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Combining electro- and magnetoencephalography data using directional archetypal analysis
Metastable microstates in electro- and magnetoencephalographic (EEG and MEG) measurements are usually determined using modified k-means accounting for polarity invariant states. However, hard state assignment approaches assume that the brain traverses microstates in a discrete rather than continuous...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9374169/ https://www.ncbi.nlm.nih.gov/pubmed/35968377 http://dx.doi.org/10.3389/fnins.2022.911034 |
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author | Olsen, Anders S. Høegh, Rasmus M. T. Hinrich, Jesper L. Madsen, Kristoffer H. Mørup, Morten |
author_facet | Olsen, Anders S. Høegh, Rasmus M. T. Hinrich, Jesper L. Madsen, Kristoffer H. Mørup, Morten |
author_sort | Olsen, Anders S. |
collection | PubMed |
description | Metastable microstates in electro- and magnetoencephalographic (EEG and MEG) measurements are usually determined using modified k-means accounting for polarity invariant states. However, hard state assignment approaches assume that the brain traverses microstates in a discrete rather than continuous fashion. We present multimodal, multisubject directional archetypal analysis as a scale and polarity invariant extension to archetypal analysis using a loss function based on the Watson distribution. With this method, EEG/MEG microstates are modeled using subject- and modality-specific archetypes that are representative, distinct topographic maps between which the brain continuously traverses. Archetypes are specified as convex combinations of unit norm input data based on a shared generator matrix, thus assuming that the timing of neural responses to stimuli is consistent across subjects and modalities. The input data is reconstructed as convex combinations of archetypes using a subject- and modality-specific continuous archetypal mixing matrix. We showcase the model on synthetic data and an openly available face perception event-related potential data set with concurrently recorded EEG and MEG. In synthetic and unimodal experiments, we compare our model to conventional Euclidean multisubject archetypal analysis. We also contrast our model to a directional clustering model with discrete state assignments to highlight the advantages of modeling state trajectories rather than hard assignments. We find that our approach successfully models scale and polarity invariant data, such as microstates, accounting for intersubject and intermodal variability. The model is readily extendable to other modalities ensuring component correspondence while elucidating spatiotemporal signal variability. |
format | Online Article Text |
id | pubmed-9374169 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93741692022-08-13 Combining electro- and magnetoencephalography data using directional archetypal analysis Olsen, Anders S. Høegh, Rasmus M. T. Hinrich, Jesper L. Madsen, Kristoffer H. Mørup, Morten Front Neurosci Neuroscience Metastable microstates in electro- and magnetoencephalographic (EEG and MEG) measurements are usually determined using modified k-means accounting for polarity invariant states. However, hard state assignment approaches assume that the brain traverses microstates in a discrete rather than continuous fashion. We present multimodal, multisubject directional archetypal analysis as a scale and polarity invariant extension to archetypal analysis using a loss function based on the Watson distribution. With this method, EEG/MEG microstates are modeled using subject- and modality-specific archetypes that are representative, distinct topographic maps between which the brain continuously traverses. Archetypes are specified as convex combinations of unit norm input data based on a shared generator matrix, thus assuming that the timing of neural responses to stimuli is consistent across subjects and modalities. The input data is reconstructed as convex combinations of archetypes using a subject- and modality-specific continuous archetypal mixing matrix. We showcase the model on synthetic data and an openly available face perception event-related potential data set with concurrently recorded EEG and MEG. In synthetic and unimodal experiments, we compare our model to conventional Euclidean multisubject archetypal analysis. We also contrast our model to a directional clustering model with discrete state assignments to highlight the advantages of modeling state trajectories rather than hard assignments. We find that our approach successfully models scale and polarity invariant data, such as microstates, accounting for intersubject and intermodal variability. The model is readily extendable to other modalities ensuring component correspondence while elucidating spatiotemporal signal variability. Frontiers Media S.A. 2022-07-29 /pmc/articles/PMC9374169/ /pubmed/35968377 http://dx.doi.org/10.3389/fnins.2022.911034 Text en Copyright © 2022 Olsen, Høegh, Hinrich, Madsen and Mørup. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Olsen, Anders S. Høegh, Rasmus M. T. Hinrich, Jesper L. Madsen, Kristoffer H. Mørup, Morten Combining electro- and magnetoencephalography data using directional archetypal analysis |
title | Combining electro- and magnetoencephalography data using directional archetypal analysis |
title_full | Combining electro- and magnetoencephalography data using directional archetypal analysis |
title_fullStr | Combining electro- and magnetoencephalography data using directional archetypal analysis |
title_full_unstemmed | Combining electro- and magnetoencephalography data using directional archetypal analysis |
title_short | Combining electro- and magnetoencephalography data using directional archetypal analysis |
title_sort | combining electro- and magnetoencephalography data using directional archetypal analysis |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9374169/ https://www.ncbi.nlm.nih.gov/pubmed/35968377 http://dx.doi.org/10.3389/fnins.2022.911034 |
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