Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging

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dc.contributor.authorHorn, Andreas
dc.contributor.authorLi, Ningfei
dc.contributor.authorDembek, Till
dc.contributor.authorKappel, Ari
dc.contributor.authorBoulay, Chadwick
dc.contributor.authorEwert, Siobhan
dc.contributor.authorTietze, Anna
dc.contributor.authorHusch, Andreas
dc.contributor.authorPerera, Thushara
dc.contributor.authorNeumann, Wolf-Julian
dc.contributor.authorReiser, Marco
dc.contributor.authorSi, Hang
dc.contributor.authorOostenveld, Robert
dc.contributor.authorRorden, Christopher
dc.contributor.authorYeh, Fang-Cheng
dc.contributor.authorFang, Qianqian
dc.contributor.authorHerrington, Todd
dc.contributor.authorVorwerk, Johannes
dc.contributor.authorKuhn, Andrea
dc.date.accessioned2019-10-28T01:06:21Z
dc.date.available2019-10-28T01:06:21Z
dc.date.issued2019-09
dc.description.abstractDeep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the preprocessing method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.en_US
dc.description.sponsorshipThis study was supported by Deutsche Forschungsgesellschaft (grants KFO 247, SPP 2041) to AAK, Stiftung Charité, Berlin Institute of Health and Prof. Klaus Thiemann Foundation to AH. TMH has received research support from the American Brain Foundation / American Academy of Neurology and NINDS grant K23NS099380. QF was supported by NIH R01-GM114365 (from NIGMS) and R01-CA204443 (from NCI). TP was supported by the Victorian Government’s Operational Infrastructure Support Programme, the Colonial Foundation and the St. Vincent’s Hospital Melbourne Research Endowment Fund. JV was supported by the National Science Foundation (NSF): US IGNITE - 10037840. NL, AK, CB, SE, AT, AH, TP, MR, HS, RO, CR, FCY, QF, TMH and JV have nothing to disclose. AAK reports personal fees and non-financial support from Medtronic, personal fees from Boston Scientific and personal fees from St. Jude Medical outside the submitted work. AH and TAD received speaker honoraria from Medtronic outside the submitted work. WJN received travel grants from Medtronic and St. Jude medical outside of the submitted work. Data collection and sharing for this project was provided by the Human Connectome Project (HCP; Principal Investigators: Bruce Rosen, M.D., Ph.D., Arthur W. Toga, Ph.D., Van J. Weeden, MD). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California. The HCP project (Principal Investigators: Bruce Rosen, M.D., Ph.D., Martinos Center at Massachusetts General Hospital; Arthur W. Toga, Ph.D., University of Southern California, Van J. Weeden, MD, Martinos Center at Massachusetts General Hospital) is supported by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH) and the National Institute of Neurological Disorders and Stroke (NINDS). HCP is the result of efforts of co-investigators from the University of Southern California, Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH), Washington University, and the University of Minnesota. Data used in the preparation of this article were obtained from the Parkinson's Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI - a public-private partnership - is funded by the Michael J. Fox Foundation for Parkinson's Research and funding partners, see www.ppmi-info.org/fundingpartners.en_US
dc.identifier.citationHorn, A., N. Li, T. A. Dembek, A. Kappel, C. Boulay, S. Ewert, A. Tietze, A. Husch, T. Perera, W. J. Neumann, M. Reisert, H. Si, R. Oostenveld, C. Rorden, F. C. Yeh, Q. Fang, T. M. Herrington, J. Vorwerk, and A. A. Kuhn. 2019. Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging. Neuroimage. 184: 293-316.en_US
dc.identifier.issn1053-8119
dc.identifier.urihttp://repository.bionicsinstitute.org:8080/handle/123456789/367
dc.language.isoenen_US
dc.publisherElsevier, Inc.en_US
dc.titleLead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imagingen_US
dc.typeArticleen_US
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