Modular pipeline for reconstruction and localization of implanted intracranial ECoG and sEEG electrodes

• Published in: PloS one Vol. 18; no. 7; p. e0287921
• Main Authors: Soper, Daniel J., Reich, Dustine, Ross, Alex, Salami, Pariya, Cash, Sydney S., Basu, Ishita, Peled, Noam, Paulk, Angelique C.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.07.2023; Public Library of Science (PLoS)
• Subjects: 3-D graphics; Algorithms; Analysis; Biology and Life Sciences; Brain; Brain - diagnostic imaging; Brain - physiology; Brain - surgery; Brain Mapping - methods; Brain research; Care and treatment; Cerebral Cortex; Cognitive ability; Cognitive tasks; Computer and Information Sciences; Convulsions & seizures; Electrocorticography - methods; Electrodes; Electrodes, Implanted; Electroencephalography; Electroencephalography - methods; Engineering and Technology; Epilepsy; Humans; Image reconstruction; Implanted electrodes (biology); Lab Protocol; Localization; Magnetic Resonance Imaging - methods; Management; Medicine and Health Sciences; Neuroimaging; Patient outcomes; Patients; Pipe lines; Registration; Research and Analysis Methods; Seizures; Social Sciences; Software packages; Substantia grisea; Visualization; United States; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0287921

Implantation of electrodes in the brain has been used as a clinical tool for decades to stimulate and record brain activity. As this method increasingly becomes the standard of care for several disorders and diseases, there is a growing need to quickly and accurately localize the electrodes once they are placed within the brain. We share here a protocol pipeline for localizing electrodes implanted in the brain, which we have applied to more than 260 patients, that is accessible to multiple skill levels and modular in execution. This pipeline uses multiple software packages to prioritize flexibility by permitting multiple different parallel outputs while minimizing the number of steps for each output. These outputs include co-registered imaging, electrode coordinates, 2D and 3D visualizations of the implants, automatic surface and volumetric localizations of the brain regions per electrode, and anonymization and data sharing tools. We demonstrate here some of the pipeline’s visualizations and automatic localization algorithms which we have applied to determine appropriate stimulation targets, to conduct seizure dynamics analysis, and to localize neural activity from cognitive tasks in previous studies. Further, the output facilitates the extraction of information such as the probability of grey matter intersection or the nearest anatomic structure per electrode contact across all data sets that go through the pipeline. We expect that this pipeline will be a useful framework for researchers and clinicians alike to localize implanted electrodes in the human brain.