Biology subjects

Thomas, C. W.

Publications and source records attributed to Thomas, C. W..

5 recordsLinked to original sources

bioRxiv2026

Investigating the Effects of Psilocybin on Cognitive Flexibility in Touchscreen and Naturalistic Variations of the Probabilistic Reversal Learning Task

RationalePsychedelic compounds such as psilocybin have attracted growing interest for their potential therapeutic effects in psychiatric disorders, with improvements in cognitive flexibility proposed as a possible mechanism of action. However, the effects of psychedelics on cognitive flexibility remain poorly understood. ObjectiveThis study aimed to examine the acute and post-acute effects of psilocybin (0.1, 0.3, 1 mg/kg) and lysergic acid diethylamide (LSD, (0.02, 0.04, 0.08 mg/kg) on cognitive flexibility in male rats. MethodsThis was tested using two variants of the probabilistic reversal learning task (PRLT): a touchscreen-based operant task and a more ethological foraging-based task. ResultsIn the touchscreen PRLT, acute psilocybin disrupted task engagement, with animals completing fewer trials and showing increased trial initiation latency, although psilocybin also showed a trend toward faster initial rule acquisition. However, psilocybin did not significantly alter the number of rule changes achieved, a canonical measure of cognitive flexibility, or feedback sensitivity. LSD similarly produced limited acute effects, although the highest dose reduced lose-shift probability, suggesting decreased sensitivity to negative feedback under some conditions. Post-acute effects of psilocybin were minimal in both PRLT variants and, where LSD effects were observed these occurred across different doses and timepoints without a consistent pattern. ConclusionsOverall, these findings suggest that serotonergic psychedelics do not robustly enhance reversal learning in these paradigms and that apparent learning effects may reflect transient disruptions in task engagement rather than improvements in cognitive flexibility. These results also highlight potential limitations of these PRLT paradigms for detecting psychedelic-induced changes in cognitive flexibility in rodents.

animal behavior and cognition↗

bioRxiv2024

Short- and long-term reconfiguration of rat prefrontal cortical networks following single doses of psilocybin

We quantify cellular- and circuit-resolution neural network dynamics following therapeutically relevant doses of the psychedelic psilocybin. Using chronically implanted Neuropixels probes, we recorded local field potentials (LFP) alongside action potentials from hundreds of neurons spanning infralimbic, prelimbic and cingulate subregions of the medial prefrontal cortex of freely-behaving adult rats. Psilocybin (0.3mg/kg or 1mg/kg i.p.) unmasked 100Hz high frequency oscillations that were most pronounced within the infralimbic cortex, persisted for approximately 1h post-injection and were accompanied by decreased net pyramidal cell firing rates and reduced signal complexity. These acute effects were more prominent during resting behaviour than during a sustained attention task. LFP 1-, 2- and 6-days post-psilocybin showed gradually-emerging increases in beta and low-gamma (20-60Hz) power, specific to the infralimbic cortex. These findings reveal features of psychedelic action not readily detectable in human brain imaging, implicating infralimbic network oscillations as potential biomarkers of psychedelic-induced network plasticity over multi-day timescales.

neuroscience↗

bioRxiv2021

Somnotate: A robust automated sleep stage classifier that exceeds human performance and identifies ambiguous states in mice

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and computational annotation methods currently ignore intermediate states because the classification features become ambiguous. However, these intermediate states contain important information regarding vigilance state dynamics. Here, we present a new classifier, "Somnotate", which produces automated annotation accuracies that exceed human expert performance on mouse electrophysiological data, is robust to errors in the training data, compatible with different recording configurations, and maintains high performance during experimental interventions. Somnotate is a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). A unique feature of Somnotate is that it quantifies and reports the certainty of its annotations, enabling the experimenter to identify ambiguous recording periods in a principled manner. We leverage this feature to identify epochs that exhibit intermediate vigilance states, revealing that many of these cluster around state transitions, whereas others correspond to failed attempts to transition. We show that the success rates of different transitions can be experimentally manipulated and explain previously observed sleep patterns. Somnotate can thus facilitate the study of sleep stage transitions and offers new insight into the mechanisms underlying sleep-wake dynamics. Author summaryTypically, the three different vigilance states - awake, REM sleep, and non-REM sleep - exhibit distinct features that are readily recognised in electrophysiological recordings. However, particularly around vigilance state transitions, epochs often exhibit features from more than one state. These intermediate vigilance states pose challenges for existing manual and automated classification methods, and are hence often ignored. Here, we present Somnotate - an open-source, highly accurate and robust sleep stage classifier, which supports research into intermediate states and sleep stage dynamics. Somnotate quantifies and reports the certainty of its annotations, enabling the experimenter to identify abnormal epochs in a principled manner. We use this feature to identify intermediate states and to detect unsuccessful attempts to switch between vigilance states. This provides new insights into the mechanisms of vigilance state transitions in mice, and creates new opportunities for future experiments.

neuroscience↗

bioRxiv2021

Psilocin acutely disrupts sleep and affects local but not global sleep homeostasis in laboratory mice

Serotonergic psychedelic drugs, such as psilocin (4-hydroxy-N,N-dimethyltryptamine), profoundly alter the quality of consciousness through mechanisms which are incompletely understood. Growing evidence suggests that a single psychedelic experience can positively impact long-term psychological well-being, with relevance for the treatment of psychiatric disorders, including depression. A prominent factor associated with psychiatric disorders is disturbed sleep, and the sleep-wake cycle is implicated in the regulation of neuronal firing and activity homeostasis. It remains unknown to what extent psychedelic agents directly affect sleep, in terms of both acute arousal and homeostatic sleep regulation. Here, chronic in vivo electrophysiological recordings were obtained in mice to track sleep-wake architecture and cortical activity after psilocin injection. Administration of psilocin led to delayed REM sleep onset and reduced NREM sleep maintenance for up to approximately 3 hours after dosing, and the acute EEG response was associated primarily with an enhanced oscillation around 4 Hz. No long-term changes in sleep-wake quantity were found. When combined with sleep deprivation, psilocin did not alter the dynamics of homeostatic sleep rebound during the subsequent recovery period, as reflected in both sleep amount and EEG slow wave activity. However, psilocin decreased the recovery rate of sleep slow wave activity following sleep deprivation in the local field potentials of electrodes targeting medial prefrontal and surrounding cortex. It is concluded that psilocin affects both global vigilance state control and local sleep homeostasis, an effect which may be relevant for its antidepressant efficacy.

neuroscience↗

bioRxiv2019

Cortical neuronal activity determines the dynamics of local sleep homeostasis

The homeostatic regulation of sleep manifests as a relative constancy of its daily amount and intensity. Theoretical descriptions of this phenomenon define "Process S", a variable with dynamics dependent only on sleep-wake history, whose levels are reflected in electroencephalogram (EEG) slow wave activity (0.5 - 4 Hz) during sleep. Here we developed novel mathematical models of Process S in mice, assuming that its dynamics are a function of the deviation of cortical neuronal firing rates from a locally defined set-point, crucially without explicit knowledge of sleep-wake state. Our results suggest that Process S tracks global sleep-wake history through an integration of local cortical neuronal activity levels over time. We posit that, instead of reflecting sleep-wake-dependent changes in specific variables and serving their homeostatic regulation, Process S may be a time-keeping mechanism which enables individuals to obtain a species-specific and ecologically-relevant quantity of sleep, even in the absence of external temporal information.

neuroscience↗