Revisiting alpha resting state dynamics underlying hallucinatory vulnerability: Insights from Hidden semi-Markov Modeling.

Autor: Honcamp H; Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands. Electronic address: h.honcamp@maastrichtuniversity.nl., Schwartze M; Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands., Amorim M; Centro de Investigação em Ciência Psicológica, Faculdade de Psicologia, Universidade de Lisboa, Lisboa, Portugal., Linden DEJ; Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands., Pinheiro AP; Centro de Investigação em Ciência Psicológica, Faculdade de Psicologia, Universidade de Lisboa, Lisboa, Portugal., Kotz SA; Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands.
Jazyk: angličtina
Zdroj: Journal of neuroscience methods [J Neurosci Methods] 2024 Jul; Vol. 407, pp. 110138. Date of Electronic Publication: 2024 Apr 20.
DOI: 10.1016/j.jneumeth.2024.110138
Abstrakt: Background: Resting state (RS) brain activity is inherently non-stationary. Hidden semi-Markov Models (HsMM) can characterize continuous RS data as a sequence of recurring and distinct brain states along with their spatio-temporal dynamics.
New Method: Recent explorations suggest that HsMM state dynamics in the alpha frequency band link to auditory hallucination proneness (HP) in non-clinical individuals. The present study aimed to replicate these findings to elucidate robust neural correlates of hallucinatory vulnerability. Specifically, we aimed to investigate the reproducibility of HsMM states across different data sets and within-data set variants as well as the replicability of the association between alpha brain state dynamics and HP.
Results: We found that most brain states are reproducible in different data sets, confirming that the HsMM characterized robust and generalizable EEG RS dynamics on a sub-second timescale. Brain state topographies and temporal dynamics of different within-data set variants showed substantial similarities and were robust against reduced data length and number of electrodes. However, the association with HP was not directly reproducible across data sets.
Comparison With Existing Methods: The HsMM optimally leverages the high temporal resolution of EEG data and overcomes time-domain restrictions of other state allocation methods.
Conclusion: The results indicate that the sensitivity of brain state dynamics to capture individual variability in HP may depend on the data recording characteristics and individual variability in RS cognition, such as mind wandering. Future studies should consider that the order in which eyes-open and eyes-closed RS data are acquired directly influences an individual's attentional state and generation of spontaneous thoughts, and thereby might mediate the link to hallucinatory vulnerability.
Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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