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Dynamic reconfiguration of functional brain networks during working memory training

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Dynamic reconfiguration of functional brain networks during working memory training. / Finc, Karolina; Bonna, Kamil; He, Xiaosong; Lydon-Staley, David M; Kühn, Simone; Duch, Włodzisław; Bassett, Danielle S.

In: NAT COMMUN, Vol. 11, No. 1, 15.05.2020, p. 2435.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Finc, K, Bonna, K, He, X, Lydon-Staley, DM, Kühn, S, Duch, W & Bassett, DS 2020, 'Dynamic reconfiguration of functional brain networks during working memory training', NAT COMMUN, vol. 11, no. 1, pp. 2435. https://doi.org/10.1038/s41467-020-15631-z

APA

Finc, K., Bonna, K., He, X., Lydon-Staley, D. M., Kühn, S., Duch, W., & Bassett, D. S. (2020). Dynamic reconfiguration of functional brain networks during working memory training. NAT COMMUN, 11(1), 2435. https://doi.org/10.1038/s41467-020-15631-z

Vancouver

Finc K, Bonna K, He X, Lydon-Staley DM, Kühn S, Duch W et al. Dynamic reconfiguration of functional brain networks during working memory training. NAT COMMUN. 2020 May 15;11(1):2435. https://doi.org/10.1038/s41467-020-15631-z

Bibtex

@article{7d1ca04d423b4621968f2de5f56d3933,
title = "Dynamic reconfiguration of functional brain networks during working memory training",
abstract = "The functional network of the brain continually adapts to changing environmental demands. The consequence of behavioral automation for task-related functional network architecture remains far from understood. We investigated the neural reflections of behavioral automation as participants mastered a dual n-back task. In four fMRI scans equally spanning a 6-week training period, we assessed brain network modularity, a substrate for adaptation in biological systems. We found that whole-brain modularity steadily increased during training for both conditions of the dual n-back task. In a dynamic analysis,we found that the autonomy of the default mode system and integration among task-positive systems were modulated by training. The automation of the n-back task through training resulted in non-linear changes in integration between the fronto-parietal and default mode systems, and integration with the subcortical system. Our findings suggest that the automation of a cognitively demanding task may result in more segregated network organization.",
keywords = "Adolescent, Adult, Algorithms, Behavior, Brain/physiology, Brain Mapping, Cognition, Electronic Data Processing, Female, Humans, Learning, Magnetic Resonance Imaging, Male, Memory, Short-Term, Models, Neurological, Models, Statistical, Nerve Net/physiology, Signal Processing, Computer-Assisted, Young Adult",
author = "Karolina Finc and Kamil Bonna and Xiaosong He and Lydon-Staley, {David M} and Simone K{\"u}hn and W{\l}odzis{\l}aw Duch and Bassett, {Danielle S}",
year = "2020",
month = may,
day = "15",
doi = "10.1038/s41467-020-15631-z",
language = "English",
volume = "11",
pages = "2435",
journal = "NAT COMMUN",
issn = "2041-1723",
publisher = "NATURE PUBLISHING GROUP",
number = "1",

}

RIS

TY - JOUR

T1 - Dynamic reconfiguration of functional brain networks during working memory training

AU - Finc, Karolina

AU - Bonna, Kamil

AU - He, Xiaosong

AU - Lydon-Staley, David M

AU - Kühn, Simone

AU - Duch, Włodzisław

AU - Bassett, Danielle S

PY - 2020/5/15

Y1 - 2020/5/15

N2 - The functional network of the brain continually adapts to changing environmental demands. The consequence of behavioral automation for task-related functional network architecture remains far from understood. We investigated the neural reflections of behavioral automation as participants mastered a dual n-back task. In four fMRI scans equally spanning a 6-week training period, we assessed brain network modularity, a substrate for adaptation in biological systems. We found that whole-brain modularity steadily increased during training for both conditions of the dual n-back task. In a dynamic analysis,we found that the autonomy of the default mode system and integration among task-positive systems were modulated by training. The automation of the n-back task through training resulted in non-linear changes in integration between the fronto-parietal and default mode systems, and integration with the subcortical system. Our findings suggest that the automation of a cognitively demanding task may result in more segregated network organization.

AB - The functional network of the brain continually adapts to changing environmental demands. The consequence of behavioral automation for task-related functional network architecture remains far from understood. We investigated the neural reflections of behavioral automation as participants mastered a dual n-back task. In four fMRI scans equally spanning a 6-week training period, we assessed brain network modularity, a substrate for adaptation in biological systems. We found that whole-brain modularity steadily increased during training for both conditions of the dual n-back task. In a dynamic analysis,we found that the autonomy of the default mode system and integration among task-positive systems were modulated by training. The automation of the n-back task through training resulted in non-linear changes in integration between the fronto-parietal and default mode systems, and integration with the subcortical system. Our findings suggest that the automation of a cognitively demanding task may result in more segregated network organization.

KW - Adolescent

KW - Adult

KW - Algorithms

KW - Behavior

KW - Brain/physiology

KW - Brain Mapping

KW - Cognition

KW - Electronic Data Processing

KW - Female

KW - Humans

KW - Learning

KW - Magnetic Resonance Imaging

KW - Male

KW - Memory, Short-Term

KW - Models, Neurological

KW - Models, Statistical

KW - Nerve Net/physiology

KW - Signal Processing, Computer-Assisted

KW - Young Adult

U2 - 10.1038/s41467-020-15631-z

DO - 10.1038/s41467-020-15631-z

M3 - SCORING: Journal article

C2 - 32415206

VL - 11

SP - 2435

JO - NAT COMMUN

JF - NAT COMMUN

SN - 2041-1723

IS - 1

ER -