Cargando…
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...
Autores principales: | , , , , , , , , , |
---|---|
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 |
_version_ | 1783689521373642752 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8104962 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT goodalesarahe fmribaseddetectionofalertnesspredictsbehavioralresponsevariability AT ahmednafis fmribaseddetectionofalertnesspredictsbehavioralresponsevariability AT zhaochong fmribaseddetectionofalertnesspredictsbehavioralresponsevariability AT dezwartjaccoa fmribaseddetectionofalertnesspredictsbehavioralresponsevariability AT ozbaypinars fmribaseddetectionofalertnesspredictsbehavioralresponsevariability AT picchionidante fmribaseddetectionofalertnesspredictsbehavioralresponsevariability AT duynjeff fmribaseddetectionofalertnesspredictsbehavioralresponsevariability AT englotdarioj fmribaseddetectionofalertnesspredictsbehavioralresponsevariability AT morganvictorial fmribaseddetectionofalertnesspredictsbehavioralresponsevariability AT changcatie fmribaseddetectionofalertnesspredictsbehavioralresponsevariability |