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Contains fulltext : 291574.pdf (Publisher’s version ) (Open Access) Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

Prediction models of brain texture changes in recent-onset psychosis (ROP) and recent-onset depression (ROD) have lately been proposed. The validation of these models transdiagnostically at the individual level and the investigation of the variability in clinical profiles are still missing. Established prevention and treatment approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of patients. We aimed to investigate the utility of brain texture changes for a) identification of the psychopathological state (ROP and ROD) and b) the association of individualized brain texture maps with clinical symptom severity and outcome profiles. We developed transdiagnostic models based on structural MRI data on 116 patients with ROD, 122 patients with ROP, and 197 healthy controls (HC) from the Personalised pROgNostic tools for early psychosIs mAnagement (PRONIA) study by applying explainable artificial intelligence and clustering analysis. We investigated the contrast texture feature as the key feature for the identification of a general psychopathological state. The discrimination power of the trained prediction model was > 72% and validated in a second independent age and sex-matched sample of 137 ROP, 94 ROD, and 159 HC. Clustering analysis was implemented to map the texture brain changes produced from an explainable artificial intelligence algorithm, in a group fashion. The explained individualized brain contrast map grouped into 8 homogeneous clusters. In each group, we investigated the association between the explained brain contrast texture map and clinical symptom severity as well as outcome profiles. Different patterns in the explained brain contrast texture map showed unique associations of brain alterations with clinical symptom severity and clinical outcomes, i.e., age, positive, negative and depressive symptoms, and functionality. In some clusters, the mean explained brain contrast texture map values and/or brain contrast texture voxels significantly contribute to the classification decision significantly predicted PANSS scores, functionality and change in functionality over time. In conclusion, we created homogeneous clusters which statistically significant predict the clinical severity and outcome profile.

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.

Circadian rhythms are imprinted in all organisms and influence virtually all aspects of physiology and behaviour in adaptation to the 24-hour day-night cycle. This recognition of a circadian timekeeping system permeating essentially all healthy functioning of body and mind quickly leads to the realisation that, in turn, human ailments should be probed for the degree to which they are rooted in or marked by disruptions and dysregulations of circadian clock functions in the human body. In this review, we will focus on psychosis as a key mental illness and foremost one of its cardinal symptoms: auditory hallucinations. We will discuss recent empirical evidence and conceptual advances probing the potential role of circadian disruption in auditory hallucinations. Moreover, a dysbalance in excitation and inhibition within cortical networks, which in turn drive a disinhibition of dopaminergic signalling will be highlighted as central physiological mechanism. Finally, we will propose two avenues for experimentally intervening on the circadian influences to potentially alleviate hallucinations in psychotic disorders.

Previous research showed that dysfunctions of fronto-striatal neural networks are implicated in the pathophysiology of obsessive–compulsive disorder (OCD). Accordingly, patients with OCD showed altered performances during decision-making tasks. As P300, evoked by oddball paradigms, is suggested to be related to attentional and cognitive processes and generated in the medial temporal lobe and orbitofrontal and cingulate cortices, it is of special interest in OCD research. Therefore, this study aimed to investigate P300 in OCD and its associations with brain activity during decision-making: P300, evoked by an auditory oddball paradigm, was analysed in 19 OCD patients and 19 healthy controls regarding peak latency, amplitude and source density power in parietal cortex areas by sLORETA. Afterwards, using a fMRI paradigm, Blood–oxygen-level-dependent (BOLD) contrast imaging was conducted during a delay-discounting paradigm. We hypothesised differences between groups regarding P300 characteristics and associations with frontal activity during delay-discounting. The P300 did not differ between groups, however, the P300 latency over the P4 electrode correlated negatively with the NEO-FFI score openness to experience in patients with OCD. In healthy controls, P300 source density power correlated with activity in frontal regions when processing rewards, a finding which was absent in OCD patients. To conclude, associations of P300 with frontal brain activation during delay-discounting were found, suggesting a contribution of attentional or context updating processes. Since this association was absent in patients with OCD, the findings could be interpreted as being indeed related to dysfunctions of fronto-striatal neural networks in patients with OCD.

Negative symptoms occur frequently in individuals at clinical high risk (CHR) for psychosis and contribute to functional impairments. The aim of this study was to predict negative symptom severity in CHR after 9 months. Predictive models either included baseline negative symptoms measured with the Structured Interview for Psychosis-Risk Syndromes (SIPS-N), whole-brain gyrification, or both to forecast negative symptoms of at least moderate severity in 94 CHR. We also conducted sequential risk stratification to stratify CHR into different risk groups based on the SIPS-N and gyrification model. Additionally, we assessed the models’ ability to predict functional outcomes in CHR and their transdiagnostic generalizability to predict negative symptoms in 96 patients with recent-onset psychosis (ROP) and 97 patients with recent-onset depression (ROD). Baseline SIPS-N and gyrification predicted moderate/severe negative symptoms with significant balanced accuracies of 68 and 62%, while the combined model achieved 73% accuracy. Sequential risk stratification stratified CHR into a high (83%), medium (40–64%), and low (19%) risk group regarding their risk of having moderate/severe negative symptoms at 9 months follow-up. The baseline SIPS-N model was also able to predict social (61%), but not role functioning (59%) at above-chance accuracies, whereas the gyrification model achieved significant accuracies in predicting both social (76%) and role (74%) functioning in CHR. Finally, only the baseline SIPS-N model showed transdiagnostic generalization to ROP (63%). This study delivers a multimodal prognostic model to identify those CHR with a clinically relevant negative symptom severity and functional impairments, potentially requiring further therapeutic consideration.

Background Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research.

Structural MRI studies in first-episode psychosis and the clinical high-risk state have consistently shown volumetric abnormalities. Aim of the present study was to introduce radiomics texture features in identification of psychosis. Radiomics texture features describe the interrelationship between voxel intensities across multiple spatial scales capturing the hidden information of underlying disease dynamics in addition to volumetric changes. Structural MR images were acquired from 77 first-episode psychosis (FEP) patients, 58 clinical high-risk patients with no later transition to psychosis (CHR_NT), 15 clinical high-risk patients with later transition (CHR_T) and 44 healthy controls (HC). Radiomics texture features were extracted from non-segmented images, and two classification schemas were performed for the identification of FEP vs. HC and FEP vs. CHR_NT. The group of CHR_T was used as external validation in both schemas. The classification of a subject’s clinical status was predicted by importing separately a) the difference of entropy feature map and b) the contrast feature map, resulting in classification accuracy above 72% in both analyses. The proposed framework enhances the classification decision for FEP, CHR_NT and HC subjects, verifies diagnosis-relevant features and may potentially contribute to identification of structural biomarkers for psychosis, beyond and above volumetric brain changes.

The introduction of clinical criteria for the operationalization of psychosis high risk provided a basis for early detection and treatment of vulnerable individuals. However, about two-thirds of people meeting clinical high-risk (CHR) criteria will never develop a psychotic disorder. In the effort to increase prognostic precision, structural and functional neuroimaging have received growing attention as a potentially useful resource in the prediction of psychotic transition in CHR patients. The present review summarizes current research on neuroimaging biomarkers in the CHR state, with a particular focus on their prognostic utility and limitations. Large, multimodal/multicenter studies are warranted to address issues important for clinical applicability such as generalizability and replicability, standardization of clinical definitions and neuroimaging methods, and consideration of contextual factors (e.g., age, comorbidity).

BackgroundPositive symptoms are a useful predictor of aggression in schizophrenia. Although a similar pattern of abnormal brain structures related to both positive symptoms and aggression has been reported, this observation has not yet been confirmed in a single sample.MethodTo study the association between positive symptoms and aggression in schizophrenia on a neurobiological level, a prospective meta-analytic approach was employed to analyze harmonized structural neuroimaging data from 10 research centers worldwide. We analyzed brain MRI scans from 902 individuals with a primary diagnosis of schizophrenia and 952 healthy controls.ResultsThe result identified a widespread cortical thickness reduction in schizophrenia compared to their controls. Two separate meta-regression analyses revealed that a common pattern of reduced cortical gray matter thickness within the left lateral temporal lobe and right midcingulate cortex was significantly associated with both positive symptoms and aggression.ConclusionThese findings suggested that positive symptoms such as formal thought disorder and auditory misperception, combined with cognitive impairments reflecting difficulties in deploying an adaptive control toward perceived threats, could escalate the likelihood of aggression in schizophrenia.

Patients with psychotic disorders present alterations in thalamocortical intrinsic functional connectivity as measured by resting-state functional magnetic resonance imaging. Specifically, thalamic intrinsic functional connectivity is increased with sensorimotor cortices (hyperconnectivity) and decreased with prefrontal limbic cortices (hypoconnectivity). Psychedelics such as lysergic acid diethlyamide (LSD) elicit similar thalamocortical hyperconnectivity with sensorimotor areas in healthy volunteers. It is unclear whether LSD also induces thalamocortical hypoconnectivity with prefrontal limbic cortices, because current findings are equivocal. Thalamocortical hyperconnectivity was associated with psychotic symptoms in patients and substance-induced altered states of consciousness in healthy volunteers. Thalamocortical dysconnectivity is likely evoked by altered neurotransmission, e.g., via dopaminergic excess in psychotic disorders and serotonergic agonism in psychedelic-induced states. It is unclear whether thalamocortical dysconnectivity is also elicited by amphetamine-type substances, broadly releasing monoamines (i.e., dopamine, norepinephrine) but producing fewer perceptual effects than psychedelics.We administrated LSD, d-amphetamine, and 3,4-methylenedioxymethamphetamine (MDMA) in 28 healthy volunteers and investigated their effects on thalamic intrinsic functional connectivity with 2 brain networks (auditory-sensorimotor and salience networks, corresponding to sensorimotor and prefrontal limbic cortices, respectively), using a double-blind, placebo-controlled, crossover design.All active substances elicited auditory-sensorimotor-thalamic hyperconnectivity compared with placebo, despite predominantly distinct pharmacological actions and subjective effects. LSD-induced effects correlated with subjective changes in perception, indicating a link between hyperconnectivity and psychedelic-type perceptual alterations. Unlike d-amphetamine and MDMA, which induced hypoconnectivity with the salience network, LSD elicited hyperconnectivity. D-amphetamine and MDMA evoked similar thalamocortical dysconnectivity patterns.Psychedelics, empathogens, and psychostimulants evoke thalamocortical hyperconnectivity with sensorimotor areas, akin to findings in patients with psychotic disorders.

BACKGROUND Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity and provide better candidates for predictive modeling. We aimed to identify clusters across patients with recent-onset depression (ROD) and recent-onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures. METHODS HYDRA (Heterogeneity through Discriminant Analysis) was trained on whole-brain volumetric measures from 577 participants from the discovery sample of the multisite PRONIA study to identify neurobiologically driven clusters, which were then externally validated in the PRONIA replication sample (n = 404) and three datasets of chronic samples (Centre for Biomedical Research Excellence, n = 146; Mind Clinical Imaging Consortium, n = 202; Munich, n = 470). RESULTS The optimal clustering solution was two transdiagnostic clusters (cluster 1: n = 153, 67 ROP, 86 ROD; cluster 2: n = 149, 88 ROP, 61 ROD; adjusted Rand index = 0.618). The two clusters contained both patients with ROP and patients with ROD. One cluster had widespread gray matter volume deficits and more positive, negative, and functional deficits (impaired cluster), and one cluster revealed a more preserved neuroanatomical signature and more core depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission, outperforming traditional diagnostic structures. CONCLUSIONS We identified two transdiagnostic neuroanatomically informed clusters that are clinically and biologically distinct, challenging current diagnostic boundaries in recent-onset mental health disorders. These results may aid understanding of the etiology of poor outcome patients transdiagnostically and improve development of stratified treatments.

Cancer is a complex disease rooted in heterogeneity, which is the phenomenon of individual cells, tissues, or patients having distinct phenotypic and/or genetic characteristics. Observed divergent disease etiology is likely rooted, at least in part, in tumor heterogeneity and the classification of distinct and important subpopulations of cells within the tumor and its associated microenvironment has remained a technical challenge. Standard next-generation sequencing of bulk tumor tissue provides an overall average genetic profile of the sample, and masks contributions from individual cells and minor populations of cells, particularly in heterogeneous samples. Only with the advent of single-cell analysis and sequencing technologies has it become possible to characterize key contributions of cellular subpopulations in order to more comprehensively characterize disease. This chapter describes a method to generate linked phenotypic and genotypic data at single-cell resolution using a real-time single-cell resolved platform. Specifically, the example method provided here is used to link cellular growth kinetics and expression of a prognostic marker protein, CA-125, in cells derived from ovarian cancer patients with their single-cell genomic profiles, but the method is translatable to other cell types and phenotypes of interest.

Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis in the identification of people with psychosis. Structural MRI were acquired from 73 CHR, 77 FEP and 44 healthy controls (HC). Chaos analysis of the grey matter distribution was performed: first, the distances of each voxel from the center of mass in the grey matter image was calculated. Next, the distances multiplied by the voxel intensity was represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts how the grey matter topology changes. The classification of a subject’s clinical status was finally predicted by a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and b) matching the lambda series with the Morlet wavelet, which resulted in 100% accuracy in distinguishing between FEP and CHR. The proposed framework using spatial-series extraction enhances the classification decision for FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.

Psychiatry has a well-established tradition of comparing drug-induced experiences to psychotic symptoms, based on shared phenomena such as altered perceptions. The present review focuses on experiences induced by classic psychedelics, which are substances capable of eliciting powerful psychoactive effects, characterized by distortions/alterations of several neurocognitive processes (e.g., hallucinations). Herein we refer to such experiences as psychedelic states. Psychosis is a clinical syndrome defined by impaired reality testing, also characterized by impaired neurocognitive processes (e.g., hallucinations and delusions). In this review we refer to acute phases of psychotic disorders as psychotic states. Neuropharmacological investigations have begun to characterize the neurobiological mechanisms underpinning the shared and distinct neurophysiological changes observed in psychedelic and psychotic states. Mounting evidence indicates changes in thalamic filtering, along with disturbances in cortico-striato-pallido-thalamo-cortical (CSPTC)-circuitry, in both altered states. Notably, alterations in thalamocortical functional connectivity were reported by functional magnetic resonance imaging (fMRI) studies. Thalamocortical dysconnectivity and its clinical relevance are well-characterized in psychotic states, particularly in schizophrenia research. Specifically, studies report hyperconnectivity between the thalamus and sensorimotor cortices and hypoconnectivity between the thalamus and prefrontal cortices, associated with patients' psychotic symptoms and cognitive disturbances, respectively. Intriguingly, studies also report hyperconnectivity between the thalamus and sensorimotor cortices in psychedelic states, correlating with altered visual and auditory perceptions. Taken together, the two altered states appear to share clinically and functionally relevant dysconnectivity patterns. In this review we discuss recent findings of thalamocortical dysconnectivity, its putative extension to CSPTC circuitry, along with its clinical implications and future directions.

Approximately 2% of adolescents and young adults display symptoms indicating a high risk for psychotic disorders. Apart from a risk of 20-35% of developing a psychotic disorder, these individuals show high rates of persisting mental health problems and functional impairment, even in the absence of a psychotic transition. Treatment in specialized centers can improve outcomes in these patients, but the need to provide timely access to care needs to be balanced against the risks of premature psychiatrization, stigmatization and unnecessary medication treatment. The transcantonal project PsyYoung aims to optimize early detection in young people, while at the same time minimizing unnecessary psychiatrization. This will be achieved through improved networking across the entire care chain and a stepped-care intervention approach.Près de 2 % des adolescents et jeunes adultes présentent des symptômes indiquant un risque élevé de développer une psychose. Outre ce risque se situant entre 20 et 35 %, ces individus présenteront des taux élevés d’autres troubles psychiques et déficits fonctionnels, même en l’absence de transition vers la psychose. Le traitement dans des centres spécialisés peut améliorer l’évolution de ces patients mais les besoins de fournir un accès rapide aux soins doivent être mis en perspective des risques de psychiatrisation prématurée, stigmatisation, et médication inutile. Le projet pluri-cantonal PsyYoung vise à optimiser la détection précoce pour les jeunes, tout en minimisant la psychiatrisation inutile. Ceci sera atteint en améliorant le réseautage de l’ensemble de la chaîne de soins et la mise en œuvre d’un modèle de soins par étapes.

Background Research on patients at clinical high risk (CHR) for psychosis has so far mainly focused on those with transition to frank psychosis (CHR-T patients). However, the majority of CHR patients do not transition (CHR-NT patients) and relatively little information is available on their clinical and functional outcome. Methods We conducted a systematic review on clinical and functional long-term outcome of CHR-NT patients. Studies were included if they had an average follow-up period of at least 24 months and reported on long-term outcome of CHR-NT patients in one or more of the following domains: (non-)remission from CHR, prevalence of clinical symptoms and/or clinical diagnoses (axis I and II), and psychosocial functioning. Results Ten publications from seven different single or multicenter studies with average follow-up durations of 2–7.5 years could be included. At the last follow-up assessment 28–71% of CHR-NT patients were not remitted from their CHR and 22–82% still had at least one clinical diagnosis. Approximately half of CHR-NT patients presented with poor psychosocial outcome at 2-year and 6-year follow-up. Conclusions The results suggest that, in the long-term, the majority of CHR-NT patients are not in full clinical remission and seem to suffer from one or more clinical disorders and psychosocial impairments. Since relatively few studies could be identified, further research is required to better understand the trajectories and clinical needs of CHR-NT patients.

SUMMARYEarly detection and specialised early intervention for people at high risk for psychotic disorders have received growing attention in the past few decades, with the aim of delaying or preventing the outbreak of explicit psychotic symptoms and improving functional outcomes. This article summarises criteria for a diagnosis of high psychosis risk, the implications for such a diagnosis and recommendations for treatment.LEARNING OBJECTIVESAfter reading this article you will be able to: •recognise signs and symptoms indicating increased psychosis risk•understand uses and limitations of screening for high psychosis risk, and interpretation of results•recognise evidence-based treatment options for patients at clinical high risk for psychosis.DECLARATION OF INTERESTC.A. has received non-financial support from Sunovion and Lundbeck in the past 36 months.

Abnormal gamma-band oscillations (GBO) have been frequently associated with the pathophysiology of schizophrenia. GBO are modulated by glutamate, a neurotransmitter, which is continuously discussed to shape the complex symptom spectrum in schizophrenia. The current study examined the effects of ketamine, a glutamate N-methyl-d-aspartate receptor (NMDAR) antagonist, on the auditory-evoked gamma-band response (aeGBR) and psychopathological outcomes in healthy volunteers to investigate neuronal mechanisms of psychotic behavior. In a placebo-controlled, randomized crossover design, the aeGBR power, phase-locking factor (PLF) during a choice reaction task, the Positive and Negative Syndrome Scale (PANSS) and the Altered State of Consciousness (5D-ASC) Rating Scale were assessed in 25 healthy subjects. Ketamine was applied in a subanaesthetic dose. Low-resolution brain electromagnetic tomography was used for EEG source localization. Significant reductions of the aeGBR power and PLF were identified under ketamine administration compared to placebo (p

HIV-1 causes a chronic infection that affects more than 37 million people worldwide. People living with human immunodeficiency virus (HIV) experience comorbidity related to chronic inflammation despite antiretroviral therapy. However, these inflammatory signaling has not been fully characterized. The role of early entry events on the activation of cellular signaling events and downstream gene expression has not been captured at the single-cell level. Here the authors describe a method that applies principles of live-cell fluorescence microscopy to an automated single-cell platform that cultures and images cells over user-customized time courses, allowing for high-throughput analysis of dynamic cellular processes. This assay can track single-cell live fluorescence microscopy of early events that immediately follow HIV-1 infection, notably the influx of calcium that accompanies exposure to the virus and the development of productive infection using a fluorescent reporter virus. MT-4 cells are loaded with a calcium-sensitive dye and cultured in isolated pens on a nanofluidic device. The cultured cells are infected with an HIV-1 reporter virus (HIV-1 NLCI). A fluorescence microscope positioned above the nanofluidic device measures calcium influx over an 8-min time course following acute HIV-1 exposure. HIV-1 productive infection is measured in those same cells over a 4-day interval. Imaging data from these time courses are analyzed to define virus-host receptor interactions and signaling pathway dynamics. The authors present an integrated, scalable alternative to traditional imaging methods using a novel optofluidic platform capable of single-cell sorting, culturing, imaging, and software automation. This assay can measure the kinetics of events under various conditions, including cell type, agonist, or antagonist effect, while measuring an array of parameters. This is the first established method for nanofluidic high-throughput longitudinal single-cell culture and imaging: This technique can be broadly adapted to study cellular signaling kinetics and dynamic molecular interactions.

This prognostic study evaluates whether psychosis transition can be predicted in patients with clinical high-risk syndromes or recent-onset depression by multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging, and polygenic risk scores for schizophrenia., Key Points Question Can a transition to psychosis be predicted in patients with clinical high-risk states or recent-onset depression by optimally integrating clinical, neurocognitive, neuroimaging, and genetic information with clinicians’ prognostic estimates? Findings In this prognostic study of 334 patients and 334 control individuals, machine learning models sequentially combining clinical and biological data with clinicians’ estimates correctly predicted disease transitions in 85.9% of cases across geographically distinct patient populations. The clinicians’ lack of prognostic sensitivity, as measured by a false-negative rate of 38.5%, was reduced to 15.4% by the sequential prognostic model. Meaning These findings suggest that an individualized prognostic workflow integrating artificial and human intelligence may facilitate the personalized prevention of psychosis in young patients with clinical high-risk syndromes or recent-onset depression., Importance Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models’ geographic generalizability; to test and integrate clinicians’ predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures Accuracy and generalizability of prognostic systems. Results A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and Relevance These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms’ and clinicians’ risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.

Schizophrenia has a prodromal phase of several years in most patients, making it possible to identify patients at clinical high risk (CHR) for developing the disorder. So far, these individuals are identified based on clinical criteria alone, and there is no reliable biomarker for predicting the transition to psychosis. It is well-established that reductions in brain volume, especially in the hippocampus, are associated with schizophrenia. Therefore, hippocampal volume may serve as a biomarker for psychosis. Several studies have already investigated hippocampal volume in CHR groups. Based on these studies, the present meta-analysis compares the baseline left and right hippocampal volume of CHR patients who developed a psychosis with that of CHR patients without such a transition. Our results show no statistically significant effect of the hippocampal volume on the transition risk for psychosis.