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fMRI-based detection of alertness predicts behavioral response variability

Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailabl...

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Autores principales: Goodale, Sarah E, Ahmed, Nafis, Zhao, Chong, de Zwart, Jacco A, Özbay, Pinar S, Picchioni, Dante, Duyn, Jeff, Englot, Dario J, Morgan, Victoria L, Chang, Catie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104962/
https://www.ncbi.nlm.nih.gov/pubmed/33960930
http://dx.doi.org/10.7554/eLife.62376
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author Goodale, Sarah E
Ahmed, Nafis
Zhao, Chong
de Zwart, Jacco A
Özbay, Pinar S
Picchioni, Dante
Duyn, Jeff
Englot, Dario J
Morgan, Victoria L
Chang, Catie
author_facet Goodale, Sarah E
Ahmed, Nafis
Zhao, Chong
de Zwart, Jacco A
Özbay, Pinar S
Picchioni, Dante
Duyn, Jeff
Englot, Dario J
Morgan, Victoria L
Chang, Catie
author_sort Goodale, Sarah E
collection PubMed
description Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease.
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spelling pubmed-81049622021-05-11 fMRI-based detection of alertness predicts behavioral response variability Goodale, Sarah E Ahmed, Nafis Zhao, Chong de Zwart, Jacco A Özbay, Pinar S Picchioni, Dante Duyn, Jeff Englot, Dario J Morgan, Victoria L Chang, Catie eLife Neuroscience Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease. eLife Sciences Publications, Ltd 2021-05-07 /pmc/articles/PMC8104962/ /pubmed/33960930 http://dx.doi.org/10.7554/eLife.62376 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication (https://creativecommons.org/publicdomain/zero/1.0/) .
spellingShingle Neuroscience
Goodale, Sarah E
Ahmed, Nafis
Zhao, Chong
de Zwart, Jacco A
Özbay, Pinar S
Picchioni, Dante
Duyn, Jeff
Englot, Dario J
Morgan, Victoria L
Chang, Catie
fMRI-based detection of alertness predicts behavioral response variability
title fMRI-based detection of alertness predicts behavioral response variability
title_full fMRI-based detection of alertness predicts behavioral response variability
title_fullStr fMRI-based detection of alertness predicts behavioral response variability
title_full_unstemmed fMRI-based detection of alertness predicts behavioral response variability
title_short fMRI-based detection of alertness predicts behavioral response variability
title_sort fmri-based detection of alertness predicts behavioral response variability
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104962/
https://www.ncbi.nlm.nih.gov/pubmed/33960930
http://dx.doi.org/10.7554/eLife.62376
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