Single-Trial EEG Connectivity of Default Mode Network Before and During Encoding Predicts Subsequent Memory Outcome

The successful memory process produces specific activity in the brain network. As the brain activity of the prestimulus and encoding phases has a crucial effect on subsequent memory outcomes (e.g., remembered or forgotten), previous studies have tried to predict the memory performance in this period...

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Main Authors: Dahye Kim, Woorim Jeong, June Sic Kim, Chun Kee Chung
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-11-01
Series:Frontiers in Systems Neuroscience
Subjects:
EEG
Online Access:https://www.frontiersin.org/articles/10.3389/fnsys.2020.591675/full
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spelling doaj-def10597ee5d4c51883022424ed981d82020-11-25T04:09:42ZengFrontiers Media S.A.Frontiers in Systems Neuroscience1662-51372020-11-011410.3389/fnsys.2020.591675591675Single-Trial EEG Connectivity of Default Mode Network Before and During Encoding Predicts Subsequent Memory OutcomeDahye Kim0Woorim Jeong1June Sic Kim2Chun Kee Chung3Chun Kee Chung4Chun Kee Chung5Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, South KoreaCollege of Sungsim General Education, Youngsan University, Yangsan, South KoreaThe Research Institute of Basic Sciences, College of Natural Sciences, Seoul National University, Seoul, South KoreaDepartment of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, South KoreaDepartment of Neurosurgery, Seoul National University Hospital, Seoul, South KoreaNeuroscience Research Institute, College of Medicine, Seoul National University, Seoul, South KoreaThe successful memory process produces specific activity in the brain network. As the brain activity of the prestimulus and encoding phases has a crucial effect on subsequent memory outcomes (e.g., remembered or forgotten), previous studies have tried to predict the memory performance in this period. Conventional studies have used the spectral power or event-related potential of specific regions as the classification feature. However, as multiple brain regions work collaboratively to process memory, it could be a better option to use functional connectivity within the memory-related brain network to predict subsequent memory performance. In this study, we acquired the EEG signals while performing an associative memory task that remembers scene–word pairs. For the connectivity analysis, we estimated the cross–mutual information within the default mode network with the time–frequency spectra at the prestimulus and encoding phases. Then, we predicted the success or failure of subsequent memory outcome with the connectivity features. We found that the classifier with support vector machine achieved the highest classification accuracy of 80.83% ± 12.65% (mean ± standard deviation) using the beta (13–30 Hz) connectivity at encoding phase among the multiple frequency bands and task phases. Using the prestimulus beta connectivity, the classification accuracy of 72.45% ± 12.52% is also achieved. Among the features, the connectivity related to the dorsomedial prefrontal cortex was found to contribute to successful memory encoding. The connectivity related to the posterior cingulate cortex was found to contribute to the failure of memory encoding. The present study showed for the first time the successful prediction with high accuracy of subsequent memory outcome using single-trial functional connectivity.https://www.frontiersin.org/articles/10.3389/fnsys.2020.591675/fullmemoryEEGsubsequent memory effectsfunctional connectivitydefault mode network
collection DOAJ
language English
format Article
sources DOAJ
author Dahye Kim
Woorim Jeong
June Sic Kim
Chun Kee Chung
Chun Kee Chung
Chun Kee Chung
spellingShingle Dahye Kim
Woorim Jeong
June Sic Kim
Chun Kee Chung
Chun Kee Chung
Chun Kee Chung
Single-Trial EEG Connectivity of Default Mode Network Before and During Encoding Predicts Subsequent Memory Outcome
Frontiers in Systems Neuroscience
memory
EEG
subsequent memory effects
functional connectivity
default mode network
author_facet Dahye Kim
Woorim Jeong
June Sic Kim
Chun Kee Chung
Chun Kee Chung
Chun Kee Chung
author_sort Dahye Kim
title Single-Trial EEG Connectivity of Default Mode Network Before and During Encoding Predicts Subsequent Memory Outcome
title_short Single-Trial EEG Connectivity of Default Mode Network Before and During Encoding Predicts Subsequent Memory Outcome
title_full Single-Trial EEG Connectivity of Default Mode Network Before and During Encoding Predicts Subsequent Memory Outcome
title_fullStr Single-Trial EEG Connectivity of Default Mode Network Before and During Encoding Predicts Subsequent Memory Outcome
title_full_unstemmed Single-Trial EEG Connectivity of Default Mode Network Before and During Encoding Predicts Subsequent Memory Outcome
title_sort single-trial eeg connectivity of default mode network before and during encoding predicts subsequent memory outcome
publisher Frontiers Media S.A.
series Frontiers in Systems Neuroscience
issn 1662-5137
publishDate 2020-11-01
description The successful memory process produces specific activity in the brain network. As the brain activity of the prestimulus and encoding phases has a crucial effect on subsequent memory outcomes (e.g., remembered or forgotten), previous studies have tried to predict the memory performance in this period. Conventional studies have used the spectral power or event-related potential of specific regions as the classification feature. However, as multiple brain regions work collaboratively to process memory, it could be a better option to use functional connectivity within the memory-related brain network to predict subsequent memory performance. In this study, we acquired the EEG signals while performing an associative memory task that remembers scene–word pairs. For the connectivity analysis, we estimated the cross–mutual information within the default mode network with the time–frequency spectra at the prestimulus and encoding phases. Then, we predicted the success or failure of subsequent memory outcome with the connectivity features. We found that the classifier with support vector machine achieved the highest classification accuracy of 80.83% ± 12.65% (mean ± standard deviation) using the beta (13–30 Hz) connectivity at encoding phase among the multiple frequency bands and task phases. Using the prestimulus beta connectivity, the classification accuracy of 72.45% ± 12.52% is also achieved. Among the features, the connectivity related to the dorsomedial prefrontal cortex was found to contribute to successful memory encoding. The connectivity related to the posterior cingulate cortex was found to contribute to the failure of memory encoding. The present study showed for the first time the successful prediction with high accuracy of subsequent memory outcome using single-trial functional connectivity.
topic memory
EEG
subsequent memory effects
functional connectivity
default mode network
url https://www.frontiersin.org/articles/10.3389/fnsys.2020.591675/full
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