Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation
Single-pulse electrical stimulation in the nervous system, often called cortico-cortical evoked potential (CCEP) measurement, is an important technique to understand how brain regions interact with one another. Voltages are measured from implanted electrodes in one brain area while stimulating anoth...
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Published in | PLoS computational biology Vol. 19; no. 5; p. e1011105 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
01.05.2023
Public Library of Science (PLoS) |
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Online Access | Get full text |
ISSN | 1553-7358 1553-734X 1553-7358 |
DOI | 10.1371/journal.pcbi.1011105 |
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Abstract | Single-pulse electrical stimulation in the nervous system, often called cortico-cortical evoked potential (CCEP) measurement, is an important technique to understand how brain regions interact with one another. Voltages are measured from implanted electrodes in one brain area while stimulating another with brief current impulses separated by several seconds. Historically, researchers have tried to understand the significance of evoked voltage polyphasic deflections by visual inspection, but no general-purpose tool has emerged to understand their shapes or describe them mathematically. We describe and illustrate a new technique to parameterize brain stimulation data, where voltage response traces are projected into one another using a semi-normalized dot product. The length of timepoints from stimulation included in the dot product is varied to obtain a temporal profile of structural significance, and the peak of the profile uniquely identifies the duration of the response. Using linear kernel PCA, a canonical response shape is obtained over this duration, and then single-trial traces are parameterized as a projection of this canonical shape with a residual term. Such parameterization allows for dissimilar trace shapes from different brain areas to be directly compared by quantifying cross-projection magnitudes, response duration, canonical shape projection amplitudes, signal-to-noise ratios, explained variance, and statistical significance. Artifactual trials are automatically identified by outliers in sub-distributions of cross-projection magnitude, and rejected. This technique, which we call “Canonical Response Parameterization” (CRP) dramatically simplifies the study of CCEP shapes, and may also be applied in a wide range of other settings involving event-triggered data. |
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AbstractList | Single-pulse electrical stimulation in the nervous system, often called cortico-cortical evoked potential (CCEP) measurement, is an important technique to understand how brain regions interact with one another. Voltages are measured from implanted electrodes in one brain area while stimulating another with brief current impulses separated by several seconds. Historically, researchers have tried to understand the significance of evoked voltage polyphasic deflections by visual inspection, but no general-purpose tool has emerged to understand their shapes or describe them mathematically. We describe and illustrate a new technique to parameterize brain stimulation data, where voltage response traces are projected into one another using a semi-normalized dot product. The length of timepoints from stimulation included in the dot product is varied to obtain a temporal profile of structural significance, and the peak of the profile uniquely identifies the duration of the response. Using linear kernel PCA, a canonical response shape is obtained over this duration, and then single-trial traces are parameterized as a projection of this canonical shape with a residual term. Such parameterization allows for dissimilar trace shapes from different brain areas to be directly compared by quantifying cross-projection magnitudes, response duration, canonical shape projection amplitudes, signal-to-noise ratios, explained variance, and statistical significance. Artifactual trials are automatically identified by outliers in sub-distributions of cross-projection magnitude, and rejected. This technique, which we call "Canonical Response Parameterization" (CRP) dramatically simplifies the study of CCEP shapes, and may also be applied in a wide range of other settings involving event-triggered data.Single-pulse electrical stimulation in the nervous system, often called cortico-cortical evoked potential (CCEP) measurement, is an important technique to understand how brain regions interact with one another. Voltages are measured from implanted electrodes in one brain area while stimulating another with brief current impulses separated by several seconds. Historically, researchers have tried to understand the significance of evoked voltage polyphasic deflections by visual inspection, but no general-purpose tool has emerged to understand their shapes or describe them mathematically. We describe and illustrate a new technique to parameterize brain stimulation data, where voltage response traces are projected into one another using a semi-normalized dot product. The length of timepoints from stimulation included in the dot product is varied to obtain a temporal profile of structural significance, and the peak of the profile uniquely identifies the duration of the response. Using linear kernel PCA, a canonical response shape is obtained over this duration, and then single-trial traces are parameterized as a projection of this canonical shape with a residual term. Such parameterization allows for dissimilar trace shapes from different brain areas to be directly compared by quantifying cross-projection magnitudes, response duration, canonical shape projection amplitudes, signal-to-noise ratios, explained variance, and statistical significance. Artifactual trials are automatically identified by outliers in sub-distributions of cross-projection magnitude, and rejected. This technique, which we call "Canonical Response Parameterization" (CRP) dramatically simplifies the study of CCEP shapes, and may also be applied in a wide range of other settings involving event-triggered data. Single-pulse electrical stimulation in the nervous system, often called cortico-cortical evoked potential (CCEP) measurement, is an important technique to understand how brain regions interact with one another. Voltages are measured from implanted electrodes in one brain area while stimulating another with brief current impulses separated by several seconds. Historically, researchers have tried to understand the significance of evoked voltage polyphasic deflections by visual inspection, but no general-purpose tool has emerged to understand their shapes or describe them mathematically. We describe and illustrate a new technique to parameterize brain stimulation data, where voltage response traces are projected into one another using a semi-normalized dot product. The length of timepoints from stimulation included in the dot product is varied to obtain a temporal profile of structural significance, and the peak of the profile uniquely identifies the duration of the response. Using linear kernel PCA, a canonical response shape is obtained over this duration, and then single-trial traces are parameterized as a projection of this canonical shape with a residual term. Such parameterization allows for dissimilar trace shapes from different brain areas to be directly compared by quantifying cross-projection magnitudes, response duration, canonical shape projection amplitudes, signal-to-noise ratios, explained variance, and statistical significance. Artifactual trials are automatically identified by outliers in sub-distributions of cross-projection magnitude, and rejected. This technique, which we call “Canonical Response Parameterization” (CRP) dramatically simplifies the study of CCEP shapes, and may also be applied in a wide range of other settings involving event-triggered data. Single-pulse electrical stimulation in the nervous system, often called cortico-cortical evoked potential (CCEP) measurement, is an important technique to understand how brain regions interact with one another. Voltages are measured from implanted electrodes in one brain area while stimulating another with brief current impulses separated by several seconds. Historically, researchers have tried to understand the significance of evoked voltage polyphasic deflections by visual inspection, but no general-purpose tool has emerged to understand their shapes or describe them mathematically. We describe and illustrate a new technique to parameterize brain stimulation data, where voltage response traces are projected into one another using a semi-normalized dot product. The length of timepoints from stimulation included in the dot product is varied to obtain a temporal profile of structural significance, and the peak of the profile uniquely identifies the duration of the response. Using linear kernel PCA, a canonical response shape is obtained over this duration, and then single-trial traces are parameterized as a projection of this canonical shape with a residual term. Such parameterization allows for dissimilar trace shapes from different brain areas to be directly compared by quantifying cross-projection magnitudes, response duration, canonical shape projection amplitudes, signal-to-noise ratios, explained variance, and statistical significance. Artifactual trials are automatically identified by outliers in sub-distributions of cross-projection magnitude, and rejected. This technique, which we call “Canonical Response Parameterization” (CRP) dramatically simplifies the study of CCEP shapes, and may also be applied in a wide range of other settings involving event-triggered data. We introduce a new machine learning technique for quantifying the structure of responses to single-pulse intracranial electrical brain stimulation. This approach allows voltage response traces of very different shape to be compared with one another. A tool like this has been needed to replace the status quo, where researchers may understand their data in terms of discovered structure rather than in terms of a pre-assigned, hand-picked, feature. The method compares single-trial responses pairwise to understand if there is a reproducible shape and how long it lasts. When significant structure is identified, the shape underlying it is isolated and each trial is parameterized in terms of this shape. This simple parameterization enables quantification of statistical significance, signal-to-noise ratio, explained variance, and average voltage of the response. Differently-shaped voltage traces from any setting can be compared with any other in a succinct mathematical framework. This versatile tool to quantify single-pulse stimulation data should facilitate a blossoming in the study of brain connectivity using implanted electrodes. |
Audience | Academic |
Author | Hermes, Dora Valencia, Gabriela Ojeda Worrell, Gregory A. Müller, Klaus-Robert Gregg, Nicholas M. Miller, Kai J. Huang, Harvey |
AuthorAffiliation | 5 Dept of Artificial Intelligence, Korea University, Seoul, Republic of Korea 7 Medical Scientist Training Program, Mayo Clinic, Rochester, Minnesota, United States of America University of Oxford, UNITED KINGDOM 1 Dept of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, United States of America 8 Dept of Neurology, Mayo Clinic, Rochester, Minnesota, United States of America 2 Dept of Biomedical Engineering & Physiology, Mayo Clinic, Rochester, Minnesota, United States of America 6 Max Planck Institute for Informatics, Saarbrücken, Germany 4 Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany 3 Google Research, Brain Team, Berlin, Germany |
AuthorAffiliation_xml | – name: University of Oxford, UNITED KINGDOM – name: 6 Max Planck Institute for Informatics, Saarbrücken, Germany – name: 1 Dept of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, United States of America – name: 4 Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany – name: 7 Medical Scientist Training Program, Mayo Clinic, Rochester, Minnesota, United States of America – name: 8 Dept of Neurology, Mayo Clinic, Rochester, Minnesota, United States of America – name: 2 Dept of Biomedical Engineering & Physiology, Mayo Clinic, Rochester, Minnesota, United States of America – name: 5 Dept of Artificial Intelligence, Korea University, Seoul, Republic of Korea – name: 3 Google Research, Brain Team, Berlin, Germany |
Author_xml | – sequence: 1 givenname: Kai J. orcidid: 0000-0002-6687-6422 surname: Miller fullname: Miller, Kai J. – sequence: 2 givenname: Klaus-Robert orcidid: 0000-0002-3861-7685 surname: Müller fullname: Müller, Klaus-Robert – sequence: 3 givenname: Gabriela Ojeda surname: Valencia fullname: Valencia, Gabriela Ojeda – sequence: 4 givenname: Harvey surname: Huang fullname: Huang, Harvey – sequence: 5 givenname: Nicholas M. orcidid: 0000-0002-6151-043X surname: Gregg fullname: Gregg, Nicholas M. – sequence: 6 givenname: Gregory A. surname: Worrell fullname: Worrell, Gregory A. – sequence: 7 givenname: Dora surname: Hermes fullname: Hermes, Dora |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37228169$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_1038_s41586_023_06459_w crossref_primary_10_1016_j_clinph_2024_11_006 crossref_primary_10_1016_j_jneumeth_2025_110389 crossref_primary_10_1016_j_clinph_2024_08_019 crossref_primary_10_1523_JNEUROSCI_2201_22_2023 crossref_primary_10_1016_j_clinph_2024_10_016 crossref_primary_10_1038_s44222_024_00185_2 |
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Copyright | Copyright: © 2023 Miller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2023 Public Library of Science 2023 Miller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023 Miller et al 2023 Miller et al |
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Snippet | Single-pulse electrical stimulation in the nervous system, often called cortico-cortical evoked potential (CCEP) measurement, is an important technique to... |
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SubjectTerms | Biology and Life Sciences Brain Brain Mapping - methods Brain stimulation Convulsions & seizures Current impulses Electric potential Electric Stimulation - methods Electrical stimulation of the brain Electrical stimuli Electrodes Electrodes, Implanted Engineering and Technology ESB Evoked potentials (Electrophysiology) Evoked Potentials - physiology Health aspects Hypotheses Implanted electrodes (biology) Inspection Measurement Medicine and Health Sciences Methods Nervous system Outliers (statistics) Parameterization Patient outcomes Patients Physical Sciences Research and Analysis Methods Social Sciences Stimulation Voltage |
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Title | Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation |
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