Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

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Abstract

Although activation/deactivation of specific brain regions have been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

Data availability

The data that support the findings of this study are openly available in Dryad Digital Repository (https://datadryad.org/).

The following data sets were generated

(2018) Data from: Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance
Available at Dryad Digital Repository under a CC0 Public Domain Dedication.

http://dx.doi.org/10.5061/dryad.35kg335

Article and author information

Author details

Ruedeerat Keerativittayayut

School of Information, Kochi University of Technology, Kami, Japan

Competing interests
The authors declare that no competing interests exist.
Ryuta Aoki

Research Center for Brain Communication, Kochi University of Technology, Kami, Japan

For correspondence
qqqqaokiq@yahoo.co.jp

Competing interests
The authors declare that no competing interests exist.
Mitra Taghizadeh Sarabi

School of Information, Kochi University of Technology, Kami, Japan

Competing interests
The authors declare that no competing interests exist.
Koji Jimura

Department of Biosciences and Informatics, Keio University, Yokohama, Japan

Competing interests
The authors declare that no competing interests exist.
Kiyoshi Nakahara

School of Information, Kochi University of Technology, Kami, Japan

For correspondence
nakahara.kiyoshi@kochi-tech.ac.jp

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-6701-6216

Funding

Japan Society for the Promotion of Science (17H00891)

Ryuta Aoki
Koji Jimura
Kiyoshi Nakahara

Japan Society for the Promotion of Science (17H06268)

Kiyoshi Nakahara

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: All experimental procedures were approved by the Ethics Committee of Kochi University of Technology. Informed consent was obtained from all participants.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.