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Zhao G, Cheng Y, Li G, Li L, Li F, Wu Y, Du C, Yan J, Cong G, Zhao Q, Wang M, Feng K, Yin S. Unveiling the Dominant Factors in Subthalamic Stimulation for Improving Depression in Parkinson's Disease. Mov Disord Clin Pract 2024. [PMID: 39262097 DOI: 10.1002/mdc3.14195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 06/07/2024] [Accepted: 08/05/2024] [Indexed: 09/13/2024] Open
Abstract
BACKGROUND Currently, the conclusions of studies on subthalamic nucleus (STN) deep brain stimulation (DBS) for improving Parkinson's disease (PD) with depression are inconsistent, and the reasons for improvement or deterioration remain unclear. METHODS The aim was to investigate the prognosis of PD with depression after bilateral STN-DBS and the factors related to the improvement in depression. The local and network effects of DBS on depression in PD (DPD) were further explored based on the volume of tissue activation (VTA). The study analyzed 80 primary PD patients who had undergone bilateral STN-DBS, comprising 47 patients with improved depression and 33 patients without improvement. Two groups of clinical profiles and stimulation parameters were compared, and the network models for improving depression were constructed. RESULTS The improvement in depression was closely associated with improvement in anxiety (odd rate [OR] = 1.067, P = 0.006) and the standardized space left y-coordinate (OR = 0.253, P = 0.005). The VTA overlapping with the left motor STN subregion is most significantly associated with improvement in depression (RSpearman = 0.53, P < 0.001; RPearson = 0.43, P < 0.001). The y-coordinates in the improvement group were closer to the optimal stimulation site for improving motor symptoms. Finally, both the structural and functional network models indicate a positive correlation between depression improvement and the connectivity of the sensorimotor cortex. CONCLUSION The amelioration of DPD is primarily attributed to the stimulation of bilateral motor STN, particularly on the left. However, this stimulatory effect manifests as an indirect influence.
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Affiliation(s)
- Guangrui Zhao
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China
- Department of Neurosurgery, Lu'an Hospital Affiliated to Anhui Medical University, Lu'an, China
| | - Yifeng Cheng
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin, China
| | - Guangfeng Li
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin, China
| | - Lanxin Li
- Department of Neurology, Huanhu Hospital, Tianjin University, Tianjin, China
| | - Feng Li
- Department of Neurology, Drum Tower Clinical College, Nanjing University of Traditional Chinese Medicine, Nanjing, China
| | - Yuzhang Wu
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China
| | - Chuan Du
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China
| | - Jingtao Yan
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China
| | - Guangyan Cong
- Department of Neurology, Lu'an Hospital Affiliated to Anhui Medical University, Lu'an, China
| | - Qiyuan Zhao
- Department of Neurology, Lu'an Hospital Affiliated to Anhui Medical University, Lu'an, China
| | - Min Wang
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin, China
| | - Keke Feng
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin, China
| | - Shaoya Yin
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin, China
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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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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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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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5
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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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Ng PR, Bush A, Vissani M, McIntyre CC, Richardson RM. Biophysical Principles and Computational Modeling of Deep Brain Stimulation. Neuromodulation 2024; 27:422-439. [PMID: 37204360 DOI: 10.1016/j.neurom.2023.04.471] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 04/02/2023] [Accepted: 04/24/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Deep brain stimulation (DBS) has revolutionized the treatment of neurological disorders, yet the mechanisms of DBS are still under investigation. Computational models are important in silico tools for elucidating these underlying principles and potentially for personalizing DBS therapy to individual patients. The basic principles underlying neurostimulation computational models, however, are not well known in the clinical neuromodulation community. OBJECTIVE In this study, we present a tutorial on the derivation of computational models of DBS and outline the biophysical contributions of electrodes, stimulation parameters, and tissue substrates to the effects of DBS. RESULTS Given that many aspects of DBS are difficult to characterize experimentally, computational models have played an important role in understanding how material, size, shape, and contact segmentation influence device biocompatibility, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Neural activation is dictated by stimulation parameters including frequency, current vs voltage control, amplitude, pulse width, polarity configurations, and waveform. These parameters also affect the potential for tissue damage, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Activation of the neural substrate also is influenced by the encapsulation layer surrounding the electrode, the conductivity of the surrounding tissue, and the size and orientation of white matter fibers. These properties modulate the effects of the electric field and determine the ultimate therapeutic response. CONCLUSION This article describes biophysical principles that are useful for understanding the mechanisms of neurostimulation.
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Affiliation(s)
| | - Alan Bush
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Matteo Vissani
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Robert Mark Richardson
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
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Malaga KA, Houshmand L, Costello JT, Chandrasekaran J, Chou KL, Patil PG. Thalamic Segmentation and Neural Activation Modeling Based on Individual Tissue Microstructure in Deep Brain Stimulation for Essential Tremor. Neuromodulation 2023; 26:1689-1698. [PMID: 36470728 DOI: 10.1016/j.neurom.2022.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/08/2022] [Accepted: 09/13/2022] [Indexed: 12/05/2022]
Abstract
OBJECTIVE Thalamic deep brain stimulation (DBS) is the primary surgical therapy for essential tremor (ET). Thalamic DBS traditionally uses an atlas-based targeting approach, which, although nominally accurate, may obscure individual anatomic differences from population norms. The objective of this study was to compare this traditional atlas-based approach with a novel quantitative modeling methodology grounded in individual tissue microstructure (N-of-1 approach). MATERIALS AND METHODS The N-of-1 approach uses individual patient diffusion tensor imaging (DTI) data to perform thalamic segmentation and volume of tissue activation (VTA) modeling. For each patient, the thalamus was individually segmented into 13 nuclei using DTI-based k-means clustering. DBS-induced VTAs associated with tremor suppression and side effects were then computed for each patient with finite-element electric-field models incorporating DTI microstructural data. Results from N-of-1 and traditional atlas-based modeling were compared for a large cohort of patients with ET treated with thalamic DBS. RESULTS The size and shape of individual N-of-1 thalamic nuclei and VTAs varied considerably across patients (N = 22). For both methods, tremor-improving therapeutic VTAs showed similar overlap with motor thalamic nuclei and greater motor than sensory nucleus overlap. For VTAs producing undesirable sustained paresthesia, 94% of VTAs overlapped with N-of-1 sensory thalamus estimates, whereas 74% of atlas-based segmentations overlapped. For VTAs producing dysarthria/motor contraction, the N-of-1 approach predicted greater spread beyond the thalamus into the internal capsule and adjacent structures than the atlas-based method. CONCLUSIONS Thalamic segmentation and VTA modeling based on individual tissue microstructure explain therapeutic stimulation equally well and side effects better than a traditional atlas-based method in DBS for ET. The N-of-1 approach may be useful in DBS targeting and programming, particularly when patient neuroanatomy deviates from population norms.
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Affiliation(s)
- Karlo A Malaga
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Layla Houshmand
- 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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Dessert GE, Thio BJ, Grill WM. Optimization of patient-specific stereo-EEG recording sensitivity. Brain Commun 2023; 5:fcad304. [PMID: 38025277 PMCID: PMC10655844 DOI: 10.1093/braincomms/fcad304] [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: 02/20/2023] [Revised: 08/11/2023] [Accepted: 11/01/2023] [Indexed: 12/01/2023] Open
Abstract
Stereo-EEG is a minimally invasive technique used to localize the origin of epileptic activity (the epileptogenic zone) in patients with drug-resistant epilepsy. However, current stereo-EEG trajectory planning methods are agnostic to the spatial recording sensitivity of implanted electrodes. In this study, we used image-based patient-specific computational models to design optimized stereo-EEG electrode configurations. Patient-specific optimized electrode configurations exhibited substantially higher recording sensitivity than clinically implanted configurations, and this may lead to a more accurate delineation of the epileptogenic zone. The optimized configurations also achieved equally good or better recording sensitivity with fewer electrodes compared with clinically implanted configurations, and this may reduce the risk for complications, including intracranial haemorrhage. This approach improves localization of the epileptogenic zone by transforming the clinical use of stereo-EEG from a discrete ad hoc sampling to an intelligent mapping of the regions of interest.
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Affiliation(s)
- Grace E Dessert
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Brandon J Thio
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
- Department of Neurobiology, Duke University, Durham, NC 27710, USA
- Department of Neurosurgery, Duke University, Durham, NC 27710, USA
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Probabilistic Subthalamic Nucleus Stimulation Sweet Spot Integration Into a Commercial Deep Brain Stimulation Programming Software Can Predict Effective Stimulation Parameters. Neuromodulation 2023; 26:348-355. [PMID: 35088739 DOI: 10.1016/j.neurom.2021.10.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 10/24/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Subthalamic nucleus (STN) deep brain stimulation (DBS) programming in patients with Parkinson disease (PD) may be challenging, especially when using segmented leads. In this study, we integrated a previously validated probabilistic STN sweet spot into a commercially available software to evaluate its predictive value for clinically effective DBS programming. MATERIALS AND METHODS A total of 14 patients with PD undergoing bilateral STN DBS with segmented leads were included. A nonlinear co-registration of a previously defined probabilistic sweet spot onto the manually segmented STN was performed together with lead reconstruction and tractography of the corticospinal tract (CST) in each patient. Contacts were ranked (level and direction), and corresponding effect and side-effect thresholds were predicted based on the overlap of the volume of activated tissue (VTA) with the sweet spot and CST. Image-based findings were correlated with postoperative clinical testing results during monopolar contact review and chronic stimulation parameter settings used after 12 months. RESULTS Image-based contact prediction showed high interrater reliability (Cohen kappa 0.851-0.91). Image-based and clinical ranking of the most efficient ring level and direction of stimulation were matched in 72% (95% CI 57.0-83.3) and 65% (95% CI 44.9-81.2), respectively, across the whole cohort. The mean difference between the predicted and clinically observed effect thresholds was 0.79 ± 0.69 mA (p = 0.72). The median difference between the predicted and clinically observed side-effect thresholds was -0.5 mA (p < 0.001, Wilcoxon paired signed rank test). CONCLUSIONS Integration of a probabilistic STN functional sweet spot into a surgical programming software shows a promising capability to predict the best level and directional contact(s) as well as stimulation settings in DBS for PD and could be used to optimize programming with segmented lead technology. This integrated image-based programming approach still needs to be evaluated on a bigger data set and in a future prospective multicenter cohort.
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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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11
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Thio BJ, Aberra AS, Dessert GE, Grill WM. Ideal current dipoles are appropriate source representations for simulating neurons for intracranial recordings. Clin Neurophysiol 2023; 145:26-35. [PMID: 36403433 PMCID: PMC9772254 DOI: 10.1016/j.clinph.2022.11.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/20/2022] [Accepted: 11/01/2022] [Indexed: 11/10/2022]
Abstract
OBJECTIVE To determine whether dipoles are an appropriate simplified representation of neural sources for stereo-EEG (sEEG). METHODS We compared the distributions of voltages generated by a dipole, biophysically realistic cortical neuron models, and extended regions of cortex to determine how well a dipole represented neural sources at different spatial scales and at electrode to neuron distances relevant for sEEG. We also quantified errors introduced by the dipole approximation of neural sources in sEEG source localization using standardized low-resolution electrotomography (sLORETA). RESULTS For pyramidal neurons, the coefficient of correlation between voltages generated by a dipole and neuron model were > 0.9 for distances > 1 mm. For small regions of cortex (∼0.1 cm2), the error in voltages between a dipole and region was < 100 µV for all distances. However, larger regions of active cortex (>5 cm2) yielded > 50 µV errors within 1.5 cm of an electrode when compared to single dipoles. Finally, source localization errors were < 5 mm when using dipoles to represent realistic neural sources. CONCLUSIONS Single dipoles are an appropriate source model to represent both single neurons and small regions of active cortex, while multiple dipoles are required to represent large regions of cortex. SIGNIFICANCE Dipoles are computationally tractable and valid source models for sEEG.
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Affiliation(s)
- Brandon J Thio
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Aman S Aberra
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Grace E Dessert
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States; Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States; Department of Neurobiology, Duke University, Durham, NC, United States; Department of Neurosurgery, Duke University, Durham, NC, United States.
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12
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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: 31] [Impact Index Per Article: 15.5] [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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13
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Widge AS, Zhang F, Gosai A, Papadimitrou G, Wilson-Braun P, Tsintou M, Palanivelu S, Noecker AM, McIntyre CC, O’Donnell L, McLaughlin NCR, Greenberg BD, Makris N, Dougherty DD, Rathi Y. Patient-specific connectomic models correlate with, but do not reliably predict, outcomes in deep brain stimulation for obsessive-compulsive disorder. Neuropsychopharmacology 2022; 47:965-972. [PMID: 34621015 PMCID: PMC8882183 DOI: 10.1038/s41386-021-01199-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/11/2021] [Accepted: 09/23/2021] [Indexed: 12/15/2022]
Abstract
Deep brain stimulation (DBS) of the ventral internal capsule/ventral striatum (VCVS) is an emerging treatment for obsessive-compulsive disorder (OCD). Recently, multiple studies using normative connectomes have correlated DBS outcomes to stimulation of specific white matter tracts. Those studies did not test whether these correlations are clinically predictive, and did not apply cross-validation approaches that are necessary for biomarker development. Further, they did not account for the possibility of systematic differences between DBS patients and the non-diagnosed controls used in normative connectomes. To address these gaps, we performed patient-specific diffusion imaging in 8 patients who underwent VCVS DBS for OCD. We delineated tracts connecting thalamus and subthalamic nucleus (STN) to prefrontal cortex via VCVS. We then calculated which tracts were likely activated by individual patients' DBS settings. We fit multiple statistical models to predict both OCD and depression outcomes from tract activation. We further attempted to predict hypomania, a VCVS DBS complication. We assessed all models' performance on held-out test sets. With this best-practices approach, no model predicted OCD response, depression response, or hypomania above chance. Coefficient inspection partly supported prior reports, in that capture of tracts projecting to cingulate cortex was associated with both YBOCS and MADRS response. In contrast to prior reports, however, tracts connected to STN were not reliably correlated with response. Thus, patient-specific imaging and a guideline-adherent analysis were unable to identify a tractographic target with sufficient effect size to drive clinical decision-making or predict individual outcomes. These findings suggest caution in interpreting the results of normative connectome studies.
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Affiliation(s)
- Alik S. Widge
- grid.17635.360000000419368657Department of Psychiatry, University of Minnesota, Minneapolis, MN USA
| | - Fan Zhang
- grid.62560.370000 0004 0378 8294Department of Radiology, Brigham and Womens Hospital, Boston, MA USA
| | - Aishwarya Gosai
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - George Papadimitrou
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Peter Wilson-Braun
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Magdalini Tsintou
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Senthil Palanivelu
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Angela M. Noecker
- grid.67105.350000 0001 2164 3847Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH USA
| | - Cameron C. McIntyre
- grid.67105.350000 0001 2164 3847Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH USA
| | - Lauren O’Donnell
- grid.62560.370000 0004 0378 8294Department of Radiology, Brigham and Womens Hospital, Boston, MA USA
| | - Nicole C. R. McLaughlin
- grid.40263.330000 0004 1936 9094Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, Providence, RI USA ,grid.273271.20000 0000 8593 9332Butler Hospital, Providence, RI USA
| | - Benjamin D. Greenberg
- grid.40263.330000 0004 1936 9094Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, Providence, RI USA ,grid.273271.20000 0000 8593 9332Butler Hospital, Providence, RI USA ,Center for Neurorestoration and Neurotechnology, VA Providence Healthcare System, Providence, RI USA
| | - Nikolaos Makris
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Darin D. Dougherty
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Yogesh Rathi
- grid.62560.370000 0004 0378 8294Department of Radiology, Brigham and Womens Hospital, Boston, MA USA ,grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
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McCarty MJ, Woolnough O, Mosher JC, Seymour J, Tandon N. The Listening Zone of Human Electrocorticographic Field Potential Recordings. eNeuro 2022; 9:ENEURO.0492-21.2022. [PMID: 35410871 PMCID: PMC9034754 DOI: 10.1523/eneuro.0492-21.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 02/09/2022] [Accepted: 03/04/2022] [Indexed: 01/05/2023] Open
Abstract
Intracranial electroencephalographic (icEEG) recordings provide invaluable insights into neural dynamics in humans because of their unmatched spatiotemporal resolution. Yet, such recordings reflect the combined activity of multiple underlying generators, confounding the ability to resolve spatially distinct neural sources. To empirically quantify the listening zone of icEEG recordings, we computed correlations between signals as a function of distance (full width at half maximum; FWHM) between 8752 recording sites in 71 patients (33 female) implanted with either subdural electrodes (SDEs), stereo-encephalography electrodes (sEEG), or high-density sEEG electrodes. As expected, for both SDEs and sEEGs, higher frequency signals exhibited a sharper fall off relative to lower frequency signals. For broadband high γ (BHG) activity, the mean FWHM of SDEs (6.6 ± 2.5 mm) and sEEGs in gray matter (7.14 ± 1.7 mm) was not significantly different; however, FWHM for low frequencies recorded by sEEGs was 2.45 mm smaller than SDEs. White matter sEEGs showed much lower power for frequencies 17-200 Hz (q < 0.01) and a much broader decay (11.3 ± 3.2 mm) than gray matter electrodes (7.14 ± 1.7 mm). The use of a bipolar referencing scheme significantly lowered FWHM for sEEGs, relative to a white matter reference or a common average reference (CAR). These results outline the influence of array design, spectral bands, and referencing schema on local field potential recordings and source localization in icEEG recordings in humans. The metrics we derive have immediate relevance to the analysis and interpretation of both cognitive and epileptic data.
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Affiliation(s)
- Meredith J McCarty
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Oscar Woolnough
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - John C Mosher
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - John Seymour
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Nitin Tandon
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
- Memorial Hermann Hospital, Texas Medical Center, Houston, TX 77030
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15
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Rogers ER, Zander HJ, Lempka SF. Neural Recruitment During Conventional, Burst, and 10-kHz Spinal Cord Stimulation for Pain. THE JOURNAL OF PAIN 2022; 23:434-449. [PMID: 34583022 PMCID: PMC8925309 DOI: 10.1016/j.jpain.2021.09.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 09/09/2021] [Accepted: 09/11/2021] [Indexed: 10/20/2022]
Abstract
Spinal cord stimulation (SCS) is a popular neurostimulation therapy for severe chronic pain. To improve stimulation efficacy, multiple modes are now used clinically, including conventional, burst, and 10-kHz SCS. Clinical observations have produced speculation that these modes target different neural elements and/or work via distinct mechanisms of action. However, in humans, these hypotheses cannot be conclusively answered via experimental methods. Therefore, we utilized computational modeling to assess the response of primary afferents, interneurons, and projection neurons to conventional, burst, and 10-kHz SCS. We found that local cell thresholds were always higher than afferent thresholds, arguing against direct recruitment of these local cells. Furthermore, although we observed relative threshold differences between conventional, burst, and 10-kHz SCS, the recruitment order was the same. Finally, contrary to previous reports, axon collateralization produced complex changes in activation thresholds of primary afferents. These results motivate future work to contextualize clinical observations across SCS paradigms. PERSPECTIVE: This article presents the first computational modeling study to investigate neural recruitment during conventional, burst, and 10-kilohertz spinal cord stimulation for chronic pain within a single modeling framework. The results provide insight into these treatments' unknown mechanisms of action and offer context to interpreting clinical observations.
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Affiliation(s)
- Evan R. Rogers
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA,Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Hans J. Zander
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA,Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Scott F. Lempka
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA,Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA,Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
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16
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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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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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18
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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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19
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Garza R, Amil AS, Nowacki A, Pollo C, Khoa Nguyen TA. Patient-Specific Anisotropic Volume of Tissue Activated with the Lead-DBS Toolbox. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6285-6288. [PMID: 34892550 DOI: 10.1109/embc46164.2021.9629810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Deep brain stimulation is an effective neurosurgical intervention for movement disorders such as Parkinson's disease. Despite its success, the underlying mechanisms are still debated. One tool to better understand them is the Volume of Tissue Activated (VTA), that estimates the region activated by electrical stimulation. Different estimation approaches exist, these typically assume isotropic tissue properties and modelling of anisotropy is often lacking.The present work was aimed at developing and testing a method for patient-specific VTA estimation that incorporated an anisotropic conduction model. Our method was implemented within the open-source toolbox Lead-DBS and is accessible to the public.The present method was further tested with two patient cases and compared to a standard Lead-DBS pipeline for VTA estimation. This showed encouraging similarities in one test scenario and expected differences in another test scenario. Further validation with a wider cohort is warranted.
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20
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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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21
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Luo M, Narasimhan S, Larson PS, Martin AJ, Konrad PE, Miga MI. Impact of brain shift on neural pathways in deep brain stimulation: a preliminary analysis via multi-physics finite element models. J Neural Eng 2021; 18. [PMID: 33740780 DOI: 10.1088/1741-2552/abf066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 03/19/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The effectiveness of deep brain stimulation (DBS) depends on electrode placement accuracy, which can be compromised by brain shift during surgery. While there have been efforts in assessing the impact of electrode misplacement due to brain shift using preop- and postop- imaging data, such analysis using preop- and intraop- imaging data via biophysical modeling has not been conducted. This work presents a preliminary study that applies a multi-physics analysis framework using finite element biomechanical and bioelectric models to examine the impact of realistic intraoperative shift on neural pathways determined by tractography. APPROACH The study examined six patients who had undergone interventional magnetic resonance (iMR)-guided DBS surgery. The modeling framework utilized a biomechanical approach to update preoperative MR to reflect shift-induced anatomical changes. Using this anatomically deformed image and its undeformed counterpart, bioelectric effects from shifting electrode leads could be simulated and neural activation differences were approximated. Specifically, for each configuration, volume of tissue activation (VTA) was computed and subsequently used for tractography estimation. Total tract volume and overlapping volume with motor regions as well as connectivity profile were compared. In addition, volumetric overlap between different fiber bundles among configurations was computed and correlated to estimated shift. MAIN RESULT The study found deformation-induced differences in tract volume, motor region overlap, and connectivity behavior, suggesting the impact of shift. There is a strong correlation (R=-0.83) between shift from intended target and intended neural pathway recruitment, where at threshold of ~2.94 mm, intended recruitment completely degrades. The determined threshold is consistent with and provides quantitative support to prior observations and literature that deviations of 2-3 mm are detrimental. SIGNIFICANCE The findings support and advance prior studies and understanding to illustrate the need to account for shift in DBS and the potentiality of computational modeling for estimating influence of shift on neural activation.
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Affiliation(s)
- Ma Luo
- Biomedical Engineering, Vanderbilt University, 5824 Stevenson Center, Nashville, Tennessee, 37232, UNITED STATES
| | - Saramati Narasimhan
- Department of Neurological Surgery, Vanderbilt University Medical Center, Village at Vanderbilt, 1500 21st Ave. South, Nashville, Tennessee, 37212, UNITED STATES
| | - Paul S Larson
- Department of Neurological Surgery, University of California San Francisco, Box 0112, 505 Parnassus Ave, Room M779, San Francisco, California, 94143, UNITED STATES
| | - Alastiar J Martin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, San Francisco, California, 94143, UNITED STATES
| | - Peter E Konrad
- Department of Neurosurgery, West Virginia University, PO Box 9183, Morgantown, West Virginia, 26506, UNITED STATES
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, 5901 Stevenson Center, Nashville, Tennessee, 37235, UNITED STATES
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22
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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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23
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Baniasadi M, Proverbio D, Gonçalves J, Hertel F, Husch A. FastField: An open-source toolbox for efficient approximation of deep brain stimulation electric fields. Neuroimage 2020; 223:117330. [DOI: 10.1016/j.neuroimage.2020.117330] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 05/26/2020] [Accepted: 08/25/2020] [Indexed: 01/25/2023] Open
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24
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Jiang F, Nguyen BT, Elahi B, Pilitsis J, Golestanirad L. Effect of Biophysical Model Complexity on Predictions of Volume of Tissue Activated (VTA) during Deep Brain Stimulation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3629-3633. [PMID: 33018788 PMCID: PMC10883758 DOI: 10.1109/embc44109.2020.9175300] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Deep brain stimulation (DBS) has evolved to an important treatment for several drug-resistant neurological and psychiatric disorders, such as epilepsy, Parkinson's disease, essential tremor and dystonia. Despite general effectiveness of DBS, however, its mechanisms of action are not completely understood. Simulations are commonly used to predict the volume of tissue activated (VTA) around DBS electrodes, which in turn helps interpreting clinical outcomes and understand therapeutic mechanisms. Computational models are commonly used to visualize the extend of volume of activated tissue (VTA) for different stimulation schemes, which in turn helps interpreting and understanding the outcomes. The degree of model complexity, however, can affect the predicted VTA. In this work we investigate the effect of volume conductor model complexity on the predicted VTA, when the VTA is estimated from activation function field metrics. Our results can help clinicians to decide what level of model complexity is suitable for their specific need.
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25
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Bogdan ID, van Laar T, Oterdoom DM, Drost G, van Dijk JMC, Beudel M. Optimal Parameters of Deep Brain Stimulation in Essential Tremor: A Meta-Analysis and Novel Programming Strategy. J Clin Med 2020; 9:jcm9061855. [PMID: 32545887 PMCID: PMC7356338 DOI: 10.3390/jcm9061855] [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: 05/21/2020] [Revised: 06/04/2020] [Accepted: 06/09/2020] [Indexed: 01/10/2023] Open
Abstract
The programming of deep brain stimulation (DBS) parameters for tremor is laborious and empirical. Despite extensive efforts, the end-result is often suboptimal. One reason for this is the poorly understood relationship between the stimulation parameters’ voltage, pulse width, and frequency. In this study, we aim to improve DBS programming for essential tremor (ET) by exploring a new strategy. At first, the role of the individual DBS parameters in tremor control was characterized using a meta-analysis documenting all the available parameters and tremor outcomes. In our novel programming strategy, we applied 10 random combinations of stimulation parameters in eight ET-DBS patients with suboptimal tremor control. Tremor severity was assessed using accelerometers and immediate and sustained patient-reported outcomes (PRO’s), including the occurrence of side-effects. The meta-analysis showed no substantial relationship between individual DBS parameters and tremor suppression. Nevertheless, with our novel programming strategy, a significantly improved (accelerometer p = 0.02, PRO p = 0.02) and sustained (p = 0.01) tremor suppression compared to baseline was achieved. Less side-effects were encountered compared to baseline. Our pilot data show that with this novel approach, tremor control can be improved in ET patients with suboptimal tremor control on DBS. In addition, this approach proved to have a beneficial effect on stimulation-related complications.
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Affiliation(s)
- I. Daria Bogdan
- Department of Neurology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands; (I.D.B.); (G.D.)
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands; (D.L.M.O.); (J.M.C.v.D.)
| | - Teus van Laar
- Department of Neurology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands; (I.D.B.); (G.D.)
- Correspondence:
| | - D.L. Marinus Oterdoom
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands; (D.L.M.O.); (J.M.C.v.D.)
| | - Gea Drost
- Department of Neurology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands; (I.D.B.); (G.D.)
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands; (D.L.M.O.); (J.M.C.v.D.)
| | - J. Marc C. van Dijk
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands; (D.L.M.O.); (J.M.C.v.D.)
| | - Martijn Beudel
- Department of Neurology, Amsterdam Neuroscience Institute, Amsterdam University Medical Center, 1007 MB Amsterdam, The Netherlands;
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Merola A, Romagnolo A, Krishna V, Pallavaram S, Carcieri S, Goetz S, Mandybur G, Duker AP, Dalm B, Rolston JD, Fasano A, Verhagen L. Current Directions in Deep Brain Stimulation for Parkinson's Disease-Directing Current to Maximize Clinical Benefit. Neurol Ther 2020; 9:25-41. [PMID: 32157562 PMCID: PMC7229063 DOI: 10.1007/s40120-020-00181-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Indexed: 12/19/2022] Open
Abstract
Several single-center studies and one large multicenter clinical trial demonstrated that directional deep brain stimulation (DBS) could optimize the volume of tissue activated (VTA) based on the individual placement of the lead in relation to the target. The ability to generate axially asymmetric fields of stimulation translates into a broader therapeutic window (TW) compared to conventional DBS. However, changing the shape and surface of stimulating electrodes (directional segmented vs. conventional ring-shaped) also demands a revision of the programming strategies employed for DBS programming. Model-based approaches have been used to predict the shape of the VTA, which can be visualized on standardized neuroimaging atlases or individual magnetic resonance imaging. While potentially useful for optimizing clinical care, these systems remain limited by factors such as patient-specific anatomical variability, postsurgical lead migrations, and inability to account for individual contact impedances and orientation of the systems of fibers surrounding the electrode. Alternative programming tools based on the functional assessment of stimulation-induced clinical benefits and side effects allow one to collect and analyze data from each electrode of the DBS system and provide an action plan of ranked alternatives for therapeutic settings based on the selection of optimal directional contacts. Overall, an increasing amount of data supports the use of directional DBS. It is conceivable that the use of directionality may reduce the need for complex programming paradigms such as bipolar configurations, frequency or pulse width modulation, or interleaving. At a minimum, stimulation through directional electrodes can be considered as another tool to improve the benefit/side effect ratio. At a maximum, directionality may become the preferred way to program because of its larger TW and lower energy consumption.
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Affiliation(s)
- Aristide Merola
- Department of Neurology, Ohio State University Wexner Medical Center, Columbus, OH, USA.
| | - Alberto Romagnolo
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
| | - Vibhor Krishna
- Department of Neurosurgery, Ohio State Wexner Medical Center, Columbus, OH, USA
| | | | | | - Steven Goetz
- Medtronic PLC Brain Modulation, Minneapolis, MN, USA
| | | | - Andrew P Duker
- Department of Neurology, Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, USA
| | - Brian Dalm
- Department of Neurosurgery, Ohio State Wexner Medical Center, Columbus, OH, USA
| | - John D Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Alfonso Fasano
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Toronto, ON, Canada
- Division of Neurology, University of Toronto, Toronto, ON, Canada
- Krembil Brain Institute, Toronto, ON, Canada
- CenteR for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
| | - Leo Verhagen
- Department of Neurological Sciences, Movement Disorder Section, Rush University, Chicago, IL, USA
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27
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Howell B, McIntyre CC. Feasibility of Interferential and Pulsed Transcranial Electrical Stimulation for Neuromodulation at the Human Scale. Neuromodulation 2020; 24:843-853. [PMID: 32147953 DOI: 10.1111/ner.13137] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 02/06/2020] [Accepted: 02/13/2020] [Indexed: 12/19/2022]
Abstract
OBJECTIVES Transcranial electrical stimulation (tES) is a promising tool for modulating neural activity, but tES has poor penetrability and spatiotemporal resolution compared to invasive techniques like deep brain stimulation (DBS). Interferential strategies for alternating-current stimulation (IF-tACS) and pulsed/intersectional strategies for transcranial direct-current stimulation (IS-tDCS) address some of the limitations of tES, but the comparative advantages and disadvantages of these new techniques is not well understood. This study's objective was to evaluate the suprathreshold and subthreshold membrane dynamics of neurons in response to IF-tACS and IS-tDCS. MATERIALS AND METHODS We analyzed the biophysics of IF-tACS and IS-tDCS using a bioelectric field model of tES. Neural responses were quantified for suprathreshold generation of action potentials in axons and for subthreshold modulation of membrane dynamics in spiking pyramidal neurons. RESULTS IF-tACS and IS-tDCS could not directly activate axons at or below 10 mA, but within this current range, these fields were able to modulate, albeit indirectly, spiking activity in the neuron model. IF-tACS facilitated phase synchronization similar to tACS, and IS-tDCS enhanced and suppressed spiking activity similar to tDCS; however, in either case, the modulatory effects of these fields were less potent than their standard counterparts at a matched field intensity. Moreover, neither IF-tACS nor IS-tDCS improved the spatial selectivity of their parent strategies. CONCLUSIONS Enhancing the spatiotemporal precision and penetrability of tES with interferential and intersectional strategies is possible at the human scale. However, IF-tACS or IS-tDCS will likely require spatial multiplexing with multiple simultaneous sources to counteract their reduced potency, compared to standard techniques, to maintain stimulation currents at tolerable levels.
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Affiliation(s)
- Bryan Howell
- 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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28
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Lu CW, Malaga KA, Chou KL, Chestek CA, Patil PG. High density microelectrode recording predicts span of therapeutic tissue activation volumes in subthalamic deep brain stimulation for Parkinson disease. Brain Stimul 2020; 13:412-419. [DOI: 10.1016/j.brs.2019.11.013] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 11/26/2019] [Accepted: 11/27/2019] [Indexed: 01/16/2023] Open
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29
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Karimi F, Attarpour A, Amirfattahi R, Nezhad AZ. Computational analysis of non-invasive deep brain stimulation based on interfering electric fields. Phys Med Biol 2019; 64:235010. [PMID: 31661678 DOI: 10.1088/1361-6560/ab5229] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Neuromodulation modalities are used as effective treatments for some brain disorders. Non-invasive deep brain stimulation (NDBS) via temporally interfering electric fields has emerged recently as a non-invasive strategy for electrically stimulating deep regions in the brain. The objective of this study is to provide insight into the fundamental mechanisms of this strategy and assess the potential uses of this method through computational analysis. Analytical and numerical methods are used to compute the electric potential and field distributions generated during NDBS in homogeneous and inhomogeneous models of the brain. The computational results are used for specifying the activated area in the brain (macroscopic approach), and quantifying its relationships to the stimulation parameters. Two automatic algorithms, using artificial neural network (ANN), are developed for the homogeneous model with two and four electrode pairs to estimate stimulation parameters. Additionally, the extracellular potentials are coupled to the compartmental axon cable model to determine the responses of the neurons to the modulated electric field in two developed models and to evaluate the precise activated area location (microscopic approach). Our results show that although the shape of the activated area was different in macroscopic and microscopic approaches, it located only at depth. Our optimization algorithms showed significant accuracy in estimating stimulation parameters. Moreover, it demonstrated that the more the electrode pairs, the more controllable the activated area. Finally, compartmental axon cable modeling results verified that neurons can demodulate and follow the electric field modulation envelope amplitude (MEA) in our models. The results of this study help develop the NDBS method and eliminate some limitations associated with the nonautomated optimization algorithm.
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Affiliation(s)
- Fariba Karimi
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran. These authors have contributed equally to this work as first authors
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30
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Nordin T, Zsigmond P, Pujol S, Westin CF, Wårdell K. White matter tracing combined with electric field simulation - A patient-specific approach for deep brain stimulation. Neuroimage Clin 2019; 24:102026. [PMID: 31795055 PMCID: PMC6880013 DOI: 10.1016/j.nicl.2019.102026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 09/04/2019] [Accepted: 10/02/2019] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Deep brain stimulation (DBS) in zona incerta (Zi) is used for symptom alleviation in essential tremor (ET). Zi is positioned along the dentato-rubro-thalamic tract (DRT). Electric field simulations with the finite element method (FEM) can be used for estimation of a volume where the stimulation affects the tissue by applying a fixed isolevel (VDBS). This work aims to develop a workflow for combined patient-specific electric field simulation and white matter tracing of the DRT, and to investigate the influence on the VDBS from different brain tissue models, lead design and stimulation modes. The novelty of this work lies in the combination of all these components. METHOD Patients with ET were implanted in Zi (lead 3389, n = 3, voltage mode; directional lead 6172, n = 1, current mode). Probabilistic reconstruction from diffusion MRI (dMRI) of the DRT (n = 8) was computed with FSL Toolbox. Brain tissue models were created for each patient (two homogenous, one heterogenous isotropic, one heterogenous anisotropic) and the respective VDBS (n = 48) calculated from the Comsol Multiphysics FEM simulations. The DRT and VDBS were visualized with 3DSlicer and superimposed on the preoperative T2 MRI, and the common volumes calculated. Dice Coefficient (DC) and level of anisotropy were used to evaluate and compare the brain models. RESULT Combined patient-specific tractography and electric field simulation was designed and evaluated, and all patients showed benefit from DBS. All VDBS overlapped the reconstructed DRT. Current stimulation showed prominent difference between the tissue models, where the homogenous grey matter deviated most (67 < DC < 69). Result from heterogenous isotropic and anisotropic models were similar (DC > 0.95), however the anisotropic model consistently generated larger volumes related to a greater extension of the electric field along the DBS lead. Independent of tissue model, the steering effect of the directional lead was evident and consistent. CONCLUSION A workflow for patient-specific electric field simulations in combination with reconstruction of DRT was successfully implemented. Accurate tissue classification is essential for electric field simulations, especially when using the current control stimulation. With an accurate targeting and tractography reconstruction, directional leads have the potential to tailor the electric field into the desired region.
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Affiliation(s)
- Teresa Nordin
- Department of Biomedical Engineering, Linköping University, Sweden.
| | - Peter Zsigmond
- Department of Neurosurgery and Clinical and Experimental Medicine, Linköping University, Sweden
| | - Sonia Pujol
- Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, USA; Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, USA
| | - Carl-Fredrik Westin
- Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, USA
| | - Karin Wårdell
- Department of Biomedical Engineering, Linköping University, Sweden
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31
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Simulation of transcranial magnetic stimulation in head model with morphologically-realistic cortical neurons. Brain Stimul 2019; 13:175-189. [PMID: 31611014 PMCID: PMC6889021 DOI: 10.1016/j.brs.2019.10.002] [Citation(s) in RCA: 162] [Impact Index Per Article: 32.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 08/30/2019] [Accepted: 10/03/2019] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) enables non-invasive modulation of brain activity with both clinical and research applications, but fundamental questions remain about the neural types and elements TMS activates and how stimulation parameters affect the neural response. OBJECTIVE To develop a multi-scale computational model to quantify the effect of TMS parameters on the direct response of individual neurons. METHODS We integrated morphologically-realistic neuronal models with TMS-induced electric fields computed in a finite element model of a human head to quantify the cortical response to TMS with several combinations of pulse waveforms and current directions. RESULTS TMS activated with lowest intensity intracortical axonal terminations in the superficial gyral crown and lip regions. Layer 5 pyramidal cells had the lowest thresholds, but layer 2/3 pyramidal cells and inhibitory basket cells were also activated at most intensities. Direct activation of layers 1 and 6 was unlikely. Neural activation was largely driven by the field magnitude, rather than the field component normal to the cortical surface. Varying the induced current direction caused a waveform-dependent shift in the activation site and provided a potential mechanism for experimentally observed differences in thresholds and latencies of muscle responses. CONCLUSIONS This biophysically-based simulation provides a novel method to elucidate mechanisms and inform parameter selection of TMS and other cortical stimulation modalities. It also serves as a foundation for more detailed network models of the response to TMS, which may include endogenous activity, synaptic connectivity, inputs from intrinsic and extrinsic axonal projections, and corticofugal axons in white matter.
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Anderson DN, Osting B, Vorwerk J, Dorval AD, Butson CR. Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes. J Neural Eng 2019; 15:026005. [PMID: 29235446 DOI: 10.1088/1741-2552/aaa14b] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Deep brain stimulation (DBS) is a growing treatment option for movement and psychiatric disorders. As DBS technology moves toward directional leads with increased numbers of smaller electrode contacts, trial-and-error methods of manual DBS programming are becoming too time-consuming for clinical feasibility. We propose an algorithm to automate DBS programming in near real-time for a wide range of DBS lead designs. APPROACH Magnetic resonance imaging and diffusion tensor imaging are used to build finite element models that include anisotropic conductivity. The algorithm maximizes activation of target tissue and utilizes the Hessian matrix of the electric potential to approximate activation of neurons in all directions. We demonstrate our algorithm's ability in an example programming case that targets the subthalamic nucleus (STN) for the treatment of Parkinson's disease for three lead designs: the Medtronic 3389 (four cylindrical contacts), the direct STNAcute (two cylindrical contacts, six directional contacts), and the Medtronic-Sapiens lead (40 directional contacts). MAIN RESULTS The optimization algorithm returns patient-specific contact configurations in near real-time-less than 10 s for even the most complex leads. When the lead was placed centrally in the target STN, the directional leads were able to activate over 50% of the region, whereas the Medtronic 3389 could activate only 40%. When the lead was placed 2 mm lateral to the target, the directional leads performed as well as they did in the central position, but the Medtronic 3389 activated only 2.9% of the STN. SIGNIFICANCE This DBS programming algorithm can be applied to cylindrical electrodes as well as novel directional leads that are too complex with modern technology to be manually programmed. This algorithm may reduce clinical programming time and encourage the use of directional leads, since they activate a larger volume of the target area than cylindrical electrodes in central and off-target lead placements.
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Affiliation(s)
- Daria Nesterovich Anderson
- Department of Bioengineering, University of Utah, Salt Lake City, UT, United States of America. Scientific Computing & Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States of America
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Cartmell SC, Tian Q, Thio BJ, Leuze C, Ye L, Williams NR, Yang G, Ben-Dor G, Deisseroth K, Grill WM, McNab JA, Halpern CH. Multimodal characterization of the human nucleus accumbens. Neuroimage 2019; 198:137-149. [PMID: 31077843 PMCID: PMC7341972 DOI: 10.1016/j.neuroimage.2019.05.019] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Revised: 04/27/2019] [Accepted: 05/07/2019] [Indexed: 01/03/2023] Open
Abstract
Dysregulation of the nucleus accumbens (NAc) is implicated in numerous neuropsychiatric disorders. Treatments targeting this area directly (e.g. deep brain stimulation) demonstrate variable efficacy, perhaps owing to non-specific targeting of a functionally heterogeneous nucleus. Here we provide support for this notion, first observing disparate behavioral effects in response to direct simulation of different locations within the NAc in a human patient. These observations motivate a segmentation of the NAc into subregions, which we produce from a diffusion-tractography based analysis of 245 young, unrelated healthy subjects. We further explore the mechanism of these stimulation-induced behavioral responses by identifying the most probable subset of axons activated using a patient-specific computational model. We validate our diffusion-based segmentation using evidence from several modalities, including MRI-based measures of function and microstructure, human post-mortem immunohistochemical staining, and cross-species comparison of cortical-NAc projections that are known to be conserved. Finally, we visualize the passage of individual axon bundles through one NAc subregion in a post-mortem human sample using CLARITY 3D histology corroborated by 7T tractography. Collectively, these findings extensively characterize human NAc subregions and provide insight into their structural and functional distinctions with implications for stereotactic treatments targeting this region.
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Affiliation(s)
- Samuel Cd Cartmell
- Department of Neurosurgery, Stanford University, Stanford, CA, 94305, USA
| | - Qiyuan Tian
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Brandon J Thio
- Department of Biomedical Engineering, Duke University, Stanford University, Stanford, CA, 94305, USA
| | - Christoph Leuze
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Li Ye
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Nolan R Williams
- Department of Psychiatry, Stanford University, Stanford, CA, 94305, USA
| | - Grant Yang
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Gabriel Ben-Dor
- Department of Psychiatry, Stanford University, Stanford, CA, 94305, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA; Department of Psychiatry, Stanford University, Stanford, CA, 94305, USA
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Stanford University, Stanford, CA, 94305, USA
| | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University, Stanford, CA, 94305, USA.
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Howell B, Gunalan K, McIntyre CC. A Driving-Force Predictor for Estimating Pathway Activation in Patient-Specific Models of Deep Brain Stimulation. Neuromodulation 2019; 22:403-415. [PMID: 30775834 PMCID: PMC6579680 DOI: 10.1111/ner.12929] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 11/30/2018] [Accepted: 12/20/2018] [Indexed: 01/03/2023]
Abstract
OBJECTIVE Detailed biophysical modeling of deep brain stimulation (DBS) provides a theoretical approach to quantify the cellular response to the applied electric field. However, the most accurate models for performing such analyses, patient-specific field-cable (FC) pathway-activation models (PAMs), are so technically demanding to implement that their use in clinical research is greatly limited. Predictive algorithms can simplify PAM calculations, but they generally fail to reproduce the output of FC models when evaluated over a wide range of clinically relevant stimulation parameters. Therefore, we set out to develop a novel driving-force (DF) predictive algorithm (DF-Howell), customized to the study of DBS, which can better match FC results. METHODS We developed the DF-Howell algorithm and compared its predictions to FC PAM results, as well as to the DF-Peterson algorithm, which is currently the most accurate and generalizable DF-based method. Comparison of the various methods was quantified within the context of subthalamic DBS using activation thresholds of axons representing the internal capsule, hyperdirect pathway, and cerebellothalamic tract for various combinations of fiber diameters, stimulus pulse widths, and electrode configurations. RESULTS The DF-Howell predictor estimated activation of the three axonal pathways with less than a 6.2% mean error with respect to the FC PAM for all 21 cases tested. In 15 of the 21 cases, DF-Howell outperformed DF-Peterson in estimating pathway activation, reducing mean-errors up to 22.5%. CONCLUSIONS DF-Howell represents an accurate predictor for estimating axonal pathway activation in patient-specific DBS models, but errors still exist relative to FC PAM calculations. Nonetheless, the tractability of DF algorithms helps to reduce the technical barriers for performing accurate biophysical modeling in clinical DBS research studies.
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Affiliation(s)
- Bryan Howell
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH, USA
- Emory University, Department of Psychiatry and Behavioral Science, Atlanta, GA, USA
| | - Kabilar Gunalan
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH, USA
| | - Cameron C. McIntyre
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH, USA
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Nguyen TAK, Nowacki A, Debove I, Petermann K, Tinkhauser G, Wiest R, Schüpbach M, Krack P, Pollo C. Directional stimulation of subthalamic nucleus sweet spot predicts clinical efficacy: Proof of concept. Brain Stimul 2019; 12:1127-1134. [PMID: 31130498 DOI: 10.1016/j.brs.2019.05.001] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 03/30/2019] [Accepted: 05/02/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Directional deep brain stimulation (dDBS) of the subthalamic nucleus for Parkinson's disease (PD) increases the therapeutic window. However, empirical programming of the neurostimulator becomes more complex given the increasing number of stimulation parameters. A better understanding of dDBS is needed to improve therapy and help guide postoperative programming. OBJECTIVE To determine whether clinical effects of dDBS can be predicted in individual patients based on lead location and volume of tissue activated (VTA) modelling. METHODS We analysed a prospective series of 28 PD patients. Imaging analysis and systematic clinical testing performed 4-6 months postoperatively yielded location, clinical efficacy and corresponding therapeutic windows for 272 directional contacts. We calculated the corresponding VTAs to build a probabilistic stimulation map using voxel-wise statistical analysis. RESULTS We found a positive and statistically significant correlation between the overlap ratio of a patient's individual stimulation volume and the probabilistic map's sweet spot -defined as the 10% voxels with the highest clinical efficacy values (average Spearman's rho = 0.43, average p ≤ 0.036). Patients who had a larger therapeutic window with directional compared to omnidirectional stimulation had a larger distance between the electrode and the sweet spot centroid (average distances 2.3 vs. 1.5 mm, p = 0.0019). CONCLUSION Our analysis provides new insights into how the definition of a probabilistic sweet spot based on directional stimulation data and individual VTA modelling can be applied to predict clinically effective directional stimulation and help guide clinicians with the intricate postoperative DBS programming.
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Affiliation(s)
- T A Khoa Nguyen
- Department of Neurosurgery, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Andreas Nowacki
- Department of Neurosurgery, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland.
| | - Ines Debove
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Katrin Petermann
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Gerd Tinkhauser
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland; Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Roland Wiest
- Department of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, and University of Bern, Bern, Switzerland
| | - Michael Schüpbach
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Paul Krack
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Claudio Pollo
- Department of Neurosurgery, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
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Peña E, Zhang S, Patriat R, Aman JE, Vitek JL, Harel N, Johnson MD. Multi-objective particle swarm optimization for postoperative deep brain stimulation targeting of subthalamic nucleus pathways. J Neural Eng 2018; 15:066020. [PMID: 30211697 PMCID: PMC6424118 DOI: 10.1088/1741-2552/aae12f] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE The effectiveness of deep brain stimulation (DBS) therapy strongly depends on precise surgical targeting of intracranial leads and on clinical optimization of stimulation settings. Recent advances in surgical targeting, multi-electrode designs, and multi-channel independent current-controlled stimulation are poised to enable finer control in modulating pathways within the brain. However, the large stimulation parameter space enabled by these technologies also poses significant challenges for efficiently identifying the most therapeutic DBS setting for a given patient. Here, we present a computational approach for programming directional DBS leads that is based on a non-convex optimization framework for neural pathway targeting. APPROACH The algorithm integrates patient-specific pre-operative 7 T MR imaging, post-operative CT scans, and multi-objective particle swarm optimization (MOPSO) methods using dominance based-criteria and incorporating multiple neural pathways simultaneously. The algorithm was evaluated on eight patient-specific models of subthalamic nucleus (STN) DBS to identify electrode configurations and stimulation amplitudes to optimally activate or avoid six clinically relevant pathways: motor territory of STN, non-motor territory of STN, internal capsule, superior cerebellar peduncle, thalamic fasciculus, and hyperdirect pathway. MAIN RESULTS Across the patient-specific models, single-electrode stimulation showed significant correlations across modeled pathways, particularly for motor and non-motor STN efferents. The MOPSO approach was able to identify multi-electrode configurations that achieved improved targeting of motor STN efferents and hyperdirect pathway afferents than that achieved by any single-electrode monopolar setting at equivalent power levels. SIGNIFICANCE These results suggest that pathway targeting with patient-specific model-based optimization algorithms can efficiently identify non-trivial electrode configurations for enhancing activation of clinically relevant pathways. However, the results also indicate that inter-pathway correlations can limit selectivity for certain pathways even with directional DBS leads.
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Affiliation(s)
- Edgar Peña
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Simeng Zhang
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Remi Patriat
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, United States
| | - Joshua E. Aman
- Department of Neurology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jerrold L. Vitek
- Department of Neurology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, United States
| | - Matthew D. Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States
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Howell B, Choi KS, Gunalan K, Rajendra J, Mayberg HS, McIntyre CC. Quantifying the axonal pathways directly stimulated in therapeutic subcallosal cingulate deep brain stimulation. Hum Brain Mapp 2018; 40:889-903. [PMID: 30311317 DOI: 10.1002/hbm.24419] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 08/21/2018] [Accepted: 10/01/2018] [Indexed: 12/20/2022] Open
Abstract
Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) is an emerging experimental therapy for treatment-resistant depression. New developments in SCC DBS surgical targeting are focused on identifying specific axonal pathways for stimulation that are estimated from patient-specific computational models. This connectomic-based biophysical modeling strategy has proven successful in improving the clinical response to SCC DBS therapy, but the DBS models used to date have been relatively simplistic, limiting the precision of the pathway activation estimates. Therefore, we used the most detailed patient-specific foundation for DBS modeling currently available (i.e., field-cable modeling) to evaluate SCC DBS in our most recent cohort of six subjects, all of which were responders to the therapy. We quantified activation of four major pathways in the SCC region: forceps minor (FM), cingulum bundle (CB), uncinate fasciculus (UF), and subcortical connections between the frontal pole and the thalamus or ventral striatum (FP). We then used the percentage of activated axons in each pathway as regressors in a linear model to predict the time it took patients to reach a stable response, or TSR. Our analysis suggests that stimulation of the left and right CBs, as well as FM are the most likely therapeutic targets for SCC DBS. In addition, the right CB alone predicted 84% of the variation in the TSR, and the correlation was positive, suggesting that activation of the right CB beyond a critical percentage may actually protract the recovery process.
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Affiliation(s)
- Bryan Howell
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.,Department of Psychiatry and Behavioral Science, Emory University, Atlanta, Georgia
| | - Ki Sueng Choi
- Department of Psychiatry and Behavioral Science, Emory University, Atlanta, Georgia.,Department of Radiology, Mount Sinai School of Medicine, New York, New York.,Department of Neurosurgery, Mount Sinai School of Medicine, New York, New York
| | - Kabilar Gunalan
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Justin Rajendra
- Scientific and Statistical Computational Core, National Institute of Mental Health, Bethesda, Maryland
| | - Helen S Mayberg
- Department of Psychiatry and Behavioral Science, Emory University, Atlanta, Georgia.,Department of Neurosurgery, Mount Sinai School of Medicine, New York, New York.,Department of Neurology, Mount Sinai School of Medicine, New York, New York.,Department of Psychiatry, Mount Sinai School of Medicine, New York, New York
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
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Maling N, Lempka SF, Blumenfeld Z, Bronte-Stewart H, McIntyre CC. Biophysical basis of subthalamic local field potentials recorded from deep brain stimulation electrodes. J Neurophysiol 2018; 120:1932-1944. [PMID: 30020838 DOI: 10.1152/jn.00067.2018] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Clinical deep brain stimulation (DBS) technology is evolving to enable chronic recording of local field potentials (LFPs) that represent electrophysiological biomarkers of the underlying disease state. However, little is known about the biophysical basis of LFPs, or how the patient's unique brain anatomy and electrode placement impact the recordings. Therefore, we developed a patient-specific computational framework to analyze LFP recordings within a clinical DBS context. We selected a subject with Parkinson's disease implanted with a Medtronic Activa PC+S DBS system and reconstructed their subthalamic nucleus (STN) and DBS electrode location using medical imaging data. The patient-specific STN volume was populated with 235,280 multicompartment STN neuron models, providing a neuron density consistent with histological measurements. Each neuron received time-varying synaptic inputs and generated transmembrane currents that gave rise to the LFP signal recorded at DBS electrode contacts residing in a finite element volume conductor model. We then used the model to study the role of synchronous beta-band inputs to the STN neurons on the recorded power spectrum. Three bipolar pairs of simultaneous clinical LFP recordings were used in combination with an optimization algorithm to customize the neural activity parameters in the model to the patient. The optimized model predicted a 2.4-mm radius of beta-synchronous neurons located in the dorsolateral STN. These theoretical results enable biophysical dissection of the LFP signal at the cellular level with direct comparison to the clinical recordings, and the model system provides a scientific platform to help guide the design of DBS technology focused on the use of subthalamic beta activity in closed-loop algorithms. NEW & NOTEWORTHY The analysis of deep brain stimulation of local field potential (LFP) data is rapidly expanding from scientific curiosity to the basis for clinical biomarkers capable of improving the therapeutic efficacy of stimulation. With this growing clinical importance comes a growing need to understand the underlying electrophysiological fundamentals of the signals and the factors contributing to their modulation. Our model reconstructs the clinical LFP from first principles and highlights the importance of patient-specific factors in dictating the signals recorded.
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Affiliation(s)
- Nicholas Maling
- Department of Biomedical Engineering, Case Western Reserve University , Cleveland, Ohio
| | - Scott F Lempka
- Department of Biomedical Engineering, University of Michigan , Ann Arbor, Michigan
| | - Zack Blumenfeld
- Department of Neurology, Stanford University , Stanford, California
| | | | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University , Cleveland, Ohio
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Gunalan K, Howell B, McIntyre CC. Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation. Neuroimage 2018; 172:263-277. [PMID: 29331449 PMCID: PMC5910209 DOI: 10.1016/j.neuroimage.2018.01.015] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 12/08/2017] [Accepted: 01/09/2018] [Indexed: 02/07/2023] Open
Abstract
Medical imaging has played a major role in defining the general anatomical targets for deep brain stimulation (DBS) therapies. However, specifics on the underlying brain circuitry that is directly modulated by DBS electric fields remain relatively undefined. Detailed biophysical modeling of DBS provides an approach to quantify the theoretical responses to stimulation at the cellular level, and has established a key role for axonal activation in the therapeutic mechanisms of DBS. Estimates of DBS-induced axonal activation can then be coupled with advances in defining the structural connectome of the human brain to provide insight into the modulated brain circuitry and possible correlations with clinical outcomes. These pathway-activation models (PAMs) represent powerful tools for DBS research, but the theoretical predictions are highly dependent upon the underlying assumptions of the particular modeling strategy used to create the PAM. In general, three types of PAMs are used to estimate activation: 1) field-cable (FC) models, 2) driving force (DF) models, and 3) volume of tissue activated (VTA) models. FC models represent the "gold standard" for analysis but at the cost of extreme technical demands and computational resources. Consequently, DF and VTA PAMs, derived from simplified FC models, are typically used in clinical research studies, but the relative accuracy of these implementations is unknown. Therefore, we performed a head-to-head comparison of the different PAMs, specifically evaluating DBS of three different axonal pathways in the subthalamic region. The DF PAM was markedly more accurate than the VTA PAMs, but none of these simplified models were able to match the results of the patient-specific FC PAM across all pathways and combinations of stimulus parameters. These results highlight the limitations of using simplified predictors to estimate axonal stimulation and emphasize the need for novel algorithms that are both biophysically realistic and computationally simple.
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Affiliation(s)
- Kabilar Gunalan
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Bryan Howell
- 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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40
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Mourdoukoutas AP, Truong DQ, Adair DK, Simon BJ, Bikson M. High-Resolution Multi-Scale Computational Model for Non-Invasive Cervical Vagus Nerve Stimulation. Neuromodulation 2018; 21:261-268. [PMID: 29076212 PMCID: PMC5895480 DOI: 10.1111/ner.12706] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 07/26/2017] [Accepted: 08/25/2017] [Indexed: 12/28/2022]
Abstract
OBJECTIVES To develop the first high-resolution, multi-scale model of cervical non-invasive vagus nerve stimulation (nVNS) and to predict vagus fiber type activation, given clinically relevant rheobase thresholds. METHODS An MRI-derived Finite Element Method (FEM) model was developed to accurately simulate key macroscopic (e.g., skin, soft tissue, muscle) and mesoscopic (cervical enlargement, vertebral arch and foramen, cerebral spinal fluid [CSF], nerve sheath) tissue components to predict extracellular potential, electric field (E-Field), and activating function along the vagus nerve. Microscopic scale biophysical models of axons were developed to compare axons of varying size (Aα-, Aβ- and Aδ-, B-, and C-fibers). Rheobase threshold estimates were based on a step function waveform. RESULTS Macro-scale accuracy was found to determine E-Field magnitudes around the vagus nerve, while meso-scale precision determined E-field changes (activating function). Mesoscopic anatomical details that capture vagus nerve passage through a changing tissue environment (e.g., bone to soft tissue) profoundly enhanced predicted axon sensitivity while encapsulation in homogenous tissue (e.g., nerve sheath) dulled axon sensitivity to nVNS. CONCLUSIONS These findings indicate that realistic and precise modeling at both macroscopic and mesoscopic scales are needed for quantitative predictions of vagus nerve activation. Based on this approach, we predict conventional cervical nVNS protocols can activate A- and B- but not C-fibers. Our state-of-the-art implementation across scales is equally valuable for models of spinal cord stimulation, cortex/deep brain stimulation, and other peripheral/cranial nerve models.
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Affiliation(s)
- Antonios P. Mourdoukoutas
- Department of Biomedical Engineering, The City College of New York, City University of New York, New York, NY
| | - Dennis Q. Truong
- Department of Biomedical Engineering, The City College of New York, City University of New York, New York, NY
| | - Devin K. Adair
- Department of Psychology, The Graduate Center, City University of New York, New York, New York
| | | | - Marom Bikson
- Department of Biomedical Engineering, The City College of New York, City University of New York, New York, NY
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Lempka SF, Howell B, Gunalan K, Machado AG, McIntyre CC. Characterization of the stimulus waveforms generated by implantable pulse generators for deep brain stimulation. Clin Neurophysiol 2018; 129:731-742. [PMID: 29448149 DOI: 10.1016/j.clinph.2018.01.015] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Revised: 11/10/2017] [Accepted: 01/06/2018] [Indexed: 01/07/2023]
Abstract
OBJECTIVE To determine the circuit elements required to theoretically describe the stimulus waveforms generated by an implantable pulse generator (IPG) during clinical deep brain stimulation (DBS). METHODS We experimentally interrogated the Medtronic Activa PC DBS IPG and defined an equivalent circuit model that accurately captured the output of the IPG. We then compared the detailed circuit model of the clinical stimulus waveforms to simplified representations commonly used in computational models of DBS. We quantified the errors associated with these simplifications using theoretical activation thresholds of myelinated axons in response to DBS. RESULTS We found that the detailed IPG model generated substantial differences in activation thresholds compared to simplified models. These differences were largest for bipolar stimulation with long pulse widths. Average errors were ∼3 to 24% for voltage-controlled stimulation and ∼2 to 11% for current-controlled stimulation. CONCLUSIONS Our results demonstrate the importance of including basic circuit elements (e.g. blocking capacitors, lead wire resistance, electrode capacitance) in model analysis of DBS. SIGNIFICANCE Computational models of DBS are now commonly used in academic research, industrial technology development, and in the selection of clinical stimulation parameters. Incorporating a realistic representation of the IPG output is necessary to improve the accuracy and utility of these clinical and scientific tools.
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Affiliation(s)
- Scott F Lempka
- Center for Neurological Restoration, Cleveland Clinic, Cleveland, OH, USA; Research Service, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA; Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA.
| | - Bryan Howell
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Kabilar Gunalan
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Andre G Machado
- Center for Neurological Restoration, Cleveland Clinic, Cleveland, OH, USA; Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Cameron C McIntyre
- Research Service, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA; Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
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42
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Schmidt C, van Rienen U. Adaptive Estimation of the Neural Activation Extent in Computational Volume Conductor Models of Deep Brain Stimulation. IEEE Trans Biomed Eng 2017; 65:1828-1839. [PMID: 29989959 DOI: 10.1109/tbme.2017.2758324] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE The aim of this study is to propose an adaptive scheme embedded into an open-source environment for the estimation of the neural activation extent during deep brain stimulation and to investigate the feasibility of approximating the neural activation extent by thresholds of the field solution. METHODS Open-source solutions for solving the field equation in volume conductor models of deep brain stimulation and computing the neural activation are embedded into a Python package to estimate the neural activation dependent on the dielectric tissue properties and axon parameters by employing a spatially adaptive scheme. Feasibility of the approximation of the neural activation extent by field thresholds is investigated to further reduce the computational expense. RESULTS The varying extents of neural activation for different patient-specific dielectric properties were estimated with the adaptive scheme. The results revealed the strong influence of the dielectric properties of the encapsulation layer in the acute and chronic phase after surgery. The computational time required to determine the neural activation extent in each studied model case was substantially reduced. CONCLUSION The neural activation extent is altered by patient-specific parameters. Threshold values of the electric potential and electric field norm facilitate a computationally efficient method to estimate the neural activation extent. SIGNIFICANCE The presented adaptive scheme is able to robustly determine neural activation extents and field threshold estimates for varying dielectric tissue properties and axon diameters while substantially reducing the computational expense.
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Horn A, Reich M, Vorwerk J, Li N, Wenzel G, Fang Q, Schmitz-Hübsch T, Nickl R, Kupsch A, Volkmann J, Kühn AA, Fox MD. Connectivity Predicts deep brain stimulation outcome in Parkinson disease. Ann Neurol 2017; 82:67-78. [PMID: 28586141 DOI: 10.1002/ana.24974] [Citation(s) in RCA: 431] [Impact Index Per Article: 61.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 06/02/2017] [Accepted: 06/02/2017] [Indexed: 12/11/2022]
Abstract
OBJECTIVE The benefit of deep brain stimulation (DBS) for Parkinson disease (PD) may depend on connectivity between the stimulation site and other brain regions, but which regions and whether connectivity can predict outcome in patients remain unknown. Here, we identify the structural and functional connectivity profile of effective DBS to the subthalamic nucleus (STN) and test its ability to predict outcome in an independent cohort. METHODS A training dataset of 51 PD patients with STN DBS was combined with publicly available human connectome data (diffusion tractography and resting state functional connectivity) to identify connections reliably associated with clinical improvement (motor score of the Unified Parkinson Disease Rating Scale [UPDRS]). This connectivity profile was then used to predict outcome in an independent cohort of 44 patients from a different center. RESULTS In the training dataset, connectivity between the DBS electrode and a distributed network of brain regions correlated with clinical response including structural connectivity to supplementary motor area and functional anticorrelation to primary motor cortex (p < 0.001). This same connectivity profile predicted response in an independent patient cohort (p < 0.01). Structural and functional connectivity were independent predictors of clinical improvement (p < 0.001) and estimated response in individual patients with an average error of 15% UPDRS improvement. Results were similar using connectome data from normal subjects or a connectome age, sex, and disease matched to our DBS patients. INTERPRETATION Effective STN DBS for PD is associated with a specific connectivity profile that can predict clinical outcome across independent cohorts. This prediction does not require specialized imaging in PD patients themselves. Ann Neurol 2017;82:67-78.
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Affiliation(s)
- Andreas Horn
- Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.,Department of Neurology, Movement Disorder and Neuromodulation Unit, Charité-Universitätsmedizin, Berlin, Germany
| | - Martin Reich
- Department of Neurology, Würzburg University Hospital, Würzburg, Germany
| | - Johannes Vorwerk
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah
| | - Ningfei Li
- Institute of Software Engineering and Theoretical Computer Science, Neural Information Processing Group, Berlin Technical University, Berlin, Germany
| | - Gregor Wenzel
- Department of Neurology, Movement Disorder and Neuromodulation Unit, Charité-Universitätsmedizin, Berlin, Germany
| | - Qianqian Fang
- Department of Bioengineering, Northeastern University, Boston, MA
| | - Tanja Schmitz-Hübsch
- Department of Neurology, Movement Disorder and Neuromodulation Unit, Charité-Universitätsmedizin, Berlin, Germany.,NeuroCure Clinical Research Center, Charité-Universitätsmedizin, Berlin, Germany
| | - Robert Nickl
- Department of Neurosurgery, Würzburg University Hospital, Würzburg, Germany
| | - Andreas Kupsch
- Clinic of Neurology and Stereotactic Neurosurgery, Otto von Guericke University, Magdeburg, Germany.,Neurology Moves, Berlin, Germany
| | - Jens Volkmann
- Department of Neurology, Würzburg University Hospital, Würzburg, Germany
| | - Andrea A Kühn
- Department of Neurology, Movement Disorder and Neuromodulation Unit, Charité-Universitätsmedizin, Berlin, Germany.,NeuroCure Clinical Research Center, Charité-Universitätsmedizin, Berlin, Germany
| | - Michael D Fox
- Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA
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Lee HM, Howell B, Grill WM, Ghovanloo M. Stimulation Efficiency With Decaying Exponential Waveforms in a Wirelessly Powered Switched-Capacitor Discharge Stimulation System. IEEE Trans Biomed Eng 2017; 65:1095-1106. [PMID: 28829301 DOI: 10.1109/tbme.2017.2741107] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The purpose of this study was to test the feasibility of using a switched-capacitor discharge stimulation (SCDS) system for electrical stimulation, and, subsequently, determine the overall energy saved compared to a conventional stimulator. We have constructed a computational model by pairing an image-based volume conductor model of the cat head with cable models of corticospinal tract (CST) axons and quantified the theoretical stimulation efficiency of rectangular and decaying exponential waveforms, produced by conventional and SCDS systems, respectively. Subsequently, the model predictions were tested in vivo by activating axons in the posterior internal capsule and recording evoked electromyography (EMG) in the contralateral upper arm muscles. Compared to rectangular waveforms, decaying exponential waveforms with time constants >500 μs were predicted to require 2%-4% less stimulus energy to activate directly models of CST axons and 0.4%-2% less stimulus energy to evoke EMG activity in vivo. Using the calculated wireless input energy of the stimulation system and the measured stimulus energies required to evoke EMG activity, we predict that an SCDS implantable pulse generator (IPG) will require 40% less input energy than a conventional IPG to activate target neural elements. A wireless SCDS IPG that is more energy efficient than a conventional IPG will reduce the size of an implant, require that less wireless energy be transmitted through the skin, and extend the lifetime of the battery in the external power transmitter.
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Gunalan K, Chaturvedi A, Howell B, Duchin Y, Lempka SF, Patriat R, Sapiro G, Harel N, McIntyre CC. Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example. PLoS One 2017; 12:e0176132. [PMID: 28441410 PMCID: PMC5404874 DOI: 10.1371/journal.pone.0176132] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 04/05/2017] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) is an established clinical therapy and computational models have played an important role in advancing the technology. Patient-specific DBS models are now common tools in both academic and industrial research, as well as clinical software systems. However, the exact methodology for creating patient-specific DBS models can vary substantially and important technical details are often missing from published reports. OBJECTIVE Provide a detailed description of the assembly workflow and parameterization of a patient-specific DBS pathway-activation model (PAM) and predict the response of the hyperdirect pathway to clinical stimulation. METHODS Integration of multiple software tools (e.g. COMSOL, MATLAB, FSL, NEURON, Python) enables the creation and visualization of a DBS PAM. An example DBS PAM was developed using 7T magnetic resonance imaging data from a single unilaterally implanted patient with Parkinson's disease (PD). This detailed description implements our best computational practices and most elaborate parameterization steps, as defined from over a decade of technical evolution. RESULTS Pathway recruitment curves and strength-duration relationships highlight the non-linear response of axons to changes in the DBS parameter settings. CONCLUSION Parameterization of patient-specific DBS models can be highly detailed and constrained, thereby providing confidence in the simulation predictions, but at the expense of time demanding technical implementation steps. DBS PAMs represent new tools for investigating possible correlations between brain pathway activation patterns and clinical symptom modulation.
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Affiliation(s)
- Kabilar Gunalan
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Ashutosh Chaturvedi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Bryan Howell
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Yuval Duchin
- Department of Radiology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Scott F. Lempka
- Center for Neurological Restoration, Cleveland Clinic, Cleveland, Ohio, United States of America
- Research Service, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, Ohio, United States of America
| | - Remi Patriat
- Department of Radiology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, United States of America
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Department of Computer Science, Duke University, Durham, North Carolina, United States of America
| | - Noam Harel
- Department of Radiology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Cameron C. McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America
- Research Service, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, Ohio, United States of America
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Peña E, Zhang S, Deyo S, Xiao Y, Johnson MD. Particle swarm optimization for programming deep brain stimulation arrays. J Neural Eng 2017; 14:016014. [PMID: 28068291 DOI: 10.1088/1741-2552/aa52d1] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. APPROACH Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. MAIN RESULTS The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (⩽9.2%) and ROA (⩽1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n = 3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of <1% between approaches. SIGNIFICANCE The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts.
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Affiliation(s)
- Edgar Peña
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
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Hemm S, Pison D, Alonso F, Shah A, Coste J, Lemaire JJ, Wårdell K. Patient-Specific Electric Field Simulations and Acceleration Measurements for Objective Analysis of Intraoperative Stimulation Tests in the Thalamus. Front Hum Neurosci 2016; 10:577. [PMID: 27932961 PMCID: PMC5122591 DOI: 10.3389/fnhum.2016.00577] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Accepted: 11/01/2016] [Indexed: 11/25/2022] Open
Abstract
Despite an increasing use of deep brain stimulation (DBS) the fundamental mechanisms of action remain largely unknown. Simulation of electric entities has previously been proposed for chronic DBS combined with subjective symptom evaluations, but not for intraoperative stimulation tests. The present paper introduces a method for an objective exploitation of intraoperative stimulation test data to identify the optimal implant position of the chronic DBS lead by relating the electric field (EF) simulations to the patient-specific anatomy and the clinical effects quantified by accelerometry. To illustrate the feasibility of this approach, it was applied to five patients with essential tremor bilaterally implanted in the ventral intermediate nucleus (VIM). The VIM and its neighborhood structures were preoperatively outlined in 3D on white matter attenuated inversion recovery MR images. Quantitative intraoperative clinical assessments were performed using accelerometry. EF simulations (n = 272) for intraoperative stimulation test data performed along two trajectories per side were set-up using the finite element method for 143 stimulation test positions. The resulting EF isosurface of 0.2 V/mm was superimposed to the outlined anatomical structures. The percentage of volume of each structure’s overlap was calculated and related to the corresponding clinical improvement. The proposed concept has been successfully applied to the five patients. For higher clinical improvements, not only the VIM but as well other neighboring structures were covered by the EF isosurfaces. The percentage of the volumes of the VIM, of the nucleus intermediate lateral of the thalamus and the prelemniscal radiations within the prerubral field of Forel increased for clinical improvements higher than 50% compared to improvements lower than 50%. The presented new concept allows a detailed and objective analysis of a high amount of intraoperative data to identify the optimal stimulation target. First results indicate agreement with published data hypothesizing that the stimulation of other structures than the VIM might be responsible for good clinical effects in essential tremor. (Clinical trial reference number: Ref: 2011-A00774-37/AU905)
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Affiliation(s)
- Simone Hemm
- Institute for Medical and Analytical Technologies, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland FHNWMuttenz, Switzerland; Department of Biomedical Engineering, Linköping UniversityLinköping, Sweden
| | - Daniela Pison
- Institute for Medical and Analytical Technologies, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland FHNW Muttenz, Switzerland
| | - Fabiola Alonso
- Department of Biomedical Engineering, Linköping University Linköping, Sweden
| | - Ashesh Shah
- Institute for Medical and Analytical Technologies, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland FHNW Muttenz, Switzerland
| | - Jérôme Coste
- Université Clermont Auvergne, Université d'Auvergne, EA 7282, Image Guided Clinical Neurosciences and Connectomics (IGCNC)Clermont-Ferrand, France; Service de Neurochirurgie, Hôpital Gabriel-Montpied, Centre Hospitalier Universitaire de Clermont-FerrandClermont-Ferrand, France
| | - Jean-Jacques Lemaire
- Université Clermont Auvergne, Université d'Auvergne, EA 7282, Image Guided Clinical Neurosciences and Connectomics (IGCNC)Clermont-Ferrand, France; Service de Neurochirurgie, Hôpital Gabriel-Montpied, Centre Hospitalier Universitaire de Clermont-FerrandClermont-Ferrand, France
| | - Karin Wårdell
- Department of Biomedical Engineering, Linköping University Linköping, Sweden
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Howell B, McIntyre CC. Role of Soft-Tissue Heterogeneity in Computational Models of Deep Brain Stimulation. Brain Stimul 2016; 10:46-50. [PMID: 27720186 DOI: 10.1016/j.brs.2016.09.001] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Revised: 08/29/2016] [Accepted: 09/05/2016] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Bioelectric field models of deep brain stimulation (DBS) are commonly utilized in research and industrial applications. However, the wide range of different representations used for the human head in these models may be responsible for substantial variance in the stimulation predictions. OBJECTIVE Determine the relative error of ignoring cerebral vasculature and soft-tissue heterogeneity outside of the brain in computational models of DBS. METHODS We used a detailed atlas of the human head, coupled to magnetic resonance imaging data, to construct a range of subthalamic DBS volume conductor models. We incrementally simplified the most detailed base model and quantified changes in the stimulation thresholds for direct activation of corticofugal axons. RESULTS Ignoring cerebral vasculature altered predictions of stimulation thresholds by <10%, whereas ignoring soft-tissue heterogeneity outside of the brain altered predictions between -44 % and 174%. CONCLUSIONS Heterogeneity in the soft tissues of the head, if unaccounted for, introduces a degree of uncertainty in predicting electrical stimulation of neural elements that is not negligible and thereby warrants consideration in future modeling studies.
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Affiliation(s)
- Bryan Howell
- 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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