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 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.
- List of abbreviations
- ANTs
- Advanced Normalization Tools, see http://stnava.github.io/ANTs/
- BSpline-SyN
- ANTs normalization method; Explicit B-spline regularization in symmetric diffeomorphic image registration
- CORE
- Algorithm for “reconstruction of electrode contact positions” defined in Horn & Kühn 2014
- DARTEL
- SPM normalization method; A fast diffeomorphic image registration algorithm.
- DiODe
- Directional Orientation Detection (Sitz et al. 2017)
- dMRI
- Diffusion-weighted MRI – in the context of diffusion-imaging based tractography.
- FA
- Fractional Anisotropy
- FGATIR
- Fast Grey Matter Acquisition T1 Inversion Recovery
- FLIRT
- FMRIB’s Linear Image Registration Tool
- FNIRT
- FSL normalization method; FMRIB’s Nonlinear Image Registration Tool
- FSL
- FMRIB Software Library, see https://fsl.fmrib.ox.ac.uk/
- GNU
- Recursive acronym for “GNU’s Not Unix.”; GNU GPL is a popular open license supported by the Free Software Foundation (see http://www.gnu.org). The Free Software Foundation (FSF) is a nonprofit with a worldwide mission to promote computer user freedom.
- Gpi
- internal segment of the globus pallidus
- Gpe
- external segment of the globus pallidus
- GQI
- Generalized q-sampling imaging, dMRI processing method implemented in DSI studio
- HCP
- Human Connectome Project
- LEDD
- Levodopa Equivalent Daily Dosage
- MAGeT Brain
- Multiple Automatically Generated Templates brain segmentation algorithm, see https://github.com/CobraLab/MAGeTbrain
- PaCER
- Precise and Convenient Electrode Reconstruction for DBS, see https://adhusch.github.io/PaCER/
- PCA
- Principal Component Analysis
- PPMI
- Parkinson‘s Disease Progression Marker Initiative
- PPN
- Pedunculopontine nucleus
- RN
- red nucleus
- ROI
- Region of Interest
- RSME
- Root-mean-square error
- QSM
- Quantitative Susceptibility Mapping
- rs-fMRI
- Resting-state functional MRI
- SHOOT
- SPM normalization method; Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation
- SPM
- Statistic Parametric Mapping, see http://www.fil.ion.ucl.ac.uk/spm/software/spm12/
- STN
- subthalamic nucleus
- SyN
- ANTs normalization method; Symmetric diffeomorphic image registration / symmetric image normalization
- TRAC
- Algorithm for “trajectory reconstruction” defined in Horn & Kühn 2014
- UPDRS
- Unified Parkinson‘s Disease Rating Scale
- VTA
- Volume of Tissue Activated