Dataset Information

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

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.

SUBMITTER: Horn A

PROVIDER: S-EPMC6286150 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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

Horn Andreas A Li Ningfei N Dembek Till A TA Kappel Ari A Boulay Chadwick C Ewert Siobhan S Tietze Anna A Husch Andreas A Perera Thushara T Neumann Wolf-Julian WJ Reisert Marco M Si Hang H Oostenveld Robert R Rorden Christopher C Yeh Fang-Cheng FC Fang Qianqian Q Herrington Todd M TM Vorwerk Johannes J Kühn Andrea A AA

NeuroImage 20180901

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 ...[more]

PMID: 30179717

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Project description:Microelectrode recordings (MERs) are often used during deep brain stimulation (DBS) surgeries to confirm the position of electrodes in patients with advanced Parkinson's disease. The present study focused on 32 patients who had undergone DBS surgery for advanced Parkinson's disease. The first objective was to confront the anatomical locations of intraoperative individual MERs as determined electrophysiologically with those determined postoperatively by image reconstructions. The second aim was to search for differences in cell characteristics among the three subthalamic nucleus (STN) subdivisions and between the STN and other identified subcortical structures. Using the DISTAL atlas implemented in the Lead-DBS image reconstruction toolbox, each MER location was determined postoperatively and attributed to specific anatomical structures (sensorimotor, associative or limbic STN; substantia nigra [SN], thalamus, nucleus reticularis polaris, zona incerta [ZI]). The STN dorsal borders determined intraoperatively from electrophysiology were then compared with the STN dorsal borders determined by the reconstructed images. Parameters of spike clusters (firing rates, amplitudes - with minimum amplitude of 60 μV -, spike durations, amplitude spectral density of β-oscillations) were compared between structures (ANOVAs on ranks). Two hundred and thirty one MERs were analyzed (144 in 34 STNs, 7 in 4 thalami, 5 in 4 ZIs, 34 in 10 SNs, 41 others). The average difference in depth of the electrophysiological dorsal STN entry in comparison with the STN entry obtained with Lead-DBS was found to be of 0.1 mm (standard deviation: 0.8 mm). All 12 analyzed MERs recorded above the electrophysiologically-determined STN entry were confirmed to be in the thalamus or zona incerta. All MERs electrophysiologically attributed to the SN were confirmed to belong to this nucleus. However, 6/34 MERs that were electrophysiologically attributed to the ventral STN were postoperatively reattributed to the SN. Furthermore, 44 MERs of 3 trajectories, which were intraoperatively attributed to the STN, were postoperatively reattributed to the pallidum or thalamus. MER parameters seemed to differ across the STN, with higher spike amplitudes (H = 10.64, p < 0.01) and less prevalent β-oscillations (H = 9.81, p < 0.01) in the limbic STN than in the sensorimotor and associative subdivisions. Some cells, especially in the SN, showed longer spikes with lower firing rates, in agreement with described characteristics of dopamine cells. However, these probabilistic electrophysiological signatures might become clinically less relevant with the development of image reconstruction tools, which deserve to be applied intraoperatively.

Project description:Effective target regions for deep brain stimulation (DBS) in Parkinson's disease (PD) have been well characterized. We sought to study whether the measured Cartesian coordinates of an implanted DBS lead are predictive of motor outcome(s). We tested the hypothesis that the position and trajectory of the DBS lead relative to the mid-commissural point (MCP) are significant predictors of clinical outcomes. We expected that due to neuroanatomical variation among individuals, a simple measure of the position of the DBS lead relative to MCP (commonly used in clinical practice) may not be a reliable predictor of clinical outcomes when utilized alone.55 PD subjects implanted with subthalamic nucleus (STN) DBS and 41 subjects implanted with globus pallidus internus (GPi) DBS were included. Lead locations in AC-PC space (x, y, z coordinates of the active contact and sagittal and coronal entry angles) measured on high-resolution CT-MRI fused images, and motor outcomes (Unified Parkinson's Disease Rating Scale) were analyzed to confirm or refute a correlation between coordinate-based lead locations and DBS motor outcomes.Coordinate-based lead locations were not a significant predictor of change in UPDRS III motor scores when comparing pre- versus post-operative values. The only potentially significant individual predictor of change in UPDRS motor scores was the antero-posterior coordinate of the GPi lead (more anterior lead locations resulted in a worse outcome), but this was only a statistical trend (p<.082).The results of the study showed that a simple measure of the position of the DBS lead relative to the MCP is not significantly correlated with PD motor outcomes, presumably because this method fails to account for individual neuroanatomical variability. However, there is broad agreement that motor outcomes depend strongly on lead location. The results suggest the need for more detailed identification of stimulation location relative to anatomical targets.

Dataset Information

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

Publications

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

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