Biology subjects

D'Amelio, T. A.

Publications and source records attributed to D'Amelio, T. A..

2 recordsLinked to original sources

bioRxiv2026

Multimodal autonomic arousal tracks dose-dependent affective dynamics during the acute effects of DMT

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 following inhalation of 20 or 40 mg of freebase N,N-dimethyltryptamine (DMT) under a semi-naturalistic blinded design, alongside time-resolved retrospective phenomenological reports. DMT induced robust increases across all autonomic markers, integrated into a multimodal index that 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, with higher doses showing prolonged autonomic engagement. DMT thus produces a transient sympathetic activation co-varying 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.

neuroscience↗

bioRxiv2026

Decoding the phenomenology of spontaneous thought using large language-model ratings on verbal retrospective free reports

AO_SCPLOWBSTRACTC_SCPLOWSpontaneous thoughts constitute most of everyday inner experience, yet long-standing methodological challenges obscure a thorough exploration of their content and neurophysiological underpinnings. Traditional approaches relying on thought probes impose strict constraints on phenomenological reports, whereas online verbal reports disrupt the natural flow of experience while interfering neural signals with motor artifacts. Here, we designed and tested an alternative approach to assess the neural basis of spontaneous thoughts combining delayed verbal retrospective free reports (RFR) with automated phenomenological ratings generated by large language models (LLMs). Twenty-two participants performed an eyes-closed free-thinking task, providing reports that were evaluated along ten phenomenological dimensions by four state-of-the-art LLMs and a panel of human raters. Machine-learning models (ML) were then trained to decode LLM-derived ratings from EEG spectral, complexity, and connectivity features. Our analyses showed that inter-rater agreement among LLMs exceeded that of human raters whereas ML models achieved above-chance accuracy for the prediction of emotional valence. These findings provide support for the use of LLMs for a scalable phenomenological annotation of spontaneous thoughts and suggest that their affective dimensions can be decoded from concurrent EEG activity.

neuroscience↗