1
|
Perera Molligoda Arachchige AS, Meuli S, Centini FR, Stomeo N, Catapano F, Politi LS. Evaluating the role of 7-Tesla magnetic resonance imaging in neurosurgery: Trends in literature since clinical approval. World J Radiol 2024; 16:274-293. [PMID: 39086607 PMCID: PMC11287432 DOI: 10.4329/wjr.v16.i7.274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/08/2024] [Accepted: 06/17/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND After approval for clinical use in 2017, early investigations of ultra-high-field abdominal magnetic resonance imaging (MRI) have demonstrated its feasibility as well as diagnostic capabilities in neuroimaging. However, there are no to few systematic reviews covering the entirety of its neurosurgical applications as well as the trends in the literature with regard to the aforementioned application. AIM To assess the impact of 7-Tesla MRI (7T MRI) on neurosurgery, focusing on its applications in diagnosis, treatment planning, and postoperative assessment, and to systematically analyze and identify patterns and trends in the existing literature related to the utilization of 7T MRI in neurosurgical contexts. METHODS A systematic search of PubMed was conducted for studies published between January 1, 2017, and December 31, 2023, using MeSH terms related to 7T MRI and neurosurgery. The inclusion criteria were: Studies involving patients of all ages, meta-analyses, systematic reviews, and original research. The exclusion criteria were: Pre-prints, studies with insufficient data (e.g., case reports and letters), non-English publications, and studies involving animal subjects. Data synthesis involved standardized extraction forms, and a narrative synthesis was performed. RESULTS We identified 219 records from PubMed within our defined period, with no duplicates or exclusions before screening. After screening, 125 articles were excluded for not meeting inclusion criteria, leaving 94 reports. Of these, 2 were irrelevant to neurosurgery and 7 were animal studies, resulting in 85 studies included in our systematic review. Data were categorized by neurosurgical procedures and diseases treated using 7T MRI. We also analyzed publications by country and the number of 7T MRI facilities per country was also presented. Experimental studies were classified into comparison and non-comparison studies based on whether 7T MRI was compared to lower field strengths. CONCLUSION 7T MRI holds great potential in improving the characterization and understanding of various neurological and psychiatric conditions that may be neurosurgically treated. These include epilepsy, pituitary adenoma, Parkinson's disease, cerebrovascular diseases, trigeminal neuralgia, traumatic head injury, multiple sclerosis, glioma, and psychiatric disorders. Superiority of 7T MRI over lower field strengths was demonstrated in terms of image quality, lesion detection, and tissue characterization. Findings suggest the need for accelerated global distribution of 7T magnetic resonance systems and increased training for radiologists to ensure safe and effective integration into routine clinical practice.
Collapse
Affiliation(s)
| | - Sarah Meuli
- Faculty of Medicine, Humanitas University, Pieve Emanuele, Milan 20072, Italy
| | | | - Niccolò Stomeo
- Department of Anaesthesiology and Intensive Care, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20090 Pieve Emanuele - Milan, Italy
| | - Federica Catapano
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20090 Pieve Emanuele - Milan, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano - Milan, Italy
| | - Letterio S Politi
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20090 Pieve Emanuele - Milan, Italy
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
| |
Collapse
|
2
|
Kornilov E, Baker Erdman H, Kahana E, Fireman S, Zarchi O, Israelashvili M, Reiner J, Glik A, Weiss P, Paz R, Bergman H, Tamir I. Interleaved Propofol-Ketamine Maintains DBS Physiology and Hemodynamic Stability: A Double-Blind Randomized Controlled Trial. Mov Disord 2024; 39:694-705. [PMID: 38396358 DOI: 10.1002/mds.29746] [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: 10/10/2023] [Revised: 12/18/2023] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND The gold standard anesthesia for deep brain stimulation (DBS) surgery is the "awake" approach, using local anesthesia alone. Although it offers high-quality microelectrode recordings and therapeutic-window assessment, it potentially causes patients extreme stress and might result in suboptimal surgical outcomes. General anesthesia or deep sedation is an alternative, but may reduce physiological testing reliability and lead localization accuracy. OBJECTIVES The aim is to investigate a novel anesthesia regimen of ketamine-induced conscious sedation for the physiological testing phase of DBS surgery. METHODS Parkinson's patients undergoing subthalamic DBS surgery were randomly divided into experimental and control groups. During physiological testing, the groups received 0.25 mg/kg/h ketamine infusion and normal saline, respectively. Both groups had moderate propofol sedation before and after physiological testing. The primary outcome was recording quality. Secondary outcomes included hemodynamic stability, lead accuracy, motor and cognitive outcome, patient satisfaction, and adverse events. RESULTS Thirty patients, 15 from each group, were included. Intraoperatively, the electrophysiological signature and lead localization were similar under ketamine and saline. Tremor amplitude was slightly lower under ketamine. Postoperatively, patients in the ketamine group reported significantly higher satisfaction with anesthesia. The improvement in Unified Parkinson's disease rating scale part-III was similar between the groups. No negative effects of ketamine on hemodynamic stability or cognition were reported perioperatively. CONCLUSIONS Ketamine-induced conscious sedation provided high quality microelectrode recordings comparable with awake conditions. Additionally, it seems to allow superior patient satisfaction and hemodynamic stability, while maintaining similar post-operative outcomes. Therefore, it holds promise as a novel alternative anesthetic regimen for DBS. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Collapse
Affiliation(s)
- Evgeniya Kornilov
- Department of Anesthesiology, Rabin Medical Center, Beilinson Hospital, Petach Tikvah, Israel
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Halen Baker Erdman
- Department of Medical Neurobiology, Hebrew University, Jerusalem, Israel
| | - Eilat Kahana
- Department of Anesthesiology, Rabin Medical Center, Beilinson Hospital, Petach Tikvah, Israel
| | - Shlomo Fireman
- Department of Anesthesiology, Rabin Medical Center, Beilinson Hospital, Petach Tikvah, Israel
| | - Omer Zarchi
- Intraoperative Neurophysiology Unit, Rabin Medical Center, Beilinson Hospital, Petach Tikvah, Israel
| | | | - Johnathan Reiner
- Department of Neurology, Rabin Medical Center, Beilinson Hospital, Petach Tikvah, Israel
| | - Amir Glik
- Department of Neurology, Rabin Medical Center, Beilinson Hospital, Petach Tikvah, Israel
- Cognitive Neurology Clinic, Rabin Medical Center, Beilinson Hospital, Petach Tikvah, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Penina Weiss
- Occupational Therapy Department, Rabin Medical Center, Beilinson Hospital, Petach Tikvah, Israel
| | - Rony Paz
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Hagai Bergman
- Department of Medical Neurobiology, Hebrew University, Jerusalem, Israel
- Department of Neurosurgery, Hadassah Medical Center, Hebrew University, Jerusalem, Israel
- The Edmond and Lily Safra Center for Brain Sciences, Hebrew University, Jerusalem, Israel
| | - Idit Tamir
- Department of Neurosurgery, Rabin Medical Center, Beilinson Hospital, Petach Tikvah, Israel
| |
Collapse
|
3
|
Özütemiz C, White M, Elvendahl W, Eryaman Y, Marjańska M, Metzger GJ, Patriat R, Kulesa J, Harel N, Watanabe Y, Grant A, Genovese G, Cayci Z. Use of a Commercial 7-T MRI Scanner for Clinical Brain Imaging: Indications, Protocols, Challenges, and Solutions-A Single-Center Experience. AJR Am J Roentgenol 2023; 221:788-804. [PMID: 37377363 PMCID: PMC10825876 DOI: 10.2214/ajr.23.29342] [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: 06/29/2023]
Abstract
The first commercially available 7-T MRI scanner (Magnetom Terra) was approved by the FDA in 2017 for clinical imaging of the brain and knee. After initial protocol development and sequence optimization efforts in volunteers, the 7-T system, in combination with an FDA-approved 1-channel transmit/32-channel receive array head coil, can now be routinely used for clinical brain MRI examinations. The ultrahigh field strength of 7-T MRI has the advantages of improved spatial resolution, increased SNR, and increased CNR but also introduces an array of new technical challenges. The purpose of this article is to describe an institutional experience with the use of the commercially available 7-T MRI scanner for routine clinical brain imaging. Specific clinical indications for which 7-T MRI may be useful for brain imaging include brain tumor evaluation with possible perfusion imaging and/or spectroscopy, radiotherapy planning; evaluation of multiple sclerosis and other demyelinating diseases, evaluation of Parkinson disease and guidance of deep brain stimulator placement, high-detail intracranial MRA and vessel wall imaging, evaluation of pituitary pathology, and evaluation of epilepsy. Detailed protocols, including sequence parameters, for these various indications are presented, and implementation challenges (including artifacts, safety, and side effects) and potential solutions are explored.
Collapse
Affiliation(s)
- Can Özütemiz
- Department of Radiology, University of Minnesota, 420 Delaware St SE, MMC 292, Minneapolis, MN 55455
| | - Matthew White
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
- Center for Clinical Imaging Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Wendy Elvendahl
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
- Center for Clinical Imaging Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Yigitcan Eryaman
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Gregory J Metzger
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Rémi Patriat
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Jeramy Kulesa
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Yoichi Watanabe
- Department of Radiation Oncology, University of Minnesota, Minneapolis, MN
| | - Andrea Grant
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Guglielmo Genovese
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Zuzan Cayci
- Department of Radiology, University of Minnesota, 420 Delaware St SE, MMC 292, Minneapolis, MN 55455
- Center for Clinical Imaging Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| |
Collapse
|
4
|
Liu Z, Zhou Y, Gao Y, Hu X. Editorial: Insights into the use of deep brain stimulation as a treatment for Parkinson's disease and related conditions. Front Neurosci 2023; 17:1322091. [PMID: 38033545 PMCID: PMC10684966 DOI: 10.3389/fnins.2023.1322091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023] Open
Affiliation(s)
- Zhi Liu
- Neurosurgery Department, The First Affiliated Hospital, Army Medical University, Chongqing, China
| | - Yi Zhou
- Department of Neurology, 980 Hospital of PLA Joint Logistics Support Forces, Shijiazhuang, Hebei, China
| | - Ya Gao
- Neuroscience Institute, Dietrich College of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Xiaofei Hu
- Department of Nuclear Medicine, Southwest Hospital, Army Medical University, Chongqing, China
| |
Collapse
|
5
|
Mathiopoulou V, Rijks N, Caan MWA, Liebrand LC, Ferreira F, de Bie RMA, van den Munckhof P, Schuurman PR, Bot M. Utilizing 7-Tesla Subthalamic Nucleus Connectivity in Deep Brain Stimulation for Parkinson Disease. Neuromodulation 2023; 26:333-339. [PMID: 35216874 DOI: 10.1016/j.neurom.2022.01.003] [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: 09/23/2021] [Revised: 12/17/2021] [Accepted: 01/10/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a highly effective surgical treatment for patients with advanced Parkinson disease (PD). Combining 7.0-Tesla (7T) T2- and diffusion-weighted imaging (DWI) sequences allows for selective segmenting of the motor part of the STN and, thus, for possible optimization of DBS. MATERIALS AND METHODS 7T T2 and DWI sequences were obtained, and probabilistic segmentation of motor, associative, and limbic STN segments was performed. Left- and right-sided motor outcome (Movement Disorders Society Unified Parkinson's Disease Rating Scale) scores were used for evaluating the correspondence between the active electrode contacts in selectively segmented STN and the clinical DBS effect. The Bejjani line was reviewed for crossing of segments. RESULTS A total of 50 STNs were segmented in 25 patients and proved highly feasible. Although the highest density of motor connections was situated in the dorsolateral STN for all patients, the exact partitioning of segments differed considerably. For all the active electrode contacts situated within the predominantly motor-connected segment of the STN, the average hemi-body Unified Parkinson's Disease Rating Scale motor improvement was 80%; outside this segment, it was 52% (p < 0.01). The Bejjani line was situated in the motor segment for 32 STNs. CONCLUSION The implementation of 7T T2 and DWI segmentation of the STN in DBS for PD is feasible and offers insight into the location of the motor segment. Segmentation-guided electrode placement is likely to further improve motor response in DBS for PD. However, commercially available DBS software for postprocessing imaging would greatly facilitate widespread implementation.
Collapse
Affiliation(s)
| | - Niels Rijks
- Department of Neurosurgery, Amsterdam UMC, Amsterdam, The Netherlands
| | - Matthan W A Caan
- Department of Biomedical Engineering and Physics, Amsterdam UMC, Amsterdam, The Netherlands
| | - Luka C Liebrand
- Department of Biomedical Engineering and Physics, Amsterdam UMC, Amsterdam, The Netherlands
| | - Francisca Ferreira
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, University College London Institute of Neurology, London, UK
| | - Rob M A de Bie
- Department of Neurology, Amsterdam UMC, Amsterdam, The Netherlands
| | | | | | - Maarten Bot
- Department of Neurosurgery, Amsterdam UMC, Amsterdam, The Netherlands.
| |
Collapse
|
6
|
Vilkhu G, Goas C, Miller JA, Kelly SM, McDonald KJ, Tsai AJ, Dviwedi A, Dalm BD, Merola A. Clinician vs. imaging-based subthalamic nucleus deep brain stimulation programming. Parkinsonism Relat Disord 2023; 106:105241. [PMID: 36525899 DOI: 10.1016/j.parkreldis.2022.105241] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 11/29/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022]
Abstract
INTRODUCTION We sought to explore whether electrode visualization tools (EVT) can accurately predict the selection of optimal Deep Brain Stimulation (DBS) electrode contacts. METHODS Twelve patients with Parkinson's disease (PD) undergoing STN-DBS at The Ohio State University were enrolled in a prospective analysis to evaluate the accuracy of EVT-based vs. standard DBS programming. EVTs were generated by the Surgical Information Sciences (SIS) system to develop a 3D model showing the implanted lead location relative to the STN. Then, imaging-based data were compared to the results of a standard monopolar review to evaluate concordance with clinical data and time spent selecting useable, non-useable, and borderline electrode contacts. RESULTS A total of 18 DBS leads (n = 68 electrode contacts) were analyzed. The concordance between EVT and standard clinical programming expressed as the kappa coefficient was 0.65 (82.35% raw agreement) for non-useable, 0.52 for useable (64.71% raw agreement), and 0.52 for borderline (58.82% raw agreement). The average time spent determining whether an electrode contact was useable, non-useable, or borderline was 1.46 ± 0.76 min with EVT vs. 61.25 ± 17.47 with standard monopolar review. Eight different categories of side effects were identified, with facial pulling and speech difficulties being observed with the most frequency. The type of side effect observed was accurately predicted using EVT 90% of the time. CONCLUSIONS This study demonstrates that next-generation EVT-based programming can be implemented into STN-DBS programming workflows with a considerable saving of time and effort spent in testing combinations of stimulation settings, particularly for the identification of non-useable electrode contacts.
Collapse
Affiliation(s)
- Gurleen Vilkhu
- Department of Neurology, Wexner Medical Center, Ohio State University, 395 W. 12th Ave. Columbus, OH, 43210, USA
| | - Clarisse Goas
- Department of Neurology, Wexner Medical Center, Ohio State University, 395 W. 12th Ave. Columbus, OH, 43210, USA
| | - Jacob A Miller
- Department of Neurology, Wexner Medical Center, Ohio State University, 395 W. 12th Ave. Columbus, OH, 43210, USA
| | - Scott M Kelly
- Department of Neurology, Wexner Medical Center, Ohio State University, 395 W. 12th Ave. Columbus, OH, 43210, USA
| | - Kelsey J McDonald
- Department of Neurology, Wexner Medical Center, Ohio State University, 395 W. 12th Ave. Columbus, OH, 43210, USA
| | - Anna J Tsai
- Department of Neurology, Wexner Medical Center, Ohio State University, 395 W. 12th Ave. Columbus, OH, 43210, USA
| | - Alok Dviwedi
- Department of Molecular and Translational Medicine, Division of Biostatistics and Epidemiology, Texas Tech University Health Sciences Center El Paso, 5001 El Paso Drive, Texas, 79905, USA
| | - Brian D Dalm
- Department of Neurology, Wexner Medical Center, Ohio State University, 395 W. 12th Ave. Columbus, OH, 43210, USA
| | - Aristide Merola
- Department of Neurology, Wexner Medical Center, Ohio State University, 395 W. 12th Ave. Columbus, OH, 43210, USA.
| |
Collapse
|
7
|
Rui-Qiang L, Xiao-Dong C, Ren-Zhe T, Cai-Zi L, Wei Y, Dou-Dou Z, Lin-Xia X, Wei-Xin S. Automatic localization of target point for subthalamic nucleus-deep brain stimulation via hierarchical attention-UNet based MRI segmentation. Med Phys 2023; 50:50-60. [PMID: 36053005 DOI: 10.1002/mp.15956] [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: 05/23/2022] [Revised: 08/16/2022] [Accepted: 08/18/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an effective treatment for patients with advanced Parkinson's disease, the outcome of this surgery is highly dependent on the accurate placement of the electrode in the optimal target of STN. PURPOSE In this study, we aim to develop a target localization pipeline for DBS surgery, considering that the heart of this matter is to achieve the STN and red nucleus segmentation, a deep learning-based automatic segmentation approach is proposed to tackle this issue. METHODS To address the problems of ambiguous boundaries and variable shape of the segmentation targets, the hierarchical attention mechanism with two different attention strategies is integrated into an encoder-decoder network for mining both semantics and fine-grained details for segmentation. The hierarchical attention mechanism is utilized to suppress irrelevant regions in magnetic resonance (MR) images while build long-range dependency among segmentation targets. Specifically, the attention gate (AG) is integrated into low-level features to suppress irrelevant regions in an input image while highlighting the salient features useful for segmentation. Besides, the self-attention involved in the transformer block is integrated into high-level features to model the global context. Ninety-nine brain magnetic resonance imaging (MRI) studies were collected from 99 patients with Parkinson's disease undergoing STN-DBS surgery, among which 80 samples were randomly selected as the training datasets for deep learning training, and ground truths (segmentation masks) were manually generated by radiologists. RESULTS We applied five-fold cross-validation on these data to train our model, the mean results on 19 test samples are used to conduct the comparison experiments, the Dice similarity coefficient (DSC), Jaccard (JA), sensitivity (SEN), and HD95 of the segmentation for STN are 88.20%, 80.32%, 90.13%, and 1.14 mm, respectively, outperforming the state-of-the-art STN segmentation method with 2.82%, 4.52%, 2.56%, and 0.02 mm respectively. The source code and trained models of this work have been released in the URL below: https://github.com/liuruiqiang/HAUNet/tree/master. CONCLUSIONS In this study, we demonstrate the effectiveness of the hierarchical attention mechanism for building global dependency on high-level semantic features and enhancing the fine-grained details on low-level features, the experimental results show that our method has considerable superiority for STN and red nucleus segmentation, which can provide accurate target localization for STN-DBS.
Collapse
Affiliation(s)
- Liu Rui-Qiang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Cai Xiao-Dong
- Department of Neurosurgery, the First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Tu Ren-Zhe
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Li Cai-Zi
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yan Wei
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhang Dou-Dou
- Department of Neurosurgery, the First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Xiao Lin-Xia
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Si Wei-Xin
- Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| |
Collapse
|
8
|
Tasserie J, Lozano AM. Editorial. 7T MRI for neuronavigation: toward better visualization during functional surgery. J Neurosurg 2022; 137:1262-1263. [PMID: 35334461 DOI: 10.3171/2021.12.jns212655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
9
|
Vitek JL, Patriat R, Ingham L, Reich MM, Volkmann J, Harel N. Lead location as a determinant of motor benefit in subthalamic nucleus deep brain stimulation for Parkinson’s disease. Front Neurosci 2022; 16:1010253. [PMID: 36267235 PMCID: PMC9577320 DOI: 10.3389/fnins.2022.1010253] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/05/2022] [Indexed: 11/19/2022] Open
Abstract
Background Subthalamic nucleus (STN) deep brain stimulation (DBS) is regarded as an effective treatment for patients with advanced Parkinson’s disease (PD). Clinical benefit, however, varies significantly across patients. Lead location has been hypothesized to play a critical role in determining motor outcome and may account for much of the observed variability reported among patients. Objective To retrospectively evaluate the relationship of lead location to motor outcomes in patients who had been implanted previously at another center by employing a novel visualization technology that more precisely determines the location of the DBS lead and its contacts with respect to each patient’s individually defined STN. Methods Anatomical models were generated using novel imaging in 40 PD patients who had undergone bilateral STN DBS (80 electrodes) at another center. Patient-specific models of each STN were evaluated to determine DBS electrode contact locations with respect to anterior to posterior and medial to lateral regions of the individualized STNs and compared to the change in the contralateral hemi-body Unified Parkinson’s Disease Rating Scale Part III (UPDRS-III) motor score. Results The greatest improvement in hemi-body motor function was found when active contacts were located within the posterolateral portion of the STN (71.5%). Motor benefit was 52 and 36% for central and anterior segments, respectively. Active contacts within the posterolateral portion also demonstrated the greatest reduction in levodopa dosage (77%). Conclusion The degree of motor benefit was dependent on the location of the stimulating contact within the STN. Although other factors may play a role, we provide further evidence in support of the hypothesis that lead location is a critical factor in determining clinical outcomes in STN DBS.
Collapse
Affiliation(s)
- Jerrold L. Vitek
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
- *Correspondence: Jerrold L. Vitek,
| | - Rémi Patriat
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | | | - Martin M. Reich
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Noam Harel
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| |
Collapse
|
10
|
Hosny M, Zhu M, Gao W, Fu Y. A novel deep learning model for STN localization from LFPs in Parkinson’s disease. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
11
|
Potel SR, Marceglia S, Meoni S, Kalia SK, Cury RG, Moro E. Advances in DBS Technology and Novel Applications: Focus on Movement Disorders. Curr Neurol Neurosci Rep 2022; 22:577-588. [PMID: 35838898 DOI: 10.1007/s11910-022-01221-7] [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] [Accepted: 06/17/2022] [Indexed: 11/30/2022]
Abstract
PURPOSE OF REVIEW Deep brain stimulation (DBS) is an established treatment in several movement disorders, including Parkinson's disease, dystonia, tremor, and Tourette syndrome. In this review, we will review and discuss the most recent findings including but not limited to clinical evidence. RECENT FINDINGS New DBS technologies include novel hardware design (electrodes, cables, implanted pulse generators) enabling new stimulation patterns and adaptive DBS which delivers potential stimulation tailored to moment-to-moment changes in the patient's condition. Better understanding of movement disorders pathophysiology and functional anatomy has been pivotal for studying the effects of DBS on the mesencephalic locomotor region, the nucleus basalis of Meynert, the substantia nigra, and the spinal cord. Eventually, neurosurgical practice has improved with more accurate target visualization or combined targeting. A rising research domain emphasizes bridging neuromodulation and neuroprotection. Recent advances in DBS therapy bring more possibilities to effectively treat people with movement disorders. Future research would focus on improving adaptive DBS, leading more clinical trials on novel targets, and exploring neuromodulation effects on neuroprotection.
Collapse
Affiliation(s)
- Sina R Potel
- Service de Neurologie, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
| | - Sara Marceglia
- Dipartimento Di Ingegneria E Architettura, Università Degli Studi Di Trieste, Trieste, Italy
| | - Sara Meoni
- Service de Neurologie, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
- Grenoble Institut Neurosciences, INSERM U1416, Grenoble, France
| | - Suneil K Kalia
- Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
| | - Rubens G Cury
- Department of Neurology, Movement Disorders Center, School of Medicine, University of São Paulo, São Paulo, Brazil
| | - Elena Moro
- Service de Neurologie, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France.
- Grenoble Institut Neurosciences, INSERM U1416, Grenoble, France.
| |
Collapse
|
12
|
Malvea A, Babaei F, Boulay C, Sachs A, Park J. Deep brain stimulation for Parkinson’s Disease: A Review and Future Outlook. Biomed Eng Lett 2022; 12:303-316. [PMID: 35892031 PMCID: PMC9308849 DOI: 10.1007/s13534-022-00226-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 12/29/2021] [Accepted: 04/03/2022] [Indexed: 11/30/2022] Open
Abstract
Parkinson's Disease (PD) is a neurodegenerative disorder that manifests as an impairment of motor and non-motor abilities due to a loss of dopamine input to deep brain structures. While there is presently no cure for PD, a variety of pharmacological and surgical therapeutic interventions have been developed to manage PD symptoms. This review explores the past, present and future outlooks of PD treatment, with particular attention paid to deep brain stimulation (DBS), the surgical procedure to deliver DBS, and its limitations. Finally, our group's efforts with respect to brain mapping for DBS targeting will be discussed.
Collapse
Affiliation(s)
- Anahita Malvea
- Faculty of Medicine, University of Ottawa, K1H 8M5 Ottawa, ON Canada
| | - Farbod Babaei
- School of Electrical Engineering and Computer Science, University of Ottawa, K1N 6N5 Ottawa, ON Canada
| | - Chadwick Boulay
- The Ottawa Hospital Research Institute, Ottawa, Ontario Canada
- The University of Ottawa Brain and Mind Research Institute, Ottawa, Ontario Canada
| | - Adam Sachs
- The Ottawa Hospital Research Institute, Ottawa, Ontario Canada
- The University of Ottawa Brain and Mind Research Institute, Ottawa, Ontario Canada
- Division of Neurosurgery, Department of Surgery, The Ottawa Hospital, Ottawa, Ontario Canada
| | - Jeongwon Park
- School of Electrical Engineering and Computer Science, University of Ottawa, K1N 6N5 Ottawa, ON Canada
- Department of Electrical and Biomedical Engineering, University of Nevada, 89557 Reno, NV USA
| |
Collapse
|
13
|
Wu C, Ferreira F, Fox M, Harel N, Hattangadi-Gluth J, Horn A, Jbabdi S, Kahan J, Oswal A, Sheth SA, Tie Y, Vakharia V, Zrinzo L, Akram H. Clinical applications of magnetic resonance imaging based functional and structural connectivity. Neuroimage 2021; 244:118649. [PMID: 34648960 DOI: 10.1016/j.neuroimage.2021.118649] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/24/2021] [Accepted: 10/10/2021] [Indexed: 12/23/2022] Open
Abstract
Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.
Collapse
Affiliation(s)
- Chengyuan Wu
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, 909 Walnut Street, Third Floor, Philadelphia, PA 19107, USA; Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, 909 Walnut Street, First Floor, Philadelphia, PA 19107, USA.
| | - Francisca Ferreira
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Michael Fox
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, 2021 Sixth Street S.E., Minneapolis, MN 55455, USA.
| | - Jona Hattangadi-Gluth
- Department of Radiation Medicine and Applied Sciences, Center for Precision Radiation Medicine, University of California, San Diego, 3855 Health Sciences Drive, La Jolla, CA 92037, USA.
| | - Andreas Horn
- Neurology Department, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Charitéplatz 1, D-10117, Berlin, Germany.
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK.
| | - Joshua Kahan
- Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, New York, NY, 10065, USA.
| | - Ashwini Oswal
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Mansfield Rd, Oxford OX1 3TH, UK.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, 7200 Cambridge, Ninth Floor, Houston, TX 77030, USA.
| | - Yanmei Tie
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Vejay Vakharia
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK.
| | - Ludvic Zrinzo
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Harith Akram
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| |
Collapse
|
14
|
Engelhardt J, Cuny E, Guehl D, Burbaud P, Damon-Perrière N, Dallies-Labourdette C, Thomas J, Branchard O, Schmitt LA, Gassa N, Zemzemi N. Prediction of Clinical Deep Brain Stimulation Target for Essential Tremor From 1.5 Tesla MRI Anatomical Landmarks. Front Neurol 2021; 12:620360. [PMID: 34777189 PMCID: PMC8579860 DOI: 10.3389/fneur.2021.620360] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 09/13/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Deep brain stimulation is an efficacious treatment for refractory essential tremor, though targeting the intra-thalamic nuclei remains challenging. Objectives: We sought to develop an inverse approach to retrieve the position of the leads in a cohort of patients operated on with optimal clinical outcomes from anatomical landmarks identifiable by 1.5 Tesla magnetic resonance imaging. Methods: The learning database included clinical outcomes and post-operative imaging from which the coordinates of the active contacts and those of anatomical landmarks were extracted. We used machine learning regression methods to build three different prediction models. External validation was performed according to a leave-one-out cross-validation. Results: Fifteen patients (29 leads) were included, with a median tremor improvement of 72% on the Fahn-Tolosa-Marin scale. Kernel ridge regression, deep neural networks, and support vector regression (SVR) were used. SVR gave the best results with a mean error of 1.33 ± 1.64 mm between the predicted target and the active contact position. Conclusion: We report an original method for the targeting in deep brain stimulation for essential tremor based on patients' radio-anatomical features. This approach will be tested in a prospective clinical trial.
Collapse
Affiliation(s)
- Julien Engelhardt
- Department of Neurosurgery, University Hospital of Bordeaux, Bordeaux, France.,Institute for Neurodegenerative Disorders, CNRS-University of Bordeaux, Bordeaux, France
| | - Emmanuel Cuny
- Department of Neurosurgery, University Hospital of Bordeaux, Bordeaux, France.,Institute for Neurodegenerative Disorders, CNRS-University of Bordeaux, Bordeaux, France
| | - Dominique Guehl
- Institute for Neurodegenerative Disorders, CNRS-University of Bordeaux, Bordeaux, France.,Department of Neurology, University Hospital of Bordeaux, Bordeaux, France
| | - Pierre Burbaud
- Institute for Neurodegenerative Disorders, CNRS-University of Bordeaux, Bordeaux, France.,Department of Neurology, University Hospital of Bordeaux, Bordeaux, France
| | - Nathalie Damon-Perrière
- Institute for Neurodegenerative Disorders, CNRS-University of Bordeaux, Bordeaux, France.,Department of Neurology, University Hospital of Bordeaux, Bordeaux, France
| | - Camille Dallies-Labourdette
- Institute for Neurodegenerative Disorders, CNRS-University of Bordeaux, Bordeaux, France.,Department of Neurology, University Hospital of Bordeaux, Bordeaux, France
| | - Juliette Thomas
- Institute for Neurodegenerative Disorders, CNRS-University of Bordeaux, Bordeaux, France.,Department of Neurology, University Hospital of Bordeaux, Bordeaux, France
| | - Olivier Branchard
- Department of Neurosurgery, University Hospital of Bordeaux, Bordeaux, France
| | | | - Narimane Gassa
- INRIA Bordeaux Sud-Ouest Research Centre, Talence, France
| | - Nejib Zemzemi
- INRIA Bordeaux Sud-Ouest Research Centre, Talence, France.,Mathematical Institute of Bordeaux, University of Bordeaux, Bordeaux, France
| |
Collapse
|
15
|
Sand D, Arkadir D, Abu Snineh M, Marmor O, Israel Z, Bergman H, Hassin-Baer S, Israeli-Korn S, Peremen Z, Geva AB, Eitan R. Deep Brain Stimulation Can Differentiate Subregions of the Human Subthalamic Nucleus Area by EEG Biomarkers. Front Syst Neurosci 2021; 15:747681. [PMID: 34744647 PMCID: PMC8565520 DOI: 10.3389/fnsys.2021.747681] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 09/16/2021] [Indexed: 01/10/2023] Open
Abstract
Introduction: Precise lead localization is crucial for an optimal clinical outcome of subthalamic nucleus (STN) deep brain stimulation (DBS) treatment in patients with Parkinson's disease (PD). Currently, anatomical measures, as well as invasive intraoperative electrophysiological recordings, are used to locate DBS electrodes. The objective of this study was to find an alternative electrophysiology tool for STN DBS lead localization. Methods: Sixty-one postoperative electrophysiology recording sessions were obtained from 17 DBS-treated patients with PD. An intraoperative physiological method automatically detected STN borders and subregions. Postoperative EEG cortical activity was measured, while STN low frequency stimulation (LFS) was applied to different areas inside and outside the STN. Machine learning models were used to differentiate stimulation locations, based on EEG analysis of engineered features. Results: A machine learning algorithm identified the top 25 evoked response potentials (ERPs), engineered features that can differentiate inside and outside STN stimulation locations as well as within STN stimulation locations. Evoked responses in the medial and ipsilateral fronto-central areas were found to be most significant for predicting the location of STN stimulation. Two-class linear support vector machine (SVM) predicted the inside (dorso-lateral region, DLR, and ventro-medial region, VMR) vs. outside [zona incerta, ZI, STN stimulation classification with an accuracy of 0.98 and 0.82 for ZI vs. VMR and ZI vs. DLR, respectively, and an accuracy of 0.77 for the within STN (DLR vs. VMR)]. Multiclass linear SVM predicted all areas with an accuracy of 0.82 for the outside and within STN stimulation locations (ZI vs. DLR vs. VMR). Conclusions: Electroencephalogram biomarkers can use low-frequency STN stimulation to localize STN DBS electrodes to ZI, DLR, and VMR STN subregions. These models can be used for both intraoperative electrode localization and postoperative stimulation programming sessions, and have a potential to improve STN DBS clinical outcomes.
Collapse
Affiliation(s)
- Daniel Sand
- Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel.,Edmond and Lily Safra Center for Brain Research, Hebrew University of Jerusalem, Jerusalem, Israel.,Elminda Ltd., Herzliya, Israel
| | - David Arkadir
- Department of Neurology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Muneer Abu Snineh
- Department of Neurology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Odeya Marmor
- Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Zvi Israel
- Brain Division, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,Functional Neurosurgery Unit, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Hagai Bergman
- Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel.,Edmond and Lily Safra Center for Brain Research, Hebrew University of Jerusalem, Jerusalem, Israel.,Functional Neurosurgery Unit, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Sharon Hassin-Baer
- Department of Neurology, Movement Disorders Institute, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Simon Israeli-Korn
- Department of Neurology, Movement Disorders Institute, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | | | - Amir B Geva
- Department of Electrical and Computer Engineering, Ben Gurion University, Beer-Sheva, Israel
| | - Renana Eitan
- Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel.,Brain Division, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,Neuropsychiatry Unit, Jerusalem Mental Health Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| |
Collapse
|
16
|
A novel deep recurrent convolutional neural network for subthalamic nucleus localization using local field potential signals. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.09.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
17
|
Sun J, Chen R, Tong Q, Ma J, Gao L, Fang J, Zhang D, Chan P, He H, Wu T. Convolutional neural network optimizes the application of diffusion kurtosis imaging in Parkinson's disease. Brain Inform 2021; 8:18. [PMID: 34585306 PMCID: PMC8479023 DOI: 10.1186/s40708-021-00139-z] [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: 03/17/2021] [Accepted: 08/11/2021] [Indexed: 11/25/2022] Open
Abstract
Objectives The literature regarding the use of diffusion-tensor imaging-derived metrics in the evaluation of Parkinson’s disease (PD) is controversial. This study attempted to assess the feasibility of a deep-learning-based method for detecting alterations in diffusion kurtosis measurements associated with PD. Methods A total of 68 patients with PD and 77 healthy controls were scanned using scanner-A (3 T Skyra) (DATASET-1). Meanwhile, an additional five healthy volunteers were scanned with both scanner-A and an additional scanner-B (3 T Prisma) (DATASET-2). Diffusion kurtosis imaging (DKI) of DATASET-2 had an extra b shell compared to DATASET-1. In addition, a 3D-convolutional neural network (CNN) was trained from DATASET-2 to harmonize the quality of scalar measures of scanner-A to a similar level as scanner-B. Whole-brain unpaired t test and Tract-Based Spatial Statistics (TBSS) were performed to validate the differences between the PD and control groups using the model-fitting method and CNN-based method, respectively. We further clarified the correlation between clinical assessments and DKI results. Results An increase in mean diffusivity (MD) was found in the left substantia nigra (SN) in the PD group. In the right SN, fractional anisotropy (FA) and mean kurtosis (MK) values were negatively correlated with Hoehn and Yahr (H&Y) scales. In the putamen (Put), FA values were positively correlated with the H&Y scales. It is worth noting that these findings were only observed with the deep learning method. There was neither a group difference nor a correlation with clinical assessments in the SN or striatum exceeding the significance level using the conventional model-fitting method. Conclusions The CNN-based method improves the robustness of DKI and can help to explore PD-associated imaging features. Supplementary Information The online version contains supplementary material available at 10.1186/s40708-021-00139-z.
Collapse
Affiliation(s)
- Junyan Sun
- Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Disease, Beijing, 100053, China
| | - Ruike Chen
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, 310027, Zhejiang, China
| | - Qiqi Tong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, 310027, Zhejiang, China.,Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Jinghong Ma
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Linlin Gao
- Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Disease, Beijing, 100053, China
| | - Jiliang Fang
- Department of Radiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Dongling Zhang
- Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Disease, Beijing, 100053, China
| | - Piu Chan
- Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Disease, Beijing, 100053, China.,Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, China.,Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, 310027, Zhejiang, China. .,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310027, Zhejiang, China.
| | - Tao Wu
- Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Disease, Beijing, 100053, China. .,Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, China. .,Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, China. .,National Clinical Research Center for Geriatric Disorders, Beijing, China.
| |
Collapse
|
18
|
Düzel E, Costagli M, Donatelli G, Speck O, Cosottini M. Studying Alzheimer disease, Parkinson disease, and amyotrophic lateral sclerosis with 7-T magnetic resonance. Eur Radiol Exp 2021; 5:36. [PMID: 34435242 PMCID: PMC8387546 DOI: 10.1186/s41747-021-00221-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 04/07/2021] [Indexed: 12/18/2022] Open
Abstract
Ultra-high-field (UHF) magnetic resonance (MR) scanners, that is, equipment operating at static magnetic field of 7 tesla (7 T) and above, enable the acquisition of data with greatly improved signal-to-noise ratio with respect to conventional MR systems (e.g., scanners operating at 1.5 T and 3 T). The change in tissue relaxation times at UHF offers the opportunity to improve tissue contrast and depict features that were previously inaccessible. These potential advantages come, however, at a cost: in the majority of UHF-MR clinical protocols, potential drawbacks may include signal inhomogeneity, geometrical distortions, artifacts introduced by patient respiration, cardiac cycle, and motion. This article reviews the 7 T MR literature reporting the recent studies on the most widespread neurodegenerative diseases: Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis.
Collapse
Affiliation(s)
- Emrah Düzel
- Otto-von-Guericke University Magdeburg, Magdeburg, Germany. .,German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany. .,University College London, London, UK.
| | - Mauro Costagli
- IRCCS Stella Maris, Pisa, Italy.,University of Genoa, Genova, Italy
| | - Graziella Donatelli
- Fondazione Imago 7, Pisa, Italy.,Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Oliver Speck
- Otto-von-Guericke University Magdeburg, Magdeburg, Germany.,German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Mirco Cosottini
- Azienda Ospedaliero Universitaria Pisana, Pisa, Italy.,University of Pisa, Pisa, Italy
| |
Collapse
|
19
|
Merola A, Singh J, Reeves K, Changizi B, Goetz S, Rossi L, Pallavaram S, Carcieri S, Harel N, Shaikhouni A, Sammartino F, Krishna V, Verhagen L, Dalm B. New Frontiers for Deep Brain Stimulation: Directionality, Sensing Technologies, Remote Programming, Robotic Stereotactic Assistance, Asleep Procedures, and Connectomics. Front Neurol 2021; 12:694747. [PMID: 34367055 PMCID: PMC8340024 DOI: 10.3389/fneur.2021.694747] [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: 04/13/2021] [Accepted: 06/14/2021] [Indexed: 11/21/2022] Open
Abstract
Over the last few years, while expanding its clinical indications from movement disorders to epilepsy and psychiatry, the field of deep brain stimulation (DBS) has seen significant innovations. Hardware developments have introduced directional leads to stimulate specific brain targets and sensing electrodes to determine optimal settings via feedback from local field potentials. In addition, variable-frequency stimulation and asynchronous high-frequency pulse trains have introduced new programming paradigms to efficiently desynchronize pathological neural circuitry and regulate dysfunctional brain networks not responsive to conventional settings. Overall, these innovations have provided clinicians with more anatomically accurate programming and closed-looped feedback to identify optimal strategies for neuromodulation. Simultaneously, software developments have simplified programming algorithms, introduced platforms for DBS remote management via telemedicine, and tools for estimating the volume of tissue activated within and outside the DBS targets. Finally, the surgical accuracy has improved thanks to intraoperative magnetic resonance or computerized tomography guidance, network-based imaging for DBS planning and targeting, and robotic-assisted surgery for ultra-accurate, millimetric lead placement. These technological and imaging advances have collectively optimized DBS outcomes and allowed “asleep” DBS procedures. Still, the short- and long-term outcomes of different implantable devices, surgical techniques, and asleep vs. awake procedures remain to be clarified. This expert review summarizes and critically discusses these recent innovations and their potential impact on the DBS field.
Collapse
Affiliation(s)
- Aristide Merola
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Jaysingh Singh
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Kevin Reeves
- Department of Psychiatry, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Barbara Changizi
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Steven Goetz
- Medtronic PLC Neuromodulation, Minneapolis, MN, United States
| | | | | | | | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Ammar Shaikhouni
- Department of Neurosurgery, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Francesco Sammartino
- Department of Neurosurgery, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Vibhor Krishna
- Department of Neurosurgery, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Leo Verhagen
- Movement Disorder Section, Department of Neurological Sciences, Rush University, Chicago, IL, United States
| | - Brian Dalm
- Department of Neurosurgery, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| |
Collapse
|
20
|
Direct visualization of deep brain stimulation targets in patients with Parkinson's disease via 3-T quantitative susceptibility mapping. Acta Neurochir (Wien) 2021; 163:1335-1345. [PMID: 33576911 DOI: 10.1007/s00701-021-04715-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 01/11/2021] [Indexed: 01/11/2023]
Abstract
BACKGROUND The direct visualization of brain nuclei on magnetic resonance (MR) images is important for target localization during deep brain stimulation (DBS) in patients with Parkinson's disease (PD). We demonstrated the superiority of 3-T high-resolution submillimeter voxel size quantitative susceptibility mapping (QSM) for delineating the subthalamic nucleus (STN) and the globus pallidus internus (GPi). METHODS Preoperative 3-T QSM and T2 weighted (T2w) images were obtained from ten patients with PD. Qualitative visualization scores were analyzed by two neurosurgeons on both images using a 4-point and 5-point scale, respectively. Images were also compared with regard to contrast-to-noise ratios (CNRs) and edge detection power for the STN and GPi. The Wilcoxon rank-sum test and the signed-rank test were used to compare measurements between the two images. RESULTS Visualization scores for the STN and GPi, the mean CNR of the STN relative to the zona incerta (ZI) and the substantia nigra, and the mean CNR of the GPi relative to the internal capsule (IC) and the globus pallidum externum, were significantly higher on QSM images than on T2w images (P < 0.01). The edge detection powers of the STN-ZI and GPi-IC on QSM were significantly larger (by 2.6- and 3.8-fold, respectively) than those on T2w images (P < 0.01). QSM detected asymmetry of the STN in two patients. CONCLUSIONS QSM images provided improved delineation ability for the STN and GPi when compared to T2w images. Our findings are important for patients with PD who undergo DBS surgery, particularly those with asymmetric bilateral nuclei.
Collapse
|
21
|
Detection of subthalamic nucleus using novel higher-order spectra features in microelectrode recordings signals. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.04.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
22
|
Watts J, Khojandi A, Shylo O, Ramdhani RA. Machine Learning's Application in Deep Brain Stimulation for Parkinson's Disease: A Review. Brain Sci 2020; 10:E809. [PMID: 33139614 PMCID: PMC7694006 DOI: 10.3390/brainsci10110809] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 10/16/2020] [Accepted: 10/29/2020] [Indexed: 01/07/2023] Open
Abstract
Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson's disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information's Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers' (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.
Collapse
Affiliation(s)
- Jeremy Watts
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA; (J.W.); (A.K.); (O.S.)
| | - Anahita Khojandi
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA; (J.W.); (A.K.); (O.S.)
| | - Oleg Shylo
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA; (J.W.); (A.K.); (O.S.)
| | - Ritesh A. Ramdhani
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
| |
Collapse
|
23
|
Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020; 4:041503. [PMID: 33094213 PMCID: PMC7556883 DOI: 10.1063/5.0011697] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 09/09/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.
Collapse
Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| |
Collapse
|
24
|
Fellous JM, Sapiro G, Rossi A, Mayberg H, Ferrante M. Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation. Front Neurosci 2019; 13:1346. [PMID: 31920509 PMCID: PMC6923732 DOI: 10.3389/fnins.2019.01346] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Accepted: 11/29/2019] [Indexed: 01/08/2023] Open
Abstract
The use of Artificial Intelligence and machine learning in basic research and clinical neuroscience is increasing. AI methods enable the interpretation of large multimodal datasets that can provide unbiased insights into the fundamental principles of brain function, potentially paving the way for earlier and more accurate detection of brain disorders and better informed intervention protocols. Despite AI's ability to create accurate predictions and classifications, in most cases it lacks the ability to provide a mechanistic understanding of how inputs and outputs relate to each other. Explainable Artificial Intelligence (XAI) is a new set of techniques that attempts to provide such an understanding, here we report on some of these practical approaches. We discuss the potential value of XAI to the field of neurostimulation for both basic scientific inquiry and therapeutic purposes, as well as, outstanding questions and obstacles to the success of the XAI approach.
Collapse
Affiliation(s)
- Jean-Marc Fellous
- Theoretical and Computational Neuroscience Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
- Department of Psychology and Biomedical Engineering, University of Arizona, Tucson, AZ, United States
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States
| | - Andrew Rossi
- Executive Functions and Reward Systems Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Helen Mayberg
- Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Michele Ferrante
- Theoretical and Computational Neuroscience Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
- Computational Psychiatry Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| |
Collapse
|
25
|
Park SC, Cha JH, Lee S, Jang W, Lee CS, Lee JK. Deep Learning-Based Deep Brain Stimulation Targeting and Clinical Applications. Front Neurosci 2019; 13:1128. [PMID: 31708729 PMCID: PMC6821714 DOI: 10.3389/fnins.2019.01128] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Accepted: 10/04/2019] [Indexed: 12/26/2022] Open
Abstract
Background The purpose of the present study was to evaluate deep learning-based image-guided surgical planning for deep brain stimulation (DBS). We developed deep learning semantic segmentation-based DBS targeting and prospectively applied the method clinically. Methods T2∗ fast gradient-echo images from 102 patients were used for training and validation. Manually drawn ground truth information was prepared for the subthalamic and red nuclei with an axial cut ∼4 mm below the anterior–posterior commissure line. A fully convolutional neural network (FCN-VGG-16) was used to ensure margin identification by semantic segmentation. Image contrast augmentation was performed nine times. Up to 102 original images and 918 augmented images were used for training and validation. The accuracy of semantic segmentation was measured in terms of mean accuracy and mean intersection over the union. Targets were calculated based on their relative distance from these segmented anatomical structures considering the Bejjani target. Results Mean accuracies and mean intersection over the union values were high: 0.904 and 0.813, respectively, for the 62 training images, and 0.911 and 0.821, respectively, for the 558 augmented training images when 360 augmented validation images were used. The Dice coefficient converted from the intersection over the union was 0.902 when 720 training and 198 validation images were used. Semantic segmentation was adaptive to high anatomical variations in size, shape, and asymmetry. For clinical application, two patients were assessed: one with essential tremor and another with bradykinesia and gait disturbance due to Parkinson’s disease. Both improved without complications after surgery, and microelectrode recordings showed subthalamic nuclei signals in the latter patient. Conclusion The accuracy of deep learning-based semantic segmentation may surpass that of previous methods. DBS targeting and its clinical application were made possible using accurate deep learning-based semantic segmentation, which is adaptive to anatomical variations.
Collapse
Affiliation(s)
- Seong-Cheol Park
- Department of Neurosurgery, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, Seoul, South Korea.,Department of Neurosurgery, Gangneung Asan Hospital, University of Ulsan, Gangneung, South Korea
| | - Joon Hyuk Cha
- Department of Neurosurgery, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, Seoul, South Korea.,School of Medicine, Inha University, Incheon, South Korea
| | - Seonhwa Lee
- Department of Neurosurgery, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, Seoul, South Korea.,Department of Bio-Convergence Engineering, College of Health Science, Korea University, Seoul, South Korea
| | - Wooyoung Jang
- Department of Neurology, Gangneung Asan Hospital, University of Ulsan, Gangneung, South Korea
| | - Chong Sik Lee
- Department of Neurology, Asan Medical Center, University of Ulsan, Seoul, South Korea
| | - Jung Kyo Lee
- Department of Neurosurgery, Asan Medical Center, University of Ulsan, Seoul, South Korea
| |
Collapse
|
26
|
Ramirez-Zamora A, Giordano J, Boyden ES, Gradinaru V, Gunduz A, Starr PA, Sheth SA, McIntyre CC, Fox MD, Vitek J, Vedam-Mai V, Akbar U, Almeida L, Bronte-Stewart HM, Mayberg HS, Pouratian N, Gittis AH, Singer AC, Creed MC, Lazaro-Munoz G, Richardson M, Rossi MA, Cendejas-Zaragoza L, D'Haese PF, Chiong W, Gilron R, Chizeck H, Ko A, Baker KB, Wagenaar J, Harel N, Deeb W, Foote KD, Okun MS. Proceedings of the Sixth Deep Brain Stimulation Think Tank Modulation of Brain Networks and Application of Advanced Neuroimaging, Neurophysiology, and Optogenetics. Front Neurosci 2019; 13:936. [PMID: 31572109 PMCID: PMC6751331 DOI: 10.3389/fnins.2019.00936] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 08/21/2019] [Indexed: 02/05/2023] Open
Abstract
The annual deep brain stimulation (DBS) Think Tank aims to create an opportunity for a multidisciplinary discussion in the field of neuromodulation to examine developments, opportunities and challenges in the field. The proceedings of the Sixth Annual Think Tank recapitulate progress in applications of neurotechnology, neurophysiology, and emerging techniques for the treatment of a range of psychiatric and neurological conditions including Parkinson’s disease, essential tremor, Tourette syndrome, epilepsy, cognitive disorders, and addiction. Each section of this overview provides insight about the understanding of neuromodulation for specific disease and discusses current challenges and future directions. This year’s report addresses key issues in implementing advanced neurophysiological techniques, evolving use of novel modulation techniques to deliver DBS, ans improved neuroimaging techniques. The proceedings also offer insights into the new era of brain network neuromodulation and connectomic DBS to define and target dysfunctional brain networks. The proceedings also focused on innovations in applications and understanding of adaptive DBS (closed-loop systems), the use and applications of optogenetics in the field of neurostimulation and the need to develop databases for DBS indications. Finally, updates on neuroethical, legal, social, and policy issues relevant to DBS research are discussed.
Collapse
Affiliation(s)
- Adolfo Ramirez-Zamora
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - James Giordano
- Neuroethics Studies Program, Department of Neurology and Department of Biochemistry, Georgetown University Medical Center, Washington, DC, United States
| | - Edward S Boyden
- Media Laboratory, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.,Center for Neurobiological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Viviana Gradinaru
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Aysegul Gunduz
- Department of Neuroscience and Department of Biomedical Engineering and Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Philip A Starr
- Graduate Program in Neuroscience, Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Sameer A Sheth
- Department of Neurological Surgery, Baylor College of Medicine, Houston, TX, United States
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Michael D Fox
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Jerrold Vitek
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Vinata Vedam-Mai
- Department of Neurosurgery, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Umer Akbar
- Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Veterans Affairs Medical Center, Brown Institute for Brain Science, Brown University, Providence, RI, United States
| | - Leonardo Almeida
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Helen M Bronte-Stewart
- Department of Neurology and Department of Neurological Sciences and Department of Neurosurgery, Stanford University, Stanford, CA, United States
| | - Helen S Mayberg
- Department of Neurology and Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Nader Pouratian
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Aryn H Gittis
- Biological Sciences and Center for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Annabelle C Singer
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University School of Medicine, Atlanta, GA, United States
| | - Meaghan C Creed
- Department of Pharmacology, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Gabriel Lazaro-Munoz
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Mark Richardson
- Center for the Neural Basis of Cognition, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Marvin A Rossi
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, United States
| | | | | | - Winston Chiong
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Ro'ee Gilron
- Graduate Program in Neuroscience, Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Howard Chizeck
- Graduate Program in Neuroscience, Department of Electrical Engineering, University of Washington, Seattle, WA, United States
| | - Andrew Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Kenneth B Baker
- Movement Disorders Program, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Joost Wagenaar
- Department of Neurology, Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | - Wissam Deeb
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Kelly D Foote
- Department of Neurosurgery, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Michael S Okun
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| |
Collapse
|
27
|
Kim J, Duchin Y, Shamir RR, Patriat R, Vitek J, Harel N, Sapiro G. Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation. Hum Brain Mapp 2019; 40:679-698. [PMID: 30379376 PMCID: PMC6519731 DOI: 10.1002/hbm.24404] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 09/04/2018] [Accepted: 09/07/2018] [Indexed: 12/20/2022] Open
Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson's disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7 T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7 T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7 T MRI and its clinical MRI pairs. We first model in the 7 T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T2 W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7 T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.
Collapse
Affiliation(s)
- Jinyoung Kim
- Surgical Information Sciences, Inc.MinneapolisMinnesota
| | - Yuval Duchin
- Surgical Information Sciences, Inc.MinneapolisMinnesota
| | | | - Remi Patriat
- Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesota
| | - Jerrold Vitek
- Department of NeurologyUniversity of MinnesotaMinneapolisMinnesota
| | - Noam Harel
- Surgical Information Sciences, Inc.MinneapolisMinnesota
- Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesota
- Department of NeurosurgeryUniversity of MinnesotaMinneapolisMinnesota
| | - Guillermo Sapiro
- Surgical Information Sciences, Inc.MinneapolisMinnesota
- Department of Electrical and Computer EngineeringDuke UniversityDurhamNorth Carolina
- Department of Biomedical EngineeringDuke UniversityDurhamNorth Carolina
- Department of Computer ScienceDuke UniversityDurhamNorth Carolina
- Department of MathematicsDuke UniversityDurhamNorth Carolina
| |
Collapse
|