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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Published inFrontiers in neuroinformatics Vol. 11; p. 62
Main Authors Hamilton, Liberty S., Chang, David L., Lee, Morgan B., Chang, Edward F.
Format Journal Article
LanguageEnglish
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
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Abstract 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.
AbstractList 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.
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.
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.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.
Author Chang, Edward F.
Hamilton, Liberty S.
Chang, David L.
Lee, Morgan B.
AuthorAffiliation 2 Center for Integrative Neuroscience, University of California, San Francisco , San Francisco, CA , United States
1 Department of Neurosurgery, University of California, San Francisco , San Francisco, CA , United States
AuthorAffiliation_xml – name: 2 Center for Integrative Neuroscience, University of California, San Francisco , San Francisco, CA , United States
– name: 1 Department of Neurosurgery, University of California, San Francisco , San Francisco, CA , United States
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/29163118$$D View this record in MEDLINE/PubMed
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Copyright © 2017 Hamilton, Chang, Lee and Chang. 2017 Hamilton, Chang, Lee and Chang
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– notice: Copyright © 2017 Hamilton, Chang, Lee and Chang. 2017 Hamilton, Chang, Lee and Chang
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Keywords epilepsy
open science
electrocorticography
image coregistration
electrode localization
intracranial recordings
subdural electrodes
surgery
Language English
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Snippet In this article, we introduce img_pipe, our open source python package for preprocessing of imaging data for use in intracranial electrocorticography (ECoG)...
In this article we introduce img_pipe, our open source python package for preprocessing of imaging data for use in intracranial electrocorticography (ECoG) and...
In this article, we introduce img_pipe , our open source python package for preprocessing of imaging data for use in intracranial electrocorticography (ECoG)...
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StartPage 62
SubjectTerms 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
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Title Semi-automated Anatomical Labeling and Inter-subject Warping of High-Density Intracranial Recording Electrodes in Electrocorticography
URI https://www.ncbi.nlm.nih.gov/pubmed/29163118
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https://pubmed.ncbi.nlm.nih.gov/PMC5671481
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