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

Ruedeerat Keerativittayayut, Ryuta Aoki, Mitra Taghizadeh Sarabi, Koji Jimura, Kiyoshi Nakahara

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Although activation/deactivation of specific brain regions has 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.

Original languageEnglish
Article numbere32696
JournaleLife
Volume7
DOIs
Publication statusPublished - 2018 Jun 18

Fingerprint

Brain
Data storage equipment
Task Performance and Analysis
Cognition
Multivariate Analysis
Chemical activation
Magnetic Resonance Imaging

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

Cite this

Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance. / Keerativittayayut, Ruedeerat; Aoki, Ryuta; Sarabi, Mitra Taghizadeh; Jimura, Koji; Nakahara, Kiyoshi.

In: eLife, Vol. 7, e32696, 18.06.2018.

Research output: Contribution to journalArticle

Keerativittayayut, Ruedeerat ; Aoki, Ryuta ; Sarabi, Mitra Taghizadeh ; Jimura, Koji ; Nakahara, Kiyoshi. / Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance. In: eLife. 2018 ; Vol. 7.
@article{815461cdfbdb49078c0dd6ce3a2617dd,
title = "Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance",
abstract = "Although activation/deactivation of specific brain regions has 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.",
author = "Ruedeerat Keerativittayayut and Ryuta Aoki and Sarabi, {Mitra Taghizadeh} and Koji Jimura and Kiyoshi Nakahara",
year = "2018",
month = "6",
day = "18",
doi = "10.7554/eLife.32696",
language = "English",
volume = "7",
journal = "eLife",
issn = "2050-084X",
publisher = "eLife Sciences Publications",

}

TY - JOUR

T1 - Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

AU - Keerativittayayut, Ruedeerat

AU - Aoki, Ryuta

AU - Sarabi, Mitra Taghizadeh

AU - Jimura, Koji

AU - Nakahara, Kiyoshi

PY - 2018/6/18

Y1 - 2018/6/18

N2 - Although activation/deactivation of specific brain regions has 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.

AB - Although activation/deactivation of specific brain regions has 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.

UR - http://www.scopus.com/inward/record.url?scp=85051969267&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051969267&partnerID=8YFLogxK

U2 - 10.7554/eLife.32696

DO - 10.7554/eLife.32696

M3 - Article

C2 - 29911970

AN - SCOPUS:85051969267

VL - 7

JO - eLife

JF - eLife

SN - 2050-084X

M1 - e32696

ER -