Seyfourian, P. | bioRxiv Science

bioRxiv2026

Brain-wide reconfiguration of burst firing by psilocybin reveals 5-HT2A-dependent circuit dynamics

Psilocybin produces rapid and lasting therapeutic effects, yet how 5-HT2A receptor activation reshapes brain-wide circuit dynamics during acute drug administration remains poorly understood. Using simultaneous multi-region Neuropixels recordings of 46,360 single units from 35 mice, together with scalp electroencephalography (EEG), pupillometry, and locomotion monitoring, we provide a brain-wide, single-unit and field-potential characterization of psilocybin's acute effects, with pharmacological dissection using the 5-HT2A antagonist ketanserin. Psilocybin selectively reconfigured burst coding, rather than mean firing rate, across cortical, thalamic, and hippocampal circuits: burst firing decreased in hippocampal CA1-CA3 and was bidirectionally modulated in the thalamus, with the reticular nucleus bursting more and first-order geniculate nuclei bursting less. Critically, most of these burst effects were abolished by ketanserin, consistent with at least partial 5-HT2A receptor dependence. These data suggest that the psychedelic state is not simply a matter of how much neurons fire, but of how they fire, pointing to a region-specific, 5-HT2A-associated reconfiguration of burst coding that may underlie the acute phenomenology of the psilocybin experience.

neuroscience↗

bioRxiv2026

Pupil-DLC: an open-source deep learning pipeline for scalable, markerless tracking of pupil dynamics across conscious and unconscious states

BackgroundPupil diameter is a non-invasive biomarker of brain state, correlating with arousal, attention, cognitive processing, and consciousness. However, existing pupillometry software often lacks scalability and robustness across diverse experimental conditions and species. New methodWe introduce Pupil-DLC, an open-source, offline, DeepLabCut-based pipeline for scalable, marker-less pupil tracking, primarily designed for mice. Trained on 21,750 manually annotated frames from over 140 videos of head-fixed mice spanning wakefulness and drug-induced states, including psychedelics and anesthesia, the dataset was deliberately selected to maximize pupil size variability and model generalization. Pupil-DLC implements a dual-model architecture: a General Model (GM) for high-throughput analysis and an Individual Model (IM) for session-specific optimization. ResultsPupil-DLC captures pupil dynamics across awake, psychedelic, and anesthetized conditions with high agreement with ground truth and equal tracking fidelity during active locomotion and quiet rest. Confidence metrics aligned with human frame quality assessments, enabling principled tuning of accuracy-retention trade-offs. As a secondary demonstration, Pupil-DLC extends to unseen human videos across diverse conditions and frame rates, including daylight and smartphone recordings, without retraining. Comparison with existing methodsPupil-DLC outperforms existing automated methods in accuracy and frame retention while maintaining computational efficiency comparable to real-time tools. These improvements stem from a learned keypoint-based representation robust to pupil shape variability, occlusions, reflections, and imaging artifacts. The GM/IM framework supports a tiered strategy balancing throughput and precision. ConclusionsPupil-DLC provides a reproducible, adaptable platform for quantifying pupil-linked brain state dynamics across experimental paradigms and species, bridging basic mouse neuroscience and translational human applications.

neuroscience↗