Journal Article

DZNE-2022-01448

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Interrater reliability of deep brain stimulation electrode localizations.

Lofredi, R. ; Auernig, C.-G. ; Ewert, S. ; Irmen, F. ; Steiner, L. A. ; Scheller, U. ; van Wijk, B. C. M. ; Oxenford, S. ; Kühn, A. A.DZNE* ; Horn, A.

2022
Academic Press Orlando, Fla.

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Please use a persistent id in citations: doi:10.1016/j.neuroimage.2022.119552

Abstract: Lead-DBS is an open-source, semi-automatized and widely applied software tool facilitating precise localization of deep brain stimulation electrodes both in native as well as in standardized stereotactic space. While automatized preprocessing steps within the toolbox have been tested and validated in previous studies, the interrater reliability in manual refinements of electrode localizations using the tool has not been objectified so far. Here, we investigate the variance introduced in this processing step by different raters when localizing electrodes based on postoperative CT or MRI. Furthermore, we compare the performance of novel trainees that received a structured training and more experienced raters with an expert user. We show that all users yield similar results with an average difference in localizations ranging between 0.52-0.75 mm with 0.07-0.12 mm increases in variability when using postoperative MRI and following normalization to standard space. Our findings may pave the way toward formal training for using Lead-DBS and demonstrate its reliability and ease-of-use for imaging research in the field of deep brain stimulation.

Keyword(s): Deep Brain Stimulation: methods (MeSH) ; Electrodes, Implanted (MeSH) ; Humans (MeSH) ; Magnetic Resonance Imaging: methods (MeSH) ; Parkinson Disease: therapy (MeSH) ; Reproducibility of Results (MeSH) ; Subthalamic Nucleus: physiology (MeSH) ; Deep brain stimulation ; Lead-DBS ; Localization ; Parkinson's disease ; Subthalamic nucleus

Note: CC BY-NC-ND: https://creativecommons.org/licenses/by-nc-nd/4.0/

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Contributing Institute(s):

1. Parkinson Genetics (AG Gasser 1)

Research Program(s):

1. 353 - Clinical and Health Care Research (POF4-353) (POF4-353)

Appears in the scientific report 2022

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