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

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  • Chiara Rossi, Vrije Universiteit Brussel
  • ,
  • Diego Vidaurre
  • Lars Costers, Vrije Universiteit Brussel, icometrix
  • ,
  • Fahimeh Akbarian, Vrije Universiteit Brussel
  • ,
  • Mark Woolrich, University of Oxford
  • ,
  • Guy Nagels, Vrije Universiteit Brussel, University of Oxford, University Hospital Brussels (UZB)
  • ,
  • Jeroen Van Schependom, Vrije Universiteit Brussel

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 languageEnglish
Article number1079
JournalCommunications Biology
Publication statusPublished - Oct 2023

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