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

Dahye Kim; Woorim Jeong; June Sic Kim; Chun Kee Chung

doi:10.3389/fnsys.2020.591675

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

Front Syst Neurosci. 2020 Nov 19:14:591675. doi: 10.3389/fnsys.2020.591675. eCollection 2020.

Authors

Dahye Kim¹, Woorim Jeong², June Sic Kim³, Chun Kee Chung^{1

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Affiliations

¹ Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, South Korea.
² College of Sungsim General Education, Youngsan University, Yangsan, South Korea.
³ The Research Institute of Basic Sciences, College of Natural Sciences, Seoul National University, Seoul, South Korea.
⁴ Department of Neurosurgery, Seoul National University Hospital, Seoul, South Korea.
⁵ Neuroscience Research Institute, College of Medicine, Seoul National University, Seoul, South Korea.

Abstract

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.

Keywords: EEG; default mode network; functional connectivity; memory; subsequent memory effects.