2021
DOI: 10.1016/j.neuroimage.2021.118070
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Age-related differences in network structure and dynamic synchrony of cognitive control

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Cited by 16 publications
(31 citation statements)
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“…In order to evaluate brain networks' characteristics over time, Hidden Markov Models represent brain activity as a succession of discrete states (i.e., characterized by matrices of specific couplings between brain regions), with each state in the finite set of states being dominant at certain time points and not at others (e.g., Tal et al, 2020). Graph theory analyses have also been conducted to examine changes with aging (e.g., Van Straaten & Stam, 2013;Hinault et al, 2021). Brain networks are described as an architecture composed of nodes (i.e., brain regions) and edges between each pair of nodes (i.e., functional or effective connectivity).…”
Section: Box 2 Methods For Characterizing Brain Dynamicsmentioning
confidence: 99%
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“…In order to evaluate brain networks' characteristics over time, Hidden Markov Models represent brain activity as a succession of discrete states (i.e., characterized by matrices of specific couplings between brain regions), with each state in the finite set of states being dominant at certain time points and not at others (e.g., Tal et al, 2020). Graph theory analyses have also been conducted to examine changes with aging (e.g., Van Straaten & Stam, 2013;Hinault et al, 2021). Brain networks are described as an architecture composed of nodes (i.e., brain regions) and edges between each pair of nodes (i.e., functional or effective connectivity).…”
Section: Box 2 Methods For Characterizing Brain Dynamicsmentioning
confidence: 99%
“…On the other hand, a relative preservation of structural integrity led to larger synchrony and less interference. Data from Hinault et al, , 2021…”
Section: Figure 5 Eeg Alpha and Gamma Plv Synchrony Was Assessed During The Completion Of An Arithmetic Verification Task Including Intermentioning
confidence: 99%
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