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.
Rossi, C, Vidaurre, D, Costers, L, Akbarian, F, Woolrich, M, Nagels, G & Van Schependom, J 2023, 'A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes', Communications Biology, vol. 6, no. 1, 1079. https://doi.org/10.1038/s42003-023-05448-z
Rossi, C., Vidaurre, D., Costers, L., Akbarian, F., Woolrich, M., Nagels, G., & Van Schependom, J. (2023). A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes. Communications Biology, 6(1), Article 1079. https://doi.org/10.1038/s42003-023-05448-z
@article{650d214d519b4cddb07d1f302aef09c0,
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.",
keywords = "Memory, Short-Term/physiology, Brain/physiology, Magnetoencephalography/methods, Attention, Neuropsychological Tests",
author = "Chiara Rossi and Diego Vidaurre and Lars Costers and Fahimeh Akbarian and Mark Woolrich and Guy Nagels and {Van Schependom}, Jeroen",
note = "{\textcopyright} 2023. Springer Nature Limited.",
year = "2023",
month = oct,
day = "23",
doi = "10.1038/s42003-023-05448-z",
language = "English",
volume = "6",
journal = "Communications Biology",
issn = "2399-3642",
publisher = "Nature Research Publishing",
number = "1",
}