Spectral and topological analyses of the cortical representation of the head position: Does hypnotizability matter?

Abstract

Introduction The aim of this exploratory study was to assess the EEG correlates of head positions (which have never been studied in humans) in participants with different psychophysiological characteristics, as encoded by their hypnotizability scores. This choice is motivated by earlier studies suggesting different processing of vestibular/neck proprioceptive information in subjects with high (highs) and low (lows) hypnotizability scores maintaining their head rotated toward one side ( RH ). Methods We analyzed EEG signals recorded in 20 highs and 19 lows in basal conditions (head forward) and during RH using spectral analysis, which captures changes localized to specific recording sites, and topological data analysis ( TDA ), which instead describes large‐scale differences in processing and representing sensorimotor information. Results Spectral analysis revealed significant differences related to head position for alpha 1, beta 2, beta 3, and gamma bands, but not to hypnotizability. TDA instead revealed global hypnotizability‐related differences in the strengths of the correlations among recording sites during RH . Significant changes were observed in lows on the left parieto‐occipital side and in highs in right frontoparietal region. Significant differences between the two groups were found in the occipital region, where changes were larger in lows than in highs . Conclusions This study reports finding of the EEG correlates of changes in the head posture for the first time, indicating that hypnotizability is related to the head posture representation/processing on large‐scale networks and that spectral and topological data analyses provide complementary results.

Imagined and Actual Movements with and without Suggestions for anesthesia in Subjects with Different Hypnotizability

“…The lows' smaller alpha decreases during MI and MA with respect to M observed at central sites seem to reflect their lower embodiment of the mental images of movement and anesthesia, generally worse cognitive performance with respect to highs. The lack of local EEG modulation in highs is consistent with the findings obtained during sensorimotor and cognitive tasks [7,29,30]. It can be accounted for a largely distributed information processing likely sustained by activating systems [39,40] which cannot be revealed by spectral analysis but is detected by topological methods [7,29].…”

“…The present findings confirmed hypnotizability-related subjective differences in the ability of motor imagery [30] and in the efficacy of suggested anesthesia [27]. It replicates earlier findings of hypnotizability related sensory-cognitive information processing [7,28,29]. It indicated that lows performing motor imagery and imagery of anesthesia do exhibit EEG cortical modulation.…”

“…Additionally, the topological analysis of EEG signals has shown a qualitatively different cortical activity during both sensorimotor and cognitive tasks. Highs exhibit a widely distributed mode of information processing whereas lows show localized changes [7]. In highs, earlier reports showed greater left activation than right anterior activation and hemispheric differences in information processing for electrodermal responses, brightness, haptic discrimination, tones evoked cortical potentials [8], temporal judgment of visual stimuli [9], and line bisection [10].…”

“…According to the feed-forward model of motor control, it was proposed that the inhibition of the peripheral reafference may be responsible for the perception of involuntariness [50]. As observed in postural imagery, the latter showed different activation modes in highs, in contrast to lows who do not exhibit local cortical changes with respect to baseline [7,28,29,30].…”

Topological Features of Electroencephalography are Reference-Invariant

“…between different subjects and tasks, using recent topological data analysis tools. These tools have been shown to be useful in the analysis of neurophysiological data (Petri et al, 2014(Petri et al, , 2013Ibáñez-Marcelo et al, 2019a;Bassett and Sporns, 2017;Giusti et al, 2016;Varley et al, 2020) because they are built to detect properties of datasets, e.g. point clouds or weighted networks, that are invariant under homeomorphic transformations, which include, deformations, rotations, contractions and any other continuous transformation.…”