A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes

Chiara Rossi; Diego Vidaurre; Lars Costers; Fahimeh Akbarian; Mark Woolrich; Guy Nagels; Jeroen Van Schependom

doi:10.1038/s42003-023-05448-z

A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes

Chiara Rossi^*
, Diego Vidaurre
, Lars Costers
, Fahimeh Akbarian
, Mark Woolrich
, Guy Nagels
, Jeroen Van Schependom^*

^*Corresponding author for this work

Department of Clinical Medicine - Center of Functionally Integrative Neuroscience

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

8 Citations (Scopus)

Abstract

The brain dynamics underlying working memory (WM) unroll via transient frequency-specific large-scale brain networks. This multidimensionality (time, space, and frequency) challenges traditional analyses. Through an unsupervised technique, the time delay embedded-hidden Markov model (TDE-HMM), we pursue a functional network analysis of magnetoencephalographic data from 38 healthy subjects acquired during an n-back task. Here we show that this model inferred task-specific networks with unique temporal (activation), spectral (phase-coupling connections), and spatial (power spectral density distribution) profiles. A theta frontoparietal network exerts attentional control and encodes the stimulus, an alpha temporo-occipital network rehearses the verbal information, and a broad-band frontoparietal network with a P300-like temporal profile leads the retrieval process and motor response. Therefore, this work provides a unified and integrated description of the multidimensional working memory dynamics that can be interpreted within the neuropsychological multi-component model of WM, improving the overall neurophysiological and neuropsychological comprehension of WM functioning.

Original language	English
Article number	1079
Journal	Communications Biology
Volume	6
Issue	1
ISSN	2399-3642
DOIs	https://doi.org/10.1038/s42003-023-05448-z
Publication status	Published - Oct 2023

Access to Document

10.1038/s42003-023-05448-zLicence: CC BY

Library access?

Check availability with the Royal Danish Library

Cite this

@article{b8ca68cf42084cbfa356be776923acdb,

title = "A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes",

abstract = "The brain dynamics underlying working memory (WM) unroll via transient frequency-specific large-scale brain networks. This multidimensionality (time, space, and frequency) challenges traditional analyses. Through an unsupervised technique, the time delay embedded-hidden Markov model (TDE-HMM), we pursue a functional network analysis of magnetoencephalographic data from 38 healthy subjects acquired during an n-back task. Here we show that this model inferred task-specific networks with unique temporal (activation), spectral (phase-coupling connections), and spatial (power spectral density distribution) profiles. A theta frontoparietal network exerts attentional control and encodes the stimulus, an alpha temporo-occipital network rehearses the verbal information, and a broad-band frontoparietal network with a P300-like temporal profile leads the retrieval process and motor response. Therefore, this work provides a unified and integrated description of the multidimensional working memory dynamics that can be interpreted within the neuropsychological multi-component model of WM, improving the overall neurophysiological and neuropsychological comprehension of WM functioning.",

author = "Chiara Rossi and Diego Vidaurre and Lars Costers and Fahimeh Akbarian and Mark Woolrich and Guy Nagels and \{Van Schependom\}, Jeroen",

note = "Publisher Copyright: {\textcopyright} 2023, Springer Nature Limited.",

year = "2023",

month = oct,

doi = "10.1038/s42003-023-05448-z",

language = "English",

volume = "6",

journal = "Communications Biology",

issn = "2399-3642",

publisher = "Springer Nature",

number = "1",

}

A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes. / Rossi, Chiara; Vidaurre, Diego; Costers, Lars et al.
In: Communications Biology, Vol. 6, No. 1, 1079, 10.2023.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

TY - JOUR

T1 - A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes

AU - Rossi, Chiara

AU - Vidaurre, Diego

AU - Costers, Lars

AU - Akbarian, Fahimeh

AU - Woolrich, Mark

AU - Nagels, Guy

AU - Van Schependom, Jeroen

PY - 2023/10

Y1 - 2023/10

N2 - The brain dynamics underlying working memory (WM) unroll via transient frequency-specific large-scale brain networks. This multidimensionality (time, space, and frequency) challenges traditional analyses. Through an unsupervised technique, the time delay embedded-hidden Markov model (TDE-HMM), we pursue a functional network analysis of magnetoencephalographic data from 38 healthy subjects acquired during an n-back task. Here we show that this model inferred task-specific networks with unique temporal (activation), spectral (phase-coupling connections), and spatial (power spectral density distribution) profiles. A theta frontoparietal network exerts attentional control and encodes the stimulus, an alpha temporo-occipital network rehearses the verbal information, and a broad-band frontoparietal network with a P300-like temporal profile leads the retrieval process and motor response. Therefore, this work provides a unified and integrated description of the multidimensional working memory dynamics that can be interpreted within the neuropsychological multi-component model of WM, improving the overall neurophysiological and neuropsychological comprehension of WM functioning.

AB - The brain dynamics underlying working memory (WM) unroll via transient frequency-specific large-scale brain networks. This multidimensionality (time, space, and frequency) challenges traditional analyses. Through an unsupervised technique, the time delay embedded-hidden Markov model (TDE-HMM), we pursue a functional network analysis of magnetoencephalographic data from 38 healthy subjects acquired during an n-back task. Here we show that this model inferred task-specific networks with unique temporal (activation), spectral (phase-coupling connections), and spatial (power spectral density distribution) profiles. A theta frontoparietal network exerts attentional control and encodes the stimulus, an alpha temporo-occipital network rehearses the verbal information, and a broad-band frontoparietal network with a P300-like temporal profile leads the retrieval process and motor response. Therefore, this work provides a unified and integrated description of the multidimensional working memory dynamics that can be interpreted within the neuropsychological multi-component model of WM, improving the overall neurophysiological and neuropsychological comprehension of WM functioning.

UR - https://www.scopus.com/pages/publications/85174679107

U2 - 10.1038/s42003-023-05448-z

DO - 10.1038/s42003-023-05448-z

M3 - Journal article

C2 - 37872313

AN - SCOPUS:85174679107

SN - 2399-3642

VL - 6

JO - Communications Biology

JF - Communications Biology

IS - 1

M1 - 1079

ER -

A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes

Abstract

Access to Document

Library access?

Other files and links

Fingerprint

Cite this