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Background and Hypothesis The emergence of psychosis in ultra-high-risk subjects (UHR) is influenced by gene-environment interactions that rely on epigenetic mechanisms such as microRNAs. However, whether they can be relevant pathophysiological biomarkers of psychosis' onset remains unknown. Study Design We present a longitudinal study of microRNA expression, measured in plasma by high-throughput sequencing at baseline and follow-up, in a prospective cohort of 81 UHR, 35 of whom developed psychosis at follow-up (converters). We combined supervised machine learning and differential graph analysis to assess the relative weighted contribution of each microRNA variation to the difference in outcome and identify outcome-specific networks. We then applied univariate models to the resulting microRNA variations common to both strategies, to interpret them as a function of demographic and clinical covariates. Study Results We identified 207 microRNA variations that significantly contributed to the classification. The differential network analysis found 276 network-specific correlations of microRNA variations. The combination of both strategies identified 25 microRNAs, whose gene targets were overrepresented in cognition and schizophrenia genome-wide association studies findings. Interpretable univariate models further supported the relevance of miR-150-5p and miR-3191-5p variations in psychosis onset, independent of age, sex, cannabis use, and medication. Conclusions In this first longitudinal study of microRNA variation during conversion to psychosis, we combined 2 methodologically independent data-driven strategies to identify a dynamic epigenetic signature of the emergence of psychosis that is pathophysiologically relevant. [ABSTRACT FROM AUTHOR]

A 17-year-old woman was referred to our centre for treatment of a recent onset psychotic episode in the context of 22q11.2 deletion syndrome. A 3 megabase A-D deletion was identified [...]

Aim: Neuroimaging‐based machine‐learning predictions of psychosis onset rely on the hypothesis that structural brain anomalies may reflect the underlying pathophysiology. Yet, current predictors remain difficult to interpret in light of brain structure. Here, we combined an advanced interpretable supervised algorithm and a model of neuroanatomical age to identify the level of brain maturation of the regions most predictive of psychosis. Methods: We used the voxel‐based morphometry of a healthy control dataset (N = 2024) and a prospective longitudinal UHR cohort (N = 82), of which 27 developed psychosis after one year. In UHR, psychosis was predicted at one year using Elastic‐Net‐Total‐Variation (Enet‐TV) penalties within a five‐fold cross‐validation, providing an interpretable map of distinct predictive regions. Using both the whole brain and each predictive region separately, a brain age predictor was then built and validated in 1605 controls, externally tested in 419 controls from an independent cohort, and applied in UHR. Brain age gaps were computed as the difference between chronological and predicted age, providing a proxy of whole‐brain and regional brain maturation. Results: Psychosis prediction was performant with 80 ± 4% of area‐under‐curve and 69 ± 5% of balanced accuracy (P < 0.001), and mainly leveraged volumetric increases in the ventromedial prefrontal cortex and decreases in the left precentral gyrus and the right orbitofrontal cortex. These regions were predicted to have delayed and accelerated maturational patterns, respectively. Conclusion: By combining an interpretable supervised model of conversion to psychosis with a brain age predictor, we showed that inter‐regional asynchronous brain maturation underlines the predictive signature of psychosis. [ABSTRACT FROM AUTHOR]

Background. Catatonia is a psychomotor syndrome frequently observed in disorders with neurodevelopmental impairments, including psychiatric disorders such as schizophrenia. The orbitofrontal cortex (OFC) has been repeatedly associated with catatonia. It presents with an important interindividual morphological variability, with three distinct H-shaped sulcal patterns, types I, II, and III, based on the continuity of the medial and lateral orbital sulci. Types II and III have been identified as neurodevelopmental risk factors for schizophrenia. The sulcal pattern of the OFC has never been investigated in catatonia despite the role of the OFC in the pathophysiology and the neurodevelopmental component of catatonia. Methods. In this context, we performed a retrospective analysis of the OFC sulcal pattern in carefully selected homogeneous and matched subgroups of schizophrenia patients with catatonia (N = 58) or without catatonia (N = 65), and healthy controls (N = 82). Results. Logistic regression analyses revealed a group effect on OFC sulcal pattern in the left (2 = 18.1; p < .001) and right (2 = 28.3; p < .001) hemispheres. Catatonia patients were found to have more type III and less type I in both hemispheres compared to healthy controls and more type III on the left hemisphere compared to schizophrenia patients without catatonia. Conclusion. Because the sulcal patterns are indirect markers of early brain development, our findings support a neurodevelopmental origin of catatonia and may shed light on the pathophysiology of this syndrome. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Methods Results Discussion Psychiatric disorders often emerge during adolescence or young adulthood, leading to significant disability among youth. The transition from Child and Adolescent Mental Health Services (CAMHS) to Adult Mental Health Services (AMHS) is critical for individuals experiencing emerging psychopathology, with delayed access to care negatively impacting long‐term outcomes. Accessing mental health services for adolescents and young adults is often complex and delayed due to challenges in service visibility, accessibility and appropriateness.This study examines the care trajectories of individuals consecutively accessing the early detection and intervention (EDI) centre C'JAAD (Evaluation Centre for Young Adults and Adolescents) in Paris (France) over the year 2021. The main goal was to clarify the role of this EDI centre in the continuity of care and transition to AMHS. Data about their history of care, hospitalisations and referral sources were collected retrospectively.The sample comprised 194 individuals, with 57.2% males and a median age of 20 years. Most patients (67.5%) were ≥18 years old upon arrival, with 31% in a situation of not being in education, employment, or training (NEET). Over one‐third (35.2%) had prior psychiatric hospitalisations. Patients were mainly referred to our EDI centre from other hospital departments (42.3%). Regarding care in CAMHS, 50.3% of the total sample had medical follow‐up during childhood, of whom 41.9% had discontinued care upon arrival at the EDI centre. The median onset age of care in CAMHS was 14, with a median duration of 12 months. Adult patients experienced an approximately 3‐year gap between the end of CAMHS care and assessment at the EDI centre.The sample's characteristics resemble those of other EDI centres, but concerns persist regarding referral timing and the NEET status of many youths. Lack of prior medical follow‐up and challenges in transitioning to AMHS underscore the need to enhance care continuity and address difficulties in accessing care during the transition to adulthood. [ABSTRACT FROM AUTHOR]

Background: Treatment resistance is a major challenge in psychiatric disorders. Early detection of potential future resistance would improve prognosis by reducing the delay to appropriate treatment adjustment and recovery. Here, we sought to determine whether neurodevelopmental markers can predict therapeutic response., Methods: Healthy controls ( N = 236), patients with schizophrenia ( N = 280) or bipolar disorder ( N = 78) with a known therapeutic outcome, were retrospectively included. Age, sex, education, early developmental abnormalities (obstetric complications, height, weight, and head circumference at birth, hyperactivity, dyslexia, epilepsy, enuresis, encopresis), neurological soft signs (NSS), and ages at first subjective impairment, clinical symptoms, treatment, and hospitalization, were recorded. A supervised algorithm leveraged NSS and age at first clinical signs to classify between resistance and response in schizophrenia., Results: Developmental abnormalities were more frequent in schizophrenia and bipolar disorder than in controls. NSS significantly differed between controls, responsive, and resistant participants with schizophrenia (5.5 ± 3.0, 7.0 ± 4.0, 15.0 ± 6.0 respectively, p = 3 × 10 -10 ) and bipolar disorder (5.5 ± 3.0, 8.3 ± 3.0, 12.5 ± 6.0 respectively, p < 1 × 10 -10 ). In schizophrenia, but not in bipolar disorder, age at first subjective impairment was three years lower, and age at first clinical signs two years lower, in resistant than responsive subjects ( p = 2 × 10 -4 and p = 9 × 10 -3 , respectively). Age at first clinical signs and NSS accurately predicted treatment response in schizophrenia (area-under-curve: 77 ± 8%, p = 1 × 10 -14 )., Conclusions: Neurodevelopmental features such as NSS and age of clinical onset provide a means to identify patients who may require rapid treatment adaptation.

Aim: Neuroimaging-based machine-learning predictions of psychosis onset rely on the hypothesis that structural brain anomalies may reflect the underlying pathophysiology. Yet, current predictors remain difficult to interpret in light of brain structure. Here, we combined an advanced interpretable supervised algorithm and a model of neuroanatomical age to identify the level of brain maturation of the regions most predictive of psychosis., Methods: We used the voxel-based morphometry of a healthy control dataset (N = 2024) and a prospective longitudinal UHR cohort (N = 82), of which 27 developed psychosis after one year. In UHR, psychosis was predicted at one year using Elastic-Net-Total-Variation (Enet-TV) penalties within a five-fold cross-validation, providing an interpretable map of distinct predictive regions. Using both the whole brain and each predictive region separately, a brain age predictor was then built and validated in 1605 controls, externally tested in 419 controls from an independent cohort, and applied in UHR. Brain age gaps were computed as the difference between chronological and predicted age, providing a proxy of whole-brain and regional brain maturation., Results: Psychosis prediction was performant with 80 ± 4% of area-under-curve and 69 ± 5% of balanced accuracy (P < 0.001), and mainly leveraged volumetric increases in the ventromedial prefrontal cortex and decreases in the left precentral gyrus and the right orbitofrontal cortex. These regions were predicted to have delayed and accelerated maturational patterns, respectively., Conclusion: By combining an interpretable supervised model of conversion to psychosis with a brain age predictor, we showed that inter-regional asynchronous brain maturation underlines the predictive signature of psychosis., (© 2023 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.)

Cognitive impairment is a core feature of schizophrenia which precedes the onset of full psychotic symptoms, even in the ultra-high-risk stage (UHR). Polygenic risk scores (PRS) can be computed for many psychiatric disorders and phenotyping traits, including scores for resilience. We explored the correlations between several PRS and neurocognition in UHR individuals. We included 107 UHR individuals; 29.9% of them converted to psychosis (UHR-C) while 57.0% did not (UHR-NC) during the 1-year follow-up. Cognitive performances were assessed with the Wechsler Adult Intelligence Scale estimating the Intelligence Quotient (IQ), the Trail Making Test, the verbal fluency, the Stroop test, and the Wisconsin card sorting test. Linear regression models were used to test their association with the PRS for schizophrenia, bipolar disorder, major depression, ADHD, cross-disorders, cognitive performance, intelligence, education attainment, and resilience to schizophrenia. UHR-C had a lower IQ than UHR-NC. The PRS for schizophrenia negatively correlated with IQ, while the PRS for cognitive performance and for resilience positively correlated with IQ. PRS for schizophrenia showed a significant correlation with working memory and processing speed indices. PRS for schizophrenia showed a higher effect on IQ in UHR-NC, and UHR-NC with high PRS for schizophrenia had a similar IQ as UHR-C. Conversely, UHR-C with a high PRS for resilience performed as well as UHR-NC. Our findings suggest that cognitive deficits may predate the onset of psychosis. The genetic architecture of schizophrenia seems to impacts the cognition in UHR-NC. Cognition is also mediated by PRS for resilience., (© 2021. The Author(s).)

The seven human 14-3-3 proteins are encoded by the YWHA-gene family. They are expressed in the brain where they play multiple roles including the modulation of synaptic plasticity and neuronal development. Previous studies have provided arguments for their involvement in schizophrenia, but their role during disease onset is unknown. We explored the peripheral-blood expression level of the seven YWHA genes in 92 young individuals at ultra-high risk for psychosis (UHR). During the study, 36 participants converted to psychosis (converters) while 56 did not (non-converters). YWHA genes expression was evaluated at baseline and after a mean follow-up of 10.3 months using multiplex quantitative PCR. Compared with non-converters, the converters had a significantly higher baseline expression levels for 5 YWHA family genes, and significantly different longitudinal changes in the expression of YWHAE, YWHAG, YWHAH, YWHAS and YWAHZ. A principal-component analysis also indicated that the YWHA expression was significantly different between converters and non-converters suggesting a dysregulation of the YWHA co-expression network. Although these results were obtained from peripheral blood which indirectly reflects brain chemistry, they indicate that this gene family may play a role in psychosis onset, opening the way to the identification of prognostic biomarkers or new drug targets.

Problematic use of psychoactive drugs, be it legal, on prescription, or not, remains a broad phenomenon when taken as a whole, with an increasingly large spectrum of used products. The polysubstance drug use is an expanding new trend. Although its epidemiological analysis is complex, needing further research, certain patterns of drug combinations can be found, allowing to identify clusters of users associated to more specific medical and social risks. Managing polysubstance users involves assessing each drug use, but also the connections between drugs and the patient's expectations for each of them. Complications, as well as psychiatric and somatic comorbidities are to be taken into account. The therapeutic tools for polysubstance drug use, mainly pharmacological, are still often limited to the sum of specific tools for each product. Prevention is crucial but has to adapt to the identified use clusters, and the gender. Notably, a good knowledge of chronic pain management and prescribed drug dependency risks is required to prevent polysubstance drug use involving opioids., (Copyright © 2016 Elsevier Masson SAS. All rights reserved.)