Aperiodic activity as a central neural feature of hypnotic susceptibility outside of hypnosis
Bibliographic record
Abstract
Abstract 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 Statement Hypnotic phenomena reflect the ability to alter one’s 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".