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.