Uncovering hidden resting state dynamics: A new perspective on auditory verbal hallucinations

Hanna, Honcamp; Schwartze, Michael; Linden, David; El-Deredy, Wael; Kotz, Sonja A.

doi:10.1016/j.neuroimage.2022.119188

Cited by 7 publications

(12 citation statements)

References 101 publications

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“…Shortened microstate durations have frequently been reported in EEG studies examining the temporal dynamics of AVH ( Nishida et al, 2013 , Rieger et al, 2016 , Strelets et al, 2003 ). It has been proposed that these deviations in temporal characteristics cause disruption of the network, which in turn emphasizes aberrant information processing and source misattribution ( Honcamp et al, 2022 , Kapur, 2003 , Lehmann et al, 2005 ). These are two mechanisms often associated with the occurrence of AVH ( Aleman et al, 2003 , Allen et al, 2005 , Corlett et al, 2019 , Johns et al, 2014 , Pinheiro et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

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Time varying dynamics of hallucinations in clinical and non-clinical voice-hearers

Marschall

Koops

Brederoo

et al. 2023

NeuroImage: Clinical

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Section: Discussionmentioning

confidence: 99%

“…Instead of following the more consistent switching patterns seen during the rest period, the brain appears to follow a less distinct path. Generally, aberrant switching patterns have been suggested to affect the interplay between the networks involved and may increase the likelihood of cognitive disfunction ( Honcamp et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Time varying dynamics of hallucinations in clinical and non-clinical voice-hearers

Marschall

Koops

Brederoo

et al. 2023

NeuroImage: Clinical

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“…While the clinical significance of these results is outside the scope of the current work, it illustrates the power and utility of this modeling approach to sensitively monitor intra-and interindividual differences in clinical populations and beyond. This improved sensitivity should also reflect better correlations with neuropsychological tests and or clinical symptoms as suggested by Honcamp et al, (2022).…”

Section: Dynamical Brain-states In Parkinson's Diseasementioning

confidence: 94%

“…In the case of empirical data, clinical studies have shown variation in the characteristics of the brain's resting state i.e., how they appear or transit (see for a comprehensive review Honcamp et al, 2022). An open question remains as to how this metastability or their features in the data correlate with symptoms or clinical severity or treatment response.…”

Section: Generative Modelsmentioning

confidence: 99%

Modeling EEG Resting-State Brain Dynamics: A proof of concept for clinical studies

Kotz

Astudillo

Araya

et al. 2023

Preprint

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Functional brain imaging has shown that the awake brain, independent of a task, spontaneously switches between a small set of functional networks. How useful this dynamical view of brain activity is for clinical studies, e.g., as early markers of subsequent structural and/or functional change or for assessing successful training or intervention effects, remains unclear. Core to addressing this question is to assess the robustness and reproducibility of the analysis methods that model, characterize, or infer the features of brain dynamics, and the accuracy by which these features represent and classify specific cognitive or altered cognitive states. This is particularly key given inter- and intra-individual variability and measurement noise. Here we used resting-state EEG from persons with Parkinson's Disease (PD) and healthy matched controls to systematically assess the reliability, robustness, and sensitivity of Hidden semi-Markov models (HsMM). These models are an example of model-based probabilistic methods for Brain-State allocations that are estimated from observed data. The method estimates model parameters, if the M/EEG recording or observations, over the scale of minutes, are emissions from hidden states that persist over short durations, before switching or transitioning to other states. We introduce an analysis pipeline that leads to sets of reproducible features of neurophysiological dynamics at the individual level. These features can be used as discriminatory variables to classify individuals and to evaluate the effect of non-pharmacological training schemes like in the current example a music-gait exercise program for Parkinson's Disease. Given the method stochasticity and the data variability, we emphasize the importance of repeating the analysis to reliably identify brain states and their dynamical trajectories that subsequently can be related to individualized variables.

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“…The HsMM is thus a promising means for accurate and highly temporally resolved characterizations of early predictive neural markers of psychopathology (H Honcamp et al, 2022;Trujillo-Barreto, Araya, & El-Deredy, 2019).…”

Section: Hsmm For Dynamic Brain State Allocationmentioning

confidence: 99%

Revisiting Alpha Resting State Dynamics Underlying Hallucinatory Vulnerability: Insights from Hidden Semi-Markov Modeling

Honcamp,

Schwartze,

Amorim

et al. 2023

Preprint

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Resting state (RS) brain activity is inherently non-stationary. Hidden Semi-Markov Models (HsMM) can characterize the continuous RS data as a sequence of recurring and distinct brain states along with their spatio-temporal dynamics. Recent explorations suggest that EEG brain state dynamics in the alpha frequency link to auditory hallucination proneness (HP) in non-clinical individuals. The present study aims 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. We found that most brain states are reproducible in different data sets, confirming that the HsMM characterized robust and generalizable EEG RS dynamics. 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. These 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. We suggest 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.

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Uncovering hidden resting state dynamics: A new perspective on auditory verbal hallucinations

Cited by 7 publications

References 101 publications

Time varying dynamics of hallucinations in clinical and non-clinical voice-hearers

Time varying dynamics of hallucinations in clinical and non-clinical voice-hearers

Modeling EEG Resting-State Brain Dynamics: A proof of concept for clinical studies

Revisiting Alpha Resting State Dynamics Underlying Hallucinatory Vulnerability: Insights from Hidden Semi-Markov Modeling

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