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

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 acro...

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Vydáno v:Frontiers in neuroinformatics Ročník 11; s. 62
Hlavní autoři: Hamilton, Liberty S., Chang, David L., Lee, Morgan B., Chang, Edward F.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Research Foundation 31.10.2017
Frontiers Media S.A
Témata:
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
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Shrnutí: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.
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Edited by: Satrajit S. Ghosh, Massachusetts Institute of Technology, United States
Reviewed by: Andrei Irimia, University of Southern California, United States; Michael S. Beauchamp, Baylor College of Medicine, United States
ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2017.00062