Neudorfer, Clemens; Butenko, Konstantin; Oxenford, Simon; Rajamani, Nanditha; Achtzehn, Johannes; Goede, Lukas; Hollunder, Barbara; Ríos, Ana Sofía; Hart, Lauren; Tasserie, Jordy; Fernando, Kavisha B; Nguyen, T A Khoa; Al-Fatly, Bassam; Vissani, Matteo; Fox, Michael; Richardson, R Mark; van Rienen, Ursula; Kühn, Andrea A; Husch, Andreas D; Opri, Enrico; ... (2023). Lead-DBS v3.0: Mapping Deep Brain Stimulation Effects to Local Anatomy and Global Networks. NeuroImage, 268, p. 119862. Elsevier 10.1016/j.neuroimage.2023.119862
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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.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurosurgery 10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research |
UniBE Contributor: |
Nguyen, Thuy Anh Khoa |
Subjects: |
500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health |
ISSN: |
1095-9572 |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
11 Jan 2023 16:43 |
Last Modified: |
10 Feb 2023 00:15 |
Publisher DOI: |
10.1016/j.neuroimage.2023.119862 |
PubMed ID: |
36610682 |
Uncontrolled Keywords: |
Deep Brain Stimulation Functional connectivity Group-level Imaging Structural connectivity |
BORIS DOI: |
10.48350/177034 |
URI: |
https://boris.unibe.ch/id/eprint/177034 |
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