Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks

• DOI: 10.1016/j.neuroimage.2023.119862
• Publication Date: 2023-01-05T08:53:16Z
• Authors: Clemens Neudorfer; Konstantin Butenko; Simon Oxenford; Nanditha Rajamani; Johannes Achtzehn; Lukas Goede; Barbara Hollunder; Ana Sofía Ríos; Lauren Hart; Jordy Tasserie; Kavisha B. Fernando; T. A. Khoa Nguyen; Bassam Al-Fatly; Matteo Vissani; Michael Fox; R. Mark Richardson; Ursula van Rienen; Andrea A. Kühn; Andreas D. Husch; Enrico Opri; Till Dembek; Ningfei Li; Andreas Horn
• Keywords: 0301 basic medicine; Deep brain stimulation; Functional connectivity; Group-level; Imaging; Structural connectivity; Structural connectivity; Deep Brain Stimulation; Brain; 610 Medicine & health; Neurosciences. Biological psychiatry. Neuropsychiatry; Parkinson Disease; Magnetic Resonance Imaging; Imaging; 3. Good health; Functional connectivity; 03 medical and health sciences; 0302 clinical medicine; Subthalamic Nucleus; Deep brain stimulation; 570 Life sciences; biology; Humans; Group-level; RC321-571; Retrospective Studies

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.