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Neuroimage. 2019 Jan 1;184:293-316. doi: 10.1016/j.neuroimage.2018.08.068. Epub 2018 Sep 1.

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

Author information

1
Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany. Electronic address: andreas.horn@charite.de.
2
Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany.
3
Department of Neurology, University Hospital of Cologne, Germany.
4
Wayne State University, Department of Neurosurgery, Detroit, Michigan, USA.
5
Ottawa Hospital Research Institute, Canada.
6
Institute of Neuroradiology, Charité - University Medicine Berlin, Germany.
7
University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Interventional Neuroscience Group, Belvaux, Luxembourg.
8
Bionics Institute, East Melbourne, Victoria, Australia; Department of Medical Bionics, University of Melbourne, Parkville, Victoria, Australia.
9
Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Germany; Institute of Neuroradiology, Charité - University Medicine Berlin, Germany.
10
Medical Physics, Department of Radiology, Faculty of Medicine, University Freiburg, Germany.
11
Numerical Mathematics and Scientific Computing, Weierstrass Institute for Applied Analysis and Stochastics (WIAS), Germany.
12
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, NL, Netherlands; NatMEG, Karolinska Institutet, Stockholm, SE, Sweden.
13
McCausland Center for Brain Imaging, University of South Carolina, Columbia, SC, USA.
14
Department of Neurological Surgery, University of Pittsburgh PA, USA.
15
Department of Bioengineering, Northeastern University, Boston, USA.
16
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
17
Scientific Computing & Imaging (SCI) Institute, University of Utah, Salt Lake City, USA.

Abstract

Deep 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.

PMID:
30179717
PMCID:
PMC6286150
[Available on 2020-01-01]
DOI:
10.1016/j.neuroimage.2018.08.068
[Indexed for MEDLINE]

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