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

• Main Authors: Esther Ibáñez‐Marcelo, Lisa Campioni, Diego Manzoni, Enrica L. Santarcangelo, Giovanni Petri
• Format: Article
• Language: English
• Published: Wiley 2019-06-01
• Series: Brain and Behavior
• Subjects: EEG; head position; hypnotizability; perception; persistent homology; spectral analysis
• Online Access: https://doi.org/10.1002/brb3.1277

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.