iElectrodes: a comprehensive Open-Source toolbox for Depth and Subdural Grid Electrode Localization

• Autores: Blenkmann, Alejandro; Phillips, Holly N.; Princich, Juan P.; Rowe, James B.; Bekinschtein, Tristán A.; Muravchik, Carlos Horacio; Kochen, Silvia
• Año de publicación: 2017
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Ingenierías y Tecnologías; SEEG; ECoG; intracranial EEG; MRI; CT; atlas; epilepsy
• Institución: Comisión de Investigaciones Científicas de la Provincia de Buenos Aires

The localization of intracranial electrodes is a fundamental step in the analysis of invasive electroencephalography (EEG) recordings in research and clinical practice. The conclusions reached from the analysis of these recordings rely on the accuracy of electrode localization in relationship to brain anatomy. However, currently available techniques for localizing electrodes from magnetic resonance (MR) and/or computerized tomography (CT) images are time consuming and/or limited to particular electrode types or shapes. Here we present iElectrodes, an open-source toolbox that provides robust and accurate semi-automatic localization of both subdural grids and depth electrodes. Using pre- and post-implantation images, the method takes 2–3 min to localize the coordinates in each electrode array and automatically number the electrodes. The proposed pre-processing pipeline allows one to work in a normalized space and to automatically obtain anatomical labels of the localized electrodes without neuroimaging experts. We validated the method with data from 22 patients implanted with a total of 1,242 electrodes. We show that localization distances were within 0.56 mm of those achieved by experienced manual evaluators. iElectrodes provided additional advantages in terms of robustness (even with severe perioperative cerebral distortions), speed (less than half the operator time compared to expert manual localization), simplicity, utility across multiple electrode types (surface and depth electrodes) and all brain regions.