bioRxivNeurosciencePreprint
Multimodal autonomic arousal tracks dose-dependent affective dynamics during the acute effects of DMT
Tomás Ariel D’AmelioiD, Tomás Gil Garbagnoli, Jerónimo Rodríguez CuelloiD, Evan Lewis-HealeyiD, Carla PallaviciniiD, Federico CavannaiD, Nicolás Marcelo BrunoiD, Laura Alethia de la FuenteiD, Stephanie Andrea MulleriD, Débora CopaiD, Tristan Andres BekinschteiniD, Diego Vidaurre HencheiD, Enzo TagliazucchiiD
Abstract
Serotonergic psychedelics induce altered states of consciousness characterised by profound changes in emotional experience. Although psychedelics modulate autonomic arousal, sympathetic engagement during their affective effects remains poorly characterised. We recorded cardiac, electrodermal, and respiratory activity in 19 participants inhaling 20 or 40 mg of N,N-dimethyltryptamine (DMT) under a semi-naturalistic blinded design, alongside time-resolved retrospective phenomenological reports. DMT increased heart rate and sudomotor activity, with a transient dose-dependent rise in respiratory volume. A multimodal index integrating the three signals selectively tracked subjective emotional intensity. Dose-dependent divergence followed modality-specific profiles: heart rate and respiratory differences emerged within the first 2 min post-inhalation, whereas electrodermal activity diverged only during the later phase. At 40 mg, peak heart rate exceeded the rate attainable by vagal withdrawal alone in most participants, indicating active sympathetic engagement. DMT thus produces a transient sympathetic activation covarying with emotional arousal, followed by gradual disengagement accompanied by pleasantness and bliss. By combining time- and cost-effective peripheral physiological measures with time-resolved phenomenological reports, this work contributes to the objective characterisation of psychedelic-induced affective states and provides a methodological basis for future biomarker research in clinical applications.
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
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