Aperiodic activity as a central neural feature of hypnotic susceptibility outside of hypnosis DOI Creative Commons
Mathieu Landry, Jason da Silva Castanheira, Catherine Boisvert

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 17, 2023

Abstract How well a person responds to hypnosis is stable trait, which exhibits considerable inter-individual diversity across the general population. Yet, its neural underpinning remains elusive. Here, we address this gap by combining EEG data, multivariate statistics, and machine learning in order identify brain patterns that differentiate between individuals high low susceptibility hypnosis. In particular, computed periodic aperiodic components of power spectrum, as graph theoretical measures derived from functional connectivity, data acquired at rest (pre-induction) under (post-induction). We found 1/f slope spectrum was best predictor hypnotic susceptibility. Our findings support idea trait linked balance cortical excitation inhibition baseline offers novel perspectives on foundations Future work can explore contribution background activity target distinguish responsiveness clinic. Significance Statement Hypnotic phenomena reflect ability alter one’s subjective experiences based targeted verbal suggestions. This varies greatly The correlates explain variability remain Addressing gap, our study employs predict By recording electroencephalography (EEG) before after induction analyzing diverse neurophysiological features, were able determine several features susceptible both during analysis revealed paramount discriminative feature non-oscillatory induction—a new finding field. outcome aligns with represents latent observable through plain five-minutes resting-state EEG.

Language: Английский

Enhanced Brain-Heart Connectivity as a Precursor of Reduced State Anxiety After Therapeutic Virtual Reality Immersion DOI Creative Commons
Idil Sezer, Philippe Moreau, Mohamad El Sayed Hussein Jomaa

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 30, 2024

Abstract State anxiety involves transient feelings of tension and nervousness in response to threats, which can escalate into disorders if persistent. Despite treatments, 30%-50% individuals show limited improvement, neurophysiological mechanisms treatment responsiveness remain unclear, requiring the development objective biomarkers. In this study, we monitored multimodal electrophysiological parameters: heart rate variability (high-frequency, low-frequency, LF/HF ratio), EEG beta alpha relative power, brain-to-heart connectivity participants with real-life state anxiety. Participants underwent a therapeutic intervention combining virtual-reality immersion, hypnotic script, breath control exercise. Real-life was captured using STAI-Y1 scale before after intervention. We observed reduced immediately 16 out 27 participants. While all participants, independently their score, showed increased HRV low frequency only treatment-responders displayed overall autonomic tone (high HRV), midline power connectivity. Notably, ratio significant linear relationship reduction, higher ratios linked greater response. These findings suggest that cognitive regulation could serve as biomarker for efficacy, elevated facilitating improved cardiac responders. Significance Statement Elevated debilitating disorders, such generalized disorder, yet efficacy remains inconsistent, reliable biomarkers predicting outcomes are lacking. This study identifies key neural physiological markers effective reduction following virtual reality-based non-pharmacological healthy Anxiety is associated heightened (LF/HF enhanced highlight role modulation nervous system functioning By highlighting these biomarkers, research aims at advancing our understanding offering insights biomarker-driven, scalable interventions.

Language: Английский

Citations

2
Ongoing Dynamics of Peak Alpha Frequency Characterize Hypnotic Induction in Highly Hypnotic-Susceptible Individuals DOI Creative Commons
Mathieu Landry, Jason da Silva Castanheira, Floriane Rousseaux

et al.

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(9), P. 883 - 883

Published: Aug. 30, 2024

Hypnotic phenomena exhibit significant inter-individual variability, with some individuals consistently demonstrating efficient responses to hypnotic suggestions, while others show limited susceptibility. Recent neurophysiological studies have added a growing body of research that shows variability in susceptibility is linked distinct neural characteristics. Building on this foundation, our previous work identified high and low can be differentiated based the arrhythmic activity observed resting-state electrophysiology (rs-EEG) outside hypnosis. However, because has largely focused mean spectral characteristics, understanding over time these features, how they relate susceptibility, still limited. Here we address gap using time-resolved assessment rhythmic alpha peaks components EEG spectrum both prior following induction. Using multivariate pattern classification, investigated whether features differ between Specifically, used classification investigate non-stationary could distinguish hypnosis before after Our analytical approach decomposition capture intricate dynamics oscillations their non-oscillatory counterpart, as well Lempel-Ziv complexity. results variations center frequency are indicative but discrimination only evident during Highly hypnotic-susceptible higher peak frequency. These findings underscore dynamic changes states related represent central feature

Language: Английский

Citations

1
Aperiodic activity as a central neural feature of hypnotic susceptibility outside of hypnosis DOI Creative Commons
Mathieu Landry, Jason da Silva Castanheira, Catherine Boisvert

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 17, 2023

Abstract How well a person responds to hypnosis is stable trait, which exhibits considerable inter-individual diversity across the general population. Yet, its neural underpinning remains elusive. Here, we address this gap by combining EEG data, multivariate statistics, and machine learning in order identify brain patterns that differentiate between individuals high low susceptibility hypnosis. In particular, computed periodic aperiodic components of power spectrum, as graph theoretical measures derived from functional connectivity, data acquired at rest (pre-induction) under (post-induction). We found 1/f slope spectrum was best predictor hypnotic susceptibility. Our findings support idea trait linked balance cortical excitation inhibition baseline offers novel perspectives on foundations Future work can explore contribution background activity target distinguish responsiveness clinic. Significance Statement Hypnotic phenomena reflect ability alter one’s subjective experiences based targeted verbal suggestions. This varies greatly The correlates explain variability remain Addressing gap, our study employs predict By recording electroencephalography (EEG) before after induction analyzing diverse neurophysiological features, were able determine several features susceptible both during analysis revealed paramount discriminative feature non-oscillatory induction—a new finding field. outcome aligns with represents latent observable through plain five-minutes resting-state EEG.

Language: Английский

Citations

3