Semi-automated Anatomical Labeling and Inter-subject Warping of High-Density Intracranial Recording Electrodes in Electrocorticography

• Published in: Frontiers in neuroinformatics Vol. 11; p. 62
• Main Authors: Hamilton, Liberty S., Chang, David L., Lee, Morgan B., Chang, Edward F.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 31.10.2017; Frontiers Media S.A
• Subjects: Automation; Cloning; Computed tomography; Cortex; EEG; electrocorticography; electrode localization; Electrodes; Epilepsy; image coregistration; Intracranial recording; intracranial recordings; Labeling; Localization; Magnetic resonance imaging; Medical imaging; Neuroimaging; Neuroscience; NMR; Nuclear magnetic resonance; Operating systems; Review boards; Software packages; surgery; United States > US; San Francisco California; California; epilepsy; open science; electrocorticography; image coregistration; electrode localization; intracranial recordings; subdural electrodes; surgery
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2017.00062

In this article, we introduce img_pipe, our open source python package for preprocessing of imaging data for use in intracranial electrocorticography (ECoG) and intracranial stereo-EEG analyses. The process of electrode localization, labeling, and warping for use in ECoG currently varies widely across laboratories, and it is usually performed with custom, lab-specific code. This python package aims to provide a standardized interface for these procedures, as well as code to plot and display results on 3D cortical surface meshes. It gives the user an easy interface to create anatomically labeled electrodes that can also be warped to an atlas brain, starting with only a preoperative T1 MRI scan and a postoperative CT scan. We describe the full capabilities of our imaging pipeline and present a step-by-step protocol for users.