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Integrated analysis of anatomical and electrophysiological human intracranial data

The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges...

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Autores principales: Stolk, Arjen, Griffin, Sandon M., van der Meij, Roemer, Dewar, Callum, Saez, Ignacio, Lin, Jack J., Piantoni, Giovanni, Schoffelen, Jan-Mathijs, Knight, Robert T., Oostenveld, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6548463/
https://www.ncbi.nlm.nih.gov/pubmed/29988107
http://dx.doi.org/10.1038/s41596-018-0009-6
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author Stolk, Arjen
Griffin, Sandon M.
van der Meij, Roemer
Dewar, Callum
Saez, Ignacio
Lin, Jack J.
Piantoni, Giovanni
Schoffelen, Jan-Mathijs
Knight, Robert T.
Oostenveld, Robert
author_facet Stolk, Arjen
Griffin, Sandon M.
van der Meij, Roemer
Dewar, Callum
Saez, Ignacio
Lin, Jack J.
Piantoni, Giovanni
Schoffelen, Jan-Mathijs
Knight, Robert T.
Oostenveld, Robert
author_sort Stolk, Arjen
collection PubMed
description The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated, taking under an hour to complete, and readily adjusted to iEEG data sets with other characteristics.
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spelling pubmed-65484632019-06-04 Integrated analysis of anatomical and electrophysiological human intracranial data Stolk, Arjen Griffin, Sandon M. van der Meij, Roemer Dewar, Callum Saez, Ignacio Lin, Jack J. Piantoni, Giovanni Schoffelen, Jan-Mathijs Knight, Robert T. Oostenveld, Robert Nat Protoc Article The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated, taking under an hour to complete, and readily adjusted to iEEG data sets with other characteristics. 2018-07 /pmc/articles/PMC6548463/ /pubmed/29988107 http://dx.doi.org/10.1038/s41596-018-0009-6 Text en http://creativecommons.org/licenses/by-nc/4.0/ The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
spellingShingle Article
Stolk, Arjen
Griffin, Sandon M.
van der Meij, Roemer
Dewar, Callum
Saez, Ignacio
Lin, Jack J.
Piantoni, Giovanni
Schoffelen, Jan-Mathijs
Knight, Robert T.
Oostenveld, Robert
Integrated analysis of anatomical and electrophysiological human intracranial data
title Integrated analysis of anatomical and electrophysiological human intracranial data
title_full Integrated analysis of anatomical and electrophysiological human intracranial data
title_fullStr Integrated analysis of anatomical and electrophysiological human intracranial data
title_full_unstemmed Integrated analysis of anatomical and electrophysiological human intracranial data
title_short Integrated analysis of anatomical and electrophysiological human intracranial data
title_sort integrated analysis of anatomical and electrophysiological human intracranial data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6548463/
https://www.ncbi.nlm.nih.gov/pubmed/29988107
http://dx.doi.org/10.1038/s41596-018-0009-6
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