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Patrick EE, Fleeting CR, Patel DR, Casauay JT, Patel A, Shepherd H, Wong JK. Modeling the volume of tissue activated in deep brain stimulation and its clinical influence: a review. Front Hum Neurosci 2024; 18:1333183. [PMID: 38660012 PMCID: PMC11039793 DOI: 10.3389/fnhum.2024.1333183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 03/26/2024] [Indexed: 04/26/2024] Open
Abstract
Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson's disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.
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Affiliation(s)
- Erin E. Patrick
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States
| | - Chance R. Fleeting
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Drashti R. Patel
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Jed T. Casauay
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Aashay Patel
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Hunter Shepherd
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Joshua K. Wong
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
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2
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Willett A, Wylie SA, Bowersock JL, Dawant BM, Rodriguez W, Ugiliweneza B, Neimat JS, van Wouwe NC. Focused stimulation of dorsal versus ventral subthalamic nucleus enhances action-outcome learning in patients with Parkinson's disease. Brain Commun 2024; 6:fcae111. [PMID: 38646144 PMCID: PMC11032193 DOI: 10.1093/braincomms/fcae111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 02/01/2024] [Accepted: 04/01/2024] [Indexed: 04/23/2024] Open
Abstract
Deep brain stimulation of the subthalamic nucleus is an effective treatment for the clinical motor symptoms of Parkinson's disease, but may alter the ability to learn contingencies between stimuli, actions and outcomes. We investigated how stimulation of the functional subregions in the subthalamic nucleus (motor and cognitive regions) modulates stimulus-action-outcome learning in Parkinson's disease patients. Twelve Parkinson's disease patients with deep brain stimulation of the subthalamic nucleus completed a probabilistic stimulus-action-outcome task while undergoing ventral and dorsal subthalamic nucleus stimulation (within subjects, order counterbalanced). The task orthogonalized action choice and outcome valence, which created four action-outcome learning conditions: action-reward, inhibit-reward, action-punishment avoidance and inhibit-punishment avoidance. We compared the effects of deep brain stimulation on learning rates across these conditions as well as on computed Pavlovian learning biases. Dorsal stimulation was associated with higher overall learning proficiency relative to ventral subthalamic nucleus stimulation. Compared to ventral stimulation, stimulating the dorsal subthalamic nucleus led to a particular advantage in learning to inhibit action to produce desired outcomes (gain reward or avoid punishment) as well as better learning proficiency across all conditions providing reward opportunities. The Pavlovian reward bias was reduced with dorsal relative to ventral subthalamic nucleus stimulation, which was reflected by improved inhibit-reward learning. Our results show that focused stimulation in the dorsal compared to the ventral subthalamic nucleus is relatively more favourable for learning action-outcome contingencies and reduces the Pavlovian bias that could lead to reward-driven behaviour. Considering the effects of deep brain stimulation of the subthalamic nucleus on learning and behaviour could be important when optimizing stimulation parameters to avoid side effects like impulsive reward-driven behaviour.
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Affiliation(s)
- Andrew Willett
- Department of Neurological Surgery, University of Louisville, Louisville, KY 40202, USA
| | - Scott A Wylie
- Department of Neurological Surgery, University of Louisville, Louisville, KY 40202, USA
| | - Jessica L Bowersock
- Department of Neurological Surgery, University of Louisville, Louisville, KY 40202, USA
| | - Benoit M Dawant
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA
| | - William Rodriguez
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA
| | - Beatrice Ugiliweneza
- Department of Neurological Surgery, University of Louisville, Louisville, KY 40202, USA
| | - Joseph S Neimat
- Department of Neurological Surgery, University of Louisville, Louisville, KY 40202, USA
| | - Nelleke C van Wouwe
- Department of Neurological Surgery, University of Louisville, Louisville, KY 40202, USA
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3
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Bower KL, Noecker AM, Frankemolle-Gilbert AM, McIntyre CC. Model-Based Analysis of Pathway Recruitment During Subthalamic Deep Brain Stimulation. Neuromodulation 2024; 27:455-463. [PMID: 37097269 PMCID: PMC10598236 DOI: 10.1016/j.neurom.2023.02.084] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/06/2023] [Accepted: 02/27/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND Subthalamic deep brain stimulation (DBS) is an established clinical therapy, but an anatomically clear definition of the underlying neural target(s) of the stimulation remains elusive. Patient-specific models of DBS are commonly used tools in the search for stimulation targets, and recent iterations of those models are focused on characterizing the brain connections that are activated by DBS. OBJECTIVE The goal of this study was to quantify axonal pathway activation in the subthalamic region from DBS at different electrode locations and stimulation settings. MATERIALS AND METHODS We used an anatomically and electrically detailed computational model of subthalamic DBS to generate recruitment curves for eight different axonal pathways of interest, at three generalized DBS electrode locations in the subthalamic nucleus (STN) (ie, central STN, dorsal STN, posterior STN). These simulations were performed with three levels of DBS electrode localization uncertainty (ie, 0.5 mm, 1.0 mm, 1.5 mm). RESULTS The recruitment curves highlight the diversity of pathways that are theoretically activated with subthalamic DBS, in addition to the dependence of the stimulation location and parameter settings on the pathway activation estimates. The three generalized DBS locations exhibited distinct pathway recruitment curve profiles, suggesting that each stimulation location would have a different effect on network activity patterns. We also found that the use of anodic stimuli could help limit activation of the internal capsule relative to other pathways. However, incorporating realistic levels of DBS electrode localization uncertainty in the models substantially limits their predictive capabilities. CONCLUSIONS Subtle differences in stimulation location and/or parameter settings can impact the collection of pathways that are activated during subthalamic DBS.
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Affiliation(s)
- Kelsey L Bower
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Angela M Noecker
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurosurgery, Duke University, Durham, NC, USA.
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4
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Beylergil SB, Noecker AM, Kilbane C, McIntyre CC, Shaikh AG. Does Vestibular Motion Perception Correlate with Axonal Pathways Stimulated by Subthalamic Deep Brain Stimulation in Parkinson's Disease? CEREBELLUM (LONDON, ENGLAND) 2024; 23:554-569. [PMID: 37308757 DOI: 10.1007/s12311-023-01576-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/01/2023] [Indexed: 06/14/2023]
Abstract
Perception of our linear motion - heading - is critical for postural control, gait, and locomotion, and it is impaired in Parkinson's disease (PD). Deep brain stimulation (DBS) has variable effects on vestibular heading perception, depending on the location of the electrodes within the subthalamic nucleus (STN). Here, we aimed to find the anatomical correlates of heading perception in PD. Fourteen PD participants with bilateral STN DBS performed a two-alternative forced-choice discrimination task where a motion platform delivered translational forward movements with a heading angle varying between 0 and 30° to the left or to the right with respect to the straight-ahead direction. Using psychometric curves, we derived the heading discrimination threshold angle of each patient from the response data. We created patient-specific DBS models and calculated the percentages of stimulated axonal pathways that are anatomically adjacent to the STN and known to play a major role in vestibular information processing. We performed correlation analyses to investigate the extent of these white matter tracts' involvement in heading perception. Significant positive correlations were identified between improved heading discrimination for rightward heading and the percentage of activated streamlines of the contralateral hyperdirect, pallido-subthalamic, and subthalamo-pallidal pathways. The hyperdirect pathways are thought to provide top-down control over STN connections to the cerebellum. In addition, STN may also antidromically activate collaterals of hyperdirect pathway that projects to the precerebellar pontine nuclei. In select cases, there was strong activation of the cerebello-thalamic projections, but it was not consistently present in all participants. Large volumetric overlap between the volume of tissue activation and the STN in the left hemisphere positively impacted rightward heading perception. Altogether, the results suggest heavy involvement of basal ganglia cerebellar network in STN-induced modulation of vestibular heading perception in PD.
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Affiliation(s)
- Sinem Balta Beylergil
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- National VA Parkinson Consortium Center, Neurology Service, Daroff-Dell'Osso Ocular Motility and Vestibular Laboratory, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Angela M Noecker
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Camilla Kilbane
- Department of Neurology, Case Western Reserve University, 11100 Euclid Avenue, Cleveland, OH, 44110, USA
- Movement Disorders Center, Neurological Institute, University Hospitals, Cleveland, OH, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Aasef G Shaikh
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
- National VA Parkinson Consortium Center, Neurology Service, Daroff-Dell'Osso Ocular Motility and Vestibular Laboratory, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA.
- Department of Neurology, Case Western Reserve University, 11100 Euclid Avenue, Cleveland, OH, 44110, USA.
- Movement Disorders Center, Neurological Institute, University Hospitals, Cleveland, OH, USA.
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5
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Ng PR, Bush A, Vissani M, McIntyre CC, Richardson RM. Biophysical Principles and Computational Modeling of Deep Brain Stimulation. Neuromodulation 2024; 27:422-439. [PMID: 37204360 DOI: 10.1016/j.neurom.2023.04.471] [Citation(s) in RCA: 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.
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Affiliation(s)
| | - Alan Bush
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Matteo Vissani
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Robert Mark Richardson
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
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6
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Bingham CS, McIntyre CC. Coupled Activation of the Hyperdirect and Cerebellothalamic Pathways with Zona Incerta Deep Brain Stimulation. Mov Disord 2024; 39:539-545. [PMID: 38321526 PMCID: PMC10963140 DOI: 10.1002/mds.29717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 10/18/2023] [Accepted: 01/02/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) of the subthalamic nucleus (STN) or ventral intermediate nucleus (VIM) are established targets for the treatment of Parkinson's disease (PD) or essential tremor (ET), respectively. However, DBS of the zona incerta (ZI) can be effective for both disorders. VIM DBS is assumed to achieve its therapeutic effect via activation of the cerebellothalamic (CBT) pathway, whereas the activation of the hyperdirect (HD) pathway likely plays a role in the mechanisms of STN DBS. Interestingly, HD pathway axons also emit collaterals to the ZI and red nucleus (RN) and the CBT pathway courses nearby to the ZI. OBJECTIVE The aim was to examine the ability of ZI DBS to mutually activate the HD and CBT pathways in a detailed computational model of human DBS. METHODS We extended a previous model of the human HD pathway to incorporate axon collaterals to the ZI and RN. The anatomical framework of the model system also included representations of the CBT pathway and internal capsule (IC) fibers of passage. We then performed detailed biophysical simulations to quantify DBS activation of the HD, CBT, and IC pathways with electrodes located in either the STN or ZI. RESULTS STN DBS and ZI DBS both robustly activated the HD pathway. However, STN DBS was limited by IC activation at higher stimulus amplitudes. Alternatively, ZI DBS avoided IC activation while simultaneously activating the HD and CBT pathways. CONCLUSIONS From both neuroanatomical and biophysical perspectives, ZI DBS represents an advantageous target for coupled activation of the HD and CBT pathways. © 2024 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Clayton S. Bingham
- Department of Biomedical Engineering, Duke University, Durham, N.C. 27708
| | - Cameron C. McIntyre
- Department of Biomedical Engineering, Duke University, Durham, N.C. 27708
- Department of Neurosurgery, Duke University, Durham, N.C. 27708
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7
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Hollunder B, Ostrem JL, Sahin IA, Rajamani N, Oxenford S, Butenko K, Neudorfer C, Reinhardt P, Zvarova P, Polosan M, Akram H, Vissani M, Zhang C, Sun B, Navratil P, Reich MM, Volkmann J, Yeh FC, Baldermann JC, Dembek TA, Visser-Vandewalle V, Alho EJL, Franceschini PR, Nanda P, Finke C, Kühn AA, Dougherty DD, Richardson RM, Bergman H, DeLong MR, Mazzoni A, Romito LM, Tyagi H, Zrinzo L, Joyce EM, Chabardes S, Starr PA, Li N, Horn A. Mapping dysfunctional circuits in the frontal cortex using deep brain stimulation. Nat Neurosci 2024; 27:573-586. [PMID: 38388734 PMCID: PMC10917675 DOI: 10.1038/s41593-024-01570-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 01/05/2024] [Indexed: 02/24/2024]
Abstract
Frontal circuits play a critical role in motor, cognitive and affective processing, and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)functions remains largely elusive. We studied 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregated the frontal cortex into circuits that had become dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to frontal, ranging from interconnections with sensorimotor cortices in dystonia, the primary motor cortex in Tourette's syndrome, the supplementary motor area in Parkinson's disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairments in the human brain.
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Affiliation(s)
- Barbara Hollunder
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jill L Ostrem
- Movement Disorders and Neuromodulation Centre, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Ilkem Aysu Sahin
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Nanditha Rajamani
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Simón Oxenford
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Konstantin Butenko
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Clemens Neudorfer
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Pablo Reinhardt
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Patricia Zvarova
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Mircea Polosan
- Université Grenoble Alpes, Grenoble, France
- Inserm, U1216, Grenoble Institut des Neurosciences, Grenoble, France
- Department of Psychiatry, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France
| | - Harith Akram
- Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, UK
- Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Matteo Vissani
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Chencheng Zhang
- Department of Neurosurgery, Rujin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bomin Sun
- Department of Neurosurgery, Rujin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Pavel Navratil
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Martin M Reich
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Juan Carlos Baldermann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Till A Dembek
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | | | | | - Pranav Nanda
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Carsten Finke
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andrea A Kühn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hagai Bergman
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem, Israel
- Department of Medical Neurobiology, Institute of Medical Research Israel-Canada, The Hebrew University, Hadassah Medical School, Jerusalem, Israel
- Department of Neurosurgery, Hadassah Medical Center, Jerusalem, Israel
| | - Mahlon R DeLong
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Luigi M Romito
- Parkinson and Movement Disorders Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Himanshu Tyagi
- Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, UK
- Department of Neuropsychiatry, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Ludvic Zrinzo
- Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, UK
- Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Eileen M Joyce
- Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, UK
- Department of Neuropsychiatry, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Stephan Chabardes
- Université Grenoble Alpes, Grenoble, France
- Inserm, U1216, Grenoble Institut des Neurosciences, Grenoble, France
- Department of Neurosurgery, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Ningfei Li
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
| | - Andreas Horn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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8
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Hollunder B, Ostrem JL, Sahin IA, Rajamani N, Oxenford S, Butenko K, Neudorfer C, Reinhardt P, Zvarova P, Polosan M, Akram H, Vissani M, Zhang C, Sun B, Navratil P, Reich MM, Volkmann J, Yeh FC, Baldermann JC, Dembek TA, Visser-Vandewalle V, Alho EJL, Franceschini PR, Nanda P, Finke C, Kühn AA, Dougherty DD, Richardson RM, Bergman H, DeLong MR, Mazzoni A, Romito LM, Tyagi H, Zrinzo L, Joyce EM, Chabardes S, Starr PA, Li N, Horn A. Mapping Dysfunctional Circuits in the Frontal Cortex Using Deep Brain Stimulation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.07.23286766. [PMID: 36945497 PMCID: PMC10029043 DOI: 10.1101/2023.03.07.23286766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Frontal circuits play a critical role in motor, cognitive, and affective processing - and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)function remains largely elusive. Here, we study 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregate the frontal cortex into circuits that became dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to rostral, ranging from interconnections with sensorimotor cortices in dystonia, with the primary motor cortex in Tourette's syndrome, the supplementary motor area in Parkinson's disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairment in the human brain.
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Affiliation(s)
- Barbara Hollunder
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jill L. Ostrem
- Movement Disorders and Neuromodulation Centre, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Ilkem Aysu Sahin
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Nanditha Rajamani
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Simón Oxenford
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Konstantin Butenko
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Clemens Neudorfer
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Pablo Reinhardt
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Patricia Zvarova
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Mircea Polosan
- Univ. Grenoble Alpes, Grenoble, France
- Inserm, U1216, Grenoble Institut des Neurosciences, Grenoble, France
- Psychiatry Department, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France
| | - Harith Akram
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Queen Square Institute of Neurology, London, UK
| | - Matteo Vissani
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Chencheng Zhang
- Department of Neurosurgery, Rujin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bomin Sun
- Department of Neurosurgery, Rujin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Pavel Navratil
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Martin M. Reich
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Juan Carlos Baldermann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Till A. Dembek
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | | | | | - Pranav Nanda
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Carsten Finke
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Andrea A. Kühn
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- NeuroCure Cluster of Excellence, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Darin D. Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - R. Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hagai Bergman
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem, Israel
- Department of Medical Neurobiology, Institute of Medical Research Israel-Canada, The Hebrew University, Hassadah Medical School, Jerusalem, Israel
- Department of Neurosurgery, Hadassah Medical Center, Jerusalem, Israel
| | - Mahlon R. DeLong
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Luigi M. Romito
- Parkinson and Movement Disorders Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Himanshu Tyagi
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Queen Square Institute of Neurology, London, UK
| | - Ludvic Zrinzo
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Queen Square Institute of Neurology, London, UK
| | - Eileen M. Joyce
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Queen Square Institute of Neurology, London, UK
| | - Stephan Chabardes
- Univ. Grenoble Alpes, Grenoble, France
- Inserm, U1216, Grenoble Institut des Neurosciences, Grenoble, France
- Department of Neurosurgery, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France
| | - Philip A. Starr
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Ningfei Li
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Horn
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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9
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Bower KL, Noecker AM, Reich M, McIntyre CC. Quantifying the Variability Associated with Postoperative Localization of Deep Brain Stimulation Electrodes. Stereotact Funct Neurosurg 2023; 101:277-284. [PMID: 37379823 PMCID: PMC10833063 DOI: 10.1159/000530462] [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: 12/15/2021] [Accepted: 03/26/2023] [Indexed: 06/30/2023]
Abstract
INTRODUCTION Computational models of deep brain stimulation (DBS) have become common tools in clinical research studies that attempt to establish correlations between stimulation locations in the brain and behavioral outcome measures. However, the accuracy of any patient-specific DBS model depends heavily upon accurate localization of the DBS electrodes within the anatomy, which is typically defined via co-registration of clinical CT and MRI datasets. Several different approaches exist for this challenging registration problem, and each approach will result in a slightly different electrode localization. The goal of this study was to better understand how different processing steps (e.g., cost-function masking, brain extraction, intensity remapping) affect the estimate of the DBS electrode location in the brain. METHODS No "gold standard" exists for this kind of analysis, as the exact location of the electrode in the living human brain cannot be determined with existing clinical imaging approaches. However, we can estimate the uncertainty associated with the electrode position, which can be used to guide statistical analyses in DBS mapping studies. Therefore, we used high-quality clinical datasets from 10 subthalamic DBS subjects and co-registered their long-term postoperative CT with their preoperative surgical targeting MRI using 9 different approaches. The distances separating all of the electrode location estimates were calculated for each subject. RESULTS On average, electrodes were located within a median distance of 0.57 mm (0.49-0.74) of one another across the different registration approaches. However, when considering electrode location estimates from short-term postoperative CTs, the median distance increased to 2.01 mm (1.55-2.78). CONCLUSIONS The results of this study suggest that electrode location uncertainty needs to be factored into statistical analyses that attempt to define correlations between stimulation locations and clinical outcomes.
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Affiliation(s)
- Kelsey L. Bower
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Angela M. Noecker
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Martin Reich
- Department of Neurology, University of Wurzburg, Germany
| | - Cameron C. McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
- Department of Biomedical Engineering, Duke University, Durham, NC
- Department of Neurosurgery, Duke University, Durham, NC
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10
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Petersen MV, McIntyre CC. Comparison of Anatomical Pathway Models with Tractography Estimates of the Pallidothalamic, Cerebellothalamic, and Corticospinal Tracts. Brain Connect 2023; 13:237-246. [PMID: 36772800 PMCID: PMC10178936 DOI: 10.1089/brain.2022.0068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Abstract
Introduction: Models of structural connectivity in the human brain are typically simulated using tractographic approaches. However, the nonlinear fitting of anatomical pathway atlases to de novo subject brains represents a simpler alternative that is hypothesized to provide more anatomically realistic results. Therefore, the goal of this study was to perform a side-by-side comparison of the streamline estimates generated by either pathway atlas fits or tractographic reconstructions in the same subjects. Methods: Our analyses focused on reconstruction of the corticospinal tract (CST), cerebellothalamic (CBT), and pallidothalamic (PT) pathways using example datasets from the Human Connectome Project (HCP). We used MRtrix3 to explore whole brain, as well as manual seed-to-target, tractography approaches. In parallel, we performed nonlinear fits of an axonal pathway atlas to each HCP dataset using Advanced Normalization Tools (ANTs). Results: The different methods produced notably different estimates for each pathway in each subject. The fitted atlas pathways were highly stereotyped and exhibited low variability in their streamline trajectories. Manual tractography resulted in pathway estimates that generally corresponded with the fitted atlas pathways, but with a higher degree of variability in the individual streamlines. Pathway reconstructions derived from whole-brain tractography exhibited the highest degree of variability and struggled to create anatomically realistic representations for either the CBT or PT pathways. Conclusion: The speed, simplicity, reproducibility, and realism of anatomical pathway model fits makes them an appealing option for some forms of structural connectivity modeling in the human brain. Impact statement Axonal pathway modeling is an important component of deep brain stimulation (DBS) research studies that seek to identify the brain connections that are directly activated by stimulation. The corticospinal tract, cerebellothalamic (CBT), and pallidothalamic (PT) pathways are specifically relevant to the study of subthalamic DBS for the treatment of Parkinson's disease. Our results suggest that anatomical pathway model fits of the CBT and PT pathways to de novo subject brains represent a more anatomically realistic option than tractographic approaches when studying subthalamic DBS.
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Affiliation(s)
- Mikkel V. Petersen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Cameron C. McIntyre
- Department of Biomedical Engineering and Duke University, Durham, North Carolina, USA
- Department of Neurosurgery, Duke University, Durham, North Carolina, USA
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11
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Agharazi H, Hardin EC, Flannery K, Beylergil SB, Noecker A, Kilbane C, Factor SA, McIntyre C, Shaikh AG. Physiological measures and anatomical correlates of subthalamic deep brain stimulation effect on gait in Parkinson's disease. J Neurol Sci 2023; 449:120647. [PMID: 37100017 DOI: 10.1016/j.jns.2023.120647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 03/25/2023] [Accepted: 04/08/2023] [Indexed: 04/28/2023]
Abstract
We examined whether conflicting visual and non-visual information leads to gait abnormalities and how the subthalamic deep brain stimulation (STN DBS) influences gait dysfunction in Parkinson's disease (PD). We used a motion capture system to measure the kinematics of the lower limbs during treadmill walking in immersive virtual reality. The visual information provided in the virtual reality paradigm was modulated to create a mismatch between the optic-flow velocity of the visual scene and the walking speed on the treadmill. In each mismatched condition, we calculated the step duration, step length, step phase, step height, and asymmetries. The key finding of our study was that mismatch between treadmill walking speed and the optic-flow velocity did not consistently alter gait parameters in PD. We also found that STN DBS improved the PD gait pattern by changing the stride length and step height. The effects on phase and left/right asymmetry were not statistically significant. The DBS parameters and location also determined its effects on gait. Statistical effects on stride length and step height were noted when the DBS volume of activated tissue (VTA) was in the dorsal aspect of the subthalamus. The statistically significant effects of STN DBS was present when VTA significantly overlapped with MR tractogrphically measured motor and pre-motor hyperdirect pathways. In summary, our results provide novel insight into ways for controlling walking behavior in PD using STN DBS.
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Affiliation(s)
- Hanieh Agharazi
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States of America
| | - Elizabeth C Hardin
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States of America; Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Katherine Flannery
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States of America
| | | | - Angela Noecker
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Camilla Kilbane
- Neurological Institute, University Hospitals, Cleveland, OH, United States of America; Department of Neurology, Case Western Reserve University, Cleveland, OH, United States of America
| | - Stewart A Factor
- Jean and Paul Amos Parkinson's Disease and Movement Disorder Program, Department of Neurology, Emory University, Atlanta, GA, United States of America
| | - Cameron McIntyre
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Aasef G Shaikh
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States of America; Neurological Institute, University Hospitals, Cleveland, OH, United States of America; Department of Neurology, Case Western Reserve University, Cleveland, OH, United States of America; Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America.
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12
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Noecker AM, Mlakar J, Petersen MV, Griswold MA, McIntyre CC. Holographic visualization for stereotactic neurosurgery research. Brain Stimul 2023; 16:411-414. [PMID: 36739892 PMCID: PMC10750300 DOI: 10.1016/j.brs.2023.02.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
Background: Stereotactic neurosurgical planning for the placement of depth electrodes requires the integration of wide-ranging 3D datasets on the anatomy of the patient. Objective: Our goal was to create an interactive group-based holographic visualization tool (HoloSNS) that facilitates evaluation of depth electrode positioning relative to the available medical imaging data, as well as models of the anatomical nuclei and structural connectivity of the brain. Methods: HoloSNS is currently designed to run on the HoloLens 2 platform, and was developed using the Unity Game Engine and the Mixed Reality Toolkit from Microsoft. Results: HoloSNS currently supports research analyses with deep brain stimulation (DBS) and/or stereo-electroencephalography (SEEG) electrodes. Two example software applications (HoloDBS and HoloSEEG) are available for free download on the Microsoft App Store. Conclusions: HoloSNS is the latest culmination of our efforts to integrate advances in brain imaging data, intracranial electrode modeling, and advanced visualization techniques to enhance stereotactic neurosurgery research.
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Affiliation(s)
- Angela M Noecker
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Jeffrey Mlakar
- Interactive Commons, Case Western Reserve University, Cleveland, OH, USA
| | - Mikkel V Petersen
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Mark A Griswold
- Interactive Commons, Case Western Reserve University, Cleveland, OH, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurosurgery, Duke University, Durham, NC, USA.
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13
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Schüller T, Kohl S, Dembek T, Tittgemeyer M, Huys D, Visser-Vandewalle V, Li N, Wehmeyer L, Barbe M, Kuhn J, Baldermann JC. Internal Capsule/Nucleus Accumbens Deep Brain Stimulation Increases Impulsive Decision Making in Obsessive-Compulsive Disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:281-289. [PMID: 36739254 DOI: 10.1016/j.bpsc.2022.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 09/22/2022] [Accepted: 10/15/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND Deep brain stimulation of the anterior limb of the internal capsule (ALIC)/nucleus accumbens is an effective treatment in patients with obsessive-compulsive disorder but may increase impulsive behavior. We aimed to investigate how active stimulation alters subdomains of impulsive decision making and whether respective effects depend on the location of stimulation sites. METHODS We assessed 15 participants with obsessive-compulsive disorder performing the Cambridge Gambling Task during active and inactive ALIC/nucleus accumbens deep brain stimulation. Specifically, we determined stimulation-induced changes in risk adjustment and delay aversion. To characterize underlying neural pathways, we computed probabilistic stimulation maps and applied fiber filtering based on normative structural connectivity data to identify "hot" and "cold" spots/fibers related to changes in impulsive decision making. RESULTS Active stimulation significantly reduced risk adjustment while increasing delay aversion, both implying increased impulsive decision making. Changes in risk adjustment were robustly associated with stimulation sites located in the central ALIC and fibers connecting the thalamus and subthalamic nucleus with the medial and lateral prefrontal cortex. Both hot spots and fibers for changes in risk adjustment were robust to leave-one-out cross-validation. Changes in delay aversion were similarly associated with central ALIC stimulation, but validation hereof was nonsignificant. CONCLUSIONS Our findings provide experimental evidence that ALIC/nucleus accumbens stimulation increases impulsive decision making in obsessive-compulsive disorder. We show that changes in risk adjustment depend on the location of stimulation volumes and affected fiber bundles. The relationship between impulsive decision making and long-term clinical outcomes requires further investigation.
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Affiliation(s)
- Thomas Schüller
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Sina Kohl
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Till Dembek
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Marc Tittgemeyer
- Max-Planck-Institute for Metabolism Research, Cologne, Germany; Cologne Cluster of Excellence in Cellular Stress and Aging associated Disease (CECAD), Cologne, Germany
| | - Daniel Huys
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Department of Psychiatry and Psychotherapy III, LVR Hospital Bonn, Bonn, Germany
| | | | - Ningfei Li
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Berlin, Germany
| | - Laura Wehmeyer
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Michael Barbe
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jens Kuhn
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Department of Psychiatry, Psychotherapy, and Psychosomatics, Johanniter Hospital Oberhausen, Oberhausen, Germany
| | - Juan Carlos Baldermann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
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14
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Roediger J, Dembek TA, Achtzehn J, Busch JL, Krämer AP, Faust K, Schneider GH, Krause P, Horn A, Kühn AA. Automated deep brain stimulation programming based on electrode location: a randomised, crossover trial using a data-driven algorithm. Lancet Digit Health 2023; 5:e59-e70. [PMID: 36528541 DOI: 10.1016/s2589-7500(22)00214-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/22/2022] [Accepted: 11/01/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is highly effective in controlling motor symptoms in patients with Parkinson's disease. However, correct selection of stimulation parameters is pivotal to treatment success and currently follows a time-consuming and demanding trial-and-error process. We aimed to assess treatment effects of stimulation parameters suggested by a recently published algorithm (StimFit) based on neuroimaging data. METHODS This double-blind, randomised, crossover, non-inferiority trial was carried out at Charité - Universitätsmedizin, Berlin, Germany, and enrolled patients with Parkinson's disease treated with directional octopolar electrodes targeted at the STN. All patients had undergone DBS programming according to our centre's standard of care (SoC) treatment before study recruitment. Based on perioperative imaging data, DBS electrodes were reconstructed and StimFit was applied to suggest optimal stimulation settings. Patients underwent motor assessments using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS-III) during OFF-medication and in OFF-stimulation and ON-stimulation states under both conditions, StimFit and SoC parameter settings. Patients were randomly assigned (1:1) to receive either StimFit-programmed DBS first and SoC-programmed DBS second, or SoC-programmed DBS first and StimFit-programmed DBS second. The allocation schedule was generated using a computerised random number generator. Both the rater and patients were masked to the sequence of SoC and StimFit stimulation conditions. All patients who participated in the study were included in the analysis. The primary endpoint of this study was the absolute mean difference between MDS-UPDRS-III scores under StimFit and SoC stimulation, with a non-inferiority margin of 5 points. The study was registered at the German Register for Clinical Trials (DRKS00023115), and is complete. FINDINGS Between July 10, 2020, and Oct 28, 2021, 35 patients were enrolled in the study; 18 received StimFit followed by SoC stimulation, and 17 received SoC followed by StimFit stimulation. Mean MDS-UPDRS-III scores improved from 47·3 (SD 17·1) at OFF-stimulation baseline to 24·7 (SD 12·4) and 26·3 (SD 12·4) under SoC and StimFit stimulation, respectively. Mean difference between motor scores was -1·6 (SD 7·1; 95% CI -4·0 to 0·9; superiority test psuperiority=0·20; n=35), establishing non-inferiority of StimFit stimulation at a margin of -5 points (non-inferiority test pnon-inferiority=0·0038). In six patients (17%), initial programming of StimFit settings resulted in acute side-effects and amplitudes were reduced until side-effects disappeared. INTERPRETATION Automated data-driven algorithms can predict stimulation parameters that lead to motor symptom control comparable to SoC treatment. This approach could significantly decrease the time necessary to obtain optimal treatment parameters. FUNDING Deutsche Forschungsgemeinschaft through NeuroCure Clinical Research Center and TRR 295.
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Affiliation(s)
- Jan Roediger
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Till A Dembek
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Johannes Achtzehn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Johannes L Busch
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Anna-Pauline Krämer
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Katharina Faust
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Gerd-Helge Schneider
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Patricia Krause
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Andreas Horn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA; MGH Neurosurgery and Center for Neurotechnology and Neurorecovery (CNTR) at MGH Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrea A Kühn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; NeuroCure Clinical Research Centre, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany; DZNE, German Center for Degenerative Diseases, Berlin, Germany.
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15
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Butenko K, Li N, Neudorfer C, Roediger J, Horn A, Wenzel GR, Eldebakey H, Kühn AA, Reich MM, Volkmann J, Rienen UV. Linking profiles of pathway activation with clinical motor improvements - A retrospective computational study. Neuroimage Clin 2022; 36:103185. [PMID: 36099807 PMCID: PMC9474565 DOI: 10.1016/j.nicl.2022.103185] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/27/2022] [Accepted: 09/02/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Deep brain stimulation (DBS) is an established therapy for patients with Parkinson's disease. In silico computer models for DBS hold the potential to inform a selection of stimulation parameters. In recent years, the focus has shifted towards DBS-induced firing in myelinated axons, deemed particularly relevant for the external modulation of neural activity. OBJECTIVE The aim of this project was to investigate correlations between patient-specific pathway activation profiles and clinical motor improvement. METHODS We used the concept of pathway activation modeling, which incorporates advanced volume conductor models and anatomically authentic fiber trajectories to estimate DBS-induced action potential initiation in anatomically plausible pathways that traverse in close proximity to targeted nuclei. We applied the method on two retrospective datasets of DBS patients, whose clinical improvement had been evaluated according to the motor part of the Unified Parkinson's Disease Rating Scale. Based on differences in outcome and activation levels for intrapatient DBS protocols in a training cohort, we derived a pathway activation profile that theoretically induces a complete alleviation of symptoms described by UPDRS-III. The profile was further enhanced by analyzing the importance of matching activation levels for individual pathways. RESULTS The obtained profile emphasized the importance of activation in pathways descending from the motor-relevant cortical regions as well as the pallidothalamic pathways. The degree of similarity of patient-specific profiles to the optimal profile significantly correlated with clinical motor improvement in a test cohort. CONCLUSION Pathway activation modeling has a translational utility in the context of motor symptom alleviation in Parkinson's patients treated with DBS.
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Affiliation(s)
- Konstantin Butenko
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany,Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany,Corresponding author.
| | - Ningfei Li
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Clemens Neudorfer
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Jan Roediger
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany,Einstein Center for Neurosciences, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Horn
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Gregor R. Wenzel
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Hazem Eldebakey
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Andrea A. Kühn
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Martin M. Reich
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Ursula van Rienen
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany,Department Life, Light & Matter, University of Rostock, Rostock, Germany,Department of Ageing of Individuals and Society, University of Rostock, Rostock, Germany,Corresponding author.
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16
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Sarikhani P, Ferleger B, Mitchell K, Ostrem J, Herron J, Mahmoudi B, Miocinovic S. Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson's Disease and essential tremor. J Neural Eng 2022; 19. [PMID: 35921806 DOI: 10.1088/1741-2552/ac86a2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 08/03/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVES Deep brain stimulation programming for movement disorders requires systematic fine tuning of stimulation parameters to ameliorate tremor and other symptoms while avoiding side effects. DBS programming can be a time-consuming process and requires clinical expertise to assess response to DBS to optimize therapy for each patient. In this study, we describe and evaluate an automated, closed-loop, and patient-specific framework for DBS programming that measures tremor using a smartwatch and automatically changes DBS parameters based on the recommendations from a closed-loop optimization algorithm thus eliminating the need for an expert clinician. APPROACH Bayesian optimization which is a sample-efficient global optimization method was used as the core of this DBS programming framework to adaptively learn each patient's response to DBS and suggest the next best settings to be evaluated. Input from a clinician was used initially to define a maximum safe amplitude, but we also implemented 'safe Bayesian optimization' to automatically discover tolerable exploration boundaries. RESULTS We tested the system in 15 patients (9 with Parkinson's disease and 6 with essential tremor). Tremor suppression at best automated settings was statistically comparable to previously established clinical settings. The optimization algorithm converged after testing 15.1±0.7 settings when maximum safe exploration boundaries were predefined, and 17.7±4.9 when the algorithm itself determined safe exploration boundaries. SIGNIFICANCE We demonstrate that fully automated DBS programming framework for treatment of tremor is efficient and safe while providing outcomes comparable to that achieved by expert clinicians.
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Affiliation(s)
- Parisa Sarikhani
- Emory University, 101 Woodruff Cir, Suite 4137, Atlanta, Georgia, 30322-1007, UNITED STATES
| | - Benjamin Ferleger
- University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, Pennsylvania, 19104-6243, UNITED STATES
| | - Kyle Mitchell
- Neurology, Duke University, 932 Morreene Rd, Durham, North Carolina, 2770, UNITED STATES
| | - Jill Ostrem
- Neurology, University of California, San Francisco, 1651 Fourth St., Suite 232, San Francisco, California, 94158, UNITED STATES
| | - Jeffrey Herron
- Electrical Engineering, University of Washington, 185 Stevens Way, Room AE100R, Campus Box 352500, Seattle, Washington, 98195, UNITED STATES
| | - Babak Mahmoudi
- Biomedical Informatics, Emory University, 101 Woodruff Cir, Atlanta, Georgia, 30322, UNITED STATES
| | - Svjetlana Miocinovic
- Neurology, Emory University, 12 Executive Park Drive Northeast, Atlanta, Georgia, 30329, UNITED STATES
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17
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Oxenford S, Roediger J, Neudorfer C, Milosevic L, Güttler C, Spindler P, Vajkoczy P, Neumann WJ, Kühn AA, Horn A. Lead-OR: a multimodal platform for deep brain stimulation surgery. eLife 2022; 11:72929. [PMID: 35594135 PMCID: PMC9177150 DOI: 10.7554/elife.72929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 05/19/2022] [Indexed: 11/25/2022] Open
Abstract
Background: Deep brain stimulation (DBS) electrode implant trajectories are stereotactically defined using preoperative neuroimaging. To validate the correct trajectory, microelectrode recordings (MERs) or local field potential recordings can be used to extend neuroanatomical information (defined by MRI) with neurophysiological activity patterns recorded from micro- and macroelectrodes probing the surgical target site. Currently, these two sources of information (imaging vs. electrophysiology) are analyzed separately, while means to fuse both data streams have not been introduced. Methods: Here, we present a tool that integrates resources from stereotactic planning, neuroimaging, MER, and high-resolution atlas data to create a real-time visualization of the implant trajectory. We validate the tool based on a retrospective cohort of DBS patients (N = 52) offline and present single-use cases of the real-time platform. Results: We establish an open-source software tool for multimodal data visualization and analysis during DBS surgery. We show a general correspondence between features derived from neuroimaging and electrophysiological recordings and present examples that demonstrate the functionality of the tool. Conclusions: This novel software platform for multimodal data visualization and analysis bears translational potential to improve accuracy of DBS surgery. The toolbox is made openly available and is extendable to integrate with additional software packages. Funding: Deutsche Forschungsgesellschaft (410169619, 424778381), Deutsches Zentrum für Luft- und Raumfahrt (DynaSti), National Institutes of Health (2R01 MH113929), and Foundation for OCD Research (FFOR). Deep brain stimulation is an established therapy for patients with Parkinson’s disease and an emerging option for other neurological conditions. Electrodes are implanted deep in the brain to stimulate precise brain regions and control abnormal brain activity in those areas. The most common target for Parkinson’s disease, for instance, is a structure called the subthalamic nucleus, which sits at the base of the brain, just above the brain stem. To ensure electrodes are placed correctly, surgeons use various sources of information to characterize the patient’s brain anatomy and decide on an implant site. These data include brain scans taken before surgery and recordings of brain activity taken during surgery to confirm the intended implant site. Sometimes, the brain activity signals from this last confirmation step may slightly alter surgical plans. It represents one of many challenges for clinical teams: to analyse, assimilate, and communicate data as it is collected during the procedure. Oxenford et al. developed a software pipeline to aggregate the data surgeons use to implant electrodes. The open-source platform, dubbed Lead-OR, visualises imaging data and brain activity recordings (termed electrophysiology data) in real time. The current set-up integrates with commercial tools and existing software for surgical planning. Oxenford et al. tested Lead-OR on data gathered retrospectively from 32 patients with Parkinson’s who had electrodes implanted in their subthalamic nucleus. The platform showed good agreement between imaging and electrophysiology data, although there were some unavoidable discrepancies, arising from limitations in the imaging pipeline and from the surgical procedure. Lead-OR was also able to correct for brain shift, which is where the brain moves ever so slightly in the skull. With further validation, this proof-of-concept software could serve as a useful decision-making tool for surgical teams implanting electrodes for deep brain stimulation. In time, if implemented, its use could improve the accuracy of electrode placement, translating into better surgical outcomes for patients. It also has the potential to integrate forthcoming ultra-high-resolution data from current brain mapping projects, and other commercial surgical planning tools.
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Affiliation(s)
- Simon Oxenford
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jan Roediger
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Clemens Neudorfer
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Luka Milosevic
- Krembil Brain Institute, University Health Network, Toronto, Canada
| | - Christopher Güttler
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Philipp Spindler
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Peter Vajkoczy
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Wolf-Julian Neumann
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andrea A Kühn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Horn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
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18
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Meier JM, Perdikis D, Blickensdörfer A, Stefanovski L, Liu Q, Maith O, Dinkelbach HÜ, Baladron J, Hamker FH, Ritter P. Virtual deep brain stimulation: Multiscale co-simulation of a spiking basal ganglia model and a whole-brain mean-field model with the virtual brain. Exp Neurol 2022; 354:114111. [DOI: 10.1016/j.expneurol.2022.114111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 04/04/2022] [Accepted: 05/05/2022] [Indexed: 11/04/2022]
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19
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Bingham CS, McIntyre CC. Subthalamic deep brain stimulation of an anatomically detailed model of the human hyperdirect pathway. J Neurophysiol 2022; 127:1209-1220. [PMID: 35320026 PMCID: PMC9054256 DOI: 10.1152/jn.00004.2022] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 11/22/2022] Open
Abstract
The motor hyperdirect pathway (HDP) is considered a key target in the treatment of Parkinson's disease with subthalamic deep brain stimulation (DBS). This hypothesis is partially derived from the association of HDP activation with evoked potentials (EPs) generated in the motor cortex and subthalamic nucleus (STN) after a DBS pulse. However, the biophysical details of how and when DBS-induced action potentials (APs) in HDP neurons reach their terminations in the cortex or STN remain unclear. Therefore, we used an anatomically detailed representation of the motor HDP, as well as the internal capsule (IC), in a model of human subthalamic DBS to explore AP activation and transmission in the HDP and IC. Our results show that small diameter HDP axons exhibited AP initiation in their subthalamic terminal arbor, which resulted in relatively long transmission latencies to cortex (∼3.5-8 ms). Alternatively, large diameter HDP axons were most likely to be directly activated in the capsular region, which resulted in short transmission times to the cortex (∼1-3 ms). However, those large diameter HDP antidromic APs would be indistinguishable from any other IC axons that were also activated by the stimulus. Conversely, DBS-induced APs in both small and large diameter HDP axons reached their synaptic boutons in the STN with similar timings, but both spanned a wide temporal range (∼0.5-5 ms). We also found that using anodic or bipolar stimulation helped to bias activation of the HDP over the IC. These computational results provide useful information for linking HDP activation with EP recordings in clinical experiments.NEW & NOTEWORTHY We used biophysical models to study pathway recruitment and conduction latencies of the hyperdirect pathway (HDP) in response to subthalamic deep brain stimulation (DBS). The model system allowed us to assess the influence of increased anatomical realism on pathway activity and the possibility of identifying HDP activity in evoked potentials (EPs) recorded in either the subthalamic nucleus (STN) or cortex. The model predicts that HDP activation is accentuated by complex axonal branching in the STN.
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Affiliation(s)
- Clayton S Bingham
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
- Department of Neurosurgery, Duke University, Durham, North Carolina
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20
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Optimal deep brain stimulation sites and networks for cervical vs. generalized dystonia. Proc Natl Acad Sci U S A 2022; 119:e2114985119. [PMID: 35357970 PMCID: PMC9168456 DOI: 10.1073/pnas.2114985119] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
We studied deep brain stimulation effects in two types of dystonia and conclude that different specific connections between the pallidum and thalamus are responsible for optimal treatment effects. Since alternative treatment options for dystonia beyond deep brain stimulation are scarce, our results will be crucial to maximize treatment outcome in this population of patients. Dystonia is a debilitating disease with few treatment options. One effective option is deep brain stimulation (DBS) to the internal pallidum. While cervical and generalized forms of isolated dystonia have been targeted with a common approach to the posterior third of the nucleus, large-scale investigations regarding optimal stimulation sites and potential network effects have not been carried out. Here, we retrospectively studied clinical results following DBS for cervical and generalized dystonia in a multicenter cohort of 80 patients. We model DBS electrode placement based on pre- and postoperative imaging and introduce an approach to map optimal stimulation sites to anatomical space. Second, we investigate which tracts account for optimal clinical improvements, when modulated. Third, we investigate distributed stimulation effects on a whole-brain functional connectome level. Our results show marked differences of optimal stimulation sites that map to the somatotopic structure of the internal pallidum. While modulation of the striatopallidofugal axis of the basal ganglia accounted for optimal treatment of cervical dystonia, modulation of pallidothalamic bundles did so in generalized dystonia. Finally, we show a common multisynaptic network substrate for both phenotypes in the form of connectivity to the cerebellum and somatomotor cortex. Our results suggest a brief divergence of optimal stimulation networks for cervical vs. generalized dystonia within the pallidothalamic loop that merge again on a thalamo-cortical level and share a common whole-brain network.
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21
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Widge AS, Zhang F, Gosai A, Papadimitrou G, Wilson-Braun P, Tsintou M, Palanivelu S, Noecker AM, McIntyre CC, O’Donnell L, McLaughlin NCR, Greenberg BD, Makris N, Dougherty DD, Rathi Y. Patient-specific connectomic models correlate with, but do not reliably predict, outcomes in deep brain stimulation for obsessive-compulsive disorder. Neuropsychopharmacology 2022; 47:965-972. [PMID: 34621015 PMCID: PMC8882183 DOI: 10.1038/s41386-021-01199-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/11/2021] [Accepted: 09/23/2021] [Indexed: 12/15/2022]
Abstract
Deep brain stimulation (DBS) of the ventral internal capsule/ventral striatum (VCVS) is an emerging treatment for obsessive-compulsive disorder (OCD). Recently, multiple studies using normative connectomes have correlated DBS outcomes to stimulation of specific white matter tracts. Those studies did not test whether these correlations are clinically predictive, and did not apply cross-validation approaches that are necessary for biomarker development. Further, they did not account for the possibility of systematic differences between DBS patients and the non-diagnosed controls used in normative connectomes. To address these gaps, we performed patient-specific diffusion imaging in 8 patients who underwent VCVS DBS for OCD. We delineated tracts connecting thalamus and subthalamic nucleus (STN) to prefrontal cortex via VCVS. We then calculated which tracts were likely activated by individual patients' DBS settings. We fit multiple statistical models to predict both OCD and depression outcomes from tract activation. We further attempted to predict hypomania, a VCVS DBS complication. We assessed all models' performance on held-out test sets. With this best-practices approach, no model predicted OCD response, depression response, or hypomania above chance. Coefficient inspection partly supported prior reports, in that capture of tracts projecting to cingulate cortex was associated with both YBOCS and MADRS response. In contrast to prior reports, however, tracts connected to STN were not reliably correlated with response. Thus, patient-specific imaging and a guideline-adherent analysis were unable to identify a tractographic target with sufficient effect size to drive clinical decision-making or predict individual outcomes. These findings suggest caution in interpreting the results of normative connectome studies.
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Affiliation(s)
- Alik S. Widge
- grid.17635.360000000419368657Department of Psychiatry, University of Minnesota, Minneapolis, MN USA
| | - Fan Zhang
- grid.62560.370000 0004 0378 8294Department of Radiology, Brigham and Womens Hospital, Boston, MA USA
| | - Aishwarya Gosai
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - George Papadimitrou
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Peter Wilson-Braun
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Magdalini Tsintou
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Senthil Palanivelu
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Angela M. Noecker
- grid.67105.350000 0001 2164 3847Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH USA
| | - Cameron C. McIntyre
- grid.67105.350000 0001 2164 3847Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH USA
| | - Lauren O’Donnell
- grid.62560.370000 0004 0378 8294Department of Radiology, Brigham and Womens Hospital, Boston, MA USA
| | - Nicole C. R. McLaughlin
- grid.40263.330000 0004 1936 9094Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, Providence, RI USA ,grid.273271.20000 0000 8593 9332Butler Hospital, Providence, RI USA
| | - Benjamin D. Greenberg
- grid.40263.330000 0004 1936 9094Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, Providence, RI USA ,grid.273271.20000 0000 8593 9332Butler Hospital, Providence, RI USA ,Center for Neurorestoration and Neurotechnology, VA Providence Healthcare System, Providence, RI USA
| | - Nikolaos Makris
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Darin D. Dougherty
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Yogesh Rathi
- grid.62560.370000 0004 0378 8294Department of Radiology, Brigham and Womens Hospital, Boston, MA USA ,grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
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22
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Tödt I, Al-Fatly B, Granert O, Kühn AA, Krack P, Rau J, Timmermann L, Schnitzler A, Paschen S, Helmers AK, Hartmann A, Bardinet E, Schuepbach M, Barbe MT, Dembek TA, Fraix V, Kübler D, Brefel-Courbon C, Gharabaghi A, Wojtecki L, Pinsker MO, Thobois S, Damier P, Witjas T, Houeto JL, Schade-Brittinger C, Vidailhet M, Horn A, Deuschl G. The Contribution of Subthalamic Nucleus Deep Brain Stimulation to the Improvement in Motor Functions and Quality of Life. Mov Disord 2022; 37:291-301. [PMID: 35112384 DOI: 10.1002/mds.28952] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 01/17/2022] [Accepted: 01/17/2022] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Subthalamic nucleus deep brain stimulation (STN-DBS) effectively treats motor symptoms and quality of life (QoL) of advanced and fluctuating early Parkinson's disease. Little is known about the relation between electrode position and changes in symptom control and ultimately QoL. OBJECTIVES The relation between the stimulated part of the STN and clinical outcomes, including the motor score of the Unified Parkinson's Disease Rating Scale (UPDRS) and the quality-of-life questionnaire, was assessed in a subcohort of the EARLYSTIM study. METHODS Sixty-nine patients from the EARLYSTIM cohort who underwent DBS, with a comprehensive clinical characterization before and 24 months after surgery, were included. Intercorrelations of clinical outcome changes, correlation between the affected functional parts of the STN, and changes in clinical outcomes were investigated. We further calculated sweet spots for different clinical parameters. RESULTS Improvements in the UPDRS III and Parkinson's Disease Questionnaire (PDQ-39) correlated positively with the extent of the overlap with the sensorimotor STN. The sweet spots for the UPDRS III (x = 11.6, y = -13.1, z = -6.3) and the PDQ-39 differed (x = 14.8, y = -12.4, z = -4.3) ~3.8 mm. CONCLUSIONS The main influence of DBS on QoL is likely mediated through the sensory-motor basal ganglia loop. The PDQ sweet spot is located in a posteroventral spatial location in the STN territory. For aspects of QoL, however, there was also evidence of improvement through stimulation of the other STN subnuclei. More research is necessary to customize the DBS target to individual symptoms of each patient. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Inken Tödt
- Department of Neurology, University Hospital Schleswig Holstein, Kiel, Germany
| | - Bassam Al-Fatly
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité Medicine University of Berlin, Berlin, Germany
| | - Oliver Granert
- Department of Neurology, University Hospital Schleswig Holstein, Kiel, Germany
| | - Andrea A Kühn
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité Medicine University of Berlin, Berlin, Germany
| | - Paul Krack
- Department of Neurology, University Hospital Bern and University of Bern, Bern, Switzerland
| | - Joern Rau
- Coordinating Center for Clinical Trials, Philipps-University, Marburg, Germany
| | - Lars Timmermann
- Department of Neurology, University Hospital Giessen and Marburg, Marburg, Germany
| | - Alfons Schnitzler
- Department of Neurology, Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Duesseldorf, Duesseldorf, Germany
| | - Steffen Paschen
- Department of Neurology, University Hospital Schleswig Holstein, Kiel, Germany
| | - Ann-Kristin Helmers
- Department of Neurosurgery, University Hospital Schleswig Holstein, Kiel, Germany
| | - Andreas Hartmann
- Assistance-Publique Hôpitaux de Paris, Center d'Investigation Clinique 9503, Institut du Cerveau et de la Moelle épinière, Paris, France.,Département de Neurologie, Université Pierre et Marie Curie-Paris 6 et INSERM, Paris, France
| | - Eric Bardinet
- Department of Neurology, NS-PARK/F-CRIN, University Hospital of Besançon, Besançon, France.,Center de Neuroimagerie de Recherche, Institut du Cerveau et de la Moelle (ICM), Paris, France
| | - Michael Schuepbach
- Department of Neurology, University Hospital Bern and University of Bern, Bern, Switzerland.,Assistance-Publique Hôpitaux de Paris, Center d'Investigation Clinique 9503, Institut du Cerveau et de la Moelle épinière, Paris, France.,Département de Neurologie, Université Pierre et Marie Curie-Paris 6 et INSERM, Paris, France.,Institute of Neurology, Konolfingen, Switzerland
| | - Michael T Barbe
- Department of Neurology, University of Cologne, Faculty of Medicine, Cologne, Germany
| | - Till A Dembek
- Department of Neurology, University of Cologne, Faculty of Medicine, Cologne, Germany
| | - Valerie Fraix
- Université Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Grenoble, France.,Neurology Department, Grenoble University Hospital, Grenoble, France
| | - Dorothee Kübler
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité Medicine University of Berlin, Berlin, Germany
| | | | - Alireza Gharabaghi
- Department of Neurosurgery and Neurotechnology Institute for Neuromodulation and Neurotechnology, University Hospital and University of Tuebingen, Tuebingen, Germany
| | - Lars Wojtecki
- Department of Neurology and Neurorehabilitation, Hospital zum Heiligen Geist GmbH & Co.KG Academic Teaching Hospital of the Heinrich-Heine-University Düsseldorf Von-Broichhausen-Allee 1, Kempen, Germany
| | - Marcus O Pinsker
- Department of Neurosurgery, University of Freiburg, Freiburg, Germany
| | - Stephane Thobois
- Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer, Service de Neurologie C, Center Expert Parkinson, Bron, France.,Université Lyon, Université Claude Bernard Lyon 1, Faculté de Médecine Lyon Sud Charles Mérieux, Oullins, France
| | | | - Tatiana Witjas
- Department of Neurology, Timone University Hospital UMR 7289, CNRS Marseille, Marseille, France
| | - Jean-Luc Houeto
- Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer, Service de Neurologie C, Center Expert Parkinson, Bron, France
| | | | - Marie Vidailhet
- Department of Neurology, Sorbonne Université, ICM UMR1127, INSERM &1127, CNRS 7225, Salpêtriere University Hospital AP-HP, Paris, France
| | - Andreas Horn
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité Medicine University of Berlin, Berlin, Germany
| | - Günther Deuschl
- Department of Neurology, University Hospital Schleswig Holstein, Kiel, Germany
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23
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Frankemolle-Gilbert AM, Howell B, Bower KL, Veltink PH, Heida T, McIntyre CC. Comparison of methodologies for modeling directional deep brain stimulation electrodes. PLoS One 2021; 16:e0260162. [PMID: 34910744 PMCID: PMC8673613 DOI: 10.1371/journal.pone.0260162] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 11/03/2021] [Indexed: 11/18/2022] Open
Abstract
Deep brain stimulation (DBS) is an established clinical therapy, and directional DBS electrode designs are now commonly used in clinical practice. Directional DBS leads have the ability to increase the therapeutic window of stimulation, but they also increase the complexity of clinical programming. Therefore, computational models of DBS have become available in clinical software tools that are designed to assist in the identification of therapeutic settings. However, the details of how the DBS model is implemented can influence the predictions of the software. The goal of this study was to compare different methods for representing directional DBS electrodes within finite element volume conductor (VC) models. We evaluated 15 different DBS VC model variants and quantified how their differences influenced estimates on the spatial extent of axonal activation from DBS. Each DBS VC model included the same representation of the brain and head, but the details of the current source and electrode contact were different for each model variant. The more complex VC models explicitly represented the DBS electrode contacts, while the more simple VC models used boundary condition approximations. The more complex VC models required 2-3 times longer to mesh, build, and solve for the DBS voltage distribution than the more simple VC models. Differences in individual axonal activation thresholds across the VC model variants were substantial (-24% to +47%). However, when comparing total activation of an axon population, or estimates of an activation volume, the differences between model variants decreased (-7% to +8%). Nonetheless, the technical details of how the electrode contact and current source are represented in the DBS VC model can directly affect estimates of the voltage distribution and electric field in the brain tissue.
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Affiliation(s)
- Anneke M. Frankemolle-Gilbert
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
- MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Bryan Howell
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Kelsey L. Bower
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Peter H. Veltink
- MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Tjitske Heida
- MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Cameron C. McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
- * E-mail:
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24
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Baldermann JC, Schüller T, Kohl S, Voon V, Li N, Hollunder B, Figee M, Haber SN, Sheth SA, Mosley PE, Huys D, Johnson KA, Butson C, Ackermans L, Bouwens van der Vlis T, Leentjens AFG, Barbe M, Visser-Vandewalle V, Kuhn J, Horn A. Connectomic Deep Brain Stimulation for Obsessive-Compulsive Disorder. Biol Psychiatry 2021; 90:678-688. [PMID: 34482949 DOI: 10.1016/j.biopsych.2021.07.010] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 06/29/2021] [Accepted: 07/01/2021] [Indexed: 01/17/2023]
Abstract
Obsessive-compulsive disorder is among the most disabling psychiatric disorders. Although deep brain stimulation is considered an effective treatment, its use in clinical practice is not fully established. This is, at least in part, due to ambiguity about the best suited target and insufficient knowledge about underlying mechanisms. Recent advances suggest that changes in broader brain networks are responsible for improvement of obsessions and compulsions, rather than local impact at the stimulation site. These findings were fueled by innovative methodological approaches using brain connectivity analyses in combination with neuromodulatory interventions. Such a connectomic approach for neuromodulation constitutes an integrative account that aims to characterize optimal target networks. In this critical review, we integrate findings from connectomic studies and deep brain stimulation interventions to characterize a neural network presumably effective in reducing obsessions and compulsions. To this end, we scrutinize methodologies and seemingly conflicting findings with the aim to merge observations to identify common and diverse pathways for treating obsessive-compulsive disorder. Ultimately, we propose a unified network that-when modulated by means of cortical or subcortical interventions-alleviates obsessive-compulsive symptoms.
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Affiliation(s)
- Juan Carlos Baldermann
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
| | - Thomas Schüller
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Sina Kohl
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Valerie Voon
- Department of Psychiatry, Cambridge University, Cambridge, United Kingdom
| | - Ningfei Li
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Berlin, Germany
| | - Barbara Hollunder
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Berlin, Germany; Einstein Center for Neurosciences, Charité - University Medicine Berlin, Berlin, Germany; Faculty of Philosophy, Humboldt University of Berlin, Berlin School of Mind and Brain, Berlin, Germany
| | - Martijn Figee
- Department of Psychiatry, Mount Sinai Hospital, New York, New York
| | - Suzanne N Haber
- Department of Pharmacology and Physiology, University of Rochester School of Medicine, Rochester, New York; Basic Neuroscience Division, Harvard Medical School, McLean Hospital, Belmont, Massachusetts
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Philip E Mosley
- Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia; Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia
| | - Daniel Huys
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Kara A Johnson
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida
| | - Christopher Butson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah; Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah
| | - Linda Ackermans
- School of Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | | | - Albert F G Leentjens
- School of Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Michael Barbe
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jens Kuhn
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Department of Psychiatry, Psychotherapy and Psychosomatic, Johanniter Hospital Oberhausen, Oberhausen, Germany
| | - Andreas Horn
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Berlin, Germany
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25
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Li N, Hollunder B, Baldermann JC, Kibleur A, Treu S, Akram H, Al-Fatly B, Strange BA, Barcia JA, Zrinzo L, Joyce EM, Chabardes S, Visser-Vandewalle V, Polosan M, Kuhn J, Kühn AA, Horn A. A Unified Functional Network Target for Deep Brain Stimulation in Obsessive-Compulsive Disorder. Biol Psychiatry 2021; 90:701-713. [PMID: 34134839 DOI: 10.1016/j.biopsych.2021.04.006] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/26/2021] [Accepted: 04/12/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND Multiple deep brain stimulation (DBS) targets have been proposed for treating intractable obsessive-compulsive disorder (OCD). Here, we investigated whether stimulation effects of different target sites would be mediated by one common or several segregated functional brain networks. METHODS First, seeding from active electrodes of 4 OCD patient cohorts (N = 50) receiving DBS to anterior limb of the internal capsule or subthalamic nucleus zones, optimal functional connectivity profiles for maximal Yale-Brown Obsessive Compulsive Scale improvements were calculated and cross-validated in leave-one-cohort-out and leave-one-patient-out designs. Second, we derived optimal target-specific connectivity patterns to determine brain regions mutually predictive of clinical outcome for both targets and others predictive for either target alone. Functional connectivity was defined using resting-state functional magnetic resonance imaging data acquired in 1000 healthy participants. RESULTS While optimal functional connectivity profiles showed both commonalities and differences between target sites, robust cross-predictions of clinical improvements across OCD cohorts and targets suggested a shared network. Connectivity to the anterior cingulate cortex, insula, and precuneus, among other regions, was predictive regardless of stimulation target. Regions with maximal connectivity to these commonly predictive areas included the insula, superior frontal gyrus, anterior cingulate cortex, and anterior thalamus, as well as the original stereotactic targets. CONCLUSIONS Pinpointing the network modulated by DBS for OCD from different target sites identified a set of brain regions to which DBS electrodes associated with optimal outcomes were functionally connected-regardless of target choice. On these grounds, we establish potential brain areas that could prospectively inform additional or alternative neuromodulation targets for obsessive-compulsive disorder.
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Affiliation(s)
- Ningfei Li
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Berlin, Germany.
| | - Barbara Hollunder
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Berlin, Germany; Charité - Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany; Berlin School of Mind and Brain, Faculty of Philosophy, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Juan Carlos Baldermann
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne; Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Astrid Kibleur
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut des Neurosciences (AK, SC, MP), Grenoble; and OpenMind Innovation (AK), Paris, France; OpenMind Innovation, Paris, France
| | - Svenja Treu
- Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Harith Akram
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom; National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Trust (UCLH), London, United Kingdom
| | - Bassam Al-Fatly
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Berlin, Germany
| | - Bryan A Strange
- Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Juan A Barcia
- Neurosurgery Department, Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - Ludvic Zrinzo
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom; National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Trust (UCLH), London, United Kingdom
| | - Eileen M Joyce
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom; National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Trust (UCLH), London, United Kingdom
| | - Stephan Chabardes
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut des Neurosciences (AK, SC, MP), Grenoble; and OpenMind Innovation (AK), Paris, France
| | | | - Mircea Polosan
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut des Neurosciences (AK, SC, MP), Grenoble; and OpenMind Innovation (AK), Paris, France
| | - Jens Kuhn
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, Cologne, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, Johanniter Hospital Oberhausen, Evangelisches Klinikum Niederrhein, Oberhausen, Germany
| | - Andrea A Kühn
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Berlin, Germany; Charité - Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany; Berlin School of Mind and Brain, Faculty of Philosophy, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Andreas Horn
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology, Berlin, Germany; Charité - Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
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26
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DiODe v2: Unambiguous and Fully-Automated Detection of Directional DBS Lead Orientation. Brain Sci 2021; 11:brainsci11111450. [PMID: 34827449 PMCID: PMC8615850 DOI: 10.3390/brainsci11111450] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 10/26/2021] [Accepted: 10/28/2021] [Indexed: 11/17/2022] Open
Abstract
Directional deep brain stimulation (DBS) leads are now widely used, but the orientation of directional leads needs to be taken into account when relating DBS to neuroanatomy. Methods that can reliably and unambiguously determine the orientation of directional DBS leads are needed. In this study, we provide an enhanced algorithm that determines the orientation of directional DBS leads from postoperative CT scans. To resolve the ambiguity of symmetric CT artifacts, which in the past, limited the orientation detection to two possible solutions, we retrospectively evaluated four different methods in 150 Cartesia™ directional leads, for which the true solution was known from additional X-ray images. The method based on shifts of the center of mass (COM) of the directional marker compared to its expected geometric center correctly resolved the ambiguity in 100% of cases. In conclusion, the DiODe v2 algorithm provides an open-source, fully automated solution for determining the orientation of directional DBS leads.
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Connectivity correlates to predict essential tremor deep brain stimulation outcome: Evidence for a common treatment pathway. NEUROIMAGE-CLINICAL 2021; 32:102846. [PMID: 34624639 PMCID: PMC8503569 DOI: 10.1016/j.nicl.2021.102846] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 08/14/2021] [Accepted: 09/27/2021] [Indexed: 11/20/2022]
Abstract
BACKGROUND AND PURPOSE Deep brain stimulation (DBS) is the most common surgical treatment for essential tremor (ET), yet there is variation in outcome and stimulation targets. This study seeks to consolidate proposed stimulation "sweet spots," as well as assess the value of structural connectivity in predicting treatment outcomes. MATERIALS AND METHODS Ninety-seven ET individuals with unilateral thalamic DBS were retrospectively included. Using normative brain connectomes, structural connectivity measures were correlated with the percentage improvement in contralateral tremor, based on the Fahn-Tolosa-Marin tremor rating scale (TRS), after parameter optimization (range 3.1-12.9 months) using a leave-one-out cross-validation in 83 individuals. The predictive feature map was used for cross-validation in a separate cohort of 14 ET individuals treated at another center. Lastly, estimated volumes of tissue activated (VTA) were used to assess a treatment "sweet spot," which was compared to seven previously reported stimulation sweet spots and their relationship to the tract identified by the predictive feature map. RESULTS In the training cohort, structural connectivity between the VTA and dentato-rubro-thalamic tract (DRTT) correlated with contralateral tremor improvement (R = 0.41; p < 0.0001). The same connectivity profile predicted outcomes in a separate validation cohort (R = 0.59; p = 0.028). The predictive feature map represented the anatomical course of the DRTT, and all seven analyzed sweet spots overlapped the predictive tract (DRTT). CONCLUSIONS Our results strongly support the possibility that structural connectivity is a predictor of contralateral tremor improvement in ET DBS. The results suggest the future potential for a patient-specific functionally based surgical target. Finally, the results showed convergence in "sweet spots" suggesting the importance of the DRTT to the outcome.
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28
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Howell B, Isbaine F, Willie JT, Opri E, Gross RE, De Hemptinne C, Starr PA, McIntyre CC, Miocinovic S. Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation. Brain Stimul 2021; 14:549-563. [PMID: 33757931 DOI: 10.1016/j.brs.2021.03.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Revised: 02/19/2021] [Accepted: 03/14/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Subthalamic deep brain stimulation (DBS) is an effective surgical treatment for Parkinson's disease and continues to advance technologically with an enormous parameter space. As such, in-silico DBS modeling systems have become common tools for research and development, but their underlying methods have yet to be standardized and validated. OBJECTIVE Evaluate the accuracy of patient-specific estimates of neural pathway activations in the subthalamic region against intracranial, cortical evoked potential (EP) recordings. METHODS Pathway activations were modeled in eleven patients using the latest advances in connectomic modeling of subthalamic DBS, focusing on the hyperdirect pathway (HDP) and corticospinal/bulbar tract (CSBT) for their relevance in human research studies. Correlations between pathway activations and respective EP amplitudes were quantified. RESULTS Good model performance required accurate lead localization and image fusions, as well as appropriate selection of fiber diameter in the biophysical model. While optimal model parameters varied across patients, good performance could be achieved using a global set of parameters that explained 60% and 73% of electrophysiologic activations of CSBT and HDP, respectively. Moreover, restricted models fit to only EP amplitudes of eight standard (monopolar and bipolar) electrode configurations were able to extrapolate variation in EP amplitudes across other directional electrode configurations and stimulation parameters, with no significant reduction in model performance across the cohort. CONCLUSIONS Our findings demonstrate that connectomic models of DBS with sufficient anatomical and electrical details can predict recruitment dynamics of white matter. These results will help to define connectomic modeling standards for preoperative surgical targeting and postoperative patient programming applications.
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Affiliation(s)
- Bryan Howell
- Department of Biomedical Engineering, Case Western Reserve University, USA
| | | | - Jon T Willie
- Department of Neurosurgery, Emory University, USA
| | - Enrico Opri
- Department of Neurology, Emory University, USA
| | | | | | - Philip A Starr
- Department of Neurological Surgery, University of California San Francisco, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, USA
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