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Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise

Mindfulness has growing empirical support for improving emotion regulation in individuals with Autism Spectrum Disorder (ASD). Mindfulness is cultivated through meditation practices. Assessing the role of mindfulness in improving emotion regulation is challenging given the reliance on self-report to...

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Autores principales: Susam, Busra T., Riek, Nathan T., Beck, Kelly, Eldeeb, Safaa, Hudac, Caitlin M., Gable, Philip A., Conner, Caitlin, Akcakaya, Murat, White, Susan, Mazefsky, Carla
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9979338/
https://www.ncbi.nlm.nih.gov/pubmed/35976834
http://dx.doi.org/10.1109/TNSRE.2022.3199151
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author Susam, Busra T.
Riek, Nathan T.
Beck, Kelly
Eldeeb, Safaa
Hudac, Caitlin M.
Gable, Philip A.
Conner, Caitlin
Akcakaya, Murat
White, Susan
Mazefsky, Carla
author_facet Susam, Busra T.
Riek, Nathan T.
Beck, Kelly
Eldeeb, Safaa
Hudac, Caitlin M.
Gable, Philip A.
Conner, Caitlin
Akcakaya, Murat
White, Susan
Mazefsky, Carla
author_sort Susam, Busra T.
collection PubMed
description Mindfulness has growing empirical support for improving emotion regulation in individuals with Autism Spectrum Disorder (ASD). Mindfulness is cultivated through meditation practices. Assessing the role of mindfulness in improving emotion regulation is challenging given the reliance on self-report tools. Electroencephalography (EEG) has successfully quantified neural responses to emotional arousal and meditation in other populations, making it ideal to objectively measure neural responses before and after mindfulness (MF) practice among individuals with ASD. We performed an EEG-based analysis during a resting state paradigm in 35 youth with ASD. Specifically, we developed a machine learning classifier and a feature and channel selection approach that separates resting states preceding (Pre-MF) and following (Post-MF) a mindfulness meditation exercise within participants. Across individuals, frontal and temporal channels were most informative. Total power in the beta band (16–30 Hz), Total power (4–30 Hz), relative power in alpha band (8–12 Hz) were the most informative EEG features. A classifier using a non-linear combination of selected EEG features from selected channel locations separated Pre-MF and Post-MF resting states with an average accuracy, sensitivity, and specificity of 80.76%, 78.24%, and 82.14% respectively. Finally, we validated that separation between Pre-MF and Post-MF is due to the MF prime rather than linear-temporal drift. This work underscores machine learning as a critical tool for separating distinct resting states within youth with ASD and will enable better classification of underlying neural responses following brief MF meditation.
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spelling pubmed-99793382023-03-02 Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise Susam, Busra T. Riek, Nathan T. Beck, Kelly Eldeeb, Safaa Hudac, Caitlin M. Gable, Philip A. Conner, Caitlin Akcakaya, Murat White, Susan Mazefsky, Carla IEEE Trans Neural Syst Rehabil Eng Article Mindfulness has growing empirical support for improving emotion regulation in individuals with Autism Spectrum Disorder (ASD). Mindfulness is cultivated through meditation practices. Assessing the role of mindfulness in improving emotion regulation is challenging given the reliance on self-report tools. Electroencephalography (EEG) has successfully quantified neural responses to emotional arousal and meditation in other populations, making it ideal to objectively measure neural responses before and after mindfulness (MF) practice among individuals with ASD. We performed an EEG-based analysis during a resting state paradigm in 35 youth with ASD. Specifically, we developed a machine learning classifier and a feature and channel selection approach that separates resting states preceding (Pre-MF) and following (Post-MF) a mindfulness meditation exercise within participants. Across individuals, frontal and temporal channels were most informative. Total power in the beta band (16–30 Hz), Total power (4–30 Hz), relative power in alpha band (8–12 Hz) were the most informative EEG features. A classifier using a non-linear combination of selected EEG features from selected channel locations separated Pre-MF and Post-MF resting states with an average accuracy, sensitivity, and specificity of 80.76%, 78.24%, and 82.14% respectively. Finally, we validated that separation between Pre-MF and Post-MF is due to the MF prime rather than linear-temporal drift. This work underscores machine learning as a critical tool for separating distinct resting states within youth with ASD and will enable better classification of underlying neural responses following brief MF meditation. 2022 2022-09-02 /pmc/articles/PMC9979338/ /pubmed/35976834 http://dx.doi.org/10.1109/TNSRE.2022.3199151 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Susam, Busra T.
Riek, Nathan T.
Beck, Kelly
Eldeeb, Safaa
Hudac, Caitlin M.
Gable, Philip A.
Conner, Caitlin
Akcakaya, Murat
White, Susan
Mazefsky, Carla
Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise
title Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise
title_full Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise
title_fullStr Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise
title_full_unstemmed Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise
title_short Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise
title_sort quantitative eeg changes in youth with asd following brief mindfulness meditation exercise
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9979338/
https://www.ncbi.nlm.nih.gov/pubmed/35976834
http://dx.doi.org/10.1109/TNSRE.2022.3199151
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