Classic serotonergic psychedelics and dissociative drugs like ketamine produce overlapping subjective effects despite different pharmacological mechanisms. Using source-localized magnetoencephalography, researchers found that LSD, psilocybin, and ketamine all caused widespread broadband reductions in spectral power. Machine learning classifiers trained on one pair of drugs could identify the third, confirming similarity. Functional connectivity analysis revealed decreases in low alpha and theta bands specific to LSD and psilocybin, linked to 5-HT2A agonism, while low beta band decreases were common to all three drugs. These findings quantify shared large-scale brain activity patterns across drug classes and suggest that beta band decoupling is an effect shared between NMDA antagonists and 5-HT2A agonists.
Spontaneous thoughts make up most of everyday inner experience, but studying them is difficult because traditional methods disrupt the natural flow of thinking or introduce motor artifacts. An alternative approach combined delayed verbal retrospective free reports with automated ratings from large language models. Twenty-two participants performed an eyes-closed free-thinking task, and their reports were evaluated on ten dimensions by four LLMs and human raters. Machine-learning models trained on EEG features achieved above-chance accuracy for predicting emotional valence. LLMs showed higher inter-rater agreement than humans, supporting their use for scalable annotation and suggesting that affective dimensions of spontaneous thoughts can be decoded from brain activity.