Segregated brain state during hypnosis

Abstract

Can the brain be shifted into a different state using a simple social cue, as tests on highly hypnotizable subjects would suggest? Demonstrating an altered global brain state is difficult. Brain activation varies greatly during wakefulness and can be voluntarily influenced. We measured the complexity of electrophysiological response to transcranial magnetic stimulation in one ‘hypnotic virtuoso’. Such a measure produces a response arguably outside the subject’s voluntary control and has been proven adequate for discriminating conscious from unconscious brain states. We show that a single-word hypnotic induction robustly shifted global neural connectivity into a state where activity remained sustained but failed to ignite strong, coherent activity in frontoparietal cortices. Changes in perturbational complexity indicate a similar move towards a more segregated state. We interpret these findings to suggest a shift in the underlying state of the brain, likely moderating subsequent hypnotic responding.

Linear and Nonlinear Quantitative EEG Analysis during Neutral Hypnosis following an Opened/Closed Eye Paradigm

“…Previous studies already applied LZC to the context of hypnosis, confirming LZC as measure able to distinguish among different mental states [30][31][32][33]. In particular, Reference [30] measured the complexity of electrophysiological response to Transcranical Magnetic Stimulation (TMS) through EEG, in one hypnotic virtuoso (i.e., a very highly hypnotizable subject who can respond to a broad spectrum of hypnotic suggestions), finding a shift from the normal metastable brain state of normal wakeful consciousness during waking rest, towards more segregated connectivity during hypnosis. However, results from such a single-subject study may not be able to generalize to the entire population, and an application of LZC to discriminate among subjects with different levels of hypnotic susceptibility is still missing.…”

“…Furthermore, LZC was found to be robust to non-stationary time-series and short data [29]. Previous studies already applied LZC to the context of hypnosis, confirming LZC as measure able to distinguish among different mental states [30][31][32][33]. In particular, Reference [30] measured the complexity of electrophysiological response to Transcranical Magnetic Stimulation (TMS) through EEG, in one hypnotic virtuoso (i.e., a very highly hypnotizable subject who can respond to a broad spectrum of hypnotic suggestions), finding a shift from the normal metastable brain state of normal wakeful consciousness during waking rest, towards more segregated connectivity during hypnosis.…”

EEG Signal Diversity Varies With Sleep Stage and Aspects of Dream Experience

“…Furthermore, there are also models primarily emphasizing different aspects of consciousness, e.g., higher order thought (HOT) theories of consciousness (Brown et al, 2019). 2 It is less clear whether PCI faithfully reflects variation in richness of experience within (approximately) the same state (Railo et al, 2017;Farnes et al, 2020;Tuominen et al, 2021), or whether it should even be expected to do so, given that ongoing activity is averaged out in the calculation of PCI. However, (Nieminen et al, 2016) suggests that TMS-evoked activity may still index within-state differences in experience, which would also seem to be consistent with IIT's emphasis on possible transitions of the system, over actual activity.…”

Implications of Noise on Neural Correlates of Consciousness: A Computational Analysis of Stochastic Systems of Mutually Connected Processes

“…Power spectral density and spectral entropy are common tools that are often used to characterize electroencephalography and magnetic resonance encephalography recordings. For example, spectral analysis of electroencephalograms is used to study the neurophysiology of sleep [ 45 ], to detect differences in brain activities of subjects under normal and hypnosis conditions [ 46 ], and healthy subjects and schizophrenic patients [ 47 ]. Electroencephalography and magnetic resonance encephalography recordings in patients with drug-resistant epilepsy reveal the altered spectral entropy for electrophysiological and hemodynamic signals [ 48 ].…”

“…We used the spectrum analysis and spectral entropy to quantify noise effects in the system of processes. These techniques are common tools that are often applied to analyzed data obtained in neurophysiological studies [ 45 , 46 , 47 , 48 ]. These tools are also commonly applied in signal processing, control systems engineering and diagnosis.…”