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 (0.3 mg/kg or 1 mg/kg i.p.) unmasked 100 Hz high frequency oscillations that were most pronounced within the infralimbic cortex, persisted for approximately 1 h post-injection and were accompanied by decreased net neuronal firing rates and reduced spike-train complexity. These acute effects were more prominent during resting behaviour than during performance of a sustained attention task. LFP 1-, 2- and 6-days post-psilocybin showed gradually-emerging increases in beta and low-gamma (20–60 Hz) 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.

Neural activity encoding recent experiences is replayed during sleep and rest to promote consolidation of memories. However, precisely which features of experience influence replay prioritisation to optimise adaptive behaviour remains unclear. Here, we trained adult male rats on a novel maze-based reinforcement learning task designed to dissociate reward outcomes from reward-prediction errors. Four variations of a reinforcement learning model were fitted to the rats' behaviour over multiple days. Behaviour was best predicted by a model incorporating replay biased by reward-prediction error, compared to the same model with no replay, random replay or reward-biased replay. Neural population recordings from the hippocampus and ventral striatum of rats trained on the task evidenced preferential reactivation of reward-prediction and reward-prediction error signals during post-task rest. These insights disentangle the influences of salience on replay, suggesting that reinforcement learning is tuned by post-learning replay biased by reward-prediction error, not by reward per se. This work therefore provides a behavioural and theoretical toolkit with which to measure and interpret the neural mechanisms linking replay and reinforcement learning.

Simultaneous localisation and mapping (SLAM) algorithms are commonly used in robotic systems for learning maps of novel environments. Brains also appear to learn maps, but the mechanisms are not known and it is unclear how to infer these maps from neural activity data. We present BrainSLAM; a method for performing SLAM using only population activity (local field potential, LFP) data simultaneously recorded from three brain regions in rats: hippocampus, prefrontal cortex, and parietal cortex. This system uses a convolutional neural network (CNN) to decode velocity and familiarity information from wavelet scalograms of neural local field potential data recorded from rats as they navigate a 2D maze. The CNN's output drives a RatSLAM-inspired architecture, powering an attractor network which performs path integration plus a separate system which performs `loop closure' (detecting previously visited locations and correcting map aliasing errors). Together, these three components can construct faithful representations of the environment while simultaneously tracking the animal's location. This is the first demonstration of inference of a spatial map from brain recordings. Our findings expand SLAM to a new modality, enabling a new method of mapping environments and facilitating a better understanding of the role of cognitive maps in navigation and decision making.

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. ### Competing Interest Statement At time of conceptualization, investigation and writing, CWT and CTG were employees of Compass Pathways. RJP, RG, SF-W and MWJ declare no competing interests.

Mutations in SYNGAP1 are a common genetic cause of intellectual disability (ID) and a risk factor for autism. SYNGAP1 encodes a synaptic GTPase-activating protein (GAP) that has both signaling and scaffolding roles. Most pathogenic variants of SYNGAP1 are predicted to result in haploinsufficiency. However, some affected individuals carry missense mutations in its calcium/lipid binding (C2) and GAP domains, suggesting that many clinical features result from loss of functions carried out by these domains. To test this hypothesis, we targeted the exons encoding the C2 and GAP domains of SYNGAP. Rats heterozygous for this deletion exhibit reduced exploration and fear extinction, altered social investigation, and spontaneous seizures—key phenotypes shared with Syngap heterozygous null rats. Together, these findings indicate that the reduction of SYNGAP C2/GAP domain function is a main feature of SYNGAP haploinsufficiency. This rat model provides an important system for the study of ID, autism, and epilepsy.

Head-fixation of mice enables high-resolution monitoring of neuronal activity coupled with precise control of environmental stimuli. Virtual reality can be used to emulate the visual experience of movement during head fixation, but a low inertia floating real-world environment (mobile homecage, MHC) has the potential to engage more sensory modalities and provide a richer experimental environment for complex behavioral tasks. However, it is not known whether mice react to this adapted environment in a similar manner to real environments, or whether the MHC can be used to implement validated, maze-based behavioral tasks. Here, we show that hippocampal place cell representations are intact in the MHC and that the system allows relatively long (20 min) whole-cell patch clamp recordings from dorsal CA1 pyramidal neurons, revealing sub-threshold membrane potential dynamics. Furthermore, mice learn the location of a liquid reward within an adapted T-maze guided by 2-dimensional spatial navigation cues and relearn the location when spatial contingencies are reversed. Bilateral infusions of scopolamine show that this learning is hippocampus-dependent and requires intact cholinergic signalling. Therefore, we characterize the MHC system as an experimental tool to study sub-threshold membrane potential dynamics that underpin complex navigation behaviors.

Sleep, circadian rhythms, and mental health are reciprocally interlinked. Disruption to the quality, continuity, and timing of sleep can precipitate or exacerbate psychiatric symptoms in susceptible individuals, while treatments that target sleep—circadian disturbances can alleviate psychopathology. Conversely, psychiatric symptoms can reciprocally exacerbate poor sleep and disrupt clock-controlled processes. Despite progress in elucidating underlying mechanisms, a cohesive approach that integrates the dynamic interactions between psychiatric disorder with both sleep and circadian processes is lacking. This review synthesizes recent evidence for sleep—circadian dysfunction as a transdiagnostic contributor to a range of psychiatric disorders, with an emphasis on biological mechanisms. We highlight observations from adolescent and young adults, who are at greatest risk of developing mental disorders, and for whom early detection and intervention promise the greatest benefit. In particular, we aim to a) integrate sleep and circadian factors implicated in the pathophysiology and treatment of mood, anxiety, and psychosis spectrum disorders, with a transdiagnostic perspective; b) highlight the need to reframe existing knowledge and adopt an integrated approach which recognizes the interaction between sleep and circadian factors; and c) identify important gaps and opportunities for further research.

Impaired behavioural flexibility is a core feature of neuropsychiatric disorders and is associated with underlying dysfunction of fronto-striatal circuitry. Reduced dosage of Cyfip1 is a risk factor for neuropsychiatric disorder, as evidenced by its involvement in the 15q11.2 (BP1-BP2) copy number variant: deletion carriers are haploinsufficient for CYFIP1 and exhibit a two- to four-fold increased risk of schizophrenia, autism and/or intellectual disability. Here, we model the contributions of Cyfip1 to behavioural flexibility and related fronto-striatal neural network function using a recently developed haploinsufficient, heterozygous knockout rat line. Using multi-site local field potential (LFP) recordings during resting state, we show that Cyfip1 heterozygous rats (Cyfip1+/-) harbor disrupted network activity spanning medial prefrontal cortex, hippocampal CA1 and ventral striatum. In particular, Cyfip1+/- rats showed reduced influence of nucleus accumbens and increased dominance of prefrontal and hippocampal inputs, compared to wildtype controls. Adult Cyfip1+/- rats were able to learn a single cue-response association, yet unable to learn a conditional discrimination task that engages fronto-striatal interactions during flexible pairing of different levers and cue combinations. Together, these results implicate Cyfip1 in development or maintenance of cortico-limbic-striatal network integrity, further supporting the hypothesis that alterations in this circuitry contribute to behavioural inflexibility observed in neuropsychiatric diseases including schizophrenia and autism.

Neural representations of space in the hippocampus and related brain areas change over timescales of days-weeks, even in familiar contexts and when behavior appears stable. It is unclear whether this ‘representational drift’ is primarily driven by the passage of time or by behavioral experience. Here we present a novel deep-learning approach for measuring network-level representational drift, quantifying drift as the rate of change in decoder error of deep neural networks as a function of train-test lag. Using this method, we analyse a longitudinal dataset of 0.5–475 Hz broadband local field potential (LFP) data recorded from dorsal hippocampal CA1, medial prefrontal cortex and parietal cortex of six rats over ∼ 30 days, during learning of a spatial navigation task in an unfamiliar environment. All three brain regions contained clear spatial representations which improve and drift over training sessions. We find that the rate of drift slows for later training sessions. Finally, we find that drift is statistically better explained by task-relevant rewarded experiences within the maze, rather than the passage of time or number of sessions the animal spent on the maze. Our use of deep neural networks to quantify drift in broadband neural time series unlocks new possibilities for testing which aspects of behavior drive representational drift.### Competing Interest StatementThe authors have declared no competing interest.

We evaluate the effectiveness of combining brain connectiv-ity metrics with signal statistics for early stage Parkinson's Disease (PD) classification using electroencephalogram data (EEG). The data is from 5 arousal states - wakeful and four sleep stages (N1, N2, N3 and REM). Our pipeline uses an Ada Boost model for classification on a challenging early stage PD classification task with with only 30 participants (11 PD, 19 Healthy Control). Evaluating 9 brain connectivity metrics we find the best connectivity metric to be different for each arousal state with Phase Lag Index achieving the highest in-dividual classification accuracy of 86% on N1 data. Further to this our pipeline using regional signal statistics achieves an accuracy of 78%, using brain connectivity only achieves an accuracy of 86% whereas combining the two achieves a best accuracy of 91%. This best performance is achieved on N1 data using Phase Lag Index (PLI) combined with statistics derived from the frequency characteristics of the EEG signal. This model also achieves a recall of 80 % and precision of 96%. Furthermore we find that o n data from each arousal state, combining PLI with regional signal statistics improves classification accuracy versus using signal statistics or brain connectivity alone. Thus we conclude that combining brain connectivity statistics with regional EEG statistics is optimal for classifier performance on early stage Parkinson's. Additionally, we find outperformance of N1 EEG for classification of Parkinson's and expect this could be due to disrupted N1 sleep in PD. This should be explored in future work.

Detecting Parkinson's Disease in its early stages using EEG data presents a significant challenge. This paper introduces a novel approach, representing EEG data as a 15-variate series of bandpower and peak frequency values/coefficients. The hypothesis is that this representation captures essential information from the noisy EEG signal, improving disease detection. Statistical features extracted from this representation are utilised as input for interpretable machine learning models, specifically Decision Tree and AdaBoost classifiers. Our classification pipeline is deployed within our proposed framework which enables high-importance data types and brain regions for classification to be identified. Interestingly, our analysis reveals that while there is no significant regional importance, the N1 sleep data type exhibits statistically significant predictive power (p < 0.01) for early-stage Parkinson's Disease classification. AdaBoost classifiers trained on the N1 data type consistently outperform baseline models, achieving over 80% accuracy and recall. Our classification pipeline statistically significantly outperforms baseline models indicating that the model has acquired useful information. Paired with the interpretability (ability to view feature importance's) of our pipeline this enables us to generate meaningful insights into the classification of early stage Parkinson's with our N1 models. In Future, these models could be deployed in the real world - the results presented in this paper indicate that more than 3 in 4 early-stage Parkinson's cases would be captured with our pipeline.

Spatial information is encoded by location-dependent hippocampal place cell firing rates and sub-second, rhythmic entrainment of spike times. These rate and temporal codes have primarily been characterized in low-dimensional environments under limited cognitive demands; but how is coding configured in complex environments when individual place cells signal several locations, individual locations contribute to multiple routes and functional demands vary? Quantifying CA1 population dynamics of male rats during a decision-making task, here we show that the phase of individual place cells' spikes relative to the local theta rhythm shifts to differentiate activity in different place fields. Theta phase coding also disambiguates repeated visits to the same location during different routes, particularly preceding spatial decisions. Using unsupervised detection of cell assemblies alongside theoretical simulation, we show that integrating rate and phase coding mechanisms dynamically recruits units to different assemblies, generating spiking sequences that disambiguate episodes of experience and multiplexing spatial information with cognitive context. Russo et al. show that context-specific place cell assemblies support hippocampal integration of past experiences into future plans during goal-directed behavior and propose a biophysical mechanism behind the formation of goal dependent theta sequences.

This editorial summarises the clinical relevance of 'chronopsychiatry', defined as the interface between circadian science and mental health science. Chronopsychiatry represents a move towards time-variable perspectives on neurobiology and symptoms, with a greater emphasis on chronotherapeutic interventions.

Neural representations of space in the hippocampus and related brain areas change over timescales of daysweeks, even when there are no apparent behavioural changes.This 'representational drift' occurs even after animals are fully familiar with a given context.Many qualities of this phenomenon are unknown, yet few tools exist to aid analysis.Here we present a novel deeplearning approach for robust quantification and analysis of ensemble level representational drift.Using this method, we analyse a longitudinal dataset of 0.5-475Hz broadband local field potential (LFP) data taken from Hippocampal, Prefrontal-Cortex and Parietal-Cortex of rats collected over multiple days, before and after a contextual rule change in a spatial navigation learning task.First, we observed clear spatial representations in all considered brain regions, despite the low frequency LFP data used.Second, we show statistically significant drift in these representations in all brain regions.Lastly, we show a statistically significant increase in the stability of representations for all considered brain regions as time and experience increases.Our general strategy for using deep neural networks to quantify drift in broadband LFP data opens up new possibilities for flexibly dissecting the features of drift in large-scale neural recordings, and how they relate to animal behaviour.

Short-term memory enables incorporation of recent experience into subsequent decision-making. This pro-cessing recruits both the prefrontal cortex and hippocampus, where neurons encode task cues, rules, and outcomes. However, precisely which information is carried when, and by which neurons, remains unclear. Using population decoding of activity in rat medial prefrontal cortex (mPFC) and dorsal hippocampal CA1, we confirm that mPFC populations lead in maintaining sample information across delays of an operant non-match to sample task, despite individual neurons firing only transiently. During sample encoding, distinct mPFC subpopulations joined distributed CA1-mPFC cell assemblies hallmarked by 4-5 Hz rhythmic modulation; CA1-mPFC assemblies re-emerged during choice episodes but were not 4-5 Hz modulated. Delay-dependent errors arose when attenuated rhythmic assembly activity heralded collapse of sustained mPFC encoding. Our results map component processes of memory-guided decisions onto heterogeneous CA1-mPFC subpopulations and the dynamics of physiologically distinct, distributed cell assemblies.

The increasing molecular, anatomical and temporal precision of modern neuroscientific methods continues to unveil a rich diversity of synapses, cells and brain regions. This heterogeneity spans all scales and allows the brain to adapt to the vast array of experiences, options and outcomes encountered across a lifetime. The prefrontal cortex (PFC) is typically portrayed as the pinnacle of this adaptation, a central hub of cognition able to flexibly integrate, process and route information according to external demands and internal state (Friedman & Robbins, 2022; Le Merre et al., 2021; Miller & Cohen, 2001). How do PFC inputs, cell-types and outputs mediate these complex interactions? How does PFC physiology configure and coordinate downstream circuits via top-down outputs to distributed recipients? And how do we move beyond “high-resolution phrenology” to capture the convergent features of PFC physiology required to inform both understanding and clinical and societal applications? A symposium entitled ‘Decoding Prefrontal Cortical Physiology: Circuits of Cognition’ debated these questions during the annual conference of The Physiological Society held online in the summer of 2021. This editorial summarises the symposium objectives, which centred on integrating rodent and non-human primate models to understand the connectivity, computational capabilities, and behavioural impacts of PFC circuits. A key goal was to synthesise evidence across experimental species and paradigms, identifying potential canonical features of PFC physiology and function. Two of the symposium speakers expand on these topics in detailed reviews: Alexander, Wood et al. (2023) interrogate the roles of ventromedial PFC in emotional regulation, integrating comparative evidence from mice, rats, marmosets and humans; and Perry et al. (2023) navigate the loops of cortico-thalamo-cortical interactions to highlight the importance of distributed, network information processing in cognition, cognitive impairment and future cognitive therapeutics. Both Alexander et al., and Roberts (2023) and Perry et al., and Mitchell (2023) exemplify the power of behavioural neuroscience in non-human primates. However, the symposium also harnessed the potential of mouse models to delineate the cellular and circuit mechanisms of PFC function, mapping the local and long-range connections that underpin prefrontal computations (Anastasiades & Carter, 2021) and the cortico-cortical population coding that enables flexible behaviour (Banerjee et al., 2020). It is clear that sustaining and integrating these complementary approaches across scales and species remains essential if we are to understand PFC well enough to treat the many disorders associated with its dysfunction (Chini & Hanganu-Opatz, 2021, Anastasiades, De Vivo et al., 2022). No two moments of life are ever identical, demanding constant updates to our mental model and behaviour. While theoretical models suggest how these updates may be achieved, there is little understanding of how they are represented in the brain. Behavioural adaptations allow us to maximise the likelihood of receiving desirable outcomes, such as rewards, whilst avoiding negative experiences, such as punishments. Flexible decision-making is strongly linked to the PFC, and work from Banerjee et al. (2020) harnessed the power of rodent circuit analyses to uncover the cellular mechanisms through which such top-down control of behaviour is mediated. They found that neurons in the lateral orbitofrontal cortex (lOFC), a subdivision of PFC, become highly active in response to unexpected rewards that occur when the association between a tactile stimulus and a sucrose-water reward is reversed in a texture discrimination based reversal learning task. Using an array of sophisticated techniques, including neural population calcium imaging and viral projection tracing, they went on to show how OFC projections to the somatosensory cortex are essential for neurons in the sensory cortex to “remap” their response to different sensory stimuli after behavioural contingencies are reversed (Banerjee et al., 2020). Reversal learning is foundational to a range of flexible behaviours; this work highlights how prefrontal outputs can signal to other brain areas, including primary sensory areas, to guide context-dependent adaptation. The myriad functions attributed to the PFC depend upon the diversity of its afferent and efferent projections, which include limbic, cognitive, autonomic and neuromodulatory brain areas. In recent years, thanks largely to the advent of optogenetics, we have developed a more precise understanding of how different inputs engage the PFC to recruit local circuit components such as inhibitory GABAergic interneurons and how this influences the activity of long-range projection neurons that mediate prefrontal outputs. These studies reveal how individual inputs are biased towards distinct regions (for example, prelimbic or infralimbic), layers, and cell types. A prime example of this is the connectivity of individual thalamic nuclei. Anastasiades, Collins et al. (2021) applied genetic and viral tools to delineate how the mediodorsal (MD) and ventromedial thalamus engage the prefrontal network. Their findings revealed that, despite both inputs originating in the higher-order thalamus, these two inputs display remarkably distinct connectivity at the level of cortical sub-layers, cell types and even specific regions of the dendritic arbour. The distinct circuit motifs activated by these two inputs have important implications for prefrontal activity during the various cognitive tasks known to require direct communication between the PFC and thalamus. The role of the cognitive thalamus in behaviour is the focus of Perry et al's. (2023) review, which outlines the cortico-thalamic and thalamo-cortical networks dynamically recruited to mediate healthy attentional control, learning and decision-making, alongside their disruption in cognitive impairment and their potential as therapeutic targets. The review focuses on the MD, anterior thalamic and pulvinar nuclei – motivated by their implication in a range of neurological and psychiatric disorders and by evidence from various perturbation studies in rodents and non-human primates. Convergent evidence shows this circuitry is recruited during rapid learning of visuospatial discriminations and probabilistic decision-making, with neurons in both PFC and MD tuned to complementary but dissociable task features. Perry et al. highlight a broader network embedding the MD thalamus as a mediator and coordinator of both limbic-cortical dialogues and cortical processing hierarchies. For instance, their brain imaging work in macaques shows that learning-associated changes in thalamic-PFC connectivity coincide with altered connectivity between PFC and parietal and temporal cortical territories. Combining such macroscopic analyses with microscopic, single-neuron recordings in network hubs, including MD thalamus and PFC, and with genetically-defined circuit mapping in mice (as outlined by other symposium speakers) holds great promise in decoding the circuit architectures of cognition. This is particularly vital given the potential utility – but enigmatic mechanisms – of circuit-targeting therapies such as deep brain stimulation (DBS) of thalamic nuclei and PFC subregions. One striking example of DBS's potential is in treatment of depression: some patients with Major Depressive Disorder (MDD) respond positively to DBS of the ventromedial PFC (vmPFC; Kennedy, Giacobbe et al., 2011). Determining which patients respond to DBS and why is essential if we are to fine-tune and improve the impact and applicability of these therapies. Alexander et al. (2023) zoom in on vmPFC and its roles in regulating cognitive and peripheral facets of emotion, for example in response to threatening stimuli and situations. The authors highlight the considerable translational challenges posed by heterogeneous cross-species anatomical definitions of vmPFC and its subdivisions – indeed, considerable efforts have been invested in agonising over which parts of the rodent brain are more or less like human equivalents (Carlén, 2017, Laubach, Amarante et al., 2018, Preuss & Wise, 2022). Nevertheless, judicious comparative studies enable translation across species, harnessing the predictive power of mouse 2-photon imaging, for example, to help design primate experiments and/or interpret human brain imaging studies. In this regard, Alexander et al. (2023) champion studies in marmosets, new world monkeys with a vmPFC anatomical and functional architecture well-suited to modelling the human brain. In particular, they review an elegant series of multi-modal measures and interventions delineating the behavioural and autonomic consequences of vmPFC stimulation and inactivation. For example, they describe how over-activation of a vmPFC subdivision (sgACC-25) in marmosets reduces tolerance and increases anxiety in response to an “uncertain threat” (an unfamiliar human experimenter), plus blunts physiological signatures of reward anticipation. These marmoset results are consistent with ACC hyperactivity associating with anxiety and anhedonia in humans and present opportunities to interrogate novel antidepressant modalities including ketamine (Alexander et al., 2019). Ultimately, Alexander et al. (2023) underscore the critical importance of aligning comparative anatomical and physiological studies with the appropriate behavioural domains. Emotion is multifaceted and some aspects – for example appetitive signalling – may be rooted in comparatively conserved circuitries encompassing rodent, non-human primate and human PFC; others, for example threat responses, may demand more judicious use of experimental species during forward- and back-translation. The PFC's complexity and flexibility make it fundamentally fascinating and clinically vital; this symposium highlighted recent advances in understanding both facets by applying the array of neuroscience tools and behavioural analyses currently available. From a translational perspective, the PFC is centre-stage in many neuropsychiatric conditions, including neurodevelopmental disorders, anxiety and depression, psychosis spectrum disorders and neurodegenerative diseases. Although the PFC's complexity and protracted development may render its wiring particularly vulnerable to genetic and/or environmental disruptors (Chini & Hanganu-Opatz, 2021, Anastasiades, De Vivo et al., 2022), none of these afflictions “reside” in a single brain region, making it vital to set PFC dysfunction in its network context. If we are to do so successfully, some contentious considerations remain: for example, how good a model of human PFC is rodent PFC and which aspects of our behaviours can be usefully modelled in rodents? The symposium reminded us that the mouse PFC is not, of course, an accurate model of the human or primate PFC – but it is a consistently informative model, allowing us to map cognition and its component algorithms onto genetically and anatomically defined circuits that are largely conserved across species. The repertoire of complex behaviours classically studied in humans and non-human primates is now extending to rodent experiments; and the molecular and cellular measures and manipulations developed in rodents are increasingly applicable to non-human primates and humans. As such, the prospects of using bidirectional translation to decode heterogeneity in the PFC and beyond is increasingly realistic and useful. Importantly, as in this symposium, such efforts will hinge on dialogue between researchers working with different species, collaborating and comparing to frame understanding of this intriguingly complex brain region. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. None. Paul Anastasiades: Conception or design of the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work Abhishek Banerjee: Conception or design of the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work Matt Jones: Conception or design of the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work. All authors have approved the final version of the manuscript and agree to be accountable for all aspects of the work. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. The authors thank The Wellcome Trust (PA, AB, MJ) and The Academy of Medical Sciences (PA) for support.

Background: Young people living with 22q11.2 Deletion Syndrome (22q11.2DS) are at increased risk of schizophrenia, intellectual disability, attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). In common with these conditions, 22q11.2DS is also associated with sleep problems. We investigated whether abnormal sleep or sleep-dependent network activity in 22q11.2DS reflects convergent, early signatures of neural circuit disruption also evident in associated neurodevelopmental conditions. Methods: In a cross-sectional design, we recorded high-density sleep EEG in young people (6–20 years) with 22q11.2DS (n=28) and their unaffected siblings (n=17), quantifying associations between sleep architecture, EEG oscillations (spindles and slow waves) and psychiatric symptoms. We also measured performance on a memory task before and after sleep. Results: 22q11.2DS was associated with significant alterations in sleep architecture, including a greater proportion of N3 sleep and lower proportions of N1 and REM sleep than in siblings. During sleep, deletion carriers showed broadband increases in EEG power with increased slow-wave and spindle amplitudes, increased spindle frequency and density, and stronger coupling between spindles and slow-waves. Spindle and slow-wave amplitudes correlated positively with overnight memory in controls, but negatively in 22q11.2DS. Mediation analyses indicated that genotype effects on anxiety, ADHD and ASD were partially mediated by sleep EEG measures. Conclusions: This study provides a detailed description of sleep neurophysiology in 22q11.2DS, highlighting alterations in EEG signatures of sleep which have been previously linked to neurodevelopment, some of which were associated with psychiatric symptoms. Sleep EEG features may therefore reflect delayed or compromised neurodevelopmental processes in 22q11.2DS, which could inform our understanding of the neurobiology of this condition and be biomarkers for neuropsychiatric disorders. Funding: This research was funded by a Lilly Innovation Fellowship Award (UB), the National Institute of Mental Health (NIMH 5UO1MH101724; MvdB), a Wellcome Trust Institutional Strategic Support Fund (ISSF) award (MvdB), the Waterloo Foundation (918-1234; MvdB), the Baily Thomas Charitable Fund (2315/1; MvdB), MRC grant Intellectual Disability and Mental Health: Assessing Genomic Impact on Neurodevelopment (IMAGINE) (MR/L011166/1; JH, MvdB and MO), MRC grant Intellectual Disability and Mental Health: Assessing Genomic Impact on Neurodevelopment 2 (IMAGINE-2) (MR/T033045/1; MvdB, JH and MO); Wellcome Trust Strategic Award ‘Defining Endophenotypes From Integrated Neurosciences’ Wellcome Trust (100202/Z/12/Z MO, JH). NAD was supported by a National Institute for Health Research Academic Clinical Fellowship in Mental Health and MWJ by a Wellcome Trust Senior Research Fellowship in Basic Biomedical Science (202810/Z/16/Z). CE and HAM were supported by Medical Research Council Doctoral Training Grants (C.B.E. 1644194, H.A.M MR/K501347/1). HMM and UB were employed by Eli Lilly & Co during the study; HMM is currently an employee of Boehringer Ingelheim Pharma GmbH & Co KG. The views and opinions expressed are those of the author(s), and not necessarily those of the NHS, the NIHR or the Department of Health funders.

Background: DNA hypomethylation at the F2RL3 (F2R like thrombin or trypsin receptor 3) locus has been associated with both smoking and atherosclerotic cardiovascular disease; whether these smoking-related associations form a pathway to disease is unknown. F2RL3 encodes protease-activated receptor 4, a potent thrombin receptor expressed on platelets. Given the role of thrombin in platelet activation and the role of thrombus formation in myocardial infarction, alterations to this biological pathway could be important for ischemic cardiovascular disease. Methods: We conducted multiple independent experiments to assess whether DNA hypomethylation at F2RL3 in response to smoking is associated with risk of myocardial infarction via changes to platelet reactivity. Using cohort data (N=3205), we explored the relationship between smoking, DNA hypomethylation at F2RL3, and myocardial infarction. We compared platelet reactivity in individuals with low versus high DNA methylation at F2RL3 (N=41). We used an in vitro model to explore the biological response of F2RL3 to cigarette smoke extract. Finally, a series of reporter constructs were used to investigate how differential methylation could impact F2RL3 gene expression. Results: Observationally, DNA methylation at F2RL3 mediated an estimated 34% of the smoking effect on increased risk of myocardial infarction. An association between methylation group (low/high) and platelet reactivity was observed in response to PAR4 (protease-activated receptor 4) stimulation. In cells, cigarette smoke extract exposure was associated with a 4.9% to 9.3% reduction in DNA methylation at F2RL3 and a corresponding 1.7-(95% CI, 1.2-2.4, P=0.04) fold increase in F2RL3 mRNA. Results from reporter assays suggest the exon 2 region of F2RL3 may help control gene expression. Conclusions: Smoking-induced epigenetic DNA hypomethylation at F2RL3 appears to increase PAR4 expression with potential downstream consequences for platelet reactivity. Combined evidence here not only identifies F2RL3 DNA methylation as a possible contributory pathway from smoking to cardiovascular disease risk but from any feature potentially influencing F2RL3 regulation in a similar manner.