
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Nanditha Rajamani
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Alexandre Boutet
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, M5T1W7, Canada
| | - Gavin J B Elias
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
| | - Jurgen Germann
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
| | - Aaron Loh
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
| | - Wissam Deeb
- UMass Chan Medical School, Department of Neurology, Worcester, MA, 01655, USA
- UMass Memorial Health, Department of Neurology, Worcester, MA, 01655, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Bryan Salvato
- University of Florida Health Jacksonville, Jacksonville, FL, USA
| | - Leonardo Brito de Almeida
- Norman Fixel Institute for Neurological Diseases, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Robert Amaral
- Cerebral Imaging Centre, Douglas Research Centre, Montreal, QC, Canada
| | - Paul B Rosenberg
- Department of Psychiatry and Behavioral Sciences and Richman Family Precision Medicine Center of Excellence, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David F Tang-Wai
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
- Department of Medicine, Division of Neurology, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Stephen Salloway
- Department of Psychiatry and Human Behavior and Neurology, Alpert Medical School of Brown University, Providence, RI, USA
- Memory & Aging Program, Butler Hospital, Providence, USA
| | | | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Research Centre, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Gwenn S Smith
- Department of Psychiatry and Behavioral Sciences and Richman Family Precision Medicine Center of Excellence, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Constantine G Lyketsos
- Department of Psychiatry and Behavioral Sciences and Richman Family Precision Medicine Center of Excellence, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | | | - Zoltan Mari
- Johns Hopkins School of Medicine, Baltimore, MD, USA
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | | | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
| | - Andreas Horn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.
- Departments of Neurology and Neurosurgery, Massachusetts General Hospital, Boston, MA, USA.
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16
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Banks GP, Heilbronner SR, Goodman W, Sheth SA. A population-normalized tractographic fiber atlas of the anterior limb of the internal capsule: relevance to surgical neuromodulation. J Neurosurg 2022; 137:1278-1288. [PMID: 35395627 DOI: 10.3171/2022.1.jns211935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 01/31/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The anterior limb of the internal capsule (ALIC) is a white matter highway that connects several subcortical structures to the prefrontal cortex. Although surgical interventions in the ALIC have been used to treat a number of psychiatric illnesses, there is significant debate regarding what fibers are targeted for intervention. This debate is partially due to an incomplete understanding of connectivity in the region. METHODS To better understand this complex structure, the authors employed a novel tractography-based approach to examine how fibers from the thalamus and subthalamic nucleus (STN) traverse the ALIC. Furthermore, the authors analyzed connections from the medial dorsal nucleus, anterior nucleus, and ventral anterior nucleus of the thalamus. RESULTS The results showed that there is an organizational gradient of thalamic fibers medially and STN fibers laterally in the ALIC that fades more anteriorly. These findings, in combination with the known corticotopic organization described by previous studies, allow for a more thorough understanding of the organization of the white matter fibers in the ALIC. CONCLUSIONS These results are important for understanding and targeting of neuromodulatory therapies in the ALIC and may help explain why differences in therapeutic effect are observed for different areas of the ALIC.
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Affiliation(s)
- Garrett P Banks
- 1Department of Neurosurgery, Columbia University Medical Center, New York, New York
| | - Sarah R Heilbronner
- 2Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | - Wayne Goodman
- 3Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas; and
| | - Sameer A Sheth
- 4Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
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17
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Butenko K, Li N, Neudorfer C, Roediger J, Horn A, Wenzel GR, Eldebakey H, Kühn AA, Reich MM, Volkmann J, Rienen UV. Linking profiles of pathway activation with clinical motor improvements - A retrospective computational study. Neuroimage Clin 2022; 36:103185. [PMID: 36099807 PMCID: PMC9474565 DOI: 10.1016/j.nicl.2022.103185] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/27/2022] [Accepted: 09/02/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Deep brain stimulation (DBS) is an established therapy for patients with Parkinson's disease. In silico computer models for DBS hold the potential to inform a selection of stimulation parameters. In recent years, the focus has shifted towards DBS-induced firing in myelinated axons, deemed particularly relevant for the external modulation of neural activity. OBJECTIVE The aim of this project was to investigate correlations between patient-specific pathway activation profiles and clinical motor improvement. METHODS We used the concept of pathway activation modeling, which incorporates advanced volume conductor models and anatomically authentic fiber trajectories to estimate DBS-induced action potential initiation in anatomically plausible pathways that traverse in close proximity to targeted nuclei. We applied the method on two retrospective datasets of DBS patients, whose clinical improvement had been evaluated according to the motor part of the Unified Parkinson's Disease Rating Scale. Based on differences in outcome and activation levels for intrapatient DBS protocols in a training cohort, we derived a pathway activation profile that theoretically induces a complete alleviation of symptoms described by UPDRS-III. The profile was further enhanced by analyzing the importance of matching activation levels for individual pathways. RESULTS The obtained profile emphasized the importance of activation in pathways descending from the motor-relevant cortical regions as well as the pallidothalamic pathways. The degree of similarity of patient-specific profiles to the optimal profile significantly correlated with clinical motor improvement in a test cohort. CONCLUSION Pathway activation modeling has a translational utility in the context of motor symptom alleviation in Parkinson's patients treated with DBS.
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Affiliation(s)
- Konstantin Butenko
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany,Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany,Corresponding author.
| | - Ningfei Li
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Clemens Neudorfer
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Jan Roediger
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany,Einstein Center for Neurosciences, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Horn
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Gregor R. Wenzel
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Hazem Eldebakey
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Andrea A. Kühn
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Martin M. Reich
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Ursula van Rienen
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany,Department Life, Light & Matter, University of Rostock, Rostock, Germany,Department of Ageing of Individuals and Society, University of Rostock, Rostock, Germany,Corresponding author.
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18
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Kokkonen A, Honkanen EA, Corp DT, Joutsa J. Neurobiological effects of deep brain stimulation: A systematic review of molecular brain imaging studies. Neuroimage 2022; 260:119473. [PMID: 35842094 DOI: 10.1016/j.neuroimage.2022.119473] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 05/28/2022] [Accepted: 07/11/2022] [Indexed: 11/29/2022] Open
Abstract
Deep brain stimulation (DBS) is an established treatment for several brain disorders, including Parkinson's disease, essential tremor, dystonia and epilepsy, and an emerging therapeutic tool in many other neurological and psychiatric disorders. The therapeutic efficacy of DBS is dependent on the stimulation target, but its mechanisms of action are still relatively poorly understood. Investigating these mechanisms is challenging, partly because the stimulation devices and electrodes have limited the use of functional MRI in these patients. Molecular brain imaging techniques, such as positron emission tomography (PET) and single photon emission tomography (SPET), offer a unique opportunity to characterize the whole brain effects of DBS. Here, we investigated the direct effects of DBS by systematically reviewing studies performing an `on' vs `off' contrast during PET or SPET imaging. We identified 62 studies (56 PET and 6 SPET studies; 531 subjects). Approximately half of the studies focused on cerebral blood flow or glucose metabolism in patients Parkinson's disease undergoing subthalamic DBS (25 studies, n = 289), therefore Activation Likelihood Estimation analysis was performed on these studies. Across disorders and stimulation targets, DBS was associated with a robust local increase in ligand uptake at the stimulation site and target-specific remote network effects. Subthalamic nucleus stimulation in Parkinson's disease showed a specific pattern of changes in the motor circuit, including increased ligand uptake in the basal ganglia, and decreased ligand uptake in the primary motor cortex, supplementary motor area and cerebellum. However, there was only a handful of studies investigating other brain disorder and stimulation site combinations (1-3 studies each), or specific neurotransmitter systems, preventing definitive conclusions of the detailed molecular effects of the stimulation in these cases.
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Affiliation(s)
- Aleksi Kokkonen
- Turku Brain and Mind Center, Clinical Neurosciences, University of Turku, Turku, Finland; Turku PET Center, Neurocenter, Turku University Hospital, Turku, Finland.
| | - Emma A Honkanen
- Turku Brain and Mind Center, Clinical Neurosciences, University of Turku, Turku, Finland; Turku PET Center, Neurocenter, Turku University Hospital, Turku, Finland
| | - Daniel T Corp
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia; Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Juho Joutsa
- Turku Brain and Mind Center, Clinical Neurosciences, University of Turku, Turku, Finland; Turku PET Center, Neurocenter, Turku University Hospital, Turku, Finland; Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Boston, MA, United States of America.
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19
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Keogh C, Saavedra F, Andrews B, FitzGerald JJ. Computation of Activating Fields for Approximation of the Orientation-Specific Neural Response to Electrical Stimulation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:5152-5155. [PMID: 36086379 DOI: 10.1109/embc48229.2022.9871706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Computational methods of determining the response of neural tissue to electrical stimulation have demonstrated value for the development of novel devices and the programming of neuromodulation therapies. Detailed biophysical models are excessively computationally intensive for many applications; simple metrics to approximate activation can speed up progress in this area. The activating function provides such a useful metric. However, this measure, defined for a specific axon orientation, is not immediately applicable to computed electric fields to assess their effects. We demonstrate a method for computation of the activating function generalized to a field in order to allow rapid computation of the effects of stimulation on neural tissue while preserving information on axon orientation. Clinical Relevance- This demonstrates a useful method of approximating the effect of electrical stimulation on nervous tissue for the development of devices and the optimization of parameters for electrical neuromodulation.
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20
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Oxenford S, Roediger J, Neudorfer C, Milosevic L, Güttler C, Spindler P, Vajkoczy P, Neumann WJ, Kühn AA, Horn A. Lead-OR: a multimodal platform for deep brain stimulation surgery. eLife 2022; 11:72929. [PMID: 35594135 PMCID: PMC9177150 DOI: 10.7554/elife.72929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 05/19/2022] [Indexed: 11/25/2022] Open
Abstract
Background: Deep brain stimulation (DBS) electrode implant trajectories are stereotactically defined using preoperative neuroimaging. To validate the correct trajectory, microelectrode recordings (MERs) or local field potential recordings can be used to extend neuroanatomical information (defined by MRI) with neurophysiological activity patterns recorded from micro- and macroelectrodes probing the surgical target site. Currently, these two sources of information (imaging vs. electrophysiology) are analyzed separately, while means to fuse both data streams have not been introduced. Methods: Here, we present a tool that integrates resources from stereotactic planning, neuroimaging, MER, and high-resolution atlas data to create a real-time visualization of the implant trajectory. We validate the tool based on a retrospective cohort of DBS patients (N = 52) offline and present single-use cases of the real-time platform. Results: We establish an open-source software tool for multimodal data visualization and analysis during DBS surgery. We show a general correspondence between features derived from neuroimaging and electrophysiological recordings and present examples that demonstrate the functionality of the tool. Conclusions: This novel software platform for multimodal data visualization and analysis bears translational potential to improve accuracy of DBS surgery. The toolbox is made openly available and is extendable to integrate with additional software packages. Funding: Deutsche Forschungsgesellschaft (410169619, 424778381), Deutsches Zentrum für Luft- und Raumfahrt (DynaSti), National Institutes of Health (2R01 MH113929), and Foundation for OCD Research (FFOR). Deep brain stimulation is an established therapy for patients with Parkinson’s disease and an emerging option for other neurological conditions. Electrodes are implanted deep in the brain to stimulate precise brain regions and control abnormal brain activity in those areas. The most common target for Parkinson’s disease, for instance, is a structure called the subthalamic nucleus, which sits at the base of the brain, just above the brain stem. To ensure electrodes are placed correctly, surgeons use various sources of information to characterize the patient’s brain anatomy and decide on an implant site. These data include brain scans taken before surgery and recordings of brain activity taken during surgery to confirm the intended implant site. Sometimes, the brain activity signals from this last confirmation step may slightly alter surgical plans. It represents one of many challenges for clinical teams: to analyse, assimilate, and communicate data as it is collected during the procedure. Oxenford et al. developed a software pipeline to aggregate the data surgeons use to implant electrodes. The open-source platform, dubbed Lead-OR, visualises imaging data and brain activity recordings (termed electrophysiology data) in real time. The current set-up integrates with commercial tools and existing software for surgical planning. Oxenford et al. tested Lead-OR on data gathered retrospectively from 32 patients with Parkinson’s who had electrodes implanted in their subthalamic nucleus. The platform showed good agreement between imaging and electrophysiology data, although there were some unavoidable discrepancies, arising from limitations in the imaging pipeline and from the surgical procedure. Lead-OR was also able to correct for brain shift, which is where the brain moves ever so slightly in the skull. With further validation, this proof-of-concept software could serve as a useful decision-making tool for surgical teams implanting electrodes for deep brain stimulation. In time, if implemented, its use could improve the accuracy of electrode placement, translating into better surgical outcomes for patients. It also has the potential to integrate forthcoming ultra-high-resolution data from current brain mapping projects, and other commercial surgical planning tools.
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Affiliation(s)
- Simon Oxenford
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jan Roediger
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Clemens Neudorfer
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Luka Milosevic
- Krembil Brain Institute, University Health Network, Toronto, Canada
| | - Christopher Güttler
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Philipp Spindler
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Peter Vajkoczy
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Wolf-Julian Neumann
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andrea A Kühn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Horn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
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21
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Pujol S, Cabeen RP, Yelnik J, François C, Fernandez Vidal S, Karachi C, Bardinet E, Cosgrove GR, Kikinis R. Somatotopic Organization of Hyperdirect Pathway Projections From the Primary Motor Cortex in the Human Brain. Front Neurol 2022; 13:791092. [PMID: 35547388 PMCID: PMC9081715 DOI: 10.3389/fneur.2022.791092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 03/04/2022] [Indexed: 11/25/2022] Open
Abstract
Background The subthalamic nucleus (STN) is an effective neurosurgical target to improve motor symptoms in Parkinson's Disease (PD) patients. MR-guided Focused Ultrasound (MRgFUS) subthalamotomy is being explored as a therapeutic alternative to Deep Brain Stimulation (DBS) of the STN. The hyperdirect pathway provides a direct connection between the cortex and the STN and is likely to play a key role in the therapeutic effects of MRgFUS intervention in PD patients. Objective This study aims to investigate the topography and somatotopy of hyperdirect pathway projections from the primary motor cortex (M1). Methods We used advanced multi-fiber tractography and high-resolution diffusion MRI data acquired on five subjects of the Human Connectome Project (HCP) to reconstruct hyperdirect pathway projections from M1. Two neuroanatomy experts reviewed the anatomical accuracy of the tracts. We extracted the fascicles arising from the trunk, arm, hand, face and tongue area from the reconstructed pathways. We assessed the variability among subjects based on the fractional anisotropy (FA) and mean diffusivity (MD) of the fibers. We evaluated the spatial arrangement of the different fascicles using the Dice Similarity Coefficient (DSC) of spatial overlap and the centroids of the bundles. Results We successfully reconstructed hyperdirect pathway projections from M1 in all five subjects. The tracts were in agreement with the expected anatomy. We identified hyperdirect pathway fascicles projecting from the trunk, arm, hand, face and tongue area in all subjects. Tract-derived measurements showed low variability among subjects, and similar distributions of FA and MD values among the fascicles projecting from different M1 areas. We found an anterolateral somatotopic arrangement of the fascicles in the corona radiata, and an average overlap of 0.63 in the internal capsule and 0.65 in the zona incerta. Conclusion Multi-fiber tractography combined with high-resolution diffusion MRI data enables the identification of the somatotopic organization of the hyperdirect pathway. Our preliminary results suggest that the subdivisions of the hyperdirect pathway projecting from the trunk, arm, hand, face, and tongue motor area are intermixed at the level of the zona incerta and posterior limb of the internal capsule, with a predominantly overlapping topographical organization in both regions. Subject-specific knowledge of the hyperdirect pathway somatotopy could help optimize target definition in MRgFUS intervention.
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Affiliation(s)
- Sonia Pujol
- Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Ryan P Cabeen
- Laboratory of Neuro Imaging, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine of the USC, University of Southern California, Los Angeles, CA, United States
| | - Jérôme Yelnik
- Sorbonne Université, CNRS, INSERM, APHP GH Pitié-Salpêtriére, Paris Brain Institute - Institut du Cerveau (ICM), Paris, France.,CENIR Platform, Institut du Cerveau (ICM), Paris, France
| | - Chantal François
- Sorbonne Université, CNRS, INSERM, APHP GH Pitié-Salpêtriére, Paris Brain Institute - Institut du Cerveau (ICM), Paris, France.,CENIR Platform, Institut du Cerveau (ICM), Paris, France
| | - Sara Fernandez Vidal
- Sorbonne Université, CNRS, INSERM, APHP GH Pitié-Salpêtriére, Paris Brain Institute - Institut du Cerveau (ICM), Paris, France.,CENIR Platform, Institut du Cerveau (ICM), Paris, France
| | - Carine Karachi
- Sorbonne Université, CNRS, INSERM, APHP GH Pitié-Salpêtriére, Paris Brain Institute - Institut du Cerveau (ICM), Paris, France.,CENIR Platform, Institut du Cerveau (ICM), Paris, France.,Department of Neurosurgery, APHP, Hôpitaux Universitaires Pitié-Salpêtriére/Charles Foix, Paris, France
| | - Eric Bardinet
- Sorbonne Université, CNRS, INSERM, APHP GH Pitié-Salpêtriére, Paris Brain Institute - Institut du Cerveau (ICM), Paris, France.,CENIR Platform, Institut du Cerveau (ICM), Paris, France
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Ron Kikinis
- Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
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22
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Cassar IR, Grill WM. The cortical evoked potential corresponds with deep brain stimulation efficacy in rats. J Neurophysiol 2022; 127:1253-1268. [PMID: 35389751 PMCID: PMC9054265 DOI: 10.1152/jn.00353.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 03/28/2022] [Accepted: 04/02/2022] [Indexed: 01/21/2023] Open
Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) antidromically activates the motor cortex (M1), and this cortical activation appears to play a role in the treatment of hypokinetic motor behaviors (Gradinaru V, Mogri M, Thompson KR, Henderson JM, Deisseroth K. Science 324: 354-359, 2009; Yu C, Cassar IR, Sambangi J, Grill WM. J Neurosci 40: 4323-4334, 2020). The synchronous antidromic activation takes the form of a short-latency cortical evoked potential (cEP) in electrocorticography (ECoG) recordings of M1. We assessed the utility of the cEP as a biomarker for STN DBS in unilateral 6-hydroxydopamine-lesioned female Sprague Dawley rats, with stimulating electrodes implanted in the STN and the ECoG recorded above M1. We quantified the correlations of the cEP magnitude and latency with changes in motor behavior from DBS and compared them to the correlation between motor behaviors and several commonly used spectral-based biomarkers. The cEP features correlated strongly with motor behaviors and were highly consistent across animals, whereas the spectral biomarkers correlated weakly with motor behaviors and were highly variable across animals. The cEP may thus be a useful biomarker for assessing the therapeutic efficacy of DBS parameters, as its features strongly correlate with motor behavior, it is consistent across time and subjects, it can be recorded under anesthesia, and it is simple to quantify with a large signal-to-noise ratio, enabling rapid, real-time evaluation. Additionally, our work provides further evidence that antidromic cortical activation mediates changes in motor behavior from STN DBS and that the dependence of DBS efficacy on stimulation frequency may be related to antidromic spike failure.NEW & NOTEWORTHY We characterize a new potential biomarker for deep brain stimulation (DBS), the cortical evoked potential (cEP), and demonstrate that it exhibits a robust correlation with motor behaviors as a function of stimulation frequency. The cEP may thus be a useful clinical biomarker for changes in motor behavior. This work also provides insight into the cortical mechanisms of DBS, suggesting that motor behaviors are strongly affected by the rate of antidromic spike failure during DBS.
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Affiliation(s)
- Isaac R Cassar
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- Department of Neurobiology, Duke University, Durham, North Carolina
- Department of Neurosurgery, Duke University, Durham, North Carolina
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23
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Bingham CS, McIntyre CC. Subthalamic deep brain stimulation of an anatomically detailed model of the human hyperdirect pathway. J Neurophysiol 2022; 127:1209-1220. [PMID: 35320026 PMCID: PMC9054256 DOI: 10.1152/jn.00004.2022] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 11/22/2022] Open
Abstract
The motor hyperdirect pathway (HDP) is considered a key target in the treatment of Parkinson's disease with subthalamic deep brain stimulation (DBS). This hypothesis is partially derived from the association of HDP activation with evoked potentials (EPs) generated in the motor cortex and subthalamic nucleus (STN) after a DBS pulse. However, the biophysical details of how and when DBS-induced action potentials (APs) in HDP neurons reach their terminations in the cortex or STN remain unclear. Therefore, we used an anatomically detailed representation of the motor HDP, as well as the internal capsule (IC), in a model of human subthalamic DBS to explore AP activation and transmission in the HDP and IC. Our results show that small diameter HDP axons exhibited AP initiation in their subthalamic terminal arbor, which resulted in relatively long transmission latencies to cortex (∼3.5-8 ms). Alternatively, large diameter HDP axons were most likely to be directly activated in the capsular region, which resulted in short transmission times to the cortex (∼1-3 ms). However, those large diameter HDP antidromic APs would be indistinguishable from any other IC axons that were also activated by the stimulus. Conversely, DBS-induced APs in both small and large diameter HDP axons reached their synaptic boutons in the STN with similar timings, but both spanned a wide temporal range (∼0.5-5 ms). We also found that using anodic or bipolar stimulation helped to bias activation of the HDP over the IC. These computational results provide useful information for linking HDP activation with EP recordings in clinical experiments.NEW & NOTEWORTHY We used biophysical models to study pathway recruitment and conduction latencies of the hyperdirect pathway (HDP) in response to subthalamic deep brain stimulation (DBS). The model system allowed us to assess the influence of increased anatomical realism on pathway activity and the possibility of identifying HDP activity in evoked potentials (EPs) recorded in either the subthalamic nucleus (STN) or cortex. The model predicts that HDP activation is accentuated by complex axonal branching in the STN.
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Affiliation(s)
- Clayton S Bingham
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
- Department of Neurosurgery, Duke University, Durham, North Carolina
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24
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Wårdell K, Nordin T, Vogel D, Zsigmond P, Westin CF, Hariz M, Hemm S. Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization. Front Neurosci 2022; 16:834026. [PMID: 35478842 PMCID: PMC9036439 DOI: 10.3389/fnins.2022.834026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 03/01/2022] [Indexed: 01/10/2023] Open
Abstract
Deep brain stimulation (DBS) is a well-established neurosurgical procedure for movement disorders that is also being explored for treatment-resistant psychiatric conditions. This review highlights important consideration for DBS simulation and data analysis. The literature on DBS has expanded considerably in recent years, and this article aims to identify important trends in the field. During DBS planning, surgery, and follow up sessions, several large data sets are created for each patient, and it becomes clear that any group analysis of such data is a big data analysis problem and has to be handled with care. The aim of this review is to provide an update and overview from a neuroengineering perspective of the current DBS techniques, technical aids, and emerging tools with the focus on patient-specific electric field (EF) simulations, group analysis, and visualization in the DBS domain. Examples are given from the state-of-the-art literature including our own research. This work reviews different analysis methods for EF simulations, tractography, deep brain anatomical templates, and group analysis. Our analysis highlights that group analysis in DBS is a complex multi-level problem and selected parameters will highly influence the result. DBS analysis can only provide clinically relevant information if the EF simulations, tractography results, and derived brain atlases are based on as much patient-specific data as possible. A trend in DBS research is creation of more advanced and intuitive visualization of the complex analysis results suitable for the clinical environment.
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Affiliation(s)
- Karin Wårdell
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Teresa Nordin
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Dorian Vogel
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
| | - Peter Zsigmond
- Department of Neurosurgery and Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Carl-Fredrik Westin
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Marwan Hariz
- Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, United Kingdom
- Department of Clinical Sciences, Neuroscience, Ume University, Umeå, Sweden
| | - Simone Hemm
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
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25
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Optimal deep brain stimulation sites and networks for cervical vs. generalized dystonia. Proc Natl Acad Sci U S A 2022; 119:e2114985119. [PMID: 35357970 PMCID: PMC9168456 DOI: 10.1073/pnas.2114985119] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
We studied deep brain stimulation effects in two types of dystonia and conclude that different specific connections between the pallidum and thalamus are responsible for optimal treatment effects. Since alternative treatment options for dystonia beyond deep brain stimulation are scarce, our results will be crucial to maximize treatment outcome in this population of patients. Dystonia is a debilitating disease with few treatment options. One effective option is deep brain stimulation (DBS) to the internal pallidum. While cervical and generalized forms of isolated dystonia have been targeted with a common approach to the posterior third of the nucleus, large-scale investigations regarding optimal stimulation sites and potential network effects have not been carried out. Here, we retrospectively studied clinical results following DBS for cervical and generalized dystonia in a multicenter cohort of 80 patients. We model DBS electrode placement based on pre- and postoperative imaging and introduce an approach to map optimal stimulation sites to anatomical space. Second, we investigate which tracts account for optimal clinical improvements, when modulated. Third, we investigate distributed stimulation effects on a whole-brain functional connectome level. Our results show marked differences of optimal stimulation sites that map to the somatotopic structure of the internal pallidum. While modulation of the striatopallidofugal axis of the basal ganglia accounted for optimal treatment of cervical dystonia, modulation of pallidothalamic bundles did so in generalized dystonia. Finally, we show a common multisynaptic network substrate for both phenotypes in the form of connectivity to the cerebellum and somatomotor cortex. Our results suggest a brief divergence of optimal stimulation networks for cervical vs. generalized dystonia within the pallidothalamic loop that merge again on a thalamo-cortical level and share a common whole-brain network.
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26
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Rao AT, Lu CW, Askari A, Malaga KA, Chou KL, Patil PG. Clinically-derived oscillatory biomarker predicts optimal subthalamic stimulation for Parkinson's disease. J Neural Eng 2022; 19. [PMID: 35272281 DOI: 10.1088/1741-2552/ac5c8c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/10/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Choosing the optimal electrode trajectory, stimulation location, and stimulation amplitude in subthalamic nucleus deep brain stimulation (STN DBS) for Parkinson's disease (PD) remains a time-consuming empirical effort. In this retrospective study, we derive a data-driven electrophysiological biomarker that predicts clinical DBS location and parameters, and we consolidate this information into a quantitative score that may facilitate an objective approach to STN DBS surgery and programming. APPROACH Random-forest feature selection was applied to a dataset of 1046 microelectrode recordings sites across 20 DBS implant trajectories to identify features of oscillatory activity that predict clinically programmed volumes of tissue activation (VTA). A cross-validated classifier was used to retrospectively predict VTA regions from these features. Spatial convolution of probabilistic classifier outputs along MER trajectories produced a biomarker score that reflects the probability of localization within a clinically optimized VTA. MAIN RESULTS Biomarker scores peaked within the VTA region and were significantly correlated with percent improvement in postoperative motor symptoms (MDS-UPRDS Part III, R = 0.61, p = 0.004). Notably, the length of STN, a common criterion for trajectory selection, did not show similar correlation (R = -0.31, p = 0.18). These findings suggest that biomarker-based trajectory selection and programming may improve motor outcomes by 9 ± 3 percentage points (p = 0.047) in this dataset. SIGNIFICANCE A clinically defined electrophysiological biomarker not only predicts VTA size and location but also correlates well with motor outcomes. Use of this biomarker for trajectory selection and initial stimulation may potentially simplify STN DBS surgery and programming.
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Affiliation(s)
- Akshay T Rao
- Biomedical Engineering, University of Michigan, 1500 East Medical Center Dr., SPC 5338, Ann Arbor, Michigan, 48109-5338, UNITED STATES
| | - Charles W Lu
- Biomedical Engineering, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, Michigan, 48109-5338, UNITED STATES
| | - Asra Askari
- Biomedical Engineering, University of Michigan, 1500 E Medical Center Drive, SPC 5338, Ann Arbor, Ann Arbor, Michigan, 48109-5338, UNITED STATES
| | - Karlo A Malaga
- Biomedical Engineering, Bucknell University, 316 Academic East Building, Lewisburg, Pennsylvania, 17837, UNITED STATES
| | - Kelvin L Chou
- Neurosurgery, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, Michigan, 48109-5338, UNITED STATES
| | - Parag G Patil
- Neurosurgery, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, Michigan, 48109-5338, UNITED STATES
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27
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Tödt I, Al-Fatly B, Granert O, Kühn AA, Krack P, Rau J, Timmermann L, Schnitzler A, Paschen S, Helmers AK, Hartmann A, Bardinet E, Schuepbach M, Barbe MT, Dembek TA, Fraix V, Kübler D, Brefel-Courbon C, Gharabaghi A, Wojtecki L, Pinsker MO, Thobois S, Damier P, Witjas T, Houeto JL, Schade-Brittinger C, Vidailhet M, Horn A, Deuschl G. The Contribution of Subthalamic Nucleus Deep Brain Stimulation to the Improvement in Motor Functions and Quality of Life. Mov Disord 2022; 37:291-301. [PMID: 35112384 DOI: 10.1002/mds.28952] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 01/17/2022] [Accepted: 01/17/2022] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Subthalamic nucleus deep brain stimulation (STN-DBS) effectively treats motor symptoms and quality of life (QoL) of advanced and fluctuating early Parkinson's disease. Little is known about the relation between electrode position and changes in symptom control and ultimately QoL. OBJECTIVES The relation between the stimulated part of the STN and clinical outcomes, including the motor score of the Unified Parkinson's Disease Rating Scale (UPDRS) and the quality-of-life questionnaire, was assessed in a subcohort of the EARLYSTIM study. METHODS Sixty-nine patients from the EARLYSTIM cohort who underwent DBS, with a comprehensive clinical characterization before and 24 months after surgery, were included. Intercorrelations of clinical outcome changes, correlation between the affected functional parts of the STN, and changes in clinical outcomes were investigated. We further calculated sweet spots for different clinical parameters. RESULTS Improvements in the UPDRS III and Parkinson's Disease Questionnaire (PDQ-39) correlated positively with the extent of the overlap with the sensorimotor STN. The sweet spots for the UPDRS III (x = 11.6, y = -13.1, z = -6.3) and the PDQ-39 differed (x = 14.8, y = -12.4, z = -4.3) ~3.8 mm. CONCLUSIONS The main influence of DBS on QoL is likely mediated through the sensory-motor basal ganglia loop. The PDQ sweet spot is located in a posteroventral spatial location in the STN territory. For aspects of QoL, however, there was also evidence of improvement through stimulation of the other STN subnuclei. More research is necessary to customize the DBS target to individual symptoms of each patient. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Inken Tödt
- Department of Neurology, University Hospital Schleswig Holstein, Kiel, Germany
| | - Bassam Al-Fatly
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité Medicine University of Berlin, Berlin, Germany
| | - Oliver Granert
- Department of Neurology, University Hospital Schleswig Holstein, Kiel, Germany
| | - Andrea A Kühn
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité Medicine University of Berlin, Berlin, Germany
| | - Paul Krack
- Department of Neurology, University Hospital Bern and University of Bern, Bern, Switzerland
| | - Joern Rau
- Coordinating Center for Clinical Trials, Philipps-University, Marburg, Germany
| | - Lars Timmermann
- Department of Neurology, University Hospital Giessen and Marburg, Marburg, Germany
| | - Alfons Schnitzler
- Department of Neurology, Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Duesseldorf, Duesseldorf, Germany
| | - Steffen Paschen
- Department of Neurology, University Hospital Schleswig Holstein, Kiel, Germany
| | - Ann-Kristin Helmers
- Department of Neurosurgery, University Hospital Schleswig Holstein, Kiel, Germany
| | - Andreas Hartmann
- Assistance-Publique Hôpitaux de Paris, Center d'Investigation Clinique 9503, Institut du Cerveau et de la Moelle épinière, Paris, France.,Département de Neurologie, Université Pierre et Marie Curie-Paris 6 et INSERM, Paris, France
| | - Eric Bardinet
- Department of Neurology, NS-PARK/F-CRIN, University Hospital of Besançon, Besançon, France.,Center de Neuroimagerie de Recherche, Institut du Cerveau et de la Moelle (ICM), Paris, France
| | - Michael Schuepbach
- Department of Neurology, University Hospital Bern and University of Bern, Bern, Switzerland.,Assistance-Publique Hôpitaux de Paris, Center d'Investigation Clinique 9503, Institut du Cerveau et de la Moelle épinière, Paris, France.,Département de Neurologie, Université Pierre et Marie Curie-Paris 6 et INSERM, Paris, France.,Institute of Neurology, Konolfingen, Switzerland
| | - Michael T Barbe
- Department of Neurology, University of Cologne, Faculty of Medicine, Cologne, Germany
| | - Till A Dembek
- Department of Neurology, University of Cologne, Faculty of Medicine, Cologne, Germany
| | - Valerie Fraix
- Université Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Grenoble, France.,Neurology Department, Grenoble University Hospital, Grenoble, France
| | - Dorothee Kübler
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité Medicine University of Berlin, Berlin, Germany
| | | | - Alireza Gharabaghi
- Department of Neurosurgery and Neurotechnology Institute for Neuromodulation and Neurotechnology, University Hospital and University of Tuebingen, Tuebingen, Germany
| | - Lars Wojtecki
- Department of Neurology and Neurorehabilitation, Hospital zum Heiligen Geist GmbH & Co.KG Academic Teaching Hospital of the Heinrich-Heine-University Düsseldorf Von-Broichhausen-Allee 1, Kempen, Germany
| | - Marcus O Pinsker
- Department of Neurosurgery, University of Freiburg, Freiburg, Germany
| | - Stephane Thobois
- Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer, Service de Neurologie C, Center Expert Parkinson, Bron, France.,Université Lyon, Université Claude Bernard Lyon 1, Faculté de Médecine Lyon Sud Charles Mérieux, Oullins, France
| | | | - Tatiana Witjas
- Department of Neurology, Timone University Hospital UMR 7289, CNRS Marseille, Marseille, France
| | - Jean-Luc Houeto
- Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer, Service de Neurologie C, Center Expert Parkinson, Bron, France
| | | | - Marie Vidailhet
- Department of Neurology, Sorbonne Université, ICM UMR1127, INSERM &1127, CNRS 7225, Salpêtriere University Hospital AP-HP, Paris, France
| | - Andreas Horn
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité Medicine University of Berlin, Berlin, Germany
| | - Günther Deuschl
- Department of Neurology, University Hospital Schleswig Holstein, Kiel, Germany
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28
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Andree A, Li N, Butenko K, Kober M, Chen JZ, Higuchi T, Fauser M, Storch A, Ip CW, Kühn AA, Horn A, van Rienen U. Deep brain stimulation electrode modeling in rats. Exp Neurol 2022; 350:113978. [PMID: 35026227 DOI: 10.1016/j.expneurol.2022.113978] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 11/13/2021] [Accepted: 01/06/2022] [Indexed: 11/26/2022]
Abstract
Deep Brain Stimulation (DBS) is an efficacious treatment option for an increasing range of brain disorders. To enhance our knowledge about the mechanisms of action of DBS and to probe novel targets, basic research in animal models with DBS is an essential research base. Beyond nonhuman primate, pig, and mouse models, the rat is a widely used animal model for probing DBS effects in basic research. Reconstructing DBS electrode placement after surgery is crucial to associate observed effects with modulating a specific target structure. Post-mortem histology is a commonly used method for reconstructing the electrode location. In humans, however, neuroimaging-based electrode localizations have become established. For this reason, we adapt the open-source software pipeline Lead-DBS for DBS electrode localizations from humans to the rat model. We validate our localization results by inter-rater concordance and a comparison with the conventional histological method. Finally, using the open-source software pipeline OSS-DBS, we demonstrate the subject-specific simulation of the VTA and the activation of axon models aligned to pathways representing neuronal fibers, also known as the pathway activation model. Both activation models yield a characterization of the impact of DBS on the target area. Our results suggest that the proposed neuroimaging-based method can precisely localize DBS electrode placements that are essentially rater-independent and yield results comparable to the histological gold standard. The advantages of neuroimaging-based electrode localizations are the possibility of acquiring them in vivo and combining electrode reconstructions with advanced imaging metrics, such as those obtained from diffusion or functional magnetic resonance imaging (MRI). This paper introduces a freely available open-source pipeline for DBS electrode reconstructions in rats. The presented initial validation results are promising.
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Affiliation(s)
- Andrea Andree
- Institute of General Electrical Engineering, University of Rostock, Albert-Einstein-Straße 2, 18059 Rostock, Germany.
| | - Ningfei Li
- Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Germany; Berlin Institute of Health, Movement Disorders and Neuromodulation Unit, Department for Neurology, Charitéplatz 1, 10117 Berlin, Germany.
| | - Konstantin Butenko
- Institute of General Electrical Engineering, University of Rostock, Albert-Einstein-Straße 2, 18059 Rostock, Germany.
| | - Maria Kober
- Department of Neurology, Rostock University Medical Center, Gehlsheimer Straße 20, 18147 Rostock, Germany.
| | - Jia Zhi Chen
- Department of Neurology, University Hospital of Würzburg, Josef-Schneider-Straße 11, 97080 Würzburg, Germany.
| | - Takahiro Higuchi
- Department of Nuclear Medicine and Comprehensive Heart Failure Center, University Hospital of Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany.
| | - Mareike Fauser
- Department of Neurology, Rostock University Medical Center, Gehlsheimer Straße 20, 18147 Rostock, Germany.
| | - Alexander Storch
- Department of Neurology, Rostock University Medical Center, Gehlsheimer Straße 20, 18147 Rostock, Germany; German Centre for Neurodegenerative Diseases (DZNE) Rostock/Greifswald, Gehlsheimer, Straße 20, 18147 Rostock, Germany; Department Ageing of Individuals and Society, University of Rostock, Gehlsheimer Straße 20, 18147 Rostock, Germany.
| | - Chi Wang Ip
- Department of Neurology, University Hospital of Würzburg, Josef-Schneider-Straße 11, 97080 Würzburg, Germany.
| | - Andrea A Kühn
- Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Germany; Berlin Institute of Health, Movement Disorders and Neuromodulation Unit, Department for Neurology, Charitéplatz 1, 10117 Berlin, Germany.
| | - Andreas Horn
- Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Germany; Berlin Institute of Health, Movement Disorders and Neuromodulation Unit, Department for Neurology, Charitéplatz 1, 10117 Berlin, Germany.
| | - Ursula van Rienen
- Institute of General Electrical Engineering, University of Rostock, Albert-Einstein-Straße 2, 18059 Rostock, Germany; Department Ageing of Individuals and Society, University of Rostock, Gehlsheimer Straße 20, 18147 Rostock, Germany; Department Life, Light & Matter, University of Rostock, Albert-Einstein-Straße 25, 18059 Rostock, Germany.
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29
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Frankemolle-Gilbert AM, Howell B, Bower KL, Veltink PH, Heida T, McIntyre CC. Comparison of methodologies for modeling directional deep brain stimulation electrodes. PLoS One 2021; 16:e0260162. [PMID: 34910744 PMCID: PMC8673613 DOI: 10.1371/journal.pone.0260162] [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: 05/17/2021] [Accepted: 11/03/2021] [Indexed: 11/18/2022] Open
Abstract
Deep brain stimulation (DBS) is an established clinical therapy, and directional DBS electrode designs are now commonly used in clinical practice. Directional DBS leads have the ability to increase the therapeutic window of stimulation, but they also increase the complexity of clinical programming. Therefore, computational models of DBS have become available in clinical software tools that are designed to assist in the identification of therapeutic settings. However, the details of how the DBS model is implemented can influence the predictions of the software. The goal of this study was to compare different methods for representing directional DBS electrodes within finite element volume conductor (VC) models. We evaluated 15 different DBS VC model variants and quantified how their differences influenced estimates on the spatial extent of axonal activation from DBS. Each DBS VC model included the same representation of the brain and head, but the details of the current source and electrode contact were different for each model variant. The more complex VC models explicitly represented the DBS electrode contacts, while the more simple VC models used boundary condition approximations. The more complex VC models required 2-3 times longer to mesh, build, and solve for the DBS voltage distribution than the more simple VC models. Differences in individual axonal activation thresholds across the VC model variants were substantial (-24% to +47%). However, when comparing total activation of an axon population, or estimates of an activation volume, the differences between model variants decreased (-7% to +8%). Nonetheless, the technical details of how the electrode contact and current source are represented in the DBS VC model can directly affect estimates of the voltage distribution and electric field in the brain tissue.
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Affiliation(s)
- Anneke M. Frankemolle-Gilbert
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
- MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Bryan Howell
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Kelsey L. Bower
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Peter H. Veltink
- MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Tjitske Heida
- MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Cameron C. McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
- * E-mail:
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30
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Wu C, Ferreira F, Fox M, Harel N, Hattangadi-Gluth J, Horn A, Jbabdi S, Kahan J, Oswal A, Sheth SA, Tie Y, Vakharia V, Zrinzo L, Akram H. Clinical applications of magnetic resonance imaging based functional and structural connectivity. Neuroimage 2021; 244:118649. [PMID: 34648960 DOI: 10.1016/j.neuroimage.2021.118649] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/24/2021] [Accepted: 10/10/2021] [Indexed: 12/23/2022] Open
Abstract
Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.
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Affiliation(s)
- Chengyuan Wu
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, 909 Walnut Street, Third Floor, Philadelphia, PA 19107, USA; Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, 909 Walnut Street, First Floor, Philadelphia, PA 19107, USA.
| | - Francisca Ferreira
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Michael Fox
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, 2021 Sixth Street S.E., Minneapolis, MN 55455, USA.
| | - Jona Hattangadi-Gluth
- Department of Radiation Medicine and Applied Sciences, Center for Precision Radiation Medicine, University of California, San Diego, 3855 Health Sciences Drive, La Jolla, CA 92037, USA.
| | - Andreas Horn
- Neurology Department, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Charitéplatz 1, D-10117, Berlin, Germany.
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK.
| | - Joshua Kahan
- Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, New York, NY, 10065, USA.
| | - Ashwini Oswal
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Mansfield Rd, Oxford OX1 3TH, UK.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, 7200 Cambridge, Ninth Floor, Houston, TX 77030, USA.
| | - Yanmei Tie
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Vejay Vakharia
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK.
| | - Ludvic Zrinzo
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Harith Akram
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
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31
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Roediger J, Dembek TA, Wenzel G, Butenko K, Kühn AA, Horn A. StimFit-A Data-Driven Algorithm for Automated Deep Brain Stimulation Programming. Mov Disord 2021; 37:574-584. [PMID: 34837245 DOI: 10.1002/mds.28878] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/07/2021] [Accepted: 11/04/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Finding the optimal deep brain stimulation (DBS) parameters from a multitude of possible combinations by trial and error is time consuming and requires highly trained medical personnel. OBJECTIVE We developed an automated algorithm to identify optimal stimulation settings in Parkinson's disease (PD) patients treated with subthalamic nucleus (STN) DBS based on imaging-derived metrics. METHODS Electrode locations and monopolar review data of 612 stimulation settings acquired from 31 PD patients were used to train a predictive model for therapeutic and adverse stimulation effects. Model performance was then evaluated within the training cohort using cross-validation and on an independent cohort of 19 patients. We inverted the model by applying a brute-force approach to determine the optimal stimulation sites in the target region. Finally, an optimization algorithm was established to identify optimal stimulation parameters. Suggested stimulation parameters were compared to the ones applied in clinical practice. RESULTS Predicted motor outcome correlated with observed outcome (R = 0.57, P < 10-10 ) across patients within the training cohort. In the test cohort, the model explained 28% of the variance in motor outcome differences between settings. The stimulation site for maximum motor improvement was located at the dorsolateral border of the STN. When compared to two empirical settings, model-based suggestions more closely matched the setting with superior motor improvement. CONCLUSION We developed and validated a data-driven model that can suggest stimulation parameters leading to optimal motor improvement while minimizing the risk of stimulation-induced side effects. This approach might provide guidance for DBS programming in the future. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Jan Roediger
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany.,Einstein Center for Neurosciences Berlin, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany
| | - Till A Dembek
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Gregor Wenzel
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany
| | - Konstantin Butenko
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany.,Berlin School of Mind and Brain, Charité University Medicine, Berlin, Germany.,NeuroCure Clinical Research Centre, Charité University Medicine, Berlin, Germany.,DZNE, German Center for Degenerative Diseases, Berlin, Germany
| | - Andreas Horn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany
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32
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Baldermann JC, Schüller T, Kohl S, Voon V, Li N, Hollunder B, Figee M, Haber SN, Sheth SA, Mosley PE, Huys D, Johnson KA, Butson C, Ackermans L, Bouwens van der Vlis T, Leentjens AFG, Barbe M, Visser-Vandewalle V, Kuhn J, Horn A. Connectomic Deep Brain Stimulation for Obsessive-Compulsive Disorder. Biol Psychiatry 2021; 90:678-688. [PMID: 34482949 DOI: 10.1016/j.biopsych.2021.07.010] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 06/29/2021] [Accepted: 07/01/2021] [Indexed: 01/17/2023]
Abstract
Obsessive-compulsive disorder is among the most disabling psychiatric disorders. Although deep brain stimulation is considered an effective treatment, its use in clinical practice is not fully established. This is, at least in part, due to ambiguity about the best suited target and insufficient knowledge about underlying mechanisms. Recent advances suggest that changes in broader brain networks are responsible for improvement of obsessions and compulsions, rather than local impact at the stimulation site. These findings were fueled by innovative methodological approaches using brain connectivity analyses in combination with neuromodulatory interventions. Such a connectomic approach for neuromodulation constitutes an integrative account that aims to characterize optimal target networks. In this critical review, we integrate findings from connectomic studies and deep brain stimulation interventions to characterize a neural network presumably effective in reducing obsessions and compulsions. To this end, we scrutinize methodologies and seemingly conflicting findings with the aim to merge observations to identify common and diverse pathways for treating obsessive-compulsive disorder. Ultimately, we propose a unified network that-when modulated by means of cortical or subcortical interventions-alleviates obsessive-compulsive symptoms.
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Affiliation(s)
- Juan Carlos Baldermann
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
| | - Thomas Schüller
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Sina Kohl
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Valerie Voon
- Department of Psychiatry, Cambridge University, Cambridge, United Kingdom
| | - Ningfei Li
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Berlin, Germany
| | - Barbara Hollunder
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Berlin, Germany; Einstein Center for Neurosciences, Charité - University Medicine Berlin, Berlin, Germany; Faculty of Philosophy, Humboldt University of Berlin, Berlin School of Mind and Brain, Berlin, Germany
| | - Martijn Figee
- Department of Psychiatry, Mount Sinai Hospital, New York, New York
| | - Suzanne N Haber
- Department of Pharmacology and Physiology, University of Rochester School of Medicine, Rochester, New York; Basic Neuroscience Division, Harvard Medical School, McLean Hospital, Belmont, Massachusetts
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Philip E Mosley
- Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia; Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia
| | - Daniel Huys
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Kara A Johnson
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida
| | - Christopher Butson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah; Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah
| | - Linda Ackermans
- School of Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | | | - Albert F G Leentjens
- School of Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Michael Barbe
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jens Kuhn
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Department of Psychiatry, Psychotherapy and Psychosomatic, Johanniter Hospital Oberhausen, Oberhausen, Germany
| | - Andreas Horn
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Berlin, Germany
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Li N, Hollunder B, Baldermann JC, Kibleur A, Treu S, Akram H, Al-Fatly B, Strange BA, Barcia JA, Zrinzo L, Joyce EM, Chabardes S, Visser-Vandewalle V, Polosan M, Kuhn J, Kühn AA, Horn A. A Unified Functional Network Target for Deep Brain Stimulation in Obsessive-Compulsive Disorder. Biol Psychiatry 2021; 90:701-713. [PMID: 34134839 DOI: 10.1016/j.biopsych.2021.04.006] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/26/2021] [Accepted: 04/12/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND Multiple deep brain stimulation (DBS) targets have been proposed for treating intractable obsessive-compulsive disorder (OCD). Here, we investigated whether stimulation effects of different target sites would be mediated by one common or several segregated functional brain networks. METHODS First, seeding from active electrodes of 4 OCD patient cohorts (N = 50) receiving DBS to anterior limb of the internal capsule or subthalamic nucleus zones, optimal functional connectivity profiles for maximal Yale-Brown Obsessive Compulsive Scale improvements were calculated and cross-validated in leave-one-cohort-out and leave-one-patient-out designs. Second, we derived optimal target-specific connectivity patterns to determine brain regions mutually predictive of clinical outcome for both targets and others predictive for either target alone. Functional connectivity was defined using resting-state functional magnetic resonance imaging data acquired in 1000 healthy participants. RESULTS While optimal functional connectivity profiles showed both commonalities and differences between target sites, robust cross-predictions of clinical improvements across OCD cohorts and targets suggested a shared network. Connectivity to the anterior cingulate cortex, insula, and precuneus, among other regions, was predictive regardless of stimulation target. Regions with maximal connectivity to these commonly predictive areas included the insula, superior frontal gyrus, anterior cingulate cortex, and anterior thalamus, as well as the original stereotactic targets. CONCLUSIONS Pinpointing the network modulated by DBS for OCD from different target sites identified a set of brain regions to which DBS electrodes associated with optimal outcomes were functionally connected-regardless of target choice. On these grounds, we establish potential brain areas that could prospectively inform additional or alternative neuromodulation targets for obsessive-compulsive disorder.
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Affiliation(s)
- Ningfei Li
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Berlin, Germany.
| | - Barbara Hollunder
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Berlin, Germany; Charité - Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany; Berlin School of Mind and Brain, Faculty of Philosophy, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Juan Carlos Baldermann
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne; Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Astrid Kibleur
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut des Neurosciences (AK, SC, MP), Grenoble; and OpenMind Innovation (AK), Paris, France; OpenMind Innovation, Paris, France
| | - Svenja Treu
- Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Harith Akram
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom; National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Trust (UCLH), London, United Kingdom
| | - Bassam Al-Fatly
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Berlin, Germany
| | - Bryan A Strange
- Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Juan A Barcia
- Neurosurgery Department, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - Ludvic Zrinzo
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom; National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Trust (UCLH), London, United Kingdom
| | - Eileen M Joyce
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom; National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Trust (UCLH), London, United Kingdom
| | - Stephan Chabardes
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut des Neurosciences (AK, SC, MP), Grenoble; and OpenMind Innovation (AK), Paris, France
| | | | - Mircea Polosan
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut des Neurosciences (AK, SC, MP), Grenoble; and OpenMind Innovation (AK), Paris, France
| | - Jens Kuhn
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, Cologne, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, Johanniter Hospital Oberhausen, Evangelisches Klinikum Niederrhein, Oberhausen, Germany
| | - Andrea A Kühn
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Berlin, Germany; Charité - Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany; Berlin School of Mind and Brain, Faculty of Philosophy, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Andreas Horn
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Berlin, Germany; Charité - Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
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34
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Sand D, Arkadir D, Abu Snineh M, Marmor O, Israel Z, Bergman H, Hassin-Baer S, Israeli-Korn S, Peremen Z, Geva AB, Eitan R. Deep Brain Stimulation Can Differentiate Subregions of the Human Subthalamic Nucleus Area by EEG Biomarkers. Front Syst Neurosci 2021; 15:747681. [PMID: 34744647 PMCID: PMC8565520 DOI: 10.3389/fnsys.2021.747681] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 09/16/2021] [Indexed: 01/10/2023] Open
Abstract
Introduction: Precise lead localization is crucial for an optimal clinical outcome of subthalamic nucleus (STN) deep brain stimulation (DBS) treatment in patients with Parkinson's disease (PD). Currently, anatomical measures, as well as invasive intraoperative electrophysiological recordings, are used to locate DBS electrodes. The objective of this study was to find an alternative electrophysiology tool for STN DBS lead localization. Methods: Sixty-one postoperative electrophysiology recording sessions were obtained from 17 DBS-treated patients with PD. An intraoperative physiological method automatically detected STN borders and subregions. Postoperative EEG cortical activity was measured, while STN low frequency stimulation (LFS) was applied to different areas inside and outside the STN. Machine learning models were used to differentiate stimulation locations, based on EEG analysis of engineered features. Results: A machine learning algorithm identified the top 25 evoked response potentials (ERPs), engineered features that can differentiate inside and outside STN stimulation locations as well as within STN stimulation locations. Evoked responses in the medial and ipsilateral fronto-central areas were found to be most significant for predicting the location of STN stimulation. Two-class linear support vector machine (SVM) predicted the inside (dorso-lateral region, DLR, and ventro-medial region, VMR) vs. outside [zona incerta, ZI, STN stimulation classification with an accuracy of 0.98 and 0.82 for ZI vs. VMR and ZI vs. DLR, respectively, and an accuracy of 0.77 for the within STN (DLR vs. VMR)]. Multiclass linear SVM predicted all areas with an accuracy of 0.82 for the outside and within STN stimulation locations (ZI vs. DLR vs. VMR). Conclusions: Electroencephalogram biomarkers can use low-frequency STN stimulation to localize STN DBS electrodes to ZI, DLR, and VMR STN subregions. These models can be used for both intraoperative electrode localization and postoperative stimulation programming sessions, and have a potential to improve STN DBS clinical outcomes.
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Affiliation(s)
- Daniel Sand
- Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel.,Edmond and Lily Safra Center for Brain Research, Hebrew University of Jerusalem, Jerusalem, Israel.,Elminda Ltd., Herzliya, Israel
| | - David Arkadir
- Department of Neurology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Muneer Abu Snineh
- Department of Neurology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Odeya Marmor
- Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Zvi Israel
- Brain Division, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,Functional Neurosurgery Unit, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Hagai Bergman
- Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel.,Edmond and Lily Safra Center for Brain Research, Hebrew University of Jerusalem, Jerusalem, Israel.,Functional Neurosurgery Unit, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Sharon Hassin-Baer
- Department of Neurology, Movement Disorders Institute, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Simon Israeli-Korn
- Department of Neurology, Movement Disorders Institute, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | | | - Amir B Geva
- Department of Electrical and Computer Engineering, Ben Gurion University, Beer-Sheva, Israel
| | - Renana Eitan
- Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel.,Brain Division, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,Neuropsychiatry Unit, Jerusalem Mental Health Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
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35
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Sobesky L, Goede L, Odekerken VJJ, Wang Q, Li N, Neudorfer C, Rajamani N, Al-Fatly B, Reich M, Volkmann J, de Bie RMA, Kühn AA, Horn A. Subthalamic and pallidal deep brain stimulation: are we modulating the same network? Brain 2021; 145:251-262. [PMID: 34453827 DOI: 10.1093/brain/awab258] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 04/05/2021] [Accepted: 06/21/2021] [Indexed: 11/14/2022] Open
Abstract
The subthalamic nucleus and internal pallidum are main target sites for deep brain stimulation in Parkinson's disease. Multiple trials that investigated subthalamic versus pallidal stimulation were unable to settle on a definitive optimal target between the two. One reason could be that the effect is mediated via a common functional network. To test this hypothesis, we calculated connectivity profiles seeding from deep brain stimulation electrodes in 94 patients that underwent subthalamic and 28 patients with pallidal treatment based on a normative connectome atlas calculated from 1,000 healthy subjects. In each cohort, we calculated connectivity profiles that were associated with optimal clinical improvements. The two maps showed striking similarity and were able to cross-predict outcomes in the respective other cohort (R = 0.37 at p < 0.001; R = 0.34 at p = 0.032). Next, we calculated an agreement map which retained regions common to both target sites. Crucially, this map was able to explain an additional amount of variance in clinical improvements of either cohort when compared to the maps calculated on the two cohorts alone. Finally, we tested profiles and predictive utility of connectivity maps calculated from different motor symptom subscores with a specific focus on bradykinesia and rigidity. While our study is based on retrospective data and indirect connectivity metrics, it may deliver empirical data to support the hypothesis of a largely overlapping network associated with effective deep brain stimulation in Parkinson's disease irrespective of the specific target.
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Affiliation(s)
- Leon Sobesky
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Lukas Goede
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Vincent J J Odekerken
- Department of Neurology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Qiang Wang
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Ningfei Li
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Clemens Neudorfer
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Nanditha Rajamani
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Bassam Al-Fatly
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Martin Reich
- Department of Neurology, University Clinic of Würzburg, Josef-Schneider-Str. 11, 97080 Würzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University Clinic of Würzburg, Josef-Schneider-Str. 11, 97080 Würzburg, Germany
| | - Rob M A de Bie
- Department of Neurology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Andreas Horn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
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36
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Charlebois CM, Caldwell DJ, Rampersad SM, Janson AP, Ojemann JG, Brooks DH, MacLeod RS, Butson CR, Dorval AD. Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation. Front Neurosci 2021; 15:691701. [PMID: 34408621 PMCID: PMC8365306 DOI: 10.3389/fnins.2021.691701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 07/12/2021] [Indexed: 11/13/2022] Open
Abstract
Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (r = 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications.
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Affiliation(s)
- Chantel M Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States
| | - David J Caldwell
- Department of Bioengineering, University of Washington, Seattle, WA, United States.,Center for Neurotechnology, University of Washington, Seattle, WA, United States.,Medical Scientist Training Program, University of Washington, Seattle, WA, United States
| | - Sumientra M Rampersad
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Andrew P Janson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Dana H Brooks
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Rob S MacLeod
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States
| | - Christopher R Butson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States.,Department of Neurology, Neurosurgery and Psychiatry, University of Utah, Salt Lake City, UT, United States
| | - Alan D Dorval
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
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37
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Scullen T, Teja N, Song SH, Couldwell M, Carr C, Mathkour M, Lee DJ, Tubbs RS, Dallapiazza RF. Use of stereoelectroencephalography beyond epilepsy: a systematic review. World Neurosurg 2021; 155:96-108. [PMID: 34217862 DOI: 10.1016/j.wneu.2021.06.105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 06/23/2021] [Accepted: 06/24/2021] [Indexed: 11/17/2022]
Affiliation(s)
- Tyler Scullen
- Tulane University School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Nikhil Teja
- Department of Psychiatry, Dartmouth-Hitchcock Medical Center, Hanover, New Hampshire, USA
| | - Seo Ho Song
- Geisel School of Medicine, Dartmouth University, Hanover, New Hampshire, USA
| | - Mitchell Couldwell
- Tulane University School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Chris Carr
- Tulane University School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Mansour Mathkour
- Tulane University School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Darrin J Lee
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - R Shane Tubbs
- Tulane University School of Medicine, Tulane University, New Orleans, Louisiana, USA; Department of Structural & Cellular Biology, Tulane University, New Orleans, Louisiana, USA; Department of Anatomical Sciences, St. George's University, Grenada
| | - Robert F Dallapiazza
- Tulane University School of Medicine, Tulane University, New Orleans, Louisiana, USA.
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38
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Pathak YJ, Greenleaf W, Verhagen Metman L, Kubben P, Sarma S, Pepin B, Lautner D, DeBates S, Benison AM, Balasingh B, Ross E. Digital Health Integration With Neuromodulation Therapies: The Future of Patient-Centric Innovation in Neuromodulation. Front Digit Health 2021; 3:618959. [PMID: 34713096 PMCID: PMC8521905 DOI: 10.3389/fdgth.2021.618959] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 04/12/2021] [Indexed: 01/30/2023] Open
Abstract
Digital health can drive patient-centric innovation in neuromodulation by leveraging current tools to identify response predictors and digital biomarkers. Iterative technological evolution has led us to an ideal point to integrate digital health with neuromodulation. Here, we provide an overview of the digital health building-blocks, the status of advanced neuromodulation technologies, and future applications for neuromodulation with digital health integration.
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Affiliation(s)
| | - Walter Greenleaf
- Department of Communication, Stanford University, Stanford, CA, United States
| | - Leo Verhagen Metman
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Pieter Kubben
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, Netherlands
| | - Sridevi Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | | | | | | | | | | | - Erika Ross
- Abbott Neuromodulation, Plano, TX, United States
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39
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Howell B, Isbaine F, Willie JT, Opri E, Gross RE, De Hemptinne C, Starr PA, McIntyre CC, Miocinovic S. Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation. Brain Stimul 2021; 14:549-563. [PMID: 33757931 DOI: 10.1016/j.brs.2021.03.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Revised: 02/19/2021] [Accepted: 03/14/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Subthalamic deep brain stimulation (DBS) is an effective surgical treatment for Parkinson's disease and continues to advance technologically with an enormous parameter space. As such, in-silico DBS modeling systems have become common tools for research and development, but their underlying methods have yet to be standardized and validated. OBJECTIVE Evaluate the accuracy of patient-specific estimates of neural pathway activations in the subthalamic region against intracranial, cortical evoked potential (EP) recordings. METHODS Pathway activations were modeled in eleven patients using the latest advances in connectomic modeling of subthalamic DBS, focusing on the hyperdirect pathway (HDP) and corticospinal/bulbar tract (CSBT) for their relevance in human research studies. Correlations between pathway activations and respective EP amplitudes were quantified. RESULTS Good model performance required accurate lead localization and image fusions, as well as appropriate selection of fiber diameter in the biophysical model. While optimal model parameters varied across patients, good performance could be achieved using a global set of parameters that explained 60% and 73% of electrophysiologic activations of CSBT and HDP, respectively. Moreover, restricted models fit to only EP amplitudes of eight standard (monopolar and bipolar) electrode configurations were able to extrapolate variation in EP amplitudes across other directional electrode configurations and stimulation parameters, with no significant reduction in model performance across the cohort. CONCLUSIONS Our findings demonstrate that connectomic models of DBS with sufficient anatomical and electrical details can predict recruitment dynamics of white matter. These results will help to define connectomic modeling standards for preoperative surgical targeting and postoperative patient programming applications.
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Affiliation(s)
- Bryan Howell
- Department of Biomedical Engineering, Case Western Reserve University, USA
| | | | - Jon T Willie
- Department of Neurosurgery, Emory University, USA
| | - Enrico Opri
- Department of Neurology, Emory University, USA
| | | | | | - Philip A Starr
- Department of Neurological Surgery, University of California San Francisco, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, USA
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40
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Schrock LE, Patriat R, Goftari M, Kim J, Johnson MD, Harel N, Vitek JL. 7T MRI and Computational Modeling Supports a Critical Role of Lead Location in Determining Outcomes for Deep Brain Stimulation: A Case Report. Front Hum Neurosci 2021; 15:631778. [PMID: 33679351 PMCID: PMC7928296 DOI: 10.3389/fnhum.2021.631778] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 01/15/2021] [Indexed: 11/13/2022] Open
Abstract
Subthalamic nucleus (STN) deep brain stimulation (DBS) is an established therapy for Parkinson’s disease motor symptoms. The ideal site for implantation within STN, however, remains controversial. While many argue that placement of a DBS lead within the sensorimotor territory of the STN yields better motor outcomes, others report similar effects with leads placed in the associative or motor territory of the STN, while still others assert that placing a DBS lead “anywhere within a 6-mm-diameter cylinder centered at the presumed middle of the STN (based on stereotactic atlas coordinates) produces similar clinical efficacy.” These discrepancies likely result from methodological differences including targeting preferences, imaging acquisition and the use of brain atlases that do not account for patient-specific anatomic variability. We present a first-in-kind within-patient demonstration of severe mood side effects and minimal motor improvement in a Parkinson’s disease patient following placement of a DBS lead in the limbic/associative territory of the STN who experienced marked improvement in motor benefit and resolution of mood side effects following repositioning the lead within the STN sensorimotor territory. 7 Tesla (7 T) magnetic resonance imaging (MRI) data were used to generate a patient-specific anatomical model of the STN with parcellation into distinct functional territories and computational modeling to assess the relative degree of activation of motor, associative and limbic territories.
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Affiliation(s)
- Lauren E Schrock
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Remi Patriat
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Mojgan Goftari
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Jiwon Kim
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN, United States
| | - Matthew D Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Jerrold L Vitek
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
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41
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Dembek TA, Baldermann JC, Petry-Schmelzer JN, Jergas H, Treuer H, Visser-Vandewalle V, Dafsari HS, Barbe MT. Sweetspot Mapping in Deep Brain Stimulation: Strengths and Limitations of Current Approaches. Neuromodulation 2021; 25:877-887. [PMID: 33476474 DOI: 10.1111/ner.13356] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 12/15/2020] [Accepted: 12/21/2020] [Indexed: 01/23/2023]
Abstract
OBJECTIVES Open questions remain regarding the optimal target, or sweetspot, for deep brain stimulation (DBS) in, for example, Parkinson's disease. Previous studies introduced different methods of mapping DBS effects to determine sweetspots. While having a direct impact on surgical targeting and postoperative programming in DBS, these methods so far have not been compared. MATERIALS AND METHODS This study investigated five previously published DBS mapping approaches regarding their potential to correctly identify a predefined target. Methods were investigated in silico in eight different use-case scenarios, which incorporated different types of clinical data, noise, and differences in underlying neuroanatomy. Dice coefficients were calculated to determine the overlap between identified sweetspots and the predefined target. Additionally, out-of-sample predictive capabilities were assessed using the amount of explained variance R2 . RESULTS The five investigated methods resulted in highly variable sweetspots. Methods based on voxel-wise statistics against average outcomes showed the best performance overall. While predictive capabilities were high, even in the best of cases Dice coefficients remained limited to values around 0.5, highlighting the overall limitations of sweetspot identification. CONCLUSIONS This study highlights the strengths and limitations of current approaches to DBS sweetspot mapping. Those limitations need to be taken into account when considering the clinical implications. All future approaches should be investigated in silico before being applied to clinical data.
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Affiliation(s)
- Till A Dembek
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | | | | | - Hannah Jergas
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Harald Treuer
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Haidar S Dafsari
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Michael T Barbe
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
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Noecker AM, Frankemolle-Gilbert AM, Howell B, Petersen MV, Beylergil SB, Shaikh AG, McIntyre CC. StimVision v2: Examples and Applications in Subthalamic Deep Brain Stimulation for Parkinson's Disease. Neuromodulation 2021; 24:248-258. [PMID: 33389779 DOI: 10.1111/ner.13350] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/16/2020] [Accepted: 12/07/2020] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Subthalamic deep brain stimulation (DBS) is an established therapy for Parkinson's disease. Connectomic DBS modeling is a burgeoning subfield of research aimed at characterizing the axonal connections activated by DBS. This article describes our approach and methods for evolving the StimVision software platform to meet the technical demands of connectomic DBS modeling in the subthalamic region. MATERIALS AND METHODS StimVision v2 was developed with Visualization Toolkit (VTK) libraries and integrates four major components: 1) medical image visualization, 2) axonal pathway visualization, 3) electrode positioning, and 4) stimulation calculation. RESULTS StimVision v2 implemented two key technological advances for connectomic DBS analyses in the subthalamic region. First was the application of anatomical axonal pathway models to patient-specific DBS models. Second was the application of a novel driving-force method to estimate the response of those axonal pathways to DBS. Example simulations with directional DBS electrodes and clinically defined therapeutic DBS settings are presented to demonstrate the general outputs of StimVision v2 models. CONCLUSIONS StimVision v2 provides the opportunity to evaluate patient-specific axonal pathway activation from subthalamic DBS using anatomically detailed pathway models and electrically detailed electric field distributions with interactive adjustment of the DBS electrode position and stimulation parameter settings.
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Affiliation(s)
- Angela M Noecker
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | | | - Bryan Howell
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Mikkel V Petersen
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Sinem Balta Beylergil
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Aasef G Shaikh
- Department of Neurology, Case Western Reserve University, Cleveland, OH, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Department of Neurology, Case Western Reserve University, Cleveland, OH, USA
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Malaga KA, Costello JT, Chou KL, Patil PG. Atlas-independent, N-of-1 tissue activation modeling to map optimal regions of subthalamic deep brain stimulation for Parkinson disease. NEUROIMAGE-CLINICAL 2020; 29:102518. [PMID: 33333464 PMCID: PMC7736726 DOI: 10.1016/j.nicl.2020.102518] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 11/25/2020] [Accepted: 11/27/2020] [Indexed: 01/13/2023]
Abstract
Neuroanatomical variations among patients are obscured in atlas-based VTA modeling. N-of-1 neuroanatomical and VTA modeling enables patient-level precision. Mean optimal stimulation is dorsomedial to the STN, near its posterior half. Individual VTAs deviate from optimal stimulation sites to varying degrees. Optimal stimulation sites for rigidity, bradykinesia, and tremor partially overlap.
Background Motor outcomes after subthalamic deep brain stimulation (STN DBS) for Parkinson disease (PD) vary considerably among patients and strongly depend on stimulation location. The objective of this retrospective study was to map the regions of optimal STN DBS for PD using an atlas-independent, fully individualized (N-of-1) tissue activation modeling approach and to assess the relationship between patient-level therapeutic volumes of tissue activation (VTAs) and motor improvement. Methods The stimulation-induced electric field for 40 PD patients treated with bilateral STN DBS was modeled using finite element analysis. Neurostimulation models were generated for each patient, incorporating their individual STN anatomy, DBS lead position and orientation, anisotropic tissue conductivity, and clinical stimulation settings. A voxel-based analysis of the VTAs was then used to map the optimal location of stimulation. The amount of stimulation in specific regions relative to the STN was measured and compared between STNs with more and less optimal stimulation, as determined by their motor improvement scores and VTA. The relationship between VTA location and motor outcome was then assessed using correlation analysis. Patient variability in terms of STN anatomy, active contact position, and VTA location were also evaluated. Results from the N-of-1 model were compared to those from a simplified VTA model. Results Tissue activation modeling mapped the optimal location of stimulation to regions medial, posterior, and dorsal to the STN centroid. These regions extended beyond the STN boundary towards the caudal zona incerta (cZI). The location of the VTA and active contact position differed significantly between STNs with more and less optimal stimulation in the dorsal-ventral and anterior-posterior directions. Therapeutic stimulation spread noticeably more in the dorsal and posterior directions, providing additional evidence for cZI as an important DBS target. There were significant linear relationships between the amount of dorsal and posterior stimulation, as measured by the VTA, and motor improvement. These relationships were more robust than those between active contact position and motor improvement. There was high variability in STN anatomy, active contact position, and VTA location among patients. Spherical VTA modeling was unable to reproduce these results and tended to overestimate the size of the VTA. Conclusion Accurate characterization of the spread of stimulation is needed to optimize STN DBS for PD. High variability in neuroanatomy, stimulation location, and motor improvement among patients highlights the need for individualized modeling techniques. The atlas-independent, N-of-1 tissue activation modeling approach presented in this study can be used to develop and evaluate stimulation strategies to improve clinical outcomes on an individual basis.
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Affiliation(s)
- Karlo A Malaga
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Joseph T Costello
- Department of Electrical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Kelvin L Chou
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA; Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA
| | - Parag G Patil
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA; Department of Neurology, University of Michigan, Ann Arbor, MI, USA; Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.
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Juárez-Paz LM. In silico Accuracy and Energy Efficiency of Two Steering Paradigms in Directional Deep Brain Stimulation. Front Neurol 2020; 11:593798. [PMID: 33193061 PMCID: PMC7661934 DOI: 10.3389/fneur.2020.593798] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 09/30/2020] [Indexed: 01/11/2023] Open
Abstract
Background: In Deep Brain Stimulation (DBS), stimulation field steering is used to achieve stimulation spatial specificity, which is critical to obtain clinical benefits and avoid side effects. Multiple Independent Current Control (MICC) and Interleaving/Multi Stim Set (Interleaving/MSS) are two stimulation field steering paradigms in commercially available DBS systems. This work investigates the stimulation field steering accuracy and energy efficiency of these two paradigms in directional DBS. Methods: Volumes of Tissue Activated (VTAs) were generated in silico using pulse widths of 60 μs and five pulse amplitude fractionalizations intended to steer the VTAs radially in 12° steps. For each fractionalization, VTAs were generated with nine pre-defined target radii. Stimulation field steering accuracy was assessed based on the VTAs rotation angle. Energy efficiency was inferred from current draw from battery values, which were calculated based on the pulse amplitudes needed to generate and steer the VTAs, as well as electrode impedance measurements of clinically implanted directional leads. Results: For radial steering, MICC needed a single VTA. In contrast, Interleaving/MSS required the generation of two VTAs, whose union and intersection created an Interleaving/MSS VTA and an Intersection VTA, respectively. MICC VTAs were 6.8 (−3.2–11.8)% larger than Interleaving/MSS VTAs. The Intersection VTAs accounted for 26.2 (16.0–32.8)% of Interleaving/MSS VTAs and were exposed to a higher stimulation frequency. For all VTA radius-fractionalization combinations, steering accuracy was 7.0 (4.5–10.5)° for MICC and 24.0 (9.0–25.3)° for Interleaving/MSS. Pulse amplitudes were 16.1 (9.2–28.6)% lower for MICC than for Interleaving/MSS, leading to a 45.9 (18.8–72.6)% lower current draw from battery for MICC. Conclusions: The results of this work show that in silico, MICC achieves a significantly better stimulation field steering accuracy and has a significantly higher energy efficiency than Interleaving/MSS. Although direct evidence still needs to be generated to translate the results of this work to clinical practice, clinical outcomes may profit from the better stimulation field steering accuracy of MICC and longevity of DBS systems may profit from its higher energy efficiency.
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Affiliation(s)
- León Mauricio Juárez-Paz
- Neuromodulation Research and Advanced Concepts, Boston Scientific Corporation, Valencia, CA, United States
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45
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Zhang S, Tagliati M, Pouratian N, Cheeran B, Ross E, Pereira E. Steering the Volume of Tissue Activated With a Directional Deep Brain Stimulation Lead in the Globus Pallidus Pars Interna: A Modeling Study With Heterogeneous Tissue Properties. Front Comput Neurosci 2020; 14:561180. [PMID: 33101000 PMCID: PMC7546409 DOI: 10.3389/fncom.2020.561180] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/20/2020] [Indexed: 12/21/2022] Open
Abstract
Objective: To study the effect of directional deep brain stimulation (DBS) electrode configuration and vertical electrode spacing on the volume of tissue activated (VTA) in the globus pallidus, pars interna (GPi). Background: Directional DBS leads may allow clinicians to precisely direct current fields to different functional networks within traditionally targeted brain areas. Modeling the shape and size of the VTA for various monopolar or bipolar configurations can inform clinical programming strategies for GPi DBS. However, many computational models of VTA are limited by assuming tissue homogeneity. Methods: We generated a multimodal image-based detailed anatomical (MIDA) computational model with a directional DBS lead (1.5 mm or 0.5 mm vertical electrode spacing) placed with segmented contact 2 at the ventral posterolateral "sensorimotor" region of the GPi. The effect of tissue heterogeneity was examined by replacing the MIDA tissues with a homogeneous tissue of conductance 0.3 S/m. DBS pulses (amplitude: 1 mA, pulse width: 60 μs, frequency: 130 Hz) were used to produce VTAs. The following DBS contact configurations were tested: single-segment monopole (2B-/Case+), two-segment monopole (2A-/2B-/Case+ and 2B-/3B-/Case+), ring monopole (2A-/2B-/2C-/Case+), one-cathode three-anode bipole (2B-/3A+/3B+/3C+), three-cathode three-anode bipole (2A-/2B-/2C-/3A+/3B+/3C+). Additionally, certain vertical configurations were repeated with 2 mA current amplitude. Results: Using a heterogeneous tissue model affected both the size and shape of the VTA in GPi. Electrodes with both 0.5 mm and 1.5 mm vertical spacing (1 mA) modeling showed that the single segment monopolar VTA was entirely contained within the GPi when the active electrode is placed at the posterolateral "sensorimotor" GPi. Two segments in a same ring and ring settings, however, produced VTAs outside of the GPi border that spread into adjacent white matter pathways, e.g., optic tract and internal capsule. Both stacked monopolar settings and vertical bipolar settings allowed activation of structures dorsal to the GPi in addition to the GPi. Modeling of the stacked monopolar settings with the DBS lead with 0.5 mm vertical electrode spacing further restricted VTAs within the GPi, but the VTA volumes were smaller compared to the equivalent settings of 1.5 mm spacing.
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Affiliation(s)
- Simeng Zhang
- Neuromodulation Division, Abbott, Plano, TX, United States
| | | | - Nader Pouratian
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States
| | - Binith Cheeran
- Neuromodulation Division, Abbott, Plano, TX, United States
| | - Erika Ross
- Neuromodulation Division, Abbott, Plano, TX, United States
| | - Erlick Pereira
- Research Institute of Molecular and Clinical Sciences, St. George's University of London, London, United Kingdom
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46
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Bower KL, McIntyre CC. Deep brain stimulation of terminating axons. Brain Stimul 2020; 13:1863-1870. [PMID: 32919091 DOI: 10.1016/j.brs.2020.09.001] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 08/05/2020] [Accepted: 09/01/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) of the subthalamic region is an established treatment for the motor symptoms of Parkinson's disease. Several types of neural elements reside in the subthalamic region, including subthalamic nucleus (STN) neurons, fibers of passage, and terminating afferents. Recent studies suggest that direct activation of a specific population of subthalamic afferents, known as the hyperdirect pathway, may be responsible for some of the therapeutic effects of subthalamic DBS. OBJECTIVE The goal of this study was to quantify how axon termination affects neural excitability from DBS. We evaluated how adjusting different stimulation parameters influenced the relative excitability of terminating axons (TAs) compared to fibers of passage (FOPs). METHODS We used finite element electric field models of DBS, coupled to multi-compartment cable models of axons, to calculate activation thresholds for populations of TAs and FOPs. These generalized models were used to evaluate the response to anodic vs. cathodic stimulation, with short vs. long stimulus pulses. RESULTS Terminating axons generally exhibited lower thresholds than fibers of passage across all tested parameters. Short pulse widths accentuated the relative excitability of TAs over FOPs. CONCLUSION(S) Our computational results demonstrate a hyperexcitability of terminating axons to DBS that is robust to variation in the stimulation parameters, as well as the axon model parameters.
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Affiliation(s)
- Kelsey L Bower
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
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47
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Normative vs. patient-specific brain connectivity in deep brain stimulation. Neuroimage 2020; 224:117307. [PMID: 32861787 DOI: 10.1016/j.neuroimage.2020.117307] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 08/17/2020] [Accepted: 08/22/2020] [Indexed: 11/22/2022] Open
Abstract
Brain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, a number of studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and their ability to predict clinical improvement. Data from 33 patients suffering from Parkinson's Disease who underwent surgery at three different centers were retrospectively collected. Stimulation-dependent connectivity profiles seeding from active contacts were estimated using three modalities, namely patient-specific diffusion-MRI data, age- and disease-matched or normative group connectome data (acquired in healthy young subjects). Based on these profiles, models of optimal connectivity were calculated and used to estimate clinical improvement in out of sample data. All three modalities resulted in highly similar optimal connectivity profiles that could largely reproduce findings from prior research based on this present novel multi-center cohort. In a data-driven approach that estimated optimal whole-brain connectivity profiles, out-of-sample predictions of clinical improvements were calculated. Using either patient-specific connectivity (R = 0.43 at p = 0.001), an age- and disease-matched group connectome (R = 0.25, p = 0.048) and a normative connectome based on healthy/young subjects (R = 0.31 at p = 0.028), significant predictions could be made. Our results of patient-specific connectivity and normative connectomes lead to similar main conclusions about which brain areas are associated with clinical improvement. Still, although results were not significantly different, they hint at the fact that patient-specific connectivity may bear the potential of explaining slightly more variance than group connectomes. Furthermore, use of normative connectomes involves datasets with high signal-to-noise acquired on specialized MRI hardware, while clinical datasets as the ones used here may not exactly match their quality. Our findings support the role of DBS electrode connectivity profiles as a promising method to investigate DBS effects and to potentially guide DBS programming.
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48
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Treu S, Strange B, Oxenford S, Neumann WJ, Kühn A, Li N, Horn A. Deep brain stimulation: Imaging on a group level. Neuroimage 2020; 219:117018. [PMID: 32505698 DOI: 10.1016/j.neuroimage.2020.117018] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 05/07/2020] [Accepted: 06/01/2020] [Indexed: 12/11/2022] Open
Abstract
Deep Brain Stimulation (DBS) is an established treatment option for movement disorders and is under investigation for treatment in a growing number of other brain diseases. It has been shown that exact electrode placement crucially affects the efficacy of DBS and this should be considered when investigating novel indications or DBS targets. To measure clinical improvement as a function of electrode placement, neuroscientific methodology and specialized software tools are needed. Such tools should have the goal to make electrode placement comparable across patients and DBS centers, and include statistical analysis options to validate and define optimal targets. Moreover, to allow for comparability across different centers, these need to be performed within an algorithmically and anatomically standardized and openly available group space. With the publication of Lead-DBS software in 2014, an open-source tool was introduced that allowed for precise electrode reconstructions based on pre- and postoperative neuroimaging data. Here, we introduce Lead Group, implemented within the Lead-DBS environment and specifically designed to meet aforementioned demands. In the present article, we showcase the various processing streams of Lead Group in a retrospective cohort of 51 patients suffering from Parkinson's disease, who were implanted with DBS electrodes to the subthalamic nucleus (STN). Specifically, we demonstrate various ways to visualize placement of all electrodes in the group and map clinical improvement values to subcortical space. We do so by using active coordinates and volumes of tissue activated, showing converging evidence of an optimal DBS target in the dorsolateral STN. Second, we relate DBS outcome to the impact of each electrode on local structures by measuring overlap of stimulation volumes with the STN. Finally, we explore the software functions for connectomic mapping, which may be used to relate DBS outcomes to connectivity estimates with remote brain areas. The manuscript is accompanied by a walkthrough tutorial which allows users to reproduce all main results presented here. All data and code needed to reproduce results are openly available.
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Affiliation(s)
- Svenja Treu
- Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Spain; Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany.
| | - Bryan Strange
- Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Spain
| | - Simon Oxenford
- Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany
| | - Wolf-Julian Neumann
- Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany
| | - Andrea Kühn
- Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany; Exzellenzcluster NeuroCure, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ningfei Li
- Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany
| | - Andreas Horn
- Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany
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49
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Anderson CJ, Anderson DN, Pulst SM, Butson CR, Dorval AD. Neural selectivity, efficiency, and dose equivalence in deep brain stimulation through pulse width tuning and segmented electrodes. Brain Stimul 2020; 13:1040-1050. [PMID: 32278715 DOI: 10.1016/j.brs.2020.03.017] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 03/26/2020] [Accepted: 03/27/2020] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Achieving deep brain stimulation (DBS) dose equivalence is challenging, especially with pulse width tuning and directional contacts. Further, the precise effects of pulse width tuning are unknown, and recent reports of the effects of pulse width tuning on neural selectivity are at odds with classic biophysical studies. METHODS We created multicompartment neuron models for two axon diameters and used finite element modeling to determine extracellular influence from standard and segmented electrodes. We analyzed axon activation profiles and calculated volumes of tissue activated. RESULTS We find that long pulse widths focus the stimulation effect on small, nearby fibers, suppressing distant white matter tract activation (responsible for some DBS side effects) and improving battery utilization when equivalent activation is maintained for small axons. Directional leads enable similar benefits to a greater degree. Reexamining previous reports of short pulse stimulation reducing side effects, we explore a possible alternate explanation: non-dose equivalent stimulation may have resulted in reduced spread of neural activation. Finally, using internal capsule avoidance as an example in the context of subthalamic stimulation, we present a patient-specific model to show how long pulse widths could help increase the biophysical therapeutic window. DISCUSSION We find agreement with classic studies and predict that long pulse widths may focus the stimulation effect on small, nearby fibers and improve power consumption. While future pre-clinical and clinical work is necessary regarding pulse width tuning, it is clear that future studies must ensure dose equivalence, noting that energy- and charge-equivalent amplitudes do not result in equivalent spread of neural activation when changing pulse width.
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Affiliation(s)
- Collin J Anderson
- University of Utah Department of Neurology, Salt Lake City, UT, USA.
| | - Daria Nesterovich Anderson
- University of Utah Department of Biomedical Engineering, Salt Lake City, UT, USA; University of Utah Department of Neurosurgery, Salt Lake City, UT, USA; University of Utah Scientific Computing and Imaging Institute, Salt Lake City, UT, USA
| | - Stefan M Pulst
- University of Utah Department of Neurology, Salt Lake City, UT, USA
| | - Christopher R Butson
- University of Utah Department of Neurology, Salt Lake City, UT, USA; University of Utah Department of Biomedical Engineering, Salt Lake City, UT, USA; University of Utah Department of Neurosurgery, Salt Lake City, UT, USA; University of Utah Scientific Computing and Imaging Institute, Salt Lake City, UT, USA; University of Utah Department of Psychiatry, Salt Lake City, UT, USA
| | - Alan D Dorval
- University of Utah Department of Biomedical Engineering, Salt Lake City, UT, USA
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50
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Reich MM, Horn A, Lange F, Roothans J, Paschen S, Runge J, Wodarg F, Pozzi NG, Witt K, Nickl RC, Soussand L, Ewert S, Maltese V, Wittstock M, Schneider GH, Coenen V, Mahlknecht P, Poewe W, Eisner W, Helmers AK, Matthies C, Sturm V, Isaias IU, Krauss JK, Kühn AA, Deuschl G, Volkmann J. Probabilistic mapping of the antidystonic effect of pallidal neurostimulation: a multicentre imaging study. Brain 2020; 142:1386-1398. [PMID: 30851091 DOI: 10.1093/brain/awz046] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 12/12/2018] [Accepted: 01/08/2019] [Indexed: 11/13/2022] Open
Abstract
Deep brain stimulation of the internal globus pallidus is a highly effective and established therapy for primary generalized and cervical dystonia, but therapeutic success is compromised by a non-responder rate of up to 25%, even in carefully-selected groups. Variability in electrode placement and inappropriate stimulation settings may account for a large proportion of this outcome variability. Here, we present probabilistic mapping data on a large cohort of patients collected from several European centres to resolve the optimal stimulation volume within the pallidal region. A total of 105 dystonia patients with pallidal deep brain stimulation were enrolled and 87 datasets (43 with cervical dystonia and 44 with generalized dystonia) were included into the subsequent 'normative brain' analysis. The average improvement of dystonia motor score was 50.5 ± 30.9% in cervical and 58.2 ± 48.8% in generalized dystonia, while 19.5% of patients did not respond to treatment (<25% benefit). We defined probabilistic maps of anti-dystonic effects by aggregating individual electrode locations and volumes of tissue activated (VTA) in normative atlas space and ranking voxel-wise for outcome distribution. We found a significant relation between motor outcome and the stimulation volume, but not the electrode location per se. The highest probability of stimulation induced motor benefit was found in a small volume covering the ventroposterior globus pallidus internus and adjacent subpallidal white matter. We then used the aggregated VTA-based outcome maps to rate patient individual VTAs and trained a linear regression model to predict individual outcomes. The prediction model showed robustness between the predicted and observed clinical improvement, with an r2 of 0.294 (P < 0.0001). The predictions deviated on average by 16.9 ± 11.6 % from observed dystonia improvements. For example, if a patient improved by 65%, the model would predict an improvement between 49% and 81%. Results were validated in an independent cohort of 10 dystonia patients, where prediction and observed benefit had a correlation of r2 = 0.52 (P = 0.02) and a mean prediction error of 10.3% (±8.9). These results emphasize the potential of probabilistic outcome brain mapping in refining the optimal therapeutic volume for pallidal neurostimulation and advancing computer-assisted planning and programming of deep brain stimulation.
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Affiliation(s)
- Martin M Reich
- Julius-Maximilians-University Würzburg, Department of Neurology, Germany.,Beth Israel Deaconess Medical Center, Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Andreas Horn
- Charite-Universitätsmedizin Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Germany
| | - Florian Lange
- Julius-Maximilians-University Würzburg, Department of Neurology, Germany
| | - Jonas Roothans
- Julius-Maximilians-University Würzburg, Department of Neurology, Germany
| | | | | | - Fritz Wodarg
- University Kiel, Department of Radiology, Germany
| | - Nicolo G Pozzi
- Julius-Maximilians-University Würzburg, Department of Neurology, Germany
| | - Karsten Witt
- University Kiel, Department of Neurology, Germany.,University Oldenburg, Department of Neurology, Germany
| | - Robert C Nickl
- Julius-Maximilians-University, Department of Neurosurgery, Germany
| | - Louis Soussand
- Beth Israel Deaconess Medical Center, Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Siobhan Ewert
- Charite-Universitätsmedizin Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Germany
| | - Virgina Maltese
- Julius-Maximilians-University Würzburg, Department of Neurology, Germany
| | | | - Gerd-Helge Schneider
- Charite-Universitätsmedizin Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Germany
| | - Volker Coenen
- Freiburg University Medical Center, Department of Stereotactic and Functional Neurosurgery, Germany
| | | | - Werner Poewe
- Department of Neurology, Innsbruck Medical University, Austria
| | - Wilhelm Eisner
- Department of Neurosurgery, Innsbruck Medical University, Austria
| | | | - Cordula Matthies
- Julius-Maximilians-University, Department of Neurosurgery, Germany
| | - Volker Sturm
- Julius-Maximilians-University, Department of Neurosurgery, Germany
| | - Ioannis U Isaias
- Julius-Maximilians-University Würzburg, Department of Neurology, Germany
| | | | - Andrea A Kühn
- Charite-Universitätsmedizin Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Germany
| | | | - Jens Volkmann
- Julius-Maximilians-University Würzburg, Department of Neurology, Germany
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