Chronopsychiatry.

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.

Key roles of C2/GAP domains in SYNGAP1-related pathophysiology.

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.

Corrigendum: L-DOPA increases slow-wave sleep duration and selectively modulates memory persistence in older adults.

[This corrects the article DOI: 10.3389/fnbeh.2023.1096720.].

Hippocampal-dependent navigation in head-fixed mice using a floating real-world environment.

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.

Differentiating neural sensitivity and bias during face-emotion processing in youth: a computational approach.

The ability to interpret face-emotion displays is critical for the development of adaptive social interactions. Using a novel variant of a computational model and fMRI data, we examined behavioral and neural associations between two metrics of face-emotion labeling (sensitivity and bias) and age in youth. Youth and adults (n = 44, M age = 20.02, s.d. = 7.44, range = 8-36) completed an explicit face-emotion labeling fMRI task including happy to angry morphed face emotions. A drift-diffusion model was applied to choice and reaction time distributions to examine sensitivity and bias in interpreting face emotions. Model fit and reliability of parameters were assessed on adult data (n = 42). Linear and quadratic slopes modeled brain activity associated with dimensions of face-emotion valence and ambiguity during interpretation. Behaviorally, age was associated with sensitivity. The bilateral anterior insula exhibited a more pronounced neural response to ambiguity with older age. Associations between sensitivity and bias metrics and activation patterns indicated that systems encoding face-emotion valence and ambiguity both contribute to the ability to discriminate face emotions. The current study provides evidence for age-related improvement in perceptual sensitivity to facial affect across adolescence and young adulthood.

Immune checkpoint therapy responders display early clonal expansion of tumor infiltrating lymphocytes.

Immune checkpoint therapy (ICT) causes durable tumour responses in a subgroup of patients, but it is not well known how T cell receptor beta (TCRß) repertoire dynamics contribute to the therapeutic response. Using murine models that exclude variation in host genetics, environmental factors and tumour mutation burden, limiting variation between animals to naturally diverse TCRß repertoires, we applied TCRseq, single cell RNAseq and flow cytometry to study TCRß repertoire dynamics in ICT responders and non-responders. Increased oligoclonal expansion of TCRß clonotypes was observed in responding tumours. Machine learning identified TCRß CDR3 signatures unique to each tumour model, and signatures associated with ICT response at various timepoints before or during ICT. Clonally expanded CD8+ T cells in responding tumours post ICT displayed effector T cell gene signatures and phenotype. An early burst of clonal expansion during ICT is associated with response, and we report unique dynamics in TCRß signatures associated with ICT response.

For human-like models, train on human-like tasks.

Bowers et al. express skepticism about deep neural networks (DNNs) as models of human vision due to DNNs' failures to account for results from psychological research. We argue that to fairly assess DNNs, we must first train them on more human-like tasks which we hypothesize will induce more human-like behaviors and representations.

A belief systems analysis of fraud beliefs following the 2020 US election.

Beliefs that the US 2020 Presidential election was fraudulent are prevalent despite substantial contradictory evidence. Why are such beliefs often resistant to counter-evidence? Is this resistance rational, and thus subject to evidence-based arguments, or fundamentally irrational? Here we surveyed 1,642 Americans during the 2020 vote count, testing fraud belief updates given hypothetical election outcomes. Participants' fraud beliefs increased when their preferred candidate lost and decreased when he won, and both effects scaled with partisan preferences, demonstrating partisan asymmetry (desirability effects). A Bayesian model of rational updating of a system of beliefs-beliefs in the true vote winner, fraud prevalence and beneficiary of fraud-accurately accounted for this partisan asymmetry, outperforming alternative models of irrational, motivated updating and models lacking the full belief system. Partisan asymmetries may not reflect motivated reasoning, but rather rational attributions over multiple potential causes of evidence. Changing such beliefs may require targeting multiple key beliefs simultaneously rather than direct debunking attempts.

L-DOPA increases slow-wave sleep duration and selectively modulates memory persistence in older adults.

Introduction: Millions of people worldwide take medications such as L-DOPA that increase dopamine to treat Parkinson's disease. Yet, we do not fully understand how L-DOPA affects sleep and memory. Our earlier research in Parkinson's disease revealed that the timing of L-DOPA relative to sleep affects dopamine's impact on long-term memory. Dopamine projections between the midbrain and hippocampus potentially support memory processes during slow wave sleep. In this study, we aimed to test the hypothesis that L-DOPA enhances memory consolidation by modulating NREM sleep. Methods: We conducted a double-blind, randomised, placebo-controlled crossover trial with healthy older adults (65-79 years, n = 35). Participants first learned a word list and were then administered long-acting L-DOPA (or placebo) before a full night of sleep. Before sleeping, a proportion of the words were re-exposed using a recognition test to strengthen memory. L-DOPA was active during sleep and the practice-recognition test, but not during initial learning. Results: The single dose of L-DOPA increased total slow-wave sleep duration by approximately 11% compared to placebo, while also increasing spindle amplitudes around slow oscillation peaks and around 1-4 Hz NREM spectral power. However, behaviourally, L-DOPA worsened memory of words presented only once compared to re-exposed words. The coupling of spindles to slow oscillation peaks correlated with these differential effects on weaker and stronger memories. To gauge whether L-DOPA affects encoding or retrieval of information in addition to consolidation, we conducted a second experiment targeting L-DOPA only to initial encoding or retrieval and found no behavioural effects. Discussion: Our results demonstrate that L-DOPA augments slow wave sleep in elderly, perhaps tuning coordinated network activity and impacting the selection of information for long-term storage. The pharmaceutical modification of slow-wave sleep and long-term memory may have clinical implications. Clinical trial registration: Eudract number: 2015-002027-26; https://doi.org/10.1186/ISRCTN90897064, ISRCTN90897064.

A Computational Model Reveals Learning Dynamics During Interpretation Bias Training With Clinical Applications.

BACKGROUND: Some psychopathologies, including anxiety and irritability, are associated with biases when judging ambiguous social stimuli. Interventions targeting these biases, or interpretation bias training (IBT), are amenable to computational modeling to describe their associative learning mechanisms. Here, we translated ALCOVE (attention learning covering map), a model of category learning, to describe learning in youths with affective psychopathology when training on more positive judgments of ambiguous face emotions. METHODS: A predominantly clinical sample comprised 71 youths (age range, 8-22 years) representing broad distributions of irritability and anxiety symptoms. Of these, 63 youths were included in the test sample by completing an IBT task with acceptable performance for computational modeling. We used a separate sample of 28 youths to translate ALCOVE for individual estimates of learning rate and generalization. In the test sample, we assessed associations between model learning estimates and irritability, anxiety, their shared variance (negative affectivity), and age. RESULTS: Age and affective symptoms were associated with category learning during IBT. Lower learning rates were associated with higher negative affectivity common in anxiety and irritability. Lower generalization, or improved discrimination between face emotions, was associated with increasing age. CONCLUSIONS: This work demonstrates a functional consequence of age- and symptom-related learning during interpretation bias. Learning measured by ALCOVE also revealed learning types not accounted for in the prior literature on IBT. This work more broadly demonstrates the utility of measurement models for understanding trial-by-trial processes and identifying individual learning styles.

Exploring the potential of using simulation games for engaging with sheep farmers about lameness recognition.

Introduction: Computer simulation games are increasingly being used in agriculture as a promising tool to study, support and influence real-life farming practices. We explored the potential of using simulation games to engage with sheep farmers on the ongoing challenge of reducing lameness. Working with UK stakeholders, we developed a game in which players are challenged with identifying all the lame sheep in a simulated flock. Here, we evaluate the game's potential to act as a tool to help assess, train and understand farmers' ability to recognize the early signs of lameness. Methods: Participants in the UK were invited to play the game in an online study, sharing with us their in-game scores alongside information relating to their real-life farming experience, how they played the game, and feedback on the game. Mixed methods were used to analyze this information in order to evaluate the game. Quantitative analyses consisted of linear modeling to test for statistical relationships between participants' in-game recall (% of the total number of lame sheep that were marked as lame), and the additional information they provided. Qualitative analyses of participants' feedback on the game consisted of thematic analysis and a Likert Scale questionnaire to contextualize the quantitative results and identify additional insights from the study. Results: Quantitative analyses identified no relationships between participants' (n = 63) recall scores and their real life farming experience, or the lameness signs they looked for when playing the game. The only relationship identified was a relationship between participants' recall score and time spent playing the game. Qualitative analyses identified that participants did not find the game sufficiently realistic or engaging, though several enjoyed playing it and saw potential for future development. Qualitative analyses also identified several interesting and less-expected insights about real-life lameness recognition practices that participants shared after playing the game. Discussion: Simulation games have potential as a tool in livestock husbandry education and research, but achieving the desired levels of realism and/or engagingness may be an obstacle to realizing this. Future research should explore this potential further, aided by larger budgets and closer collaboration with farmers, stockpeople, and veterinarians.

A Statistical Foundation for Derived Attention.

According to the theory of derived attention, organisms attend to cues with strong associations. Prior work has shown that - combined with a Rescorla-Wagner style learning mechanism - derived attention explains phenomena such as learned predictiveness, inattention to blocked cues, and value-based salience. We introduce a Bayesian derived attention model that explains a wider array of results than previous models and gives further insight into the principle of derived attention. Our approach combines Bayesian linear regression with the assumption that the associations of any cue with various outcomes share the same prior variance, which can be thought of as the inherent importance of that cue. The new model simultaneously estimates cue-outcome associations and prior variance through approximate Bayesian learning. A significant cue will develop large associations, leading the model to estimate a high prior variance and hence develop larger associations from that cue to novel outcomes. This provides a normative, statistical explanation for derived attention. Through simulation, we show that this Bayesian derived attention model not only explains the same phenomena as previous versions, but also retrospective revaluation. It also makes a novel prediction: inattention after backward blocking. We hope that further development of the Bayesian derived attention model will shed light on the complex relationship between uncertainty and predictiveness effects on attention.

Distinct hippocampal-prefrontal neural assemblies coordinate memory encoding, maintenance, and recall.

Short-term memory enables incorporation of recent experience into subsequent decision-making. This processing 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.

Taking eDNA underground: Factors affecting eDNA detection of subterranean fauna in groundwater.

Stygofauna are aquatic fauna that have evolved to live underground. The impacts of anthropogenic climate change, extraction and pollution on groundwater pose major threats to groundwater health, prompting the need for efficient and reliable means to detect and monitor stygofaunal communities. Conventional survey techniques for these species rely on morphological identification and can be biased, labour-intensive and often indeterminate to lower taxonomic levels. By contrast, environmental DNA (eDNA)-based methods have the potential to dramatically improve on existing stygofaunal survey methods in a large range of habitats and for all life stages, reducing the need for the destructive manual collection of often critically endangered species or for specialized taxonomic expertise. We compared eDNA and haul-net samples collected in 2020 and 2021 from 19 groundwater bores and a cave on Barrow Island, northwest Western Australia, and assessed how sampling factors influenced the quality of eDNA detection of stygofauna. The two detection methods were complementary; eDNA metabarcoding was able to detect soft-bodied taxa and fish often missed by nets, but only detected seven of the nine stygofaunal crustacean orders identified from haul-net specimens. Our results also indicated that eDNA metabarcoding could detect 54%-100% of stygofauna from shallow-water samples and 82%-90% from sediment samples. However, there was significant variation in stygofaunal diversity between sample years and sampling types. The findings of this study demonstrate that haul-net sampling has a tendency to underestimate stygofaunal diversity and that eDNA metabarcoding of groundwater can substantially improve the efficiency of stygofaunal surveys.

Inaccurate self-report of olfactory dysfunction in REM Sleep Behaviour Disorder and implications for prognosis.

Introduction: The earliest stages of alpha-synucleinopathies are accompanied by non-specific prodromal symptoms such as diminished sense of smell, constipation and depression, as well as more specific prodromal conditions including REM Sleep Behaviour Disorder (RBD). While the majority of RBD patients will develop an alpha-synucleinopathy, one of the greatest clinical challenges is determining whether and when individual patients will phenoconvert. Clinical evaluation of a patient presenting with RBD should therefore include robust and objective assessments of known alpha-synucleinopathy prodromes. Methods: This study compared olfactory function self-report measures with psychophysical 'Sniffin' Stick 16-item Identification' test scores in Control (n = 19), RBD (n = 16) and PD (n = 17) participants. Results: We confirm that olfactory test scores are significantly diminished in RBD and PD groups compared to Controls (p < 0.001, One-Way ANOVA with Tukey-Kramer Post-Hoc, effect size = 0.401). However, RBD participants were only 56 % accurate when self-reporting olfactory dysfunction, hence markedly less likely to perceive or acknowledge their own hyposmia compared to Controls (p = 0.045, Fisher's Exact Test, effect-size = 0.35). Conclusion: When isolated RBD presents with hyposmia, there is an increased likelihood of phenoconversion to Parkinson's Disease (PD) or Dementia with Lewy Bodies (DLB); unawareness of olfactory dysfunction in an individual with isolated RBD may therefore confound differential diagnosis and prognosis. Our results evidence the fallibility of olfactory function self-report in the context of RBD prognosis, indicating that clinical assessments of RBD patients should include more reliable measures of olfactory status.

Non-coding RNAs associated with Prader-Willi syndrome regulate transcription of neurodevelopmental genes in human induced pluripotent stem cells.

Mutations and aberrant gene expression during cellular differentiation lead to neurodevelopmental disorders, such as Prader-Willi syndrome (PWS), which results from the deletion of an imprinted locus on paternally inherited chromosome 15. We analyzed chromatin-associated RNA in human induced pluripotent cells (iPSCs) upon depletion of hybrid small nucleolar long non-coding RNAs (sno-lncRNAs) and 5' snoRNA capped and polyadenylated long non-coding RNAs (SPA-lncRNAs) transcribed from the locus deleted in PWS. We found that rapid ablation of these lncRNAs affects transcription of specific gene classes. Downregulated genes contribute to neurodevelopment and neuronal maintenance, while upregulated genes are predominantly involved in the negative regulation of cellular metabolism and apoptotic processes. Our data reveal the importance of SPA-lncRNAs and sno-lncRNAs in controlling gene expression in iPSCs and provide a platform for synthetic experimental approaches in PWS studies. We conclude that ncRNAs transcribed from the PWS locus are critical regulators of a transcriptional signature, which is important for neuronal differentiation and development.

Extended depth-of-field light-sheet microscopy improves imaging of large volumes at high numerical aperture.

Light-sheet microscopes must compromise among field of view, optical sectioning, resolution, and detection efficiency. High-numerical-aperture (NA) detection objective lenses provide higher resolution, but their narrow depth of field inefficiently captures the fluorescence signal generated throughout the thickness of the illumination light sheet when imaging large volumes. Here, we present ExD-SPIM (extended depth-of-field selective-plane illumination microscopy), an improved light-sheet microscopy strategy that solves this limitation by extending the depth of field (DOF) of high-NA detection objectives to match the thickness of the illumination light sheet. This extension of the DOF uses a phase mask to axially stretch the point-spread function of the objective lens while largely preserving lateral resolution. This matching of the detection DOF to the illumination-sheet thickness increases the total fluorescence collection, reduces the background, and improves the overall signal-to-noise ratio (SNR), as shown by numerical simulations, imaging of bead phantoms, and imaging living animals. In comparison to conventional light sheet imaging with low-NA detection that yields equivalent DOF, the results show that ExD-SPIM increases the SNR by more than threefold and dramatically reduces the rate of photobleaching. Compared to conventional high-NA detection, ExD-SPIM improves the signal sensitivity and volumetric coverage of whole-brain activity imaging, increasing the number of detected neurons by over a third.

A systematic review of the validity of non-invasive sleep-measuring devices in mid-to-late life adults: Future utility for Alzheimer's disease research.

Changes in sleep during mid-to-late life are associated with risk for Alzheimer's disease (AD). Mechanistic understanding of this association necessitates measurement tools able to quantify these sleep changes longitudinally and accurately. We conducted a systematic review with meta-analysis of validity studies of non-invasive sleep-measuring devices published since 2015 that record sleep metrics associated with AD in adults over 40 (mean 52.9, SD 6.1 years). We reviewed 52 studies, including 32 wearable and ten non-wearable single or multi-sensor devices validated against polysomnography (minimum one night). The apnoea hypopnoea index and oxygen desaturation index were accurately measured across devices. Total sleep time and sleep efficiency were significantly overestimated (p < 0.001) by mean 33.2 minutes and 7.6%, respectively. Slow wave sleep duration was inaccurately measured except by a headband device with electroencephalography. There was no significant difference in accuracy between participants with and without sleep disorders. Studies were undermined by high risk of bias from closed-access algorithms and classification thresholds, and incomplete reporting of accuracy data. Only one study investigated slow wave activity, and none investigated sleep spindles. Nonetheless, we have identified devices that could be used in future studies of sleep and AD risk and discuss some of the limitations of available research.