Ogez, D. | Search bioRxiv

bioRxiv2026

From language to pain: brain-behavior representations of hypnotic verbal suggestions for pain modulation

Language can shape pain by conveying conceptual information about future somatosensory experiences. However, how verbal input is encoded in the brain and how these representations are later implemented during pain modulation remains unclear. We used a standardized hypnotic procedure with fMRI to test how verbal suggestions of hypoalgesia and hyperalgesia (vs neutral control) modulate pain. We quantified convergent neural responses across individuals during both suggestion encoding and pain processing, using inter-subject correlation and multivoxel pattern similarity approaches, respectively, and related these measures to individual differences in hypnotic suggestibility using representational similarity analysis. More suggestible individuals showed similar temporal dynamics in language-related regions during encoding and more similar hypoalgesia-specific patterns in the left parahippocampal gyrus (PHG) during pain. In contrast, lower suggestibility was associated with convergent responses in sensorimotor and monitoring systems across phases, demonstrating different neural implementation in individuals less successful in converting suggestions into hypoalgesia. These findings identify the PHG as a key region linking the effect of verbal suggestions to subsequent pain relief and show that hypnotic suggestibility structures how neural dynamics during language processing translate into altered pain experiences. This study offers a mechanistic lens for understanding how medical hypnosis and language-based therapies influence pain.

neuroscience↗

bioRxiv2023

Unravelling the neural dynamics of hypnotic susceptibility: Aperiodic neural activity as a central feature of hypnosis

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

neuroscience↗