• Media type: E-Article
  • Title: A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes
  • Contributor: Rossi, Chiara; Vidaurre, Diego; Costers, Lars; Akbarian, Fahimeh; Woolrich, Mark; Nagels, Guy; Van Schependom, Jeroen
  • Published: Springer Science and Business Media LLC, 2023
  • Published in: Communications Biology, 6 (2023) 1
  • Language: English
  • DOI: 10.1038/s42003-023-05448-z
  • ISSN: 2399-3642
  • Origination:
  • Footnote:
  • Description: AbstractThe 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.
  • Access State: Open Access