A classification-based generative approach to selective targeting of global slow oscillations during sleep
BackgroundGiven sleep’s crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2024-02-01
|
Series: | Frontiers in Human Neuroscience |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2024.1342975/full |
_version_ | 1827352852495859712 |
---|---|
author | Mahmoud Alipour Mahmoud Alipour SangCheol Seok Sara C. Mednick Paola Malerba Paola Malerba |
author_facet | Mahmoud Alipour Mahmoud Alipour SangCheol Seok Sara C. Mednick Paola Malerba Paola Malerba |
author_sort | Mahmoud Alipour |
collection | DOAJ |
description | BackgroundGiven sleep’s crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during sleep can enhance memory performance, especially when used in a closed-loop (cl-tACS) mode that coordinates with sleep slow oscillations (SOs, 0.5−1.5Hz). However, sleep tACS research is characterized by mixed results across individuals, which are often attributed to individual variability.Objective/HypothesisThis study targets a specific type of SOs, widespread on the electrode manifold in a short delay (“global SOs”), due to their close relationship with long-term memory consolidation. We propose a model-based approach to optimize cl-tACS paradigms, targeting global SOs not only by considering their temporal properties but also their spatial profile.MethodsWe introduce selective targeting of global SOs using a classification-based approach. We first estimate the current elicited by various stimulation paradigms, and optimize parameters to match currents found in natural sleep during a global SO. Then, we employ an ensemble classifier trained on sleep data to identify effective paradigms. Finally, the best stimulation protocol is determined based on classification performance.ResultsOur study introduces a model-driven cl-tACS approach that specifically targets global SOs, with the potential to extend to other brain dynamics. This method establishes a connection between brain dynamics and stimulation optimization.ConclusionOur research presents a novel approach to optimize cl-tACS during sleep, with a focus on targeting global SOs. This approach holds promise for improving cl-tACS not only for global SOs but also for other physiological events, benefiting both research and clinical applications in sleep and cognition. |
first_indexed | 2024-03-08T03:12:05Z |
format | Article |
id | doaj.art-baae63bcb2cf4dcdbe76d807bbd129e8 |
institution | Directory Open Access Journal |
issn | 1662-5161 |
language | English |
last_indexed | 2024-03-08T03:12:05Z |
publishDate | 2024-02-01 |
publisher | Frontiers Media S.A. |
record_format | Article |
series | Frontiers in Human Neuroscience |
spelling | doaj.art-baae63bcb2cf4dcdbe76d807bbd129e82024-02-13T04:25:20ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612024-02-011810.3389/fnhum.2024.13429751342975A classification-based generative approach to selective targeting of global slow oscillations during sleepMahmoud Alipour0Mahmoud Alipour1SangCheol Seok2Sara C. Mednick3Paola Malerba4Paola Malerba5Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, United StatesThe Ohio State University School of Medicine, Columbus, OH, United StatesCenter for Gene Therapy, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, United StatesDepartment of Cognitive Sciences, University of California, Irvine, Irvine CA, United StatesCenter for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, United StatesThe Ohio State University School of Medicine, Columbus, OH, United StatesBackgroundGiven sleep’s crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during sleep can enhance memory performance, especially when used in a closed-loop (cl-tACS) mode that coordinates with sleep slow oscillations (SOs, 0.5−1.5Hz). However, sleep tACS research is characterized by mixed results across individuals, which are often attributed to individual variability.Objective/HypothesisThis study targets a specific type of SOs, widespread on the electrode manifold in a short delay (“global SOs”), due to their close relationship with long-term memory consolidation. We propose a model-based approach to optimize cl-tACS paradigms, targeting global SOs not only by considering their temporal properties but also their spatial profile.MethodsWe introduce selective targeting of global SOs using a classification-based approach. We first estimate the current elicited by various stimulation paradigms, and optimize parameters to match currents found in natural sleep during a global SO. Then, we employ an ensemble classifier trained on sleep data to identify effective paradigms. Finally, the best stimulation protocol is determined based on classification performance.ResultsOur study introduces a model-driven cl-tACS approach that specifically targets global SOs, with the potential to extend to other brain dynamics. This method establishes a connection between brain dynamics and stimulation optimization.ConclusionOur research presents a novel approach to optimize cl-tACS during sleep, with a focus on targeting global SOs. This approach holds promise for improving cl-tACS not only for global SOs but also for other physiological events, benefiting both research and clinical applications in sleep and cognition.https://www.frontiersin.org/articles/10.3389/fnhum.2024.1342975/fullsleepmemory consolidationelectrical brain stimulationglobal slow oscillationsoptimization |
spellingShingle | Mahmoud Alipour Mahmoud Alipour SangCheol Seok Sara C. Mednick Paola Malerba Paola Malerba A classification-based generative approach to selective targeting of global slow oscillations during sleep Frontiers in Human Neuroscience sleep memory consolidation electrical brain stimulation global slow oscillations optimization |
title | A classification-based generative approach to selective targeting of global slow oscillations during sleep |
title_full | A classification-based generative approach to selective targeting of global slow oscillations during sleep |
title_fullStr | A classification-based generative approach to selective targeting of global slow oscillations during sleep |
title_full_unstemmed | A classification-based generative approach to selective targeting of global slow oscillations during sleep |
title_short | A classification-based generative approach to selective targeting of global slow oscillations during sleep |
title_sort | classification based generative approach to selective targeting of global slow oscillations during sleep |
topic | sleep memory consolidation electrical brain stimulation global slow oscillations optimization |
url | https://www.frontiersin.org/articles/10.3389/fnhum.2024.1342975/full |
work_keys_str_mv | AT mahmoudalipour aclassificationbasedgenerativeapproachtoselectivetargetingofglobalslowoscillationsduringsleep AT mahmoudalipour aclassificationbasedgenerativeapproachtoselectivetargetingofglobalslowoscillationsduringsleep AT sangcheolseok aclassificationbasedgenerativeapproachtoselectivetargetingofglobalslowoscillationsduringsleep AT saracmednick aclassificationbasedgenerativeapproachtoselectivetargetingofglobalslowoscillationsduringsleep AT paolamalerba aclassificationbasedgenerativeapproachtoselectivetargetingofglobalslowoscillationsduringsleep AT paolamalerba aclassificationbasedgenerativeapproachtoselectivetargetingofglobalslowoscillationsduringsleep AT mahmoudalipour classificationbasedgenerativeapproachtoselectivetargetingofglobalslowoscillationsduringsleep AT mahmoudalipour classificationbasedgenerativeapproachtoselectivetargetingofglobalslowoscillationsduringsleep AT sangcheolseok classificationbasedgenerativeapproachtoselectivetargetingofglobalslowoscillationsduringsleep AT saracmednick classificationbasedgenerativeapproachtoselectivetargetingofglobalslowoscillationsduringsleep AT paolamalerba classificationbasedgenerativeapproachtoselectivetargetingofglobalslowoscillationsduringsleep AT paolamalerba classificationbasedgenerativeapproachtoselectivetargetingofglobalslowoscillationsduringsleep |