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Ragguett RM, Eagleson R, de Ribaupierre S. Evaluating normalized registration and preprocessing methodologies for the analysis of brain MRI in pediatric patients with shunt-treated hydrocephalus. Front Neurosci 2024; 18:1405363. [PMID: 38887369 PMCID: PMC11182356 DOI: 10.3389/fnins.2024.1405363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 05/06/2024] [Indexed: 06/20/2024] Open
Abstract
Introduction Registration to a standardized template (i.e. "normalization") is a critical step when performing neuroimaging studies. We present a comparative study involving the evaluation of general-purpose registration algorithms for pediatric patients with shunt treated hydrocephalus. Our sample dataset presents a number of intersecting challenges for registration, representing the potentially large deformations to both brain structures and overall brain shape, artifacts from shunts, and morphological differences corresponding to age. The current study assesses the normalization accuracy of shunt-treated hydrocephalus patients using freely available neuroimaging registration tools. Methods Anatomical neuroimages from eight pediatric patients with shunt-treated hydrocephalus were normalized. Four non-linear registration algorithms were assessed in addition to the preprocessing steps of skull-stripping and bias-correction. Registration accuracy was assessed using the Dice Coefficient (DC) and Hausdorff Distance (HD) in subcortical and cortical regions. Results A total of 592 registrations were performed. On average, normalizations performed using the brain extracted and bias-corrected images had a higher DC and lower HD compared to full head/ non-biased corrected images. The most accurate registration was achieved using SyN by ANTs with skull-stripped and bias corrected images. Without preprocessing, the DARTEL Toolbox was able to produce normalized images with comparable accuracy. The use of a pediatric template as an intermediate registration did not improve normalization. Discussion Using structural neuroimages from patients with shunt-treated pediatric hydrocephalus, it was demonstrated that there are tools which perform well after specified pre-processing steps were taken. Overall, these results provide insight to the performance of registration programs that can be used for normalization of brains with complex pathologies.
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Affiliation(s)
| | - Roy Eagleson
- School of Biomedical Engineering, Western University, London, ON, Canada
- Department of Electrical and Computer Engineering, Western University, London, ON, Canada
- Centre for Brain and Mind, Western University, London, ON, Canada
| | - Sandrine de Ribaupierre
- School of Biomedical Engineering, Western University, London, ON, Canada
- Centre for Brain and Mind, Western University, London, ON, Canada
- Department of Clinical Neurological Sciences, Schulich School of Medicine, Western University, London, ON, Canada
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2
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Vogel D, Nordin T, Feiler S, Wårdell K, Coste J, Lemaire JJ, Hemm S. Probabilistic stimulation mapping from intra-operative thalamic deep brain stimulation data in essential tremor. J Neural Eng 2024; 21:036017. [PMID: 38701768 DOI: 10.1088/1741-2552/ad4742] [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: 11/17/2023] [Accepted: 05/03/2024] [Indexed: 05/05/2024]
Abstract
Deep brain stimulation (DBS) is a therapy for Parkinson's disease (PD) and essential tremor (ET). The mechanism of action of DBS is still incompletely understood. Retrospective group analysis of intra-operative data recorded from ET patients implanted in the ventral intermediate nucleus of the thalamus (Vim) is rare. Intra-operative stimulation tests generate rich data and their use in group analysis has not yet been explored.Objective.To implement, evaluate, and apply a group analysis workflow to generate probabilistic stimulation maps (PSMs) using intra-operative stimulation data from ET patients implanted in Vim.Approach.A group-specific anatomical template was constructed based on the magnetic resonance imaging scans of 6 ET patients and 13 PD patients. Intra-operative test data (total:n= 1821) from the 6 ET patients was analyzed: patient-specific electric field simulations together with tremor assessments obtained by a wrist-based acceleration sensor were transferred to this template. Occurrence and weighted mean maps were generated. Voxels associated with symptomatic response were identified through a linear mixed model approach to form a PSM. Improvements predicted by the PSM were compared to those clinically assessed. Finally, the PSM clusters were compared to those obtained in a multicenter study using data from chronic stimulation effects in ET.Main results.Regions responsible for improvement identified on the PSM were in the posterior sub-thalamic area (PSA) and at the border between the Vim and ventro-oral nucleus of the thalamus (VO). The comparison with literature revealed a center-to-center distance of less than 5 mm and an overlap score (Dice) of 0.4 between the significant clusters. Our workflow and intra-operative test data from 6 ET-Vim patients identified effective stimulation areas in PSA and around Vim and VO, affirming existing medical literature.Significance.This study supports the potential of probabilistic analysis of intra-operative stimulation test data to reveal DBS's action mechanisms and to assist surgical planning.
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Affiliation(s)
- Dorian Vogel
- Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Hofackerstrasse 30, Muttenz, Switzerland
| | - Teresa Nordin
- Department of Biomedical Engineering, Linköping University, Campus US, Linköping, Sweden
| | - Stefanie Feiler
- Dynamics and statistics of complex systems, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Hofackerstrasse 30, Muttenz, Switzerland
| | - Karin Wårdell
- Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Hofackerstrasse 30, Muttenz, Switzerland
- Department of Biomedical Engineering, Linköping University, Campus US, Linköping, Sweden
| | - Jérôme Coste
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, Clermont-Ferrand, France
- Service de Neurochirurgie, Hôpital Gabriel-Montpied, Centre Hospitalier Universitaire de Clermont-Ferrand, 58 rue Montalembert, Clermont-Ferrand, France
| | - Jean-Jacques Lemaire
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, Clermont-Ferrand, France
- Service de Neurochirurgie, Hôpital Gabriel-Montpied, Centre Hospitalier Universitaire de Clermont-Ferrand, 58 rue Montalembert, Clermont-Ferrand, France
| | - Simone Hemm
- Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Hofackerstrasse 30, Muttenz, Switzerland
- Department of Biomedical Engineering, Linköping University, Campus US, Linköping, Sweden
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3
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Varga Z, Keller J, Robinson SD, Serranova T, Nepozitek J, Zogala D, Trnka J, Ruzicka E, Sonka K, Dusek P. Whole brain pattern of iron accumulation in REM sleep behavior disorder. Hum Brain Mapp 2024; 45:e26675. [PMID: 38590155 PMCID: PMC11002348 DOI: 10.1002/hbm.26675] [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: 10/17/2023] [Revised: 03/10/2024] [Accepted: 03/21/2024] [Indexed: 04/10/2024] Open
Abstract
Isolated REM sleep behavior disorder (iRBD) is an early stage of synucleinopathy with most patients progressing to Parkinson's disease (PD) or related conditions. Quantitative susceptibility mapping (QSM) in PD has identified pathological iron accumulation in the substantia nigra (SN) and variably also in basal ganglia and cortex. Analyzing whole-brain QSM across iRBD, PD, and healthy controls (HC) may help to ascertain the extent of neurodegeneration in prodromal synucleinopathy. 70 de novo PD patients, 70 iRBD patients, and 60 HCs underwent 3 T MRI. T1 and susceptibility-weighted images were acquired and processed to space standardized QSM. Voxel-based analyses of grey matter magnetic susceptibility differences comparing all groups were performed on the whole brain and upper brainstem levels with the statistical threshold set at family-wise error-corrected p-values <.05. Whole-brain analysis showed increased susceptibility in the bilateral fronto-parietal cortex of iRBD patients compared to both PD and HC. This was not associated with cortical thinning according to the cortical thickness analysis. Compared to iRBD, PD patients had increased susceptibility in the left amygdala and hippocampal region. Upper brainstem analysis revealed increased susceptibility within the bilateral SN for both PD and iRBD compared to HC; changes were located predominantly in nigrosome 1 in the former and nigrosome 2 in the latter group. In the iRBD group, abnormal dopamine transporter SPECT was associated with increased susceptibility in nigrosome 1. iRBD patients display greater fronto-parietal cortex involvement than incidental early-stage PD cohort indicating more widespread subclinical neuropathology. Dopaminergic degeneration in the substantia nigra is paralleled by susceptibility increase, mainly in nigrosome 1.
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Affiliation(s)
- Zsoka Varga
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - Jiri Keller
- Radiodiagnostic DepartmentNa Homolce HospitalPragueCzech Republic
| | - Simon Daniel Robinson
- Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaAustria
| | - Tereza Serranova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - Jiri Nepozitek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - David Zogala
- Department of Nuclear Medicine, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - Jiri Trnka
- Department of Nuclear Medicine, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - Evzen Ruzicka
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - Karel Sonka
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
| | - Petr Dusek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
- Department of Radiology, First Faculty of MedicineCharles University and General University Hospital in PragueCzech Republic
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4
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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: 13] [Impact Index Per Article: 13.0] [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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5
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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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6
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Nguyen HP, Lee HJ, Kim S. Feasibility study for the automatic surgical planning method based on statistical model. J Orthop Surg Res 2023; 18:398. [PMID: 37264435 DOI: 10.1186/s13018-023-03870-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/21/2023] [Indexed: 06/03/2023] Open
Abstract
PURPOSE In this study, we proposed establishing an automatic computer-assisted surgical planning approach based on average population models. METHODS We built the average population models from humerus datasets using the Advanced Normalization Toolkits (ANTs) and Shapeworks. Experiments include (1) evaluation of the average population models before surgical planning and (2) validation of the average population models in the context of predicting clinical landmarks on the humerus from the new dataset that was not involved in the process of building the average population model. The evaluation experiment consists of explained variation and distance model. The validation experiment calculated the root-mean-square error (RMSE) between the expert-determined clinical ground truths and the landmarks transferred from the average population model to the new dataset. The evaluation results and validation results when using the templates built from ANTs were compared to when using the mean shape generated from Shapeworks. RESULTS The average population models predicted clinical locations on the new dataset with acceptable errors when compared to the ground truth determined by an expert. However, the templates built from ANTs present better accuracy in landmark prediction when compared to the mean shape built from the Shapeworks. CONCLUSION The average population model could be utilized to assist anatomical landmarks checking automatically and following surgical decisions for new patients who are not involved in the dataset used to generate the average population model.
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Affiliation(s)
- Hang Phuong Nguyen
- Department of Electrical, Electronic, and Computer Engineering, University of Ulsan, Ulsan, Korea
| | - Hyun-Joo Lee
- Department of Orthopaedic Surgery, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Korea
| | - Sungmin Kim
- Department of Electrical, Electronic, and Computer Engineering, University of Ulsan, Ulsan, Korea.
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7
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Xu Y, Qin G, Tan B, Fan S, An Q, Gao Y, Fan H, Xie H, Wu D, Liu H, Yang G, Fang H, Xiao Z, Zhang J, Zhang H, Shi L, Yang A. Deep Brain Stimulation Electrode Reconstruction: Comparison between Lead-DBS and Surgical Planning System. J Clin Med 2023; 12:jcm12051781. [PMID: 36902568 PMCID: PMC10002993 DOI: 10.3390/jcm12051781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/12/2023] [Accepted: 02/16/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND Electrode reconstruction for postoperative deep brain simulation (DBS) can be achieved manually using a surgical planning system such as Surgiplan, or in a semi-automated manner using software such as the Lead-DBS toolbox. However, the accuracy of Lead-DBS has not been thoroughly addressed. METHODS In our study, we compared the DBS reconstruction results of Lead-DBS and Surgiplan. We included 26 patients (21 with Parkinson's disease and 5 with dystonia) who underwent subthalamic nucleus (STN)-DBS, and reconstructed the DBS electrodes using the Lead-DBS toolbox and Surgiplan. The electrode contact coordinates were compared between Lead-DBS and Surgiplan with postoperative CT and MRI. The relative positions of the electrode and STN were also compared between the methods. Finally, the optimal contact during follow-up was mapped onto the Lead-DBS reconstruction results to check for overlap between the contacts and the STN. RESULTS We found significant differences in all axes between Lead-DBS and Surgiplan with postoperative CT, with the mean variance for the X, Y, and Z coordinates being -0.13, -1.16, and 0.59 mm, respectively. Y and Z coordinates showed significant differences between Lead-DBS and Surgiplan with either postoperative CT or MRI. However, no significant difference in the relative distance of the electrode and the STN was found between the methods. All optimal contacts were located in the STN, with 70% of them located within the dorsolateral region of the STN in the Lead-DBS results. CONCLUSIONS Although significant differences in electrode coordinates existed between Lead-DBS and Surgiplan, our results suggest that the coordinate difference was around 1 mm, and Lead-DBS can capture the relative distance between the electrode and the DBS target, suggesting it is reasonably accurate for postoperative DBS reconstruction.
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Affiliation(s)
- Yichen Xu
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Guofan Qin
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Bojing Tan
- Department of Neurosurgery, Capital Institute of Pediatrics, Beijing 100020, China
| | - Shiying Fan
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Qi An
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Yuan Gao
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Houyou Fan
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Hutao Xie
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Delong Wu
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Huanguang Liu
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Guang Yang
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150007, China
| | - Huaying Fang
- Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing 100089, China
- Academy for Multidisciplinary Studies, Capital Normal University, Beijing 100089, China
| | - Zunyu Xiao
- Molecular Imaging Research Center, Harbin Medical University, Harbin 150076, China
| | - Jianguo Zhang
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China
| | - Hua Zhang
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
- Correspondence: (H.Z.); (L.S.); (A.Y.)
| | - Lin Shi
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
- Correspondence: (H.Z.); (L.S.); (A.Y.)
| | - Anchao Yang
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China
- Correspondence: (H.Z.); (L.S.); (A.Y.)
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8
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Baniasadi M, Petersen MV, Gonçalves J, Horn A, Vlasov V, Hertel F, Husch A. DBSegment: Fast and robust segmentation of deep brain structures considering domain generalization. Hum Brain Mapp 2023; 44:762-778. [PMID: 36250712 PMCID: PMC9842883 DOI: 10.1002/hbm.26097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/30/2022] [Accepted: 09/15/2022] [Indexed: 01/25/2023] Open
Abstract
Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage.
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Affiliation(s)
- Mehri Baniasadi
- National Department of Neurosurgery, Centre Hospitalier deLuxembourg Center for Systems Biomedicine, University of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Mikkel V. Petersen
- Department of Clinical Medicine, Center of Functionally Integrative NeuroscienceUniversity of AarhusAarhusDenmark
| | - Jorge Gonçalves
- Luxembourg Center for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Andreas Horn
- Neuromodulation and Movement Disorders Unit, Department of NeurologyCharité–Universitätsmedizin BerlinBerlinGermany
- MGH Neurosurgery and Center for Neurotechnology and Neurorecovery at MGH Neurology Massachusetts General HospitalHarvard Medical SchoolBostonUSA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's HospitalHarvard Medical SchoolBostonUSA
| | - Vanja Vlasov
- Luxembourg Center for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Frank Hertel
- National Department of NeurosurgeryCentre Hospitalier de LuxembourgLuxembourg
| | - Andreas Husch
- Luxembourg Center for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
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9
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Oxenford S, Roediger J, Neudorfer C, Milosevic L, Güttler C, Spindler P, Vajkoczy P, Neumann WJ, Kühn A, Horn A. Lead-OR: A multimodal platform for deep brain stimulation surgery. eLife 2022; 11:e72929. [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] [MESH Headings] [Grants] [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).
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Affiliation(s)
- Simón Oxenford
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
| | - Jan Roediger
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
- Charité — Universitätsmedizin Berlin, Einstein Center for Neurosciences BerlinBerlinGermany
| | - Clemens Neudorfer
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
- Center for Brain Circuit Therapeutics Department of Neurology, Brigham & Women’s Hospital, Harvard Medical SchoolBostonUnited States
- MGH Neurosurgery & Center for Neurotechnology and Neurorecovery (CNTR) at MGH Neurology Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
| | - Luka Milosevic
- Institute of Biomedical Engineering, University of TorontoTorontoCanada
- Krembil Brain Institute, University Health NetworkTorontoCanada
| | - Christopher Güttler
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
| | - Philipp Spindler
- Department of Neurosurgery, Charité — Universitätsmedizin BerlinBerlinGermany
| | - Peter Vajkoczy
- Department of Neurosurgery, Charité — Universitätsmedizin BerlinBerlinGermany
| | - Wolf-Julian Neumann
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
| | - Andrea Kühn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
| | - Andreas Horn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
- Center for Brain Circuit Therapeutics Department of Neurology, Brigham & Women’s Hospital, Harvard Medical SchoolBostonUnited States
- MGH Neurosurgery & Center for Neurotechnology and Neurorecovery (CNTR) at MGH Neurology Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
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10
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Wårdell K, Nordin T, Vogel D, Zsigmond P, Westin CF, Hariz M, Hemm S. Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization. Front Neurosci 2022; 16:834026. [PMID: 35478842 PMCID: PMC9036439 DOI: 10.3389/fnins.2022.834026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 03/01/2022] [Indexed: 01/10/2023] Open
Abstract
Deep brain stimulation (DBS) is a well-established neurosurgical procedure for movement disorders that is also being explored for treatment-resistant psychiatric conditions. This review highlights important consideration for DBS simulation and data analysis. The literature on DBS has expanded considerably in recent years, and this article aims to identify important trends in the field. During DBS planning, surgery, and follow up sessions, several large data sets are created for each patient, and it becomes clear that any group analysis of such data is a big data analysis problem and has to be handled with care. The aim of this review is to provide an update and overview from a neuroengineering perspective of the current DBS techniques, technical aids, and emerging tools with the focus on patient-specific electric field (EF) simulations, group analysis, and visualization in the DBS domain. Examples are given from the state-of-the-art literature including our own research. This work reviews different analysis methods for EF simulations, tractography, deep brain anatomical templates, and group analysis. Our analysis highlights that group analysis in DBS is a complex multi-level problem and selected parameters will highly influence the result. DBS analysis can only provide clinically relevant information if the EF simulations, tractography results, and derived brain atlases are based on as much patient-specific data as possible. A trend in DBS research is creation of more advanced and intuitive visualization of the complex analysis results suitable for the clinical environment.
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Affiliation(s)
- Karin Wårdell
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Teresa Nordin
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Dorian Vogel
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
| | - Peter Zsigmond
- Department of Neurosurgery and Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Carl-Fredrik Westin
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Marwan Hariz
- Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, United Kingdom
- Department of Clinical Sciences, Neuroscience, Ume University, Umeå, Sweden
| | - Simone Hemm
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
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11
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Optimal deep brain stimulation sites and networks for cervical vs. generalized dystonia. Proc Natl Acad Sci U S A 2022; 119:e2114985119. [PMID: 35357970 PMCID: PMC9168456 DOI: 10.1073/pnas.2114985119] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
We studied deep brain stimulation effects in two types of dystonia and conclude that different specific connections between the pallidum and thalamus are responsible for optimal treatment effects. Since alternative treatment options for dystonia beyond deep brain stimulation are scarce, our results will be crucial to maximize treatment outcome in this population of patients. Dystonia is a debilitating disease with few treatment options. One effective option is deep brain stimulation (DBS) to the internal pallidum. While cervical and generalized forms of isolated dystonia have been targeted with a common approach to the posterior third of the nucleus, large-scale investigations regarding optimal stimulation sites and potential network effects have not been carried out. Here, we retrospectively studied clinical results following DBS for cervical and generalized dystonia in a multicenter cohort of 80 patients. We model DBS electrode placement based on pre- and postoperative imaging and introduce an approach to map optimal stimulation sites to anatomical space. Second, we investigate which tracts account for optimal clinical improvements, when modulated. Third, we investigate distributed stimulation effects on a whole-brain functional connectome level. Our results show marked differences of optimal stimulation sites that map to the somatotopic structure of the internal pallidum. While modulation of the striatopallidofugal axis of the basal ganglia accounted for optimal treatment of cervical dystonia, modulation of pallidothalamic bundles did so in generalized dystonia. Finally, we show a common multisynaptic network substrate for both phenotypes in the form of connectivity to the cerebellum and somatomotor cortex. Our results suggest a brief divergence of optimal stimulation networks for cervical vs. generalized dystonia within the pallidothalamic loop that merge again on a thalamo-cortical level and share a common whole-brain network.
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12
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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: 37] [Impact Index Per Article: 12.3] [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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13
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Sobesky L, Goede L, Odekerken VJJ, Wang Q, Li N, Neudorfer C, Rajamani N, Al-Fatly B, Reich M, Volkmann J, de Bie RMA, Kühn AA, Horn A. Subthalamic and pallidal deep brain stimulation: are we modulating the same network? Brain 2021; 145:251-262. [PMID: 34453827 DOI: 10.1093/brain/awab258] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 04/05/2021] [Accepted: 06/21/2021] [Indexed: 11/14/2022] Open
Abstract
The subthalamic nucleus and internal pallidum are main target sites for deep brain stimulation in Parkinson's disease. Multiple trials that investigated subthalamic versus pallidal stimulation were unable to settle on a definitive optimal target between the two. One reason could be that the effect is mediated via a common functional network. To test this hypothesis, we calculated connectivity profiles seeding from deep brain stimulation electrodes in 94 patients that underwent subthalamic and 28 patients with pallidal treatment based on a normative connectome atlas calculated from 1,000 healthy subjects. In each cohort, we calculated connectivity profiles that were associated with optimal clinical improvements. The two maps showed striking similarity and were able to cross-predict outcomes in the respective other cohort (R = 0.37 at p < 0.001; R = 0.34 at p = 0.032). Next, we calculated an agreement map which retained regions common to both target sites. Crucially, this map was able to explain an additional amount of variance in clinical improvements of either cohort when compared to the maps calculated on the two cohorts alone. Finally, we tested profiles and predictive utility of connectivity maps calculated from different motor symptom subscores with a specific focus on bradykinesia and rigidity. While our study is based on retrospective data and indirect connectivity metrics, it may deliver empirical data to support the hypothesis of a largely overlapping network associated with effective deep brain stimulation in Parkinson's disease irrespective of the specific target.
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Affiliation(s)
- Leon Sobesky
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Lukas Goede
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Vincent J J Odekerken
- Department of Neurology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Qiang Wang
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Ningfei Li
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Clemens Neudorfer
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Nanditha Rajamani
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Bassam Al-Fatly
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Martin Reich
- Department of Neurology, University Clinic of Würzburg, Josef-Schneider-Str. 11, 97080 Würzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University Clinic of Würzburg, Josef-Schneider-Str. 11, 97080 Würzburg, Germany
| | - Rob M A de Bie
- Department of Neurology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Andreas Horn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
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Therapies to Restore Consciousness in Patients with Severe Brain Injuries: A Gap Analysis and Future Directions. Neurocrit Care 2021; 35:68-85. [PMID: 34236624 PMCID: PMC8266715 DOI: 10.1007/s12028-021-01227-y] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 03/04/2021] [Indexed: 02/06/2023]
Abstract
Background/Objective For patients with disorders of consciousness (DoC) and their families, the search for new therapies has been a source of hope and frustration. Almost all clinical trials in patients with DoC have been limited by small sample sizes, lack of placebo groups, and use of heterogeneous outcome measures. As a result, few therapies have strong evidence to support their use; amantadine is the only therapy recommended by current clinical guidelines, specifically for patients with DoC caused by severe traumatic brain injury. To foster and advance development of consciousness-promoting therapies for patients with DoC, the Curing Coma Campaign convened a Coma Science Work Group to perform a gap analysis. Methods We consider five classes of therapies: (1) pharmacologic; (2) electromagnetic; (3) mechanical; (4) sensory; and (5) regenerative. For each class of therapy, we summarize the state of the science, identify gaps in knowledge, and suggest future directions for therapy development. Results Knowledge gaps in all five therapeutic classes can be attributed to the lack of: (1) a unifying conceptual framework for evaluating therapeutic mechanisms of action; (2) large-scale randomized controlled trials; and (3) pharmacodynamic biomarkers that measure subclinical therapeutic effects in early-phase trials. To address these gaps, we propose a precision medicine approach in which clinical trials selectively enroll patients based upon their physiological receptivity to targeted therapies, and therapeutic effects are measured by complementary behavioral, neuroimaging, and electrophysiologic endpoints. Conclusions This personalized approach can be realized through rigorous clinical trial design and international collaboration, both of which will be essential for advancing the development of new therapies and ultimately improving the lives of patients with DoC. Supplementary Information The online version contains supplementary material available at 10.1007/s12028-021-01227-y.
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15
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Lee DJ, Drummond NM, Saha U, De Vloo P, Dallapiazza RF, Gramer R, Al-Ozzi TM, Lam J, Loh A, Elias GJB, Boutet A, Germann J, Hodaie M, Fasano A, Munhoz RP, Hutchison W, Cohn M, Chen R, Kalia SK, Lozano AM. Acute low frequency dorsal subthalamic nucleus stimulation improves verbal fluency in Parkinson's disease. Brain Stimul 2021; 14:754-760. [PMID: 33940243 DOI: 10.1016/j.brs.2021.04.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 04/12/2021] [Accepted: 04/26/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Parkinson's disease (PD) is a common neurodegenerative disorder that results in movement-related dysfunction and has variable cognitive impairment. Deep brain stimulation (DBS) of the dorsal subthalamic nucleus (STN) has been shown to be effective in improving motor symptoms; however, cognitive impairment is often unchanged, and in some cases, worsened particularly on tasks of verbal fluency. Traditional DBS strategies use high frequency gamma stimulation for motor symptoms (∼130 Hz), but there is evidence that low frequency theta oscillations (5-12 Hz) are important in cognition. METHODS We tested the effects of stimulation frequency and location on verbal fluency among patients who underwent STN DBS implantation with externalized leads. During baseline cognitive testing, STN field potentials were recorded and the individual patients' peak theta frequency power was identified during each cognitive task. Patients repeated cognitive testing at five different stimulation settings: no stimulation, dorsal contact gamma (130 Hz), ventral contact gamma, dorsal theta (peak baseline theta) and ventral theta (peak baseline theta) frequency stimulation. RESULTS Acute left dorsal peak theta frequency STN stimulation improves overall verbal fluency compared to no stimulation and to either dorsal or ventral gamma stimulation. Stratifying by type of verbal fluency probes, verbal fluency in episodic categories was improved with dorsal theta stimulation compared to all other conditions, while there were no differences between stimulation conditions in non-episodic probe conditions. CONCLUSION Here, we provide evidence that dorsal STN theta stimulation may improve verbal fluency, suggesting a potential possibility of integrating theta stimulation into current DBS paradigms to improve cognitive outcomes.
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Affiliation(s)
- Darrin J Lee
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada; Department of Neurological Surgery, University of Southern California, 1200 North State Street, Suite 3300, Los Angeles, CA, 90033, USA; USC Neurorestoration Center, Keck School of Medicine of USC, 1333 San Pablo Street, McKibben Hall B51, Los Angeles, CA, 90033, USA.
| | - Neil M Drummond
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada
| | - Utpal Saha
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; USC Neurorestoration Center, Keck School of Medicine of USC, 1333 San Pablo Street, McKibben Hall B51, Los Angeles, CA, 90033, USA
| | - Philippe De Vloo
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada; Department of Neurosurgery, University Hospitals Leuven - KU Leuven, Herestraat 49, 3000, Leuven, Vlaams-Brabant, Belgium
| | - Robert F Dallapiazza
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
| | - Robert Gramer
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
| | - Tameem M Al-Ozzi
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
| | - Jordan Lam
- Department of Neurological Surgery, University of Southern California, 1200 North State Street, Suite 3300, Los Angeles, CA, 90033, USA; USC Neurorestoration Center, Keck School of Medicine of USC, 1333 San Pablo Street, McKibben Hall B51, Los Angeles, CA, 90033, USA
| | - Aaron Loh
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada
| | - Gavin J B Elias
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada
| | - Alexandre Boutet
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Jurgen Germann
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada
| | - Mojgan Hodaie
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
| | - Alfonso Fasano
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada; Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Toronto, Ontario, Canada
| | - Renato P Munhoz
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada; Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Toronto, Ontario, Canada
| | - William Hutchison
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
| | - Melanie Cohn
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Department of Psychology, University of Toronto, Toronto, Canada
| | - Robert Chen
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada; Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Toronto, Ontario, Canada
| | - Suneil K Kalia
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
| | - Andres M Lozano
- Krembil Research Institute, University Health Network, 60 Leonard Avenue, Toronto, ON, M5T 2S8, Canada; Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
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16
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Elias GJB, Boutet A, Joel SE, Germann J, Gwun D, Neudorfer C, Gramer RM, Algarni M, Paramanandam V, Prasad S, Beyn ME, Horn A, Madhavan R, Ranjan M, Lozano CS, Kühn AA, Ashe J, Kucharczyk W, Munhoz RP, Giacobbe P, Kennedy SH, Woodside DB, Kalia SK, Fasano A, Hodaie M, Lozano AM. Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy. Ann Neurol 2020; 89:426-443. [PMID: 33252146 DOI: 10.1002/ana.25975] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 11/24/2020] [Accepted: 11/25/2020] [Indexed: 12/19/2022]
Abstract
Deep brain stimulation (DBS) depends on precise delivery of electrical current to target tissues. However, the specific brain structures responsible for best outcome are still debated. We applied probabilistic stimulation mapping to a retrospective, multidisorder DBS dataset assembled over 15 years at our institution (ntotal = 482 patients; nParkinson disease = 303; ndystonia = 64; ntremor = 39; ntreatment-resistant depression/anorexia nervosa = 76) to identify the neuroanatomical substrates of optimal clinical response. Using high-resolution structural magnetic resonance imaging and activation volume modeling, probabilistic stimulation maps (PSMs) that delineated areas of above-mean and below-mean response for each patient cohort were generated and defined in terms of their relationships with surrounding anatomical structures. Our results show that overlap between PSMs and individual patients' activation volumes can serve as a guide to predict clinical outcomes, but that this is not the sole determinant of response. In the future, individualized models that incorporate advancements in mapping techniques with patient-specific clinical variables will likely contribute to the optimization of DBS target selection and improved outcomes for patients. ANN NEUROL 2021;89:426-443.
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Affiliation(s)
- Gavin J B Elias
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Ontario, Canada.,Krembil Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Alexandre Boutet
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Ontario, Canada.,Krembil Research Institute, University of Toronto, Toronto, Ontario, Canada.,Joint Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | | | - Jürgen Germann
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Dave Gwun
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Clemens Neudorfer
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Robert M Gramer
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Musleh Algarni
- Krembil Research Institute, University of Toronto, Toronto, Ontario, Canada.,Edmond J. Safra Program in Parkinson's Disease and Morton and Gloria Shulman Movement Disorders Clinic, University Health Network, Toronto, Ontario, Canada
| | - Vijayashankar Paramanandam
- Krembil Research Institute, University of Toronto, Toronto, Ontario, Canada.,Edmond J. Safra Program in Parkinson's Disease and Morton and Gloria Shulman Movement Disorders Clinic, University Health Network, Toronto, Ontario, Canada
| | - Sreeram Prasad
- Krembil Research Institute, University of Toronto, Toronto, Ontario, Canada.,Edmond J. Safra Program in Parkinson's Disease and Morton and Gloria Shulman Movement Disorders Clinic, University Health Network, Toronto, Ontario, Canada
| | - Michelle E Beyn
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Andreas Horn
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité-Universitätsmedizin, Berlin, Germany
| | | | - Manish Ranjan
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Christopher S Lozano
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Andrea A Kühn
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité-Universitätsmedizin, Berlin, Germany
| | - Jeff Ashe
- GE Global Research, Toronto, Ontario, Canada
| | - Walter Kucharczyk
- Krembil Research Institute, University of Toronto, Toronto, Ontario, Canada.,Joint Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Renato P Munhoz
- Krembil Research Institute, University of Toronto, Toronto, Ontario, Canada.,Edmond J. Safra Program in Parkinson's Disease and Morton and Gloria Shulman Movement Disorders Clinic, University Health Network, Toronto, Ontario, Canada
| | - Peter Giacobbe
- Department of Psychiatry, Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Sidney H Kennedy
- Krembil Research Institute, University of Toronto, Toronto, Ontario, Canada.,Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
| | - D Blake Woodside
- Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
| | - Suneil K Kalia
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Ontario, Canada.,Krembil Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Alfonso Fasano
- Krembil Research Institute, University of Toronto, Toronto, Ontario, Canada.,Edmond J. Safra Program in Parkinson's Disease and Morton and Gloria Shulman Movement Disorders Clinic, University Health Network, Toronto, Ontario, Canada
| | - Mojgan Hodaie
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Ontario, Canada.,Krembil Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Ontario, Canada.,Krembil Research Institute, University of Toronto, Toronto, Ontario, Canada
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17
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Normative vs. patient-specific brain connectivity in deep brain stimulation. Neuroimage 2020; 224:117307. [PMID: 32861787 DOI: 10.1016/j.neuroimage.2020.117307] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 08/17/2020] [Accepted: 08/22/2020] [Indexed: 11/22/2022] Open
Abstract
Brain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, a number of studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and their ability to predict clinical improvement. Data from 33 patients suffering from Parkinson's Disease who underwent surgery at three different centers were retrospectively collected. Stimulation-dependent connectivity profiles seeding from active contacts were estimated using three modalities, namely patient-specific diffusion-MRI data, age- and disease-matched or normative group connectome data (acquired in healthy young subjects). Based on these profiles, models of optimal connectivity were calculated and used to estimate clinical improvement in out of sample data. All three modalities resulted in highly similar optimal connectivity profiles that could largely reproduce findings from prior research based on this present novel multi-center cohort. In a data-driven approach that estimated optimal whole-brain connectivity profiles, out-of-sample predictions of clinical improvements were calculated. Using either patient-specific connectivity (R = 0.43 at p = 0.001), an age- and disease-matched group connectome (R = 0.25, p = 0.048) and a normative connectome based on healthy/young subjects (R = 0.31 at p = 0.028), significant predictions could be made. Our results of patient-specific connectivity and normative connectomes lead to similar main conclusions about which brain areas are associated with clinical improvement. Still, although results were not significantly different, they hint at the fact that patient-specific connectivity may bear the potential of explaining slightly more variance than group connectomes. Furthermore, use of normative connectomes involves datasets with high signal-to-noise acquired on specialized MRI hardware, while clinical datasets as the ones used here may not exactly match their quality. Our findings support the role of DBS electrode connectivity profiles as a promising method to investigate DBS effects and to potentially guide DBS programming.
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18
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Treu S, Strange B, Oxenford S, Neumann WJ, Kühn A, Li N, Horn A. Deep brain stimulation: Imaging on a group level. Neuroimage 2020; 219:117018. [PMID: 32505698 DOI: 10.1016/j.neuroimage.2020.117018] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 05/07/2020] [Accepted: 06/01/2020] [Indexed: 12/11/2022] Open
Abstract
Deep Brain Stimulation (DBS) is an established treatment option for movement disorders and is under investigation for treatment in a growing number of other brain diseases. It has been shown that exact electrode placement crucially affects the efficacy of DBS and this should be considered when investigating novel indications or DBS targets. To measure clinical improvement as a function of electrode placement, neuroscientific methodology and specialized software tools are needed. Such tools should have the goal to make electrode placement comparable across patients and DBS centers, and include statistical analysis options to validate and define optimal targets. Moreover, to allow for comparability across different centers, these need to be performed within an algorithmically and anatomically standardized and openly available group space. With the publication of Lead-DBS software in 2014, an open-source tool was introduced that allowed for precise electrode reconstructions based on pre- and postoperative neuroimaging data. Here, we introduce Lead Group, implemented within the Lead-DBS environment and specifically designed to meet aforementioned demands. In the present article, we showcase the various processing streams of Lead Group in a retrospective cohort of 51 patients suffering from Parkinson's disease, who were implanted with DBS electrodes to the subthalamic nucleus (STN). Specifically, we demonstrate various ways to visualize placement of all electrodes in the group and map clinical improvement values to subcortical space. We do so by using active coordinates and volumes of tissue activated, showing converging evidence of an optimal DBS target in the dorsolateral STN. Second, we relate DBS outcome to the impact of each electrode on local structures by measuring overlap of stimulation volumes with the STN. Finally, we explore the software functions for connectomic mapping, which may be used to relate DBS outcomes to connectivity estimates with remote brain areas. The manuscript is accompanied by a walkthrough tutorial which allows users to reproduce all main results presented here. All data and code needed to reproduce results are openly available.
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Affiliation(s)
- Svenja Treu
- Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Spain; Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany.
| | - Bryan Strange
- Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Spain
| | - Simon Oxenford
- Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany
| | - Wolf-Julian Neumann
- Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany
| | - Andrea Kühn
- Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany; Exzellenzcluster NeuroCure, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ningfei Li
- Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany
| | - Andreas Horn
- Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany
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