1
|
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.
Collapse
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
| |
Collapse
|
2
|
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: 0] [Impact Index Per Article: 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.
Collapse
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
| |
Collapse
|
3
|
Jia Q, Liu Y, Lv S, Wang Y, Jiao P, Xu W, Xu Z, Wang M, Cai X. Wireless closed-loop deep brain stimulation using microelectrode array probes. J Zhejiang Univ Sci B 2024:1-21. [PMID: 38423536 DOI: 10.1631/jzus.b2300400] [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: 06/05/2023] [Accepted: 08/25/2023] [Indexed: 03/02/2024]
Abstract
Deep brain stimulation (DBS), including optical stimulation and electrical stimulation, has been demonstrated considerable value in exploring pathological brain activity and developing treatments for neural disorders. Advances in DBS microsystems based on implantable microelectrode array (MEA) probes have opened up new opportunities for closed-loop DBS (CL-DBS) in situ. This technology can be used to detect damaged brain circuits and test the therapeutic potential for modulating the output of these circuits in a variety of diseases simultaneously. Despite the success and rapid utilization of MEA probe-based CL-DBS microsystems, key challenges, including excessive wired communication, need to be urgently resolved. In this review, we considered recent advances in MEA probe-based wireless CL-DBS microsystems and outlined the major issues and promising prospects in this field. This technology has the potential to offer novel therapeutic options for psychiatric disorders in the future.
Collapse
Affiliation(s)
- Qianli Jia
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaoyao Liu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shiya Lv
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiding Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peiyao Jiao
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaojie Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mixia Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xinxia Cai
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China. ,
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China. ,
| |
Collapse
|
4
|
Goetz SM, Howell B, Wang B, Li Z, Sommer MA, Peterchev AV, Grill WM. Isolating two sources of variability of subcortical stimulation to quantify fluctuations of corticospinal tract excitability. Clin Neurophysiol 2022; 138:134-142. [PMID: 35397278 PMCID: PMC9271909 DOI: 10.1016/j.clinph.2022.02.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/17/2022] [Accepted: 02/01/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Investigate the variability previously found with cortical stimulation and handheld transcranial magnetic stimulation (TMS) coils, criticized for its high potential of coil position fluctuations, bypassing the cortex using deep brain electrical stimulation (DBS) of the corticospinal tract with fixed electrodes where both latent variations of the coil position of TMS are eliminated and cortical excitation fluctuations should be absent. METHODS Ten input-output curves were recorded from five anesthetized cats with implanted DBS electrodes targeting the corticospinal tract. Goodness of fit of regressions with a conventional single variability source as well as a dual variability source model was quantified using a Schwarz Bayesian Information approach to avoid overfitting. RESULTS Motor evoked potentials (MEPs) through DBS of the corticospinal tract revealed short-term fluctuations in excitability of the targeted neuron pathway reflecting endogenous input-side variability at similar magnitude as TMS despite bypassing cortical networks. CONCLUSION Input-side variability, i.e., variability resulting in changing MEP amplitudes as if the stimulation strength was modulated, also emerges in electrical stimulation at a similar degree and is not primarily a result of varying stimulation, such as minor coil movements in TMS. More importantly, this variability component is present, although the cortex is bypassed. Thus, it may be of spinal origin, which can include cortical input from spinal projections. Further, the nonlinearity of the compound variability entails complex heteroscedastic non-Gaussian distributions and typically does not allow simple linear averages in statistical analysis of MEPs. As the average is dominated by outliers, it risks bias. With appropriate regression, the net effects of excitatory and inhibitory inputs to the targeted neuron pathways become noninvasively observable and quantifiable. SIGNIFICANCE The neural responses evoked by artificial stimulation in the cerebral cortex are variable. For example, MEPs in response to repeated presentations of the same stimulus can vary from no response to saturation across trials. Several sources of such variability have been suggested, and most of them may be technical in nature, but localization is missing.
Collapse
Affiliation(s)
- Stefan M Goetz
- Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK; Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC 27710, USA; Department of Neurosurgery, Duke University, Durham, NC 27710, USA; Department of Electrical & Computer Engineering, Duke University, Durham, NC 27708, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC 27710, USA.
| | - Bryan Howell
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Boshuo Wang
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC 27710, USA
| | - Zhongxi Li
- Department of Electrical & Computer Engineering, Duke University, Durham, NC 27708, USA
| | - Marc A Sommer
- Duke Institute of Brain Sciences, Duke University, Durham, NC 27710, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA; Department of Neurobiology, Duke University, Durham, NC 27710, USA
| | - Angel V Peterchev
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC 27710, USA; Department of Neurosurgery, Duke University, Durham, NC 27710, USA; Department of Electrical & Computer Engineering, Duke University, Durham, NC 27708, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC 27710, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Warren M Grill
- Department of Neurosurgery, Duke University, Durham, NC 27710, USA; Department of Electrical & Computer Engineering, Duke University, Durham, NC 27708, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC 27710, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA; Department of Neurobiology, Duke University, Durham, NC 27710, USA
| |
Collapse
|
5
|
Yuen J, Rusheen AE, Price JB, Barath AS, Shin H, Kouzani AZ, Berk M, Blaha CD, Lee KH, Oh Y. Biomarkers for Deep Brain Stimulation in Animal Models of Depression. Neuromodulation 2022; 25:161-170. [PMID: 35125135 PMCID: PMC8655028 DOI: 10.1111/ner.13483] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 04/20/2021] [Accepted: 05/11/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVES Despite recent advances in depression treatment, many patients still do not respond to serial conventional therapies and are considered "treatment resistant." Deep brain stimulation (DBS) has therapeutic potential in this context. This comprehensive review of recent studies of DBS for depression in animal models identifies potential biomarkers for improving therapeutic efficacy and predictability of conventional DBS to aid future development of closed-loop control of DBS systems. MATERIALS AND METHODS A systematic search was performed in Pubmed, EMBASE, and Cochrane Review using relevant keywords. Overall, 56 animal studies satisfied the inclusion criteria. RESULTS Outcomes were divided into biochemical/physiological, electrophysiological, and behavioral categories. Promising biomarkers include biochemical assays (in particular, microdialysis and electrochemical measurements), which provide real-time results in awake animals. Electrophysiological tests, showing changes at both the target site and downstream structures, also revealed characteristic changes at several anatomic targets (such as the medial prefrontal cortex and locus coeruleus). However, the substantial range of models and DBS targets limits the ability to draw generalizable conclusions in animal behavioral models. CONCLUSIONS Overall, DBS is a promising therapeutic modality for treatment-resistant depression. Different outcomes have been used to assess its efficacy in animal studies. From the review, electrophysiological and biochemical markers appear to offer the greatest potential as biomarkers for depression. However, to develop closed-loop DBS for depression, additional preclinical and clinical studies with a focus on identifying reliable, safe, and effective biomarkers are warranted.
Collapse
Affiliation(s)
- Jason Yuen
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55905, USA,Deakin University, IMPACT – the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong VIC 3216, Australia
| | - Aaron E. Rusheen
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55905, USA,Medical Scientist Training Program, Mayo Clinic, Rochester, MN 55905, USA
| | - J. Blair Price
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Hojin Shin
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55905, USA,Department of Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Abbas Z. Kouzani
- School of Engineering, Deakin University, Geelong VIC 3216, Australia
| | - Michael Berk
- Deakin University, IMPACT – the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong VIC 3216, Australia
| | - Charles D. Blaha
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Kendall H. Lee
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55905, USA,Department of Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Yoonbae Oh
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55905, USA,Department of Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
6
|
Saldanha RL, Urdaneta ME, Otto KJ. The Role of Electrode-Site Placement in the Long-Term Stability of Intracortical Microstimulation. Front Neurosci 2021; 15:712578. [PMID: 34566563 PMCID: PMC8455844 DOI: 10.3389/fnins.2021.712578] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/19/2021] [Indexed: 11/13/2022] Open
Abstract
Intracortical microelectrodes are neuroprosthetic devices used in brain-machine interfaces to both record and stimulate neural activity in the brain. These technologies have been improved by advances in microfabrication, which have led to the creation of subcellular and high-density microelectrodes. The greater number of independent stimulation channels in these devices allows for improved neuromodulation selectivity, compared to single-site microelectrodes. Elements of electrode design such as electrode-site placement can influence the long-term performance of neuroprostheses. Previous studies have shown that electrode-sites placed on the edge of a planar microelectrode have greater chronic recording functionality than sites placed in the center. However, the effect of electrode-site placement on long-term intracortical microstimulation (ICMS) is still unknown. Here, we show that, in rats chronically implanted with custom-made planar silicon microelectrodes, electrode-sites on the tip of the device outperformed those on both the edge and center in terms of the effect per charge delivered, though there is still a slight advantage to using edge sites over center sites for ICMS. Longitudinal analysis of ICMS detection thresholds over a 16-week period revealed that while all sites followed a similar trend over time, the tip and edge sites consistently elicited the behavioral response with less charge compared to center sites. Furthermore, we quantified channel activity over time and found that edge sites remained more active than center sites over time, though the rate of decay of active sites for center and edge sites was comparable. Our results demonstrate that electrode-site placement plays an important role in the long-term stability of intracortical microstimulation and could be a potential factor to consider in the design of future intracortical electrodes.
Collapse
Affiliation(s)
- Ramya L Saldanha
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Morgan E Urdaneta
- Department of Neuroscience, University of Florida, Gainesville, FL, United States
| | - Kevin J Otto
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States.,Department of Neuroscience, University of Florida, Gainesville, FL, United States.,Department of Materials Science and Engineering, University of Florida, Gainesville, FL, United States.,Department of Neurology, University of Florida, Gainesville, FL, United States.,Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States
| |
Collapse
|
7
|
Patel B, Chiu S, Wong JK, Patterson A, Deeb W, Burns M, Zeilman P, Wagle-Shukla A, Almeida L, Okun MS, Ramirez-Zamora A. Deep brain stimulation programming strategies: segmented leads, independent current sources, and future technology. Expert Rev Med Devices 2021; 18:875-891. [PMID: 34329566 DOI: 10.1080/17434440.2021.1962286] [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] [Indexed: 02/06/2023]
Abstract
Introduction: Advances in neuromodulation and deep brain stimulation (DBS) technologies have facilitated opportunities for improved clinical benefit and side effect management. However, new technologies have added complexity to clinic-based DBS programming.Areas covered: In this article, we review basic basal ganglia physiology, proposed mechanisms of action and technical aspects of DBS. We discuss novel DBS technologies for movement disorders including the role of advanced imaging software, lead design, IPG design, novel programming techniques including directional stimulation and coordinated reset neuromodulation. Additional topics include the use of potential biomarkers, such as local field potentials, electrocorticography, and adaptive stimulation. We will also discuss future directions including optogenetically inspired DBS.Expert opinion: The introduction of DBS for the management of movement disorders has expanded treatment options. In parallel with our improved understanding of brain physiology and neuroanatomy, new technologies have emerged to address challenges associated with neuromodulation, including variable effectiveness, side-effects, and programming complexity. Advanced functional neuroanatomy, improved imaging, real-time neurophysiology, improved electrode designs, and novel programming techniques have collectively been driving improvements in DBS outcomes.
Collapse
Affiliation(s)
- Bhavana Patel
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Shannon Chiu
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Joshua K Wong
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Addie Patterson
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Wissam Deeb
- Department of Neurology, University of Massachusetts College of Medicine, Worcester, MA, USA
| | - Matthew Burns
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Pamela Zeilman
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Aparna Wagle-Shukla
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Leonardo Almeida
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Michael S Okun
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Adolfo Ramirez-Zamora
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| |
Collapse
|
8
|
Karczewski AM, Dingle AM, Poore SO. The Need to Work Arm in Arm: Calling for Collaboration in Delivering Neuroprosthetic Limb Replacements. Front Neurorobot 2021; 15:711028. [PMID: 34366820 PMCID: PMC8334559 DOI: 10.3389/fnbot.2021.711028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 06/22/2021] [Indexed: 11/21/2022] Open
Abstract
Over the last few decades there has been a push to enhance the use of advanced prosthetics within the fields of biomedical engineering, neuroscience, and surgery. Through the development of peripheral neural interfaces and invasive electrodes, an individual's own nervous system can be used to control a prosthesis. With novel improvements in neural recording and signal decoding, this intimate communication has paved the way for bidirectional and intuitive control of prostheses. While various collaborations between engineers and surgeons have led to considerable success with motor control and pain management, it has been significantly more challenging to restore sensation. Many of the existing peripheral neural interfaces have demonstrated success in one of these modalities; however, none are currently able to fully restore limb function. Though this is in part due to the complexity of the human somatosensory system and stability of bioelectronics, the fragmentary and as-yet uncoordinated nature of the neuroprosthetic industry further complicates this advancement. In this review, we provide a comprehensive overview of the current field of neuroprosthetics and explore potential strategies to address its unique challenges. These include exploration of electrodes, surgical techniques, control methods, and prosthetic technology. Additionally, we propose a new approach to optimizing prosthetic limb function and facilitating clinical application by capitalizing on available resources. It is incumbent upon academia and industry to encourage collaboration and utilization of different peripheral neural interfaces in combination with each other to create versatile limbs that not only improve function but quality of life. Despite the rapidly evolving technology, if the field continues to work in divided "silos," we will delay achieving the critical, valuable outcome: creating a prosthetic limb that is right for the patient and positively affects their life.
Collapse
Affiliation(s)
| | - Aaron M. Dingle
- Division of Plastic Surgery, Department of Surgery, University of Wisconsin–Madison, Madison, WI, United States
| | | |
Collapse
|
9
|
Kolaya E, Firestein BL. Deep brain stimulation: Challenges at the tissue-electrode interface and current solutions. Biotechnol Prog 2021; 37:e3179. [PMID: 34056871 DOI: 10.1002/btpr.3179] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/19/2021] [Accepted: 05/27/2021] [Indexed: 11/08/2022]
Abstract
Deep brain stimulation (DBS) is used to treat the motor symptoms of Parkinson's disease patients by stimulating the subthalamic nucleus. However, optimization of DBS is still needed since the performance of the neural electrodes is limited by the body's response to the implant. This review discusses the issues with DBS, such as placement of electrodes, foreign body response, and electrode degradation. The current solutions to these technical issues include modifications to electrode material, coatings, and geometry.
Collapse
Affiliation(s)
- Emily Kolaya
- Biomedical Engineering Graduate Program, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Bonnie L Firestein
- Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| |
Collapse
|
10
|
Vedam-Mai V, Deisseroth K, Giordano J, Lazaro-Munoz G, Chiong W, Suthana N, Langevin JP, Gill J, Goodman W, Provenza NR, Halpern CH, Shivacharan RS, Cunningham TN, Sheth SA, Pouratian N, Scangos KW, Mayberg HS, Horn A, Johnson KA, Butson CR, Gilron R, de Hemptinne C, Wilt R, Yaroshinsky M, Little S, Starr P, Worrell G, Shirvalkar P, Chang E, Volkmann J, Muthuraman M, Groppa S, Kühn AA, Li L, Johnson M, Otto KJ, Raike R, Goetz S, Wu C, Silburn P, Cheeran B, Pathak YJ, Malekmohammadi M, Gunduz A, Wong JK, Cernera S, Wagle Shukla A, Ramirez-Zamora A, Deeb W, Patterson A, Foote KD, Okun MS. Proceedings of the Eighth Annual Deep Brain Stimulation Think Tank: Advances in Optogenetics, Ethical Issues Affecting DBS Research, Neuromodulatory Approaches for Depression, Adaptive Neurostimulation, and Emerging DBS Technologies. Front Hum Neurosci 2021; 15:644593. [PMID: 33953663 PMCID: PMC8092047 DOI: 10.3389/fnhum.2021.644593] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/10/2021] [Indexed: 12/20/2022] Open
Abstract
We estimate that 208,000 deep brain stimulation (DBS) devices have been implanted to address neurological and neuropsychiatric disorders worldwide. DBS Think Tank presenters pooled data and determined that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. The DBS Think Tank was founded in 2012 providing a space where clinicians, engineers, researchers from industry and academia discuss current and emerging DBS technologies and logistical and ethical issues facing the field. The emphasis is on cutting edge research and collaboration aimed to advance the DBS field. The Eighth Annual DBS Think Tank was held virtually on September 1 and 2, 2020 (Zoom Video Communications) due to restrictions related to the COVID-19 pandemic. The meeting focused on advances in: (1) optogenetics as a tool for comprehending neurobiology of diseases and on optogenetically-inspired DBS, (2) cutting edge of emerging DBS technologies, (3) ethical issues affecting DBS research and access to care, (4) neuromodulatory approaches for depression, (5) advancing novel hardware, software and imaging methodologies, (6) use of neurophysiological signals in adaptive neurostimulation, and (7) use of more advanced technologies to improve DBS clinical outcomes. There were 178 attendees who participated in a DBS Think Tank survey, which revealed the expansion of DBS into several indications such as obesity, post-traumatic stress disorder, addiction and Alzheimer’s disease. This proceedings summarizes the advances discussed at the Eighth Annual DBS Think Tank.
Collapse
Affiliation(s)
- Vinata Vedam-Mai
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, United States.,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
| | - James Giordano
- Department of Neurology and Neuroethics Studies Program, Georgetown University Medical Center, Washington, DC, United States
| | - Gabriel Lazaro-Munoz
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Winston Chiong
- Weill Institute for Neurosciences, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States
| | - Nanthia Suthana
- Department of Neurosurgery, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jean-Philippe Langevin
- Department of Neurosurgery, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Neurosurgery Service, Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, United States
| | - Jay Gill
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Wayne Goodman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Nicole R Provenza
- School of Engineering, Brown University, Providence, RI, United States
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, United States
| | - Rajat S Shivacharan
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, United States
| | - Tricia N Cunningham
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, United States
| | - Sameer A Sheth
- Department of Neurological Surgery, Baylor College of Medicine, Houston, TX, United States
| | - Nader Pouratian
- Department of Neurosurgery, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine W Scangos
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Helen S Mayberg
- Department of Neurology and Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Andreas Horn
- Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Berlin, Germany
| | - Kara A Johnson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
| | - Christopher R Butson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
| | - Ro'ee Gilron
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Coralie de Hemptinne
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States.,Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Robert Wilt
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Maria Yaroshinsky
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Simon Little
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Philip Starr
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Prasad Shirvalkar
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States.,Department of Anesthesiology (Pain Management) and Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Edward Chang
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Jens Volkmann
- Neurologischen Klinik Universitätsklinikum Würzburg, Würzburg, Germany
| | - Muthuraman Muthuraman
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Andrea A Kühn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Matthew Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Kevin J Otto
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Robert Raike
- Restorative Therapies Group Implantables, Research and Core Technology, Medtronic, Minneapolis, MN, United States
| | - Steve Goetz
- Restorative Therapies Group Implantables, Research and Core Technology, Medtronic, Minneapolis, MN, United States
| | - Chengyuan Wu
- Department of Neurological Surgery, Thomas Jefferson University Hospitals, Philadelphia, PA, United States
| | - Peter Silburn
- Asia Pacific Centre for Neuromodulation, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Binith Cheeran
- Neuromodulation Division, Abbott, Plano, TX, United States
| | - Yagna J Pathak
- Neuromodulation Division, Abbott, Plano, TX, United States
| | | | - Aysegul Gunduz
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States.,J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Joshua K Wong
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Stephanie Cernera
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States.,J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Aparna Wagle Shukla
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Adolfo Ramirez-Zamora
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Wissam Deeb
- Department of Neurology, University of Massachusetts, Worchester, MA, United States
| | - Addie Patterson
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| |
Collapse
|
11
|
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.
Collapse
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.
| |
Collapse
|
12
|
Bingham CS, Paknahad J, Girard CBC, Loizos K, Bouteiller JMC, Song D, Lazzi G, Berger TW. Admittance Method for Estimating Local Field Potentials Generated in a Multi-Scale Neuron Model of the Hippocampus. Front Comput Neurosci 2020; 14:72. [PMID: 32848687 PMCID: PMC7417331 DOI: 10.3389/fncom.2020.00072] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 06/26/2020] [Indexed: 01/26/2023] Open
Abstract
Significant progress has been made toward model-based prediction of neral tissue activation in response to extracellular electrical stimulation, but challenges remain in the accurate and efficient estimation of distributed local field potentials (LFP). Analytical methods of estimating electric fields are a first-order approximation that may be suitable for model validation, but they are computationally expensive and cannot accurately capture boundary conditions in heterogeneous tissue. While there are many appropriate numerical methods of solving electric fields in neural tissue models, there isn't an established standard for mesh geometry nor a well-known rule for handling any mismatch in spatial resolution. Moreover, the challenge of misalignment between current sources and mesh nodes in a finite-element or resistor-network method volume conduction model needs to be further investigated. Therefore, using a previously published and validated multi-scale model of the hippocampus, the authors have formulated an algorithm for LFP estimation, and by extension, bidirectional communication between discretized and numerically solved volume conduction models and biologically detailed neural circuit models constructed in NEURON. Development of this algorithm required that we assess meshes of (i) unstructured tetrahedral and grid-based hexahedral geometries as well as (ii) differing approaches for managing the spatial misalignment of current sources and mesh nodes. The resulting algorithm is validated through the comparison of Admittance Method predicted evoked potentials with analytically estimated LFPs. Establishing this method is a critical step toward closed-loop integration of volume conductor and NEURON models that could lead to substantial improvement of the predictive power of multi-scale stimulation models of cortical tissue. These models may be used to deepen our understanding of hippocampal pathologies and the identification of efficacious electroceutical treatments.
Collapse
Affiliation(s)
- Clayton S. Bingham
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Javad Paknahad
- Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Christopher B. C. Girard
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Kyle Loizos
- Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Jean-Marie C. Bouteiller
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Gianluca Lazzi
- Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Theodore W. Berger
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| |
Collapse
|
13
|
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.
Collapse
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
| |
Collapse
|
14
|
Lehto LJ, Canna A, Wu L, Sierra A, Zhurakovskaya E, Ma J, Pearce C, Shaio M, Filip P, Johnson MD, Low WC, Gröhn O, Tanila H, Mangia S, Michaeli S. Orientation selective deep brain stimulation of the subthalamic nucleus in rats. Neuroimage 2020; 213:116750. [PMID: 32198048 DOI: 10.1016/j.neuroimage.2020.116750] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 02/22/2020] [Accepted: 03/13/2020] [Indexed: 11/28/2022] Open
Abstract
Deep brain stimulation (DBS) has become an important tool in the management of a wide spectrum of diseases in neurology and psychiatry. Target selection is a vital aspect of DBS so that only the desired areas are stimulated. Segmented leads and current steering have been shown to be promising additions to DBS technology enabling better control of the stimulating electric field. Recently introduced orientation selective DBS (OS-DBS) is a related development permitting sensitization of the stimulus to axonal pathways with different orientations by freely controlling the primary direction of the electric field using multiple contacts. Here, we used OS-DBS to stimulate the subthalamic nucleus (STN) in healthy rats while simultaneously monitoring the induced brain activity with fMRI. Maximal activation of the sensorimotor and basal ganglia-thalamocortical networks was observed when the electric field was aligned mediolaterally in the STN pointing in the lateral direction, while no cortical activation was observed with the electric field pointing medially to the opposite direction. Such findings are consistent with mediolateral main direction of the STN fibers, as seen with high resolution diffusion imaging and histology. The asymmetry of the OS-DBS dipolar field distribution using three contacts along with the potential stimulation of the internal capsule, are also discussed. We conclude that OS-DBS offers an additional degree of flexibility for optimization of DBS of the STN which may enable a better treatment response.
Collapse
Affiliation(s)
- Lauri J Lehto
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Antonietta Canna
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA; Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Salerno, Italy
| | - Lin Wu
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Alejandra Sierra
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Ekaterina Zhurakovskaya
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA; A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jun Ma
- Department of Neurosurgery, University of Minnesota, Minneapolis, USA
| | - Clairice Pearce
- Department of Neurosurgery, University of Minnesota, Minneapolis, USA
| | - Maple Shaio
- Department of Neurosurgery, University of Minnesota, Minneapolis, USA
| | - Pavel Filip
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA; First Department of Neurology, Faculty of Medicine, Masaryk University and University Hospital of St. Anne, Brno, Czech Republic
| | - Matthew D Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, USA
| | - Walter C Low
- Department of Neurosurgery, University of Minnesota, Minneapolis, USA
| | - Olli Gröhn
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Heikki Tanila
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Silvia Mangia
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Shalom Michaeli
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA.
| |
Collapse
|
15
|
Preston C, Alvarez AM, Barragan A, Becker J, Kasoff WS, Witte RS. High resolution transcranial acoustoelectric imaging of current densities from a directional deep brain stimulator. J Neural Eng 2020; 17:016074. [PMID: 31978914 PMCID: PMC7446234 DOI: 10.1088/1741-2552/ab6fc3] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE New innovations in deep brain stimulation (DBS) enable directional current steering-allowing more precise electrical stimulation of the targeted brain structures for Parkinson's disease, essential tremor and other neurological disorders. While intra-operative navigation through MRI or CT approaches millimeter accuracy for placing the DBS leads, no existing modality provides feedback of the currents as they spread from the contacts through the brain tissue. In this study, we investigate transcranial acoustoelectric imaging (tAEI) as a new modality to non-invasively image and characterize current produced from a directional DBS lead. tAEI uses ultrasound (US) to modulate tissue resistivity to generate detectable voltage signals proportional to the local currents. APPROACH An 8-channel directional DBS lead (Infinity 6172ANS, Abbott Inc) was inserted inside three adult human skulls submerged in 0.9% NaCl. A 2.5 MHz linear array delivered US pulses through the transtemporal window and focused near the contacts on the lead, while a custom amplifier and acquisition system recorded the acoustoelectric (AE) interaction used to generate images. MAIN RESULTS tAEI detected monopolar current with stimulation pulses as short as 100 µs with an SNR ranging from 10-27 dB when using safe US pressure (mechanical indices <0.78) and injected current of ~2 mA peak amplitude. Adjacent contacts were discernable along the length and within each ring of the lead with a mean radial separation between contacts of 2.10 and 1.34 mm, respectively. SIGNIFICANCE These results demonstrate the feasibility of tAEI for high resolution mapping of directional DBS currents using clinically-relevant stimulation parameters. This new modality may improve the accuracy for placing the DBS leads, guide calibration and programming, and monitor long-term performance of DBS for treatment of Parkinson's disease.
Collapse
Affiliation(s)
- Chet Preston
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States of America
| | - Alexander M Alvarez
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States of America
| | - Andres Barragan
- Department of Computer Science, University of Arizona, Tucson, AZ, United States of America
| | - Jennifer Becker
- Department of Medical Imaging, University of Arizona, Tucson, AZ, United States of America
| | - Willard S Kasoff
- Department of Surgery, University of Arizona, Tucson, AZ, United States of America
| | - Russell S Witte
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States of America
- Department of Medical Imaging, University of Arizona, Tucson, AZ, United States of America
| |
Collapse
|
16
|
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.
Collapse
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.
| |
Collapse
|
17
|
Pelot NA, Behrend CE, Grill WM. On the parameters used in finite element modeling of compound peripheral nerves. J Neural Eng 2018; 16:016007. [PMID: 30507555 DOI: 10.1088/1741-2552/aaeb0c] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Computational modeling is an important tool for developing and optimizing implantable neural stimulation devices, but requires accurate electrical and geometrical parameter values to improve predictive value. We quantified the effects of perineurial (resistive sheath around each fascicle) and endoneurial (within each fascicle) parameter values for modeling peripheral nerve stimulation. APPROACH We implemented 3D finite element models of compound peripheral nerves and cuff electrodes to quantify activation and block thresholds of model axons. We also implemented a 2D finite element model of a bundle of axons to estimate the bulk transverse endoneurial resistivity; we compared numerical estimates to an analytical solution. MAIN RESULTS Since the perineurium is highly resistive, potentials were approximately constant over the cross section of a fascicle, and the perineurium resistivity, longitudinal endoneurial resistivity, and fascicle diameter had important effects on thresholds. Activation thresholds increased up to ~130% for higher perineurium resistivity (~400 versus 2200 Ω m) and by ~35%-250% for lower longitudinal endoneurial resistivity (3.5 versus 0.75 Ω m), with larger increases for smaller diameter axons and for axons in larger fascicles. Further, thresholds increased by ~30%-180% for larger fascicle radii, yielding a larger increase with higher perineurium resistivity. Thresholds were largely insensitive to the transverse endoneurial resistivity, but estimates of the bulk resistivity increased with extracellular resistivity and axonal area fraction; the numerical and analytical estimates were in strong agreement except at high axonal area fractions, where structured axon placements that achieved tighter packing produced electric field inhomogeneities. SIGNIFICANCE We performed a systematic investigation of the effects of values and methods for modeling the perineurium and endoneurium on thresholds for neural stimulation and block. These results provide guidance for future modeling studies, including parameter selection, data interpretation, and comparison to experimental results.
Collapse
Affiliation(s)
- Nicole A Pelot
- Department of Biomedical Engineering, Duke University, Room 1427, Fitzpatrick CIEMAS, 101 Science Drive, Campus Box 90281, Durham, NC 27708, United States of America
| | | | | |
Collapse
|
18
|
Ineichen C, Shepherd NR, Sürücü O. Understanding the Effects and Adverse Reactions of Deep Brain Stimulation: Is It Time for a Paradigm Shift Toward a Focus on Heterogenous Biophysical Tissue Properties Instead of Electrode Design Only? Front Hum Neurosci 2018; 12:468. [PMID: 30538625 PMCID: PMC6277493 DOI: 10.3389/fnhum.2018.00468] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 11/06/2018] [Indexed: 02/02/2023] Open
Abstract
Deep brain stimulation (DBS) has been proven to be an effective treatment modality for various late-stage neurological and psychiatric disorders. However, knowledge on the electrical field distribution in the brain tissue is still scarce. Most recent attempts to understand electric field spread were primarily focused on the effect of different electrodes on rather simple tissue models. The influence of microanatomic, biophysical tissue properties in particular has not been investigated in depth. Ethical concerns restrict thorough research on field distribution in human in vivo brain tissue. By means of a simplified model, we investigated the electric field distribution in a broader area of the subthalamic nucleus (STN). Pivotal biophysical parameters including conductivity, permittivity and permeability of brain tissue were incorporated in the model. A brain tissue model was created with the finite element method (FEM). Stimulation was mimicked with parameters used for monopolar stimulation of patients suffering from Parkinson's disease. Our results were visualized with omnidirectional and segmented electrodes. The stimulated electric field was visualized with superimpositions on a stereotactic atlas (Morel). Owing to the effects of regional tissue properties near the stimulating electrode, marked field distortions occur. Such effects include, for example, isolating effects of heavily myelinated neighboring structures, e.g., the internal capsule. In particular, this may be illustrated through the analysis of a larger coronal area. While omnidirectional stimulation has been associated with vast current leakage, higher targeting precision was obtained with segmented electrodes. Finally, targeting was improved when the influence of microanatomic structures on the electric spread was considered. Our results confirm that lead design is not the sole influence on current spread. An omnidirectional lead configuration does not automatically result in an omnidirectional spread of current. In turn, segmented electrodes do not automatically imply an improved steering of current. Our findings may provide an explanation for side-effects secondary to current leakage. Furthermore, a possible explanation for divergent results in the comparison of the intraoperative awake patient and the postoperative setting is given. Due to the major influence of biophysical tissue properties on electric field shape, the local microanatomy should be considered for precise surgical targeting and optimal hardware implantation.
Collapse
Affiliation(s)
- Christian Ineichen
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, Zurich, Switzerland.,Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland
| | | | | |
Collapse
|
19
|
Preston C, Kasoff WS, Witte RS. Selective Mapping of Deep Brain Stimulation Lead Currents Using Acoustoelectric Imaging. ULTRASOUND IN MEDICINE & BIOLOGY 2018; 44:2345-2357. [PMID: 30119863 PMCID: PMC6163075 DOI: 10.1016/j.ultrasmedbio.2018.06.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Revised: 06/15/2018] [Accepted: 06/27/2018] [Indexed: 05/26/2023]
Abstract
We describe a new application of acoustoelectric imaging for non-invasive mapping of the location, magnitude and polarity of current generated by a clinical deep brain stimulation (DBS) device. Ultrasound at 1MHz was focused near the DBS device as short current pulses were injected across different DBS leads. A recording electrode detected the high-frequency acoustoelectric interaction signal. Linear scans of the US beam produced time-varying images of the magnitude and polarity of the induced current, enabling precise localization of the DBS leads within 0.70mm, a detection threshold of 1.75mA at 1 MPa and a sensitivity of 0.52 ± 0.07 μV/(mA*MPa). Monopole and dipole configurations in saline were repeated through a human skullcap. Despite 13.8-dB ultrasound attenuation through bone, acoustoelectric imaging was still >10dB above background with a sensitivity of 0.56 ± 0.10 μV/(mA*MPa). This proof-of-concept study indicates that selective mapping of lead currents through a DBS device may be possible using non-invasive acoustoelectric imaging.
Collapse
Affiliation(s)
- Chet Preston
- Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, USA
| | - Willard S Kasoff
- Department of Surgery, University of Arizona, Tucson, Arizona, USA
| | - Russell S Witte
- Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, USA; Department of Medical Imaging, University of Arizona, Tucson, Arizona, USA.
| |
Collapse
|
20
|
Santaniello S, Gale JT, Sarma SV. Systems approaches to optimizing deep brain stimulation therapies in Parkinson's disease. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2018; 10:e1421. [PMID: 29558564 PMCID: PMC6148418 DOI: 10.1002/wsbm.1421] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 01/29/2018] [Accepted: 02/01/2018] [Indexed: 01/17/2023]
Abstract
Over the last 30 years, deep brain stimulation (DBS) has been used to treat chronic neurological diseases like dystonia, obsessive-compulsive disorders, essential tremor, Parkinson's disease, and more recently, dementias, depression, cognitive disorders, and epilepsy. Despite its wide use, DBS presents numerous challenges for both clinicians and engineers. One challenge is the design of novel, more efficient DBS therapies, which are hampered by the lack of complete understanding about the cellular mechanisms of therapeutic DBS. Another challenge is the existence of redundancy in clinical outcomes, that is, different DBS programs can result in similar clinical benefits but very little information (e.g., predictive models, longitudinal data, metrics, etc.) is available to select one program over another. Finally, there is high variability in patients' responses to DBS, which forces clinicians to carefully adjust the stimulation settings to each patient via lengthy programming sessions. Researchers in neural engineering and systems biology have been tackling these challenges over the past few years with the specific goal of developing novel DBS therapies, design methodologies, and computational tools that optimize the therapeutic effects of DBS in each patient. Furthermore, efforts are being made to automatically adapt the DBS treatment to the fluctuations of disease symptoms. A review of the quantitative approaches currently available for the treatment of Parkinson's disease is presented here with an emphasis on the contributions that systems theoretical approaches have provided to understand the global dynamics of complex neuronal circuits in the brain under DBS. This article is categorized under: Translational, Genomic, and Systems Medicine > Therapeutic Methods Analytical and Computational Methods > Computational Methods Analytical and Computational Methods > Dynamical Methods Physiology > Mammalian Physiology in Health and Disease.
Collapse
Affiliation(s)
- Sabato Santaniello
- Biomedical Engineering Department and CT Institute for the Brain and Cognitive Sciences, University of Connecticut; ORCID-ID: 0000-0002-2133-9471
| | - John T. Gale
- Department of Neurosurgery, Emory University School of Medicine
| | - Sridevi V. Sarma
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University
| |
Collapse
|
21
|
Slopsema JP, Peña E, Patriat R, Lehto LJ, Gröhn O, Mangia S, Harel N, Michaeli S, Johnson MD. Clinical deep brain stimulation strategies for orientation-selective pathway activation. J Neural Eng 2018; 15:056029. [PMID: 30095084 DOI: 10.1088/1741-2552/aad978] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE This study investigated stimulation strategies to increase the selectivity of activating axonal pathways within the brain based on their orientations relative to clinical deep brain stimulation (DBS) lead implants. APPROACH Previous work has shown how varying electrode shape and controlling the primary electric field direction through preclinical electrode arrays can produce orientation-selective axonal stimulation. Here, we significantly extend those results using computational models to evaluate the degree to which clinical DBS leads can direct stimulus-induced electric fields and generate orientation-selective activation of fiber pathways in the brain. Orientation-selective pulse paradigms were evaluated in conceptual models and in patient-specific models of subthalamic nucleus (STN)-DBS for treating Parkinson's disease. MAIN RESULTS Single-contact monopolar or two-contact bipolar stimulation through clinical DBS leads with cylindrical electrodes primarily activated axons orientated parallel to the lead. Conversely, multi-contact monopolar stimulation with a cathode-leading pulse waveform selectively activated axons perpendicular to the DBS lead. Clinical DBS leads with segmented rows of electrodes and a single current source provided additional angular resolution for activating axons oriented 0°, ±22.5°, ±45°, ±67.5°, or 90° relative to the lead shaft. Employing multiple independent current sources to deliver unequal amounts of current through these leads further increased the angular resolution of activation relative to the lead shaft. The patient-specific models indicated that multi-contact cathode configurations, which are rarely used in clinical practice, could increase activation of the hyperdirect pathway collaterals projecting into STN (a putative therapeutic target), while minimizing direct activation of the corticospinal tract of internal capsule, which can elicit sensorimotor side-effects when stimulated. SIGNIFICANCE When combined with patient-specific tissue anisotropy and patient-specific anatomical morphologies of neural pathways responsible for therapy and side effects, orientation-selective DBS approaches show potential to significantly improve clinical outcomes of DBS therapy for a range of existing and investigational clinical indications.
Collapse
Affiliation(s)
- Julia P Slopsema
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, United States of America
| | | | | | | | | | | | | | | | | |
Collapse
|
22
|
Bingham CS, Loizos K, Yu GJ, Gilbert A, Bouteiller JMC, Song D, Lazzi G, Berger TW. Model-Based Analysis of Electrode Placement and Pulse Amplitude for Hippocampal Stimulation. IEEE Trans Biomed Eng 2018; 65:2278-2289. [PMID: 29993519 PMCID: PMC6224291 DOI: 10.1109/tbme.2018.2791860] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Objective: The ideal form of a neural-interfacing device is highly dependent upon the anatomy of the region with which it is meant to interface. Multiple-electrode arrays provide a system which can be adapted to various neural geometries. Computational models of stimulating systems have proven useful for evaluating electrode placement and stimulation protocols, but have yet to be adequately adapted to the unique features of the hippocampus. Methods: As an approach to understanding potential memory restorative devices, an Admittance Method-NEURON model was constructed to predict the direct and synaptic response of a region of the rat dentate gyrus to electrical stimulation of the perforant path. Results: A validation of estimated local field potentials against experimental recordings is performed and results of a bi-linear electrode placement and stimulation amplitude parameter search are presented. Conclusion: The parametric analysis presented herein suggests that stimulating electrodes placed between the lateral and medial perforant path, near the crest of the dentate gyrus, yield a larger relative population response to given stimuli. Significance: Beyond deepening understanding of the hippocampal tissue system, establishment of this model provides a method to evaluate candidate stimulating devices and protocols.
Collapse
|
23
|
Antal A, Alekseichuk I, Bikson M, Brockmöller J, Brunoni AR, Chen R, Cohen LG, Dowthwaite G, Ellrich J, Flöel A, Fregni F, George MS, Hamilton R, Haueisen J, Herrmann CS, Hummel FC, Lefaucheur JP, Liebetanz D, Loo CK, McCaig CD, Miniussi C, Miranda PC, Moliadze V, Nitsche MA, Nowak R, Padberg F, Pascual-Leone A, Poppendieck W, Priori A, Rossi S, Rossini PM, Rothwell J, Rueger MA, Ruffini G, Schellhorn K, Siebner HR, Ugawa Y, Wexler A, Ziemann U, Hallett M, Paulus W. Low intensity transcranial electric stimulation: Safety, ethical, legal regulatory and application guidelines. Clin Neurophysiol 2017; 128:1774-1809. [PMID: 28709880 PMCID: PMC5985830 DOI: 10.1016/j.clinph.2017.06.001] [Citation(s) in RCA: 646] [Impact Index Per Article: 92.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 05/29/2017] [Accepted: 06/06/2017] [Indexed: 12/11/2022]
Abstract
Low intensity transcranial electrical stimulation (TES) in humans, encompassing transcranial direct current (tDCS), transcutaneous spinal Direct Current Stimulation (tsDCS), transcranial alternating current (tACS), and transcranial random noise (tRNS) stimulation or their combinations, appears to be safe. No serious adverse events (SAEs) have been reported so far in over 18,000 sessions administered to healthy subjects, neurological and psychiatric patients, as summarized here. Moderate adverse events (AEs), as defined by the necessity to intervene, are rare, and include skin burns with tDCS due to suboptimal electrode-skin contact. Very rarely mania or hypomania was induced in patients with depression (11 documented cases), yet a causal relationship is difficult to prove because of the low incidence rate and limited numbers of subjects in controlled trials. Mild AEs (MAEs) include headache and fatigue following stimulation as well as prickling and burning sensations occurring during tDCS at peak-to-baseline intensities of 1-2mA and during tACS at higher peak-to-peak intensities above 2mA. The prevalence of published AEs is different in studies specifically assessing AEs vs. those not assessing them, being higher in the former. AEs are frequently reported by individuals receiving placebo stimulation. The profile of AEs in terms of frequency, magnitude and type is comparable in healthy and clinical populations, and this is also the case for more vulnerable populations, such as children, elderly persons, or pregnant women. Combined interventions (e.g., co-application of drugs, electrophysiological measurements, neuroimaging) were not associated with further safety issues. Safety is established for low-intensity 'conventional' TES defined as <4mA, up to 60min duration per day. Animal studies and modeling evidence indicate that brain injury could occur at predicted current densities in the brain of 6.3-13A/m2 that are over an order of magnitude above those produced by tDCS in humans. Using AC stimulation fewer AEs were reported compared to DC. In specific paradigms with amplitudes of up to 10mA, frequencies in the kHz range appear to be safe. In this paper we provide structured interviews and recommend their use in future controlled studies, in particular when trying to extend the parameters applied. We also discuss recent regulatory issues, reporting practices and ethical issues. These recommendations achieved consensus in a meeting, which took place in Göttingen, Germany, on September 6-7, 2016 and were refined thereafter by email correspondence.
Collapse
Affiliation(s)
- A Antal
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Georg August University, Göttingen, Germany.
| | - I Alekseichuk
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Georg August University, Göttingen, Germany
| | - M Bikson
- Department of Biomedical Engineering, The City College of New York, New York, USA
| | - J Brockmöller
- Department of Clinical Pharmacology, University Medical Center Goettingen, Germany
| | - A R Brunoni
- Service of Interdisciplinary Neuromodulation, Department and Institute of Psychiatry, Laboratory of Neurosciences (LIM-27) and Interdisciplinary Center for Applied Neuromodulation University Hospital, University of São Paulo, São Paulo, Brazil
| | - R Chen
- Division of Neurology, Department of Medicine, University of Toronto and Krembil Research Institute, Toronto, Ontario, Canada
| | - L G Cohen
- Human Cortical Physiology and Neurorehabilitation Section, National Institute of Neurological Disorders and Stroke NIH, Bethesda, USA
| | | | - J Ellrich
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark; Institute of Physiology and Pathophysiology, University of Erlangen-Nürnberg, Erlangen, Germany; EBS Technologies GmbH, Europarc Dreilinden, Germany
| | - A Flöel
- Universitätsmedizin Greifswald, Klinik und Poliklinik für Neurologie, Greifswald, Germany
| | - F Fregni
- Spaulding Neuromodulation Center, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA
| | - M S George
- Brain Stimulation Division, Medical University of South Carolina, and Ralph H. Johnson Veterans Affairs Medical Center, Charleston, SC, USA
| | - R Hamilton
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - J Haueisen
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Germany
| | - C S Herrmann
- Experimental Psychology Lab, Department of Psychology, European Medical School, Carl von Ossietzky Universität, Oldenburg, Germany
| | - F C Hummel
- Defitech Chair of Clinical Neuroengineering, Centre of Neuroprosthetics (CNP) and Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Clinique Romande de Réadaptation, Swiss Federal Institute of Technology (EPFL Valais), Sion, Switzerland
| | - J P Lefaucheur
- Department of Physiology, Henri Mondor Hospital, Assistance Publique - Hôpitaux de Paris, and EA 4391, Nerve Excitability and Therapeutic Team (ENT), Faculty of Medicine, Paris Est Créteil University, Créteil, France
| | - D Liebetanz
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Georg August University, Göttingen, Germany
| | - C K Loo
- School of Psychiatry & Black Dog Institute, University of New South Wales, Sydney, Australia
| | - C D McCaig
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, Scotland, UK
| | - C Miniussi
- Center for Mind/Brain Sciences CIMeC, University of Trento, Rovereto, Italy; Cognitive Neuroscience Section, IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - P C Miranda
- Institute of Biophysics and Biomedical Engineering, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - V Moliadze
- Institute of Medical Psychology and Medical Sociology, University Hospital of Schleswig-Holstein (UKSH), Campus Kiel, Christian-Albrechts-University, Kiel, Germany
| | - M A Nitsche
- Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany; Department of Neurology, University Hospital Bergmannsheil, Bochum, Germany
| | - R Nowak
- Neuroelectrics, Barcelona, Spain
| | - F Padberg
- Department of Psychiatry and Psychotherapy, Munich Center for Brain Stimulation, Ludwig-Maximilian University Munich, Germany
| | - A Pascual-Leone
- Division of Cognitive Neurology, Harvard Medical Center and Berenson-Allen Center for Noninvasive Brain Stimulation at Beth Israel Deaconess Medical Center, Boston, USA
| | - W Poppendieck
- Department of Information Technology, Mannheim University of Applied Sciences, Mannheim, Germany
| | - A Priori
- Center for Neurotechnology and Experimental Brain Therapeutich, Department of Health Sciences, University of Milan Italy; Deparment of Clinical Neurology, University Hospital Asst Santi Paolo E Carlo, Milan, Italy
| | - S Rossi
- Department of Medicine, Surgery and Neuroscience, Human Physiology Section and Neurology and Clinical Neurophysiology Section, Brain Investigation & Neuromodulation Lab, University of Siena, Italy
| | - P M Rossini
- Area of Neuroscience, Institute of Neurology, University Clinic A. Gemelli, Catholic University, Rome, Italy
| | | | - M A Rueger
- Department of Neurology, University Hospital of Cologne, Germany
| | | | | | - H R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark
| | - Y Ugawa
- Department of Neurology, Fukushima Medical University, Fukushima, Japan; Fukushima Global Medical Science Center, Advanced Clinical Research Center, Fukushima Medical University, Japan
| | - A Wexler
- Department of Science, Technology & Society, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - U Ziemann
- Department of Neurology & Stroke, and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - M Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - W Paulus
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Georg August University, Göttingen, Germany
| |
Collapse
|
24
|
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.
Collapse
|
25
|
Trevathan JK, Yousefi A, Park HO, Bartoletta JJ, Ludwig KA, Lee KH, Lujan JL. Computational Modeling of Neurotransmitter Release Evoked by Electrical Stimulation: Nonlinear Approaches to Predicting Stimulation-Evoked Dopamine Release. ACS Chem Neurosci 2017; 8:394-410. [PMID: 28076681 DOI: 10.1021/acschemneuro.6b00319] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Neurochemical changes evoked by electrical stimulation of the nervous system have been linked to both therapeutic and undesired effects of neuromodulation therapies used to treat obsessive-compulsive disorder, depression, epilepsy, Parkinson's disease, stroke, hypertension, tinnitus, and many other indications. In fact, interest in better understanding the role of neurochemical signaling in neuromodulation therapies has been a focus of recent government- and industry-sponsored programs whose ultimate goal is to usher in an era of personalized medicine by creating neuromodulation therapies that respond to real-time changes in patient status. A key element to achieving these precision therapeutic interventions is the development of mathematical modeling approaches capable of describing the nonlinear transfer function between neuromodulation parameters and evoked neurochemical changes. Here, we propose two computational modeling frameworks, based on artificial neural networks (ANNs) and Volterra kernels, that can characterize the input/output transfer functions of stimulation-evoked neurochemical release. We evaluate the ability of these modeling frameworks to characterize subject-specific neurochemical kinetics by accurately describing stimulation-evoked dopamine release across rodent (R2 = 0.83 Volterra kernel, R2 = 0.86 ANN), swine (R2 = 0.90 Volterra kernel, R2 = 0.93 ANN), and non-human primate (R2 = 0.98 Volterra kernel, R2 = 0.96 ANN) models of brain stimulation. Ultimately, these models will not only improve understanding of neurochemical signaling in healthy and diseased brains but also facilitate the development of neuromodulation strategies capable of controlling neurochemical release via closed-loop strategies.
Collapse
Affiliation(s)
| | - Ali Yousefi
- Department
of Neurologic Surgery, Massachusetts General Hospital and Harvard Medical School, 25 Shattuck Street, Boston, Massachusetts 02115, United States
| | | | | | | | | | | |
Collapse
|
26
|
Lehto LJ, Slopsema JP, Johnson MD, Shatillo A, Teplitzky BA, Utecht L, Adriany G, Mangia S, Sierra A, Low WC, Gröhn O, Michaeli S. Orientation selective deep brain stimulation. J Neural Eng 2017; 14:016016. [PMID: 28068296 DOI: 10.1088/1741-2552/aa5238] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Target selectivity of deep brain stimulation (DBS) therapy is critical, as the precise locus and pattern of the stimulation dictates the degree to which desired treatment responses are achieved and adverse side effects are avoided. There is a clear clinical need to improve DBS technology beyond currently available stimulation steering and shaping approaches. We introduce orientation selective neural stimulation as a concept to increase the specificity of target selection in DBS. APPROACH This concept, which involves orienting the electric field along an axonal pathway, was tested in the corpus callosum of the rat brain by freely controlling the direction of the electric field on a plane using a three-electrode bundle, and monitoring the response of the neurons using functional magnetic resonance imaging (fMRI). Computational models were developed to further analyze axonal excitability for varied electric field orientation. MAIN RESULTS Our results demonstrated that the strongest fMRI response was observed when the electric field was oriented parallel to the axons, while almost no response was detected with the perpendicular orientation of the electric field relative to the primary fiber tract. These results were confirmed by computational models of the experimental paradigm quantifying the activation of radially distributed axons while varying the primary direction of the electric field. SIGNIFICANCE The described strategies identify a new course for selective neuromodulation paradigms in DBS based on axonal fiber orientation.
Collapse
Affiliation(s)
- Lauri J Lehto
- Center for Magnetic Resonance Research, Radiology, University of Minnesota, 2021 6th St SE, Minneapolis, MN 55455, USA
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
27
|
Delhaye BP, Saal HP, Bensmaia SJ. Key considerations in designing a somatosensory neuroprosthesis. ACTA ACUST UNITED AC 2016; 110:402-408. [PMID: 27815182 DOI: 10.1016/j.jphysparis.2016.11.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2016] [Revised: 10/20/2016] [Accepted: 11/01/2016] [Indexed: 12/22/2022]
Abstract
In recent years, a consensus has emerged that somatosensory feedback needs to be provided for upper limb neuroprostheses to be useful. An increasingly promising approach to sensory restoration is to electrically stimulate neurons along the somatosensory neuraxis to convey information about the state of the prosthetic limb and about contact with objects. To date, efforts toward artificial sensory feedback have consisted mainly of demonstrating that some sensory information could be conveyed using a small number of stimulation patterns, generally delivered through single electrodes. However impressive these achievements are, results from different studies are hard to compare, as each research team implements different stimulation patterns and tests the elicited sensations differently. A critical question is whether different stimulation strategies will generalize from contrived laboratory settings to activities of daily living. Here, we lay out some key specifications that an artificial somatosensory channel should meet, discuss how different approaches should be evaluated, and caution about looming challenges that the field of sensory restoration will face.
Collapse
Affiliation(s)
- Benoit P Delhaye
- Department of Organismal Biology and Anatomy, University of Chicago, United States
| | - Hannes P Saal
- Department of Organismal Biology and Anatomy, University of Chicago, United States
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, United States.
| |
Collapse
|
28
|
Klinger NV, Mittal S. Clinical efficacy of deep brain stimulation for the treatment of medically refractory epilepsy. Clin Neurol Neurosurg 2016; 140:11-25. [DOI: 10.1016/j.clineuro.2015.11.009] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2015] [Revised: 10/26/2015] [Accepted: 11/12/2015] [Indexed: 10/22/2022]
|
29
|
Model-based analysis and design of waveforms for efficient neural stimulation. PROGRESS IN BRAIN RESEARCH 2015; 222:147-62. [PMID: 26541380 DOI: 10.1016/bs.pbr.2015.07.031] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The design space for electrical stimulation of the nervous system is extremely large, and because the response to stimulation is highly nonlinear, the selection of stimulation parameters to achieve a desired response is a challenging problem. Computational models of the response of neurons to extracellular stimulation allow analysis of the effects of stimulation parameters on neural excitation and provide an approach to select or design optimal parameters of stimulation. Here, I review the use of computational models to understand the effects of stimulation waveform on the energy efficiency of neural excitation and to design novel stimulation waveforms to increase the efficiency of neural stimulation.
Collapse
|
30
|
Spherical statistics for characterizing the spatial distribution of deep brain stimulation effects on neuronal activity. J Neurosci Methods 2015; 255:52-65. [PMID: 26275582 DOI: 10.1016/j.jneumeth.2015.08.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Revised: 07/29/2015] [Accepted: 08/01/2015] [Indexed: 11/24/2022]
Abstract
BACKGROUND Computational models of deep brain stimulation (DBS) have played a key role in understanding its physiological mechanisms. By estimating a volume of tissue directly modulated by DBS, one can relate the neuronal pathways within those volumes to the therapeutic efficacy of a particular DBS setting. NEW METHOD A spherical statistical framework is described to quantify and determine salient features of such morphologies using visualization techniques, empirical shape analysis, and formal hypothesis testing. This framework is shown using a 3D model of thalamocortical neurons surrounding a radially-segmented DBS array. RESULTS We show that neuronal population volumes modulated by various DBS electrode configurations can be characterized by parametric distribution models, such as Kent and Watson girdle models. Distribution parameters were found to change with stimulus settings, including amplitude and radial distance from the DBS array. Increasing stimulation amplitude through a single electrode resulted in more diffuse neuronal activation and increased rotational symmetry about the mean direction of the activated population. When stimulation amplitude was held constant, the activated neuronal population distribution was more concentrated with distance from the DBS array and was also more rotationally asymmetric. We also show how data representation (e.g. stimulus-entrained cell body vs. axon node) can significantly alter model distribution shape. COMPARISON TO EXISTING METHODS This statistical framework provides a quantitative method to analyze the spatial morphologies of DBS-induced effects on neuronal activity. CONCLUSIONS The application of spherical statistics to assess spatial distributions of neuronal activity has potential usefulness for numerous other recording, labeling, and stimulation modalities.
Collapse
|