Evidence map›Paper›PMID 32271781›Full record

ArticlePloS one2020

Higher hypnotic suggestibility is associated with the lower EEG signal variability in theta, alpha, and beta frequency bands.

Soheil Keshmiri, Maryam Alimardani, Masahiro Shiomi, Hidenobu Sumioka, Hiroshi Ishiguro, Kazuo Hiraki

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Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Entropy and the Brain: An Overview.Entropy (Basel, Switzerland) · 2020
    Review
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Soheil KeshmiriAdvanced Telecommunications Research Institute International (ATR), Kyoto, Japan.ORCID 0000-0003-0854-0354
Maryam AlimardaniAdvanced Telecommunications Research Institute International (ATR), Kyoto, Japan.ORCID 0000-0003-3077-7657
Masahiro ShiomiAdvanced Telecommunications Research Institute International (ATR), Kyoto, Japan.
Hidenobu SumiokaAdvanced Telecommunications Research Institute International (ATR), Kyoto, Japan.
Hiroshi IshiguroAdvanced Telecommunications Research Institute International (ATR), Kyoto, Japan.
Kazuo HirakiDepartment of General Systems Studies, Tokyo University, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Variation of information in the firing rate of neural population, as reflected in different frequency bands of electroencephalographic (EEG) time series, provides direct evidence for change in neural responses of the brain to hypnotic suggestibility. However, realization of an effective biomarker for spiking behaviour of neural population proves to be an elusive subject matter with its impact evident in highly contrasting results in the literature. In this article, we took an information-theoretic stance on analysis of the EEG time series of the brain activity during hypnotic suggestions, thereby capturing the variability in pattern of brain neural activity in terms of its information content. For this purpose, we utilized differential entropy (DE, i.e., the average information content in a continuous time series) of theta, alpha, and beta frequency bands of fourteen-channel EEG time series recordings that pertain to the brain neural responses of twelve carefully selected high and low hypnotically suggestible individuals. Our results show that the higher hypnotic suggestibility is associated with a significantly lower variability in information content of theta, alpha, and beta frequencies. Moreover, they indicate that such a lower variability is accompanied by a significantly higher functional connectivity (FC, a measure of spatiotemporal synchronization) in the parietal and the parieto-occipital regions in the case of theta and alpha frequency bands and a non-significantly lower FC in the central region's beta frequency band. Our results contribute to the field in two ways. First, they identify the applicability of DE as a unifying measure to reproduce the similar observations that are separately reported through adaptation of different hypnotic biomarkers in the literature. Second, they extend these previous findings that were based on neutral hypnosis (i.e., a hypnotic procedure that involves no specific suggestions other than those for becoming hypnotized) to the case of hypnotic suggestions, thereby identifying their presence as a potential signature of hypnotic experience.

Indexed as

ElectroencephalographyHypnosisSignal Processing, Computer-AssistedAdultBrainEntropyFemaleHumansMale

Identifiers

PMID32271781
PMCPMC7145105

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.