Lysergic acid diethylamide (LSD) reduces associative brain connectivity while increasing sensory-somatomotor and thalamic connectivity. These neural effects, along with the subjective experience, are fully blocked by ketanserin, a selective 5-HT2A receptor antagonist. The spatial pattern of LSD's effects across the brain matches the distribution of 5-HT2A receptor gene expression in humans. These results strongly implicate the 5-HT2A receptor in LSD's neuropharmacology, informing the neurobiology of psychedelics and guiding development of psychedelic-based therapeutics.
Psilocybin reduces connectivity in associative brain regions while increasing connectivity in sensory regions, a pattern that emerges over time from administration to peak effects. Baseline connectivity predicts the extent of these changes. The shifts correlate with spatial gene expression patterns of the serotonin 2A and 1A receptors, pinpointing their critical role in the psychedelic state. These findings suggest that sensory integration and associative disintegration may underlie the psychedelic experience, and baseline connectivity could serve as a predictive marker for personalized psychedelic treatment.
A computational model that simulates how LSD affects human brain activity shows that the drug alters communication between cortical areas by increasing the sensitivity of pyramidal neurons via the serotonin-2A receptor. The model accurately reproduced changes in functional connectivity observed in brain scans, and fitting it to individual participants captured personal differences in drug response related to altered consciousness. This approach links molecular drug actions to large-scale brain network changes, offering a path toward personalized medicine.
Ketamine is a promising treatment for treatment-resistant depression, but why people respond differently is poorly understood. In a single-blind placebo-controlled study, 40 healthy participants received acute ketamine. Using data-driven global brain connectivity, the neural and behavioral effects of ketamine were found to be multi-dimensional, reflecting robust inter-individual variability. Ketamine's principal neural gradient matched somatostatin and parvalbumin cortical gene expression patterns, while the mean effect did not. Behavioral symptom variation mapped onto distinct neural gradients resolvable at the single-subject level. These results highlight the importance of individual variation for developing precise pharmacological biomarkers in psychiatry.
Ketamine is a promising therapy for treatment-resistant depression, but why some people respond better than others remains unclear. The molecular mechanisms of ketamine are not yet connected to its effects on brain activity and behavior.