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Climate change and other anthropogenic pressures are altering species distributions. Several studies have indicated that tri-colored bats (Perimyotis subflavus) are extending their distributional range westward in the United States. Montane and subalpine habitats consist of a mosaic of forest types including lodgepole pine woodlands and montane meadows, which provide an opportunity to study how a newly arriving species, typically associated with lowland riparian systems, is adapting to novel environmental conditions. The objective of this study is to document tri-colored bat activity in these novel habitats using acoustic surveys and to quantify what factors are influencing activity patterns in habitats and at elevations not previously documented. We selected sites at 2700m elevation that differed in stand structure, driven primarily by beetle kill outbreaks, and were in various stages of secondary succession. We used acoustic monitoring to model habitat activity patterns using nonparametric multiplicative regression. Results showed that tri-colored bats used meadows and lodgepole pine stands undergoing secondary succession following bark beetle outbreaks. Activity was highest in meadows and early time-since-kill (TSK) forests in the beginning of the survey period, and tri-colored bats had increased activity in late TSK forest habitats at the end of the survey period in early August. Temporally, activity was lowest during the middle of the survey period (mid-July) indicating that tri-colored bats moved away from our study area. However, activity significantly returned by the end of the survey. Our study demonstrates that tri-colored bats are not restricting their activity to lower elevation riparian areas in the Colorado foothills, but instead appear to be using high elevation habitat types in areas dominated by lodgepole pine and subalpine meadows. We hope this study will support conservation efforts for this species following the proposed U.S. Fish and Wildlife Service listing.

The hippocampal formation has been implicated in the pathophysiology of schizophrenia, with patients showing impairments in spatial and relational cognition, structural changes in entorhinal cortex and reduced theta coherence with medial prefrontal cortex. Both the entorhinal cortex and medial prefrontal cortex exhibit a 6-fold (or ‘hexadirectional’) modulation of neural activity during virtual navigation that is indicative of grid cell populations and associated with accurate spatial navigation. Here, we examined whether these grid-like patterns are disrupted in schizophrenia. We asked 17 participants with diagnoses of schizophrenia and 23 controls (matched for age, sex and IQ) to perform a virtual reality spatial navigation task during magnetoencephalography. The control group showed stronger 4–10 Hz theta power during movement onset, as well as hexadirectional modulation of theta band oscillatory activity in the right entorhinal cortex whose directional stability across trials correlated with navigational accuracy. This hexadirectional modulation was absent in schizophrenia patients, with a significant difference between groups. These results suggest that impairments in spatial and relational cognition associated with schizophrenia may arise from disrupted grid firing patterns in entorhinal cortex.

Paranoid delusions or unfounded beliefs that others intend to deliberately cause harm are a frequent and burdensome symptom in early psychosis, but their emergence and consolidation still remains opaque. Recent theories suggest that aberrant prediction errors lead to a brittle model of the world providing a breeding ground for delusions. Here, we employ a Bayesian approach to test for a more unstable model of the world and investigate the computational mechanisms underlying emerging paranoia.We modelled behaviour of 18 first-episode psychosis patients (FEP), 19 individuals at clinical high-risk for psychosis (CHR-P), and 19 healthy controls (HC) during an advice-taking task, designed to probe learning about others’ changing intentions. We formulated competing hypotheses comparing the standard Hierarchical Gaussian Filter (HGF), a Bayesian belief updating scheme, with a mean-reverting HGF to model an altered perception of volatility.There was a significant group-by-volatility interaction on advice-taking suggesting that CHR-P and FEP displayed reduced adaptability to environmental volatility. Model comparison favored the standard HGF in HC, but the mean-reverting HGF in CHR-P and FEP in line with perceiving increased volatility, although model attributions in CHR-P were heterogeneous. We observed correlations between shifts in perceived volatility and positive symptoms generally as well as with frequency of paranoid delusions specifically.Our results suggest that FEP are characterised by a different computational mechanism – perceiving the environment as increasingly volatile – in line with Bayesian accounts of psychosis. This approach may prove useful to investigate heterogeneity in CHR-P and identify vulnerability for transition to psychosis.

BACKGROUND: Diminished synaptic gain-the sensitivity of postsynaptic responses to neural inputs-may be a fundamental synaptic pathology in schizophrenia. Evidence for this is indirect, however. Furthermore, it is unclear whether pyramidal cells or interneurons (or both) are affected, or how these deficits relate to symptoms. \ud \ud METHODS: People with schizophrenia diagnoses (PScz) (n = 108), their relatives (n = 57), and control subjects (n = 107) underwent 3 electroencephalography (EEG) paradigms-resting, mismatch negativity, and 40-Hz auditory steady-state response-and resting functional magnetic resonance imaging. Dynamic causal modeling was used to quantify synaptic connectivity in cortical microcircuits. \ud \ud RESULTS: Classic group differences in EEG features between PScz and control subjects were replicated, including increased theta and other spectral changes (resting EEG), reduced mismatch negativity, and reduced 40-Hz power. Across all 4 paradigms, characteristic PScz data features were all best explained by models with greater self-inhibition (decreased synaptic gain) in pyramidal cells. Furthermore, disinhibition in auditory areas predicted abnormal auditory perception (and positive symptoms) in PScz in 3 paradigms. \ud \ud CONCLUSIONS: First, characteristic EEG changes in PScz in 3 classic paradigms are all attributable to the same underlying parameter change: greater self-inhibition in pyramidal cells. Second, psychotic symptoms in PScz relate to disinhibition in neural circuits. These findings are more commensurate with the hypothesis that in PScz, a primary loss of synaptic gain on pyramidal cells is then compensated by interneuron downregulation (rather than the converse). They further suggest that psychotic symptoms relate to this secondary downregulation.

Background Psychotic experiences emerge from abnormalities in perception and belief formation, and occur more commonly in those experiencing childhood trauma. Yet, which precise aspects of belief formation are atypical in psychosis is not well understood. We used a computational modelling approach to characterise belief-updating in young adults in the general population, examine their relationship with psychotic outcomes and trauma, and the extent to which they mediate the trauma-psychosis relationship.MethodsWe used data from 3,360 individuals from the Avon Longitudinal Study of Parents and Children birth cohort who completed assessments for psychotic outcomes, depression, anxiety, and two belief-updating tasks at age 24, and had data available on traumatic events assessed from birth to late adolescence. Unadjusted and adjusted regression and counterfactual mediation methods were used for the analyses.ResultsBasic behavioural measures of belief-updating (‘draws to decision’ and ‘disconfirmatory updating’) were not associated with psychotic experiences. However, computational modelling revealed an association between increased decision noise with both psychotic experiences and trauma exposure, although ConclusionsThese findings challenge the hypothesis that atypical belief-updating mechanisms (as indexed by the computational models and behavioural measures we employed) underlie the development of psychotic phenomena.

The aberrant salience hypothesis proposes that striatal dopamine dysregulation causes misattribution of salience to irrelevant stimuli leading to psychosis. Recently, new lines of preclinical evidence on information coding by subcortical dopamine coupled with computational models of the brain's ability to predict and make inferences about the world (predictive processing) provide a new perspective on this hypothesis. We review these and summarize the evidence for dopamine dysfunction, reward processing, and salience abnormalities in people at clinical high risk of psychosis (CHR) relative to findings in patients with psychosis. This review identifies consistent evidence for dysregulated subcortical dopamine function in people at CHR, but also indicates a number of areas where neurobiological processes are different in CHR subjects relative to patients with psychosis, particularly in reward processing. We then consider how predictive processing models may explain psychotic symptoms in terms of alterations in prediction error and precision signaling using Bayesian approaches. We also review the potential role of environmental risk factors, particularly early adverse life experiences, in influencing the prior expectations that individuals have about their world in terms of computational models of the progression from being at CHR to frank psychosis. We identify a number of key outstanding questions, including the relative roles of prediction error or precision signaling in the development of symptoms and the mechanism underlying dopamine dysfunction. Finally, we discuss how the integration of computational psychiatry with biological investigation may inform the treatment for people at CHR of psychosis.

Sensory attenuation refers to the decreased intensity of a sensory percept when a sensation is self‐generated compared with when it is externally triggered. However, the underlying brain regions and network interactions that give rise to this phenomenon remain to be determined. To address this issue, we recorded magnetoencephalographic (MEG) data from 35 healthy controls during an auditory task in which pure tones were either elicited through a button press or passively presented. We analyzed the auditory M100 at sensor‐ and source‐level and identified movement‐related magnetic fields (MRMFs). Regression analyses were used to further identify brain regions that contributed significantly to sensory attenuation, followed by a dynamic causal modeling (DCM) approach to explore network interactions between generators. Attenuation of the M100 was pronounced in right Heschl's gyrus (HES), superior temporal cortex (ST), thalamus, rolandic operculum (ROL), precuneus and inferior parietal cortex (IPL). Regression analyses showed that right postcentral gyrus (PoCG) and left precentral gyrus (PreCG) predicted M100 sensory attenuation. In addition, DCM results indicated that auditory sensory attenuation involved bi‐directional information flow between thalamus, IPL, and auditory cortex. In summary, our data show that sensory attenuation is mediated by bottom‐up and top‐down information flow in a thalamocortical network, providing support for the role of predictive processing in sensory‐motor system., We examined auditory sensory attenuation using a combination of magnetoencephalography (MEG) and computational modeling. Our data show that sensory attenuation is mediated by bottom‐up and top‐down information flow in a thalamocortical network, providing support for the role of predictive processing in sensorimotor systems.

Childhood trauma is a risk factor for psychosis. Amphetamine increases synaptic striatal dopamine levels and can induce positive psychotic symptoms in healthy individuals and patients with schizophrenia. Socio-developmental hypotheses of psychosis propose that childhood trauma and other environmental risk factors sensitize the dopamine system to increase the risk of psychotic symptoms, but this remains to be tested in humans. We used [11C]-(+)-PHNO positron emission tomography to measure striatal dopamine-2/3 receptor (D2/3R) availability and ventral striatal dexamphetamine-induced dopamine release in healthy participants (n = 24). The relationships between dexamphetamine-induced dopamine release, dexamphetamine-induced positive psychotic symptoms using the Positive and Negative Syndrome Scale (PANSS), and childhood trauma using the Childhood Trauma Questionnaire (CTQ) were assessed using linear regression and mediation analyses, with childhood trauma as the independent variable, dexamphetamine-induced dopamine release as the mediator variable, and dexamphetamine-induced symptoms as the dependent variable. There was a significant interaction between childhood trauma and ventral striatal dopamine release in predicting dexamphetamine-induced positive psychotic symptoms (standardized β = 1.83, p = 0.003), but a mediation analysis was not significant (standardized β = −0.18, p = 0.158). There were no significant effects of dopamine release and childhood trauma on change in negative (p = 0.280) or general PANSS symptoms (p = 0.061), and there was no relationship between ventral striatal baseline D2/3R availability and positive symptoms (p = 0.368). This indicates childhood trauma and dopamine release interact to influence the induction of positive psychotic symptoms. This is not consistent with a simple sensitization hypothesis, but suggests that childhood trauma moderates the cognitive response to dopamine release to make psychotic experiences more likely.

Canonical correlation analysis (CCA) and partial least squares (PLS) are powerful multivariate methods for capturing associations across 2 modalities of data (e.g., brain and behavior). However, when the sample size is similar to or smaller than the number of variables in the data, standard CCA and PLS models may overfit, i.e., find spurious associations that generalize poorly to new data. Dimensionality reduction and regularized extensions of CCA and PLS have been proposed to address this problem, yet most studies using these approaches have some limitations. This work gives a theoretical and practical introduction into the most common CCA/PLS models and their regularized variants. We examine the limitations of standard CCA and PLS when the sample size is similar to or smaller than the number of variables. We discuss how dimensionality reduction and regularization techniques address this problem and explain their main advantages and disadvantages. We highlight crucial aspects of the CCA/PLS analysis framework, including optimizing the hyperparameters of the model and testing the identified associations for statistical significance. We apply the described CCA/PLS models to simulated data and real data from the Human Connectome Project and Alzheimer's Disease Neuroimaging Initiative (both of n500). We use both low- and high-dimensionality versions of these data (i.e., ratios between sample size and variables in the range of ∼1-10 and ∼0.1-0.01, respectively) to demonstrate the impact of data dimensionality on the models. Finally, we summarize the key lessons of the tutorial.

This paper characterizes impulsive behavior using a patch-leaving paradigm and active inference—a framework for describing Bayes optimal behavior. This paradigm comprises different environments (patches) with limited resources that decline over time at different rates. The challenge is to decide when to leave the current patch for another to maximize reward. We chose this task because it offers an operational characterization of impulsive behavior, namely, maximizing proximal reward at the expense of future gain. We use a Markov decision process formulation of active inference to simulate behavioral and electrophysiological responses under different models and prior beliefs. Our main finding is that there are at least three distinct causes of impulsive behavior, which we demonstrate by manipulating three different components of the Markov decision process model. These components comprise (i) the depth of planning, (ii) the capacity to maintain and process information, and (iii) the perceived value of immediate (relative to delayed) rewards. We show how these manipulations change beliefs and subsequent choices through variational message passing. Furthermore, we appeal to the process theories associated with this message passing to simulate neuronal correlates. In future work, we will use this scheme to identify the prior beliefs that underlie different sorts of impulsive behavior—and ask whether different causes of impulsivity can be inferred from the electrophysiological correlates of choice behavior.

Fueled by developments in computational neuroscience, there has been increasing interest in the underlying neurocomputational mechanisms of psychosis. One successful approach involves predictive coding and Bayesian inference. Here, inferences regarding the current state of the world are made by combining prior beliefs with incoming sensory signals. Mismatches between prior beliefs and incoming signals constitute prediction errors that drive new learning. Psychosis has been suggested to result from a decreased precision in the encoding of prior beliefs relative to the sensory data, thereby garnering maladaptive inferences. Here, we review the current evidence for aberrant predictive coding and discuss challenges for this canonical predictive coding account of psychosis. For example, hallucinations and delusions may relate to distinct alterations in predictive coding, despite their common co-occurrence. More broadly, some studies implicate weakened prior beliefs in psychosis, and others find stronger priors. These challenges might be answered with a more nuanced view of predictive coding. Different priors may be specified for different sensory modalities and their integration, and deficits in each modality need not be uniform. Furthermore, hierarchical organization may be critical. Altered processes at lower levels of a hierarchy need not be linearly related to processes at higher levels (and vice versa). Finally, canonical theories do not highlight active inference-the process through which the effects of our actions on our sensations are anticipated and minimized. It is possible that conflicting findings might be reconciled by considering these complexities, portending a framework for psychosis more equipped to deal with its many manifestations.

Delusions are, by popular definition, false beliefs that are held with certainty and resistant to contradictory evidence. They seem at odds with the notion that the brain at least approximates Bayesian inference. This is especially the case in schizophrenia, a disorder thought to relate to decreased – rather than increased – certainty in the brain's model of the world. We use an active inference Markov decision process model (a Bayes-optimal decision-making agent) to perform a simple task involving social and non-social inferences. We show that even moderate changes in some model parameters – decreasing confidence in sensory input and increasing confidence in states implied by its own (especially habitual) actions – can lead to delusions as defined above. Incorporating affect in the model increases delusions, specifically in the social domain. The model also reproduces some classic psychological effects, including choice-induced preference change, and an optimism bias in inferences about oneself. A key observation is that no change in a single parameter is both necessary and sufficient for delusions; rather, delusions arise due to conditional dependencies that create ‘basins of attraction’ which trap Bayesian beliefs. Simulating the effects of antidopaminergic antipsychotics – by reducing the model's confidence in its actions – demonstrates that the model can escape from these attractors, through this synthetic pharmacotherapy.

Diminished synaptic gain – the sensitivity of postsynaptic responses to neural inputs – may be a fundamental synaptic pathology in schizophrenia. Evidence for this is indirect, however. Furthermore, it is unclear whether pyramidal cells or interneurons (or both) are affected, or how these deficits relate to symptoms. Participants with schizophrenia (Scz, n=108), their relatives (n=57), and controls (n=107) underwent three electroencephalography paradigms – resting, mismatch negativity, and 40 Hz auditory steady-state response – and resting functional magnetic resonance imaging. Dynamic causal modelling was used to quantify synaptic connectivity in cortical microcircuits. Across all four paradigms, characteristic Scz data features were best explained by models with greater self-inhibition (decreased synaptic gain), in pyramidal cells. Furthermore, disinhibition in auditory areas predicted abnormal auditory perception (and positive symptoms) in Scz, in three paradigms. Thus, psychotic symptoms of Scz may result from a downregulation of inhibitory interneurons that may compensate for diminished postsynaptic gain in pyramidal cells.

Environmental change due to climate warming is accelerating in most of the world’s arid regions, pushing already xeric ecosystems towards drier conditions and expanding desert landscapes. These changes will alter community structures and species interactions globally. Because many arid regions, especially semi-arid zones, are relatively high in bat species richness, reactions of bat populations to increasing and intensifying droughts will affect food web dynamics and ecosystem services on nearly every continent. Although there is evidence that some species have preadaptations for surviving decreasing available water resources in arid regions, many species may succumb to the pace of climate warming and landscape xerification. As drought and aridity increase, many drinking sources will inevitably be lost, increasing competition for progressively limited resources, thereby affecting bats during the reproductive season when they are most vulnerable. Herein, we review how climate warming and increasing drought are currently altering bat population dynamics and model future viability of some species and populations. Documented declines in some bat populations, reductions in species richness, and changes in distributional ranges are already occurring in hardest hit areas and some models have been developed to predict future outcomes for bats living on the leading edge of climate disruption.

Summary Mismatch negativity (MMN) is a differential electrophysiological response measuring cortical adaptability to unpredictable stimuli. MMN is consistently attenuated in patients with psychosis. However, the genetics of MMN are uncharted, limiting the validation of MMN as a psychosis endophenotype. Here, we perform a transcriptome-wide association study of 728 individuals, which reveals 2 genes (FAM89A and ENGASE) whose expression in cortical tissues is associated with MMN. Enrichment analyses of neurodevelopmental expression signatures show that genes associated with MMN tend to be overexpressed in the frontal cortex during prenatal development but are significantly downregulated in adulthood. Endophenotype ranking value calculations comparing MMN and three other candidate psychosis endophenotypes (lateral ventricular volume and two auditory-verbal learning measures) find MMN to be considerably superior. These results yield promising insights into sensory processing in the cortex and endorse the notion of MMN as a psychosis endophenotype., Graphical abstract, Highlights • Expression of FAM89A and ENGASE is associated with reduced mismatch negativity (MMN) • Genes regulating neurotransmitter levels increase in MMN transcriptome-wide association • Genes influencing MMN are underexpressed in adult brain cortex • MMN ranks above verbal memory and ventricular volume as psychosis endophenotype, Bhat et al. identify two genes, FAM89A and ENGASE, whose expression in cortical tissue is negatively associated with mismatch negativity (MMN), an electrophysiological measure of cortical responses to unexpected sensory stimuli. They find enrichment of neurotransmission-regulating genes in these associations and endorse MMN as an endophenotype for psychosis.

Choosing actions that result in advantageous outcomes is a fundamental function of nervous systems. All computational decision-making models contain a mechanism that controls the variability of (or confidence in) action selection, but its neural implementation is unclear—especially in humans. We investigated this mechanism using two influential decision-making frameworks: active inference (AI) and reinforcement learning (RL). In AI, the precision (inverse variance) of beliefs about policies controls action selection variability—similar to decision ‘noise’ parameters in RL—and is thought to be encoded by striatal dopamine signaling. We tested this hypothesis by administering a ‘go/no-go’ task to 75 healthy participants, and measuring striatal dopamine 2/3 receptor (D2/3R) availability in a subset (n = 25) using [11C]-(+)-PHNO positron emission tomography. In behavioral model comparison, RL performed best across the whole group but AI performed best in participants performing above chance levels. Limbic striatal D2/3R availability had linear relationships with AI policy precision (P = 0.029) as well as with RL irreducible decision ‘noise’ (P = 0.020), and this relationship with D2/3R availability was confirmed with a ‘decision stochasticity’ factor that aggregated across both models (P = 0.0006). These findings are consistent with occupancy of inhibitory striatal D2/3Rs decreasing the variability of action selection in humans., Cerebral Cortex, 30 (6), ISSN:1047-3211, ISSN:1460-2199

Probabilistic models of cognition typically assume that agents make inferences about current states by combining new sensory information with fixed beliefs about the past, an approach known as Bayesian filtering. This is computationally parsimonious, but, in general, leads to suboptimal beliefs about past states, since it ignores the fact that new observations typically contain information about the past as well as the present. This is disadvantageous both because knowledge of past states may be intrinsically valuable, and because it impairs learning about fixed or slowly changing parameters of the environment. For these reasons, in offline data analysis it is usual to infer on every set of states using the entire time series of observations, an approach known as (fixed-interval) Bayesian smoothing. Unfortunately, however, this is impractical for real agents, since it requires the maintenance and updating of beliefs about an ever-growing set of states. We propose an intermediate approach, finite retrospective inference (FRI), in which agents perform update beliefs about a limited number of past states. (Formally, this represents online fixed-lag smoothing with a sliding window.) This can be seen as a form of bounded rationality in which agents seek to optimise the accuracy of their beliefs subject to computational and other resource costs. We show through simulation that this approach has the capacity to significantly increase the accuracy of both inference and learning, using a simple variational scheme applied to both randomly generated Hidden Markov models (HMMs), and a specific application of the HMM, in the form of the widely used probabilistic reversal task. Our proposal thus constitutes a theoretical contribution to normative accounts of bounded rationality, which makes testable empirical predictions that can be explored in future work.

In this article, we develop a computational model of obsessive-compulsive disorder (OCD). We propose that OCD is characterized by a difficulty in relying on past events to predict the consequences of patients' own actions and the unfolding of possible events. Clinically, this corresponds both to patients' difficulty in trusting their own actions (and therefore repeating them), and to their common preoccupation with unlikely chains of events. Critically, we develop this idea on the basis of the well-developed framework of the Bayesian brain, where this impairment is formalized as excessive uncertainty regarding state transitions. We illustrate the validity of this idea using quantitative simulations and use these to form specific empirical predictions. These predictions are evaluated in relation to existing evidence, and are used to delineate directions for future research. We show how seemingly unrelated findings and phenomena in OCD can be explained by the model, including a persistent experience that actions were not adequately performed and a tendency to repeat actions; excessive information gathering (i.e., checking); indecisiveness and pathological doubt; overreliance on habits at the expense of goal-directed behavior; and overresponsiveness to sensory stimuli, thoughts, and feedback. We discuss the relationship and interaction between our model and other prominent models of OCD, including models focusing on harm-avoidance, not-just-right experiences, or impairments in goal-directed behavior. Finally, we outline potential clinical implications and suggest lines for future research. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Mismatch negativity (MMN) is a differential electrophysiological response measuring cortical adaptability to unpredictable stimuli. MMN is consistently attenuated in patients with psychosis. However, the genetics of MMN are uncharted, limiting the validation of MMN as a psychosis endophenotype. We therefore performed a transcriptome-wide association study of 728 individuals, which revealed two genes (FAM89A and ENGASE) whose expression in cortical tissues is associated with MMN. Enrichment analyses of neurodevelopmental expression signatures showed that genes associated with MMN tend to be overexpressed in frontal cortex during prenatal development but significantly downregulated in adulthood. In our Endophenotype Ranking Value calculation comparing MMN and three other candidate psychosis endophenotypes (lateral ventricular volume and two auditory-verbal learning measures), MMN was considerably superior. These results yield promising insights into sensory processing in the cortex and endorse the notion of MMN as a psychosis endophenotype.

The tricolored bat (Perimyotis subflavus) is widely distributed throughout eastern North America. However, this species has also undergone severe population declines in areas where white-nose syndrome has taken hold. Previous records in Colorado showed what appeared to be vagrant individuals with no evidence of established populations. Herein we provide new records for Boulder and Weld Counties, Colorado. We also provide evidence of reproduction supporting the hypothesis of westward expansion of this species. Because the tricolored bat has been significantly impacted by white-nose syndrome in eastern North America, the Rocky Mountain West may provide at least a temporary refugium from this disease.

In the Rocky Mountain region of North America, climate change is occurring at an accelerated rate and its effects are already changing population and community organization. Although some data have been gathered on changes in latitudinal migration patterns of animals, almost nothing is known about altitudinal migrations and climate change. Myotis bats of the Rocky Mountains provide a good model for understanding these patterns because they undergo significant seasonal altitudinal movements between high- and low-elevation. In order to test if climate change is affecting spring down-slope migrations of myotis species, I compared May and June capture data gathered over the last 21 years from 31 survey sites located between 1650 m a.s.l. and 2250 m a.s.l. on the eastern slopes of the Rocky Mountains, Colorado. Although three of the Myotis species maintained stable or slightly increasing capture numbers, indicating typical down-slope spring migrations, two species showed significant declines in spring captures at lower elevations. When climate data for May and June were tested against capture data, the apparent causative factors for these declines differed between species. Declines in the capture of fringed myotis did not closely track annual patterns of mean June temperatures across years. Instead a steady decline occurred regardless of yearly variation in temperatures. However, declining captures of long-legged myotis showed clear synchrony with variations in mean June temperatures wherein more captures occurred in years having cooler temperatures, and notably fewer captures in years having warmer June temperatures. In addition, zero captures of long-legged myotis occurred in five of the last six years when mean June temperatures have shown an average increase in 1.8°C over previous years. These data indicate species-specific responses to climate warming for bats in the Colorado Rocky Mountains and that loss of spring down-slope migrations from hibernacula may be how some species mitigate climate warming effects.

The “dysconnection hypothesis” of psychosis suggests that a disruption of functional integration underlies cognitive deficits and clinical symptoms. Impairments in the P300 potential are well documented in psychosis. Intrinsic (self-)connectivity in a frontoparietal cortical hierarchy during a P300 experiment was investigated. Dynamic Causal Modeling was used to estimate how evoked activity results from the dynamics of coupled neural populations and how neural coupling changes with the experimental factors. Twenty-four patients with psychotic disorder, twenty-four unaffected relatives, and twenty-five controls underwent EEG recordings during an auditory oddball paradigm. Sixteen frontoparietal network models (including primary auditory, superior parietal, and superior frontal sources) were analyzed and an optimal model of neural coupling, explaining diagnosis and genetic risk effects, as well as their interactions with task condition were identified. The winning model included changes in connectivity at all three hierarchical levels. Patients showed decreased self-inhibition—that is, increased cortical excitability—in left superior frontal gyrus across task conditions, compared with unaffected participants. Relatives had similar increases in excitability in left superior frontal and right superior parietal sources, and a reversal of the normal synaptic gain changes in response to targets relative to standard tones. It was confirmed that both subjects with psychotic disorder and their relatives show a context-independent loss of synaptic gain control at the highest hierarchy levels. The relatives also showed abnormal gain modulation responses to task-relevant stimuli. These may be caused by NMDA-receptor and/or GABAergic pathologies that change the excitability of superficial pyramidal cells and may be a potential biological marker for psychosis.

Communication between the frontal and medial temporal lobes is impaired in schizophrenia. Adams et al. find that when retrieving memories, subjects with schizophrenia have lower theta phase oscillatory coupling between these areas, and lower α-GABAA receptor availability in the hippocampal area where coupling is lost., Frontotemporal dysconnectivity is a key pathology in schizophrenia. The specific nature of this dysconnectivity is unknown, but animal models imply dysfunctional theta phase coupling between hippocampus and medial prefrontal cortex (mPFC). We tested this hypothesis by examining neural dynamics in 18 participants with a schizophrenia diagnosis, both medicated and unmedicated; and 26 age, sex and IQ matched control subjects. All participants completed two tasks known to elicit hippocampal-prefrontal theta coupling: a spatial memory task (during magnetoencephalography) and a memory integration task. In addition, an overlapping group of 33 schizophrenia and 29 control subjects underwent PET to measure the availability of GABAARs expressing the α5 subunit (concentrated on hippocampal somatostatin interneurons). We demonstrate—in the spatial memory task, during memory recall—that theta power increases in left medial temporal lobe (mTL) are impaired in schizophrenia, as is theta phase coupling between mPFC and mTL. Importantly, the latter cannot be explained by theta power changes, head movement, antipsychotics, cannabis use, or IQ, and is not found in other frequency bands. Moreover, mPFC-mTL theta coupling correlated strongly with performance in controls, but not in subjects with schizophrenia, who were mildly impaired at the spatial memory task and no better than chance on the memory integration task. Finally, mTL regions showing reduced phase coupling in schizophrenia magnetoencephalography participants overlapped substantially with areas of diminished α5-GABAAR availability in the wider schizophrenia PET sample. These results indicate that mPFC-mTL dysconnectivity in schizophrenia is due to a loss of theta phase coupling, and imply α5-GABAARs (and the cells that express them) have a role in this process.

The disrupted‐in‐schizophrenia 1 (DISC1) protein has been implicated in a range of biological mechanisms underlying chronic mental disorders such as schizophrenia. Schizophrenia is associated with abnormal striatal dopamine signalling, and all antipsychotic drugs block striatal dopamine 2/3 receptors (D_{2/3}Rs). Importantly, the DISC1 protein directly interacts and forms a protein complex with the dopamine D_{2} receptor (D_{2}R) that inhibits agonist‐induced D_{2}R internalisation. Moreover, animal studies have found large striatal increases in the proportion of D_{2}R receptors in a high affinity state (D_{2}^{high}R in DISC1 rodent models. Here, we investigated the relationship between the three most common polymorphisms altering the amino‐acid sequence of the DISC1 protein (Ser704Cys (rs821616), Leu607Phe (rs6675281) and Arg264Gln (rs3738401)) and striatal D_{2/3}R availability in 41 healthy human volunteers, using [{11}^C] ‐(+)‐PHNO positron emission tomography. We found no association between DISC1 polymorphisms and D_{2/3}R availability in the striatum and D_{2}R availability in the caudate and putamen. Therefore, despite a direct interaction between DISC1 and the D_{2}R, none of its main functional polymorphisms impact striatal D_{2/3}R binding potential, suggesting DISC1 variants act through other mechanisms.

The mechanisms of visual hallucinations in Lewy body disease are unknown. Zarkali et al. use two-tone stimuli to show that Lewy body hallucinations are associated with altered integration of top-down predictions with sensory evidence. Specifically, results show an increased relative weighting of prior knowledge in Lewy body hallucinations providing important mechanistic insights., Hallucinations are a common and distressing feature of many psychiatric and neurodegenerative conditions. In Lewy body disease, visual hallucinations are a defining feature, associated with worse outcomes; yet their mechanisms remain unclear and treatment options are limited. Here, we show that hallucinations in Lewy body disease are associated with altered integration of top-down predictions with incoming sensory evidence, specifically with an increased relative weighting of prior knowledge. We tested 37 individuals with Lewy body disease, 17 habitual hallucinators and 20 without hallucinations, and 20 age-matched healthy individuals. We employed an image-based learning paradigm to test whether people with Lewy body disease and visual hallucinations show higher dependence on prior knowledge. We used two-tone images that are difficult to disambiguate without any prior information but generate a strong percept when information is provided. We measured discrimination sensitivity before and after this information was provided. We observed that in people with Lewy body disease who experience hallucinations, there was greater improvement in discrimination sensitivity after information was provided, compared to non-hallucinators and controls. This suggests that people with Lewy body disease and hallucinations place higher relative weighting on prior knowledge than those who do not hallucinate. Importantly, increased severity of visual hallucinations was associated with an increased effect of prior knowledge. Together these findings suggest that visual hallucinations in Lewy body disease are linked to a shift towards top-down influences on perception and away from sensory evidence, perhaps due to an increase in sensory noise. This provides important mechanistic insights to how hallucinations develop in Lewy body disease, with potential for revealing new therapeutic targets., Graphical Abstract Graphical Abstract

The Disrupted-in-Schizophrenia 1 (DISC1) protein has been implicated in a range of biological mechanisms underlying chronic mental disorders such as schizophrenia. Schizophrenia is associated with abnormal striatal dopamine signalling, and all antipsychotic drugs block striatal dopamine 2/3 receptors (D(2/3)Rs). Importantly, the DISC1 protein directly interacts and forms a protein complex with the dopamine D(2) receptor (D(2)R) that inhibits agonist-induced D(2)R internalization. Moreover, animal studies have found large striatal increases in the proportion of D(2)R receptors in a high affinity state (D(2)(high)R) in DISC1 rodent models. Here, we investigated the relationship between the three most common polymorphisms altering the amino-acid sequence of the DISC1 protein (Ser704Cys (rs821616), Leu607Phe (rs6675281) and Arg264Gln (rs3738401)) and striatal D(2/3)R availability in 41 healthy human volunteers, using [(11)C]-(+)-PHNO positron emission tomography. We found no association between DISC1 polymorphisms and D(2/3)R availability in the striatum and D(2)R availability in the caudate and putamen. Therefore, despite a direct interaction between DISC1 and the D(2)R, none of its main functional polymorphisms impact striatal D(2/3)R binding potential, suggesting DISC1 variants act through other mechanisms.

Hallucinations, including auditory verbal hallucinations (AVH), occur in both the healthy population and in psychotic conditions such as schizophrenia (often developing after a prodromal period). In addition, hallucinations can be in-context (they can be consistent with the environment, such as when one hallucinates the end of a sentence that has been repeated many times), or out-of-context (such as the bizarre hallucinations associated with schizophrenia). In previous work, we introduced a model of hallucinations as false (positive) inferences based on a (Markov decision process) formulation of active inference. In this work, we extend this model to include content–to disclose the computational mechanisms behind in- and out-of-context hallucinations. In active inference, sensory information is used to disambiguate alternative hypotheses about the causes of sensations. Sensory information is balanced against prior beliefs, and when this balance is tipped in the favor of prior beliefs, hallucinations can occur. We show that in-context hallucinations arise when (simulated) subjects cannot use sensory information to correct prior beliefs about hearing a voice, but beliefs about content (i.e. the sequential order of a sentence) remain accurate. When hallucinating subjects also have inaccurate beliefs about state transitions, out-of-context hallucinations occur; i.e. their hallucinated speech content is disordered. Note that out-of-context hallucinations in this setting does not refer to inference about context, but rather to false perceptual inference that emerges when the confidence in–or precision of–sensory evidence is reduced. Furthermore, subjects with inaccurate beliefs about state transitions but an intact ability to use sensory information do not hallucinate and are reminiscent of prodromal patients. This work demonstrates the different computational mechanisms that may underlie the spectrum of hallucinatory experience–from the healthy population to psychotic states.

Working memory performance is thought to depend on both striatal dopamine 2/3 receptors (D2/3Rs) and task-induced functional organisation in key cortical brain networks. Here, we combine functional magnetic resonance imaging and D2/3R positron emission tomography in 51 healthy volunteers, to investigate the relationship between working memory performance, task-induced default mode network (DMN) functional connectivity changes, and striatal D2/3R availability. Increasing working memory load was associated with reduced DMN functional connectivity, which was itself associated with poorer task performance. Crucially, the magnitude of the DMN connectivity reduction correlated with striatal D2/3R availability, particularly in the caudate, and this relationship mediated the relationship between striatal D2/3R availability and task performance. These results inform our understanding of natural variation in working memory performance, and have implications for understanding age-related cognitive decline and cognitive impairments in neuropsychiatric disorders where dopamine signalling is altered.