Dynamic network coding of working-memory domains and working-memory processes
File(s)
Published version
Author(s)
Soreq, Eyal
Leech, Robert
Hampshire, Adam
Type
Journal Article
Abstract
The classic mapping of distinct aspects of working memory (WM) to mutually exclusive brain areas is at odds with the distributed processing mechanisms proposed by contemporary network science theory. Here, we use machine
learning to determine how aspects of WM are dynamically coded in the human brain. Using cross-validation across independent fMRI studies, we demonstrate that stimulus domains (spatial, number and fractal) and WM processes
(encode, maintain, probe) are classifiable with high accuracy from the patterns of network activity and connectivity
that they evoke. This is the case even when focusing on ‘multiple demands’ brain regions, which are active across all
WM conditions. Contrary to early neuropsychological perspectives, these aspects of WM do not map exclusively to
brain areas or processing streams; however, the mappings from that literature form salient features within the
corresponding multivariate connectivity patterns. Furthermore, connectivity patterns provide the most precise basis
for classification and become fine-tuned as maintenance load increases. These results accord with a network-coding
mechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.
learning to determine how aspects of WM are dynamically coded in the human brain. Using cross-validation across independent fMRI studies, we demonstrate that stimulus domains (spatial, number and fractal) and WM processes
(encode, maintain, probe) are classifiable with high accuracy from the patterns of network activity and connectivity
that they evoke. This is the case even when focusing on ‘multiple demands’ brain regions, which are active across all
WM conditions. Contrary to early neuropsychological perspectives, these aspects of WM do not map exclusively to
brain areas or processing streams; however, the mappings from that literature form salient features within the
corresponding multivariate connectivity patterns. Furthermore, connectivity patterns provide the most precise basis
for classification and become fine-tuned as maintenance load increases. These results accord with a network-coding
mechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.
Date Issued
2019-02-25
Date Acceptance
2019-01-18
Citation
Nature Communications, 2019, 10 (1)
ISSN
2041-1723
Publisher
Nature Research (part of Springer Nature)
Journal / Book Title
Nature Communications
Volume
10
Issue
1
Copyright Statement
© The Author(s) 2019. This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this license, visit http://creativecommons.org/
licenses/by/4.0/ .
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this license, visit http://creativecommons.org/
licenses/by/4.0/ .
Sponsor
Commission of the European Communities
Medical Research Council (MRC)
Grant Number
Marie Curie CIG
PNSBAKR
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
FUNCTIONAL CONNECTIVITY
PREFRONTAL CORTEX
NEURAL MECHANISMS
HUMAN BRAIN
FMRI
REGIONS
ORGANIZATION
INFORMATION
PATTERNS
MODELS
Adolescent
Adult
Brain
Brain Mapping
Female
Humans
Magnetic Resonance Imaging
Male
Memory, Short-Term
Social Networking
Young Adult
Brain
Humans
Magnetic Resonance Imaging
Brain Mapping
Memory, Short-Term
Adolescent
Adult
Female
Male
Young Adult
Social Networking
Publication Status
Published
Article Number
936
Date Publish Online
2019-02-25