Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Childhood trauma moderates schizotypy-related brain morphology: analyses of 1182 healthy individuals from the ENIGMA schizotypy working group.

BACKGROUND: Schizotypy represents an index of psychosis-proneness in the general population, often associated with childhood trauma exposure. Both schizotypy and childhood trauma are linked to structural brain alterations, and it is possible that trauma exposure moderates the extent of brain morphological differences associated with schizotypy. METHODS: We addressed this question using data from a total of 1182 healthy adults (age range: 18-65 years old, 647 females/535 males), pooled from nine sites worldwide, contributing to the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Schizotypy working group. All participants completed both the Schizotypal Personality Questionnaire Brief version (SPQ-B), and the Childhood Trauma Questionnaire (CTQ), and underwent a 3D T1-weighted brain MRI scan from which regional indices of subcortical gray matter volume and cortical thickness were determined. RESULTS: A series of multiple linear regressions revealed that differences in cortical thickness in four regions-of-interest were significantly associated with interactions between schizotypy and trauma; subsequent moderation analyses indicated that increasing levels of schizotypy were associated with thicker left caudal anterior cingulate gyrus, right middle temporal gyrus and insula, and thinner left caudal middle frontal gyrus, in people exposed to higher (but not low or average) levels of childhood trauma. This was found in the context of morphological changes directly associated with increasing levels of schizotypy or increasing levels of childhood trauma exposure. CONCLUSIONS: These results suggest that alterations in brain regions critical for higher cognitive and integrative processes that are associated with schizotypy may be enhanced in individuals exposed to high levels of trauma.

Mismatch negativity generation in subjects at risk for psychosis: source analysis is more sensitive than surface electrodes in risk prediction.

Background: Deficits of mismatch negativity (MMN) in patients with schizophrenia have been demonstrated many times and there is growing evidence that alterations of MMN already exist in individuals at risk for psychosis. The present study examines differences in MMN between subjects fulfilling ultra-high risk (UHR) or only basic symptoms criteria and it addresses the question, if MMN source analysis can improve prediction of transition to psychosis. Methods: The MMN to duration, frequency, and intensity deviants was recorded in 50 healthy controls and 161 individuals at risk for psychosis classified into three subgroups: only basic symptoms (n = 74), only ultra-high risk (n = 13) and persons who fulfill both risk criteria (n = 74). Based on a three-source model of MMN generation, we conducted an MMN source analysis and compared the amplitudes of surface electrodes and sources among the three groups. Results: Significant differences in MMN generation among the four groups were revealed at surface electrodes Cz and C4 (p < 0.05) and at the frontal source (p < 0.001) for duration deviant stimuli. The 15 subjects from the risk groups who subsequently developed a manifest psychosis had a significantly lower MMN amplitude at frontal source (p = 0.019) without showing significant differences at surface electrodes. Low activity at frontal MMN source increased the risk of transition to manifest disease by the factor 3.12 in UHR subjects. Conclusion: MMN activity differed significantly between subjects presenting only basic symptoms and subjects which additionally meet UHR criteria. The largest differences between groups as well as between individuals with and without transition were observed at the frontal source. The present results suggest that source analysis is more sensitive than surface electrodes in psychosis risk prediction by MMN.

A dopamine receptor D2 genetic polymorphism associated with transition to mental disorders in a cohort of individuals with at-risk mental state for psychosis.

OBJECTIVES: To test the association of 45 single nucleotide polymorphisms (SNPs) with transition to psychiatric disorders in a cohort of individuals at ultrahigh risk (UHR) mental state for psychosis. METHODS: Through general population screening, 88 non-help-seeking UHR subjects and 130 healthy control individuals were genotyped for 45 SNPs related to psychosis. They were followed for a mean of 2.5 years, and conversion to psychotic and to general psychiatric disorders was assessed. Genotype frequencies between controls, converters, and non-converters were analyzed. RESULTS: There were no differences in sociodemographics between controls and UHR. Also, UHR converters and non-converters had no differences in their baseline symptoms scores. The dopamine receptor D2 gene (DRD2) SNP rs6277 was significantly more common among UHR who transitioned to psychosis (p < 0.001) and to UHR who transitioned to any psychiatric disorders (p = 0.001) when compared to UHR who did not transition. The rs6277 T allele was related to psychiatric morbidity in a dose-response fashion, being significantly more frequent in UHR converters than UHR non-converters and control subjects (p = 0.003). CONCLUSION: Our findings suggest that rs6277 could potentially constitute a genetic marker of transition to psychiatric disorders in subjects with at-risk mental states, warranting further investigation in larger samples.

A dopamine receptor D2 genetic polymorphism associated with transition to mental disorders in a cohort of individuals with at-risk mental state for psychosis

Objectives: To test the association of 45 single nucleotide polymorphisms (SNPs) with transition to psychiatric disorders in a cohort of individuals at ultrahigh risk (UHR) mental state for psychosis. Methods: Through general population screening, 88 non-help-seeking UHR subjects and 130 healthy control individuals were genotyped for 45 SNPs related to psychosis. They were followed for a mean of 2.5 years, and conversion to psychotic and to general psychiatric disorders was assessed. Genotype frequencies between controls, converters, and non-converters were analyzed. Results: There were no differences in sociodemographics between controls and UHR. Also, UHR converters and non-converters had no differences in their baseline symptoms scores. The dopamine receptor D2 gene (DRD2) SNP rs6277 was significantly more common among UHR who transitioned to psychosis (p < 0.001) and to UHR who transitioned to any psychiatric disorders (p = 0.001) when compared to UHR who did not transition. The rs6277 T allele was related to psychiatric morbidity in a dose-response fashion, being significantly more frequent in UHR converters than UHR non-converters and control subjects (p = 0.003). Conclusion: Our findings suggest that rs6277 could potentially constitute a genetic marker of transition to psychiatric disorders in subjects with at-risk mental states, warranting further investigation in larger samples.

White matter microstructure and the clinical risk for psychosis: A diffusion tensor imaging study of individuals with basic symptoms and at ultra-high risk.

BACKGROUND: Widespread white matter abnormalities are a frequent finding in chronic schizophrenia patients. More inconsistent results have been provided by the sparser literature on at-risk states for psychosis, i.e., emerging subclinical symptoms. However, considering risk as a homogenous construct, an approach of earlier studies, may impede our understanding of neuro-progression into psychosis. METHODS: An analysis was conducted of 3-Tesla MRI diffusion and symptom data from 112 individuals (mean age, 21.97 ± 4.19) within two at-risk paradigm subtypes, only basic symptoms (n = 43) and ultra-high risk (n = 37), and controls (n = 32). Between-group comparisons (involving three study groups and further split based on the subsequent transition to schizophrenia) of four diffusion-tensor-imaging-derived scalars were performed using voxelwise tract-based spatial statistics, followed by correlational analyses with Structured Interview for Prodromal Syndromes responses. RESULTS: Relative to controls, fractional anisotropy was lower in the splenium of the corpus callosum of ultra-high-risk individuals, but only before stringent multiple-testing correction, and negatively correlated with General Symptom severity among at-risk individuals. At-risk participants who transitioned to schizophrenia within 3 years, compared to those that did not transition, had more severe WM differences in fractional anisotropy and radial diffusivity (particularly in the corpus callosum, anterior corona radiata, and motor/sensory tracts), which were even more extensive compared to healthy controls. CONCLUSIONS: These findings align with the subclinical symptom presentation and more extensive disruptions in converters, suggestive of severity-related demyelination or axonal pathology. Fine-grained but detectable differences among ultra-high-risk subjects (i.e., with brief limited intermittent and/or attenuated psychotic symptoms) point to the splenium as a discrete site of emerging psychopathology, while basic symptoms alone were not associated with altered fractional anisotropy.

Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages.

Importance: Approaches are needed to stratify individuals in early psychosis stages beyond positive symptom severity to investigate specificity related to affective and normative variation and to validate solutions with premorbid, longitudinal, and genetic risk measures. Objective: To use machine learning techniques to cluster, compare, and combine subgroup solutions using clinical and brain structural imaging data from early psychosis and depression stages. Design, Setting, and Participants: A multisite, naturalistic, longitudinal cohort study (10 sites in 5 European countries; including major follow-up intervals at 9 and 18 months) with a referred patient sample of those with clinical high risk for psychosis (CHR-P), recent-onset psychosis (ROP), recent-onset depression (ROD), and healthy controls were recruited between February 1, 2014, to July 1, 2019. Data were analyzed between January 2020 and January 2022. Main Outcomes and Measures: A nonnegative matrix factorization technique separately decomposed clinical (287 variables) and parcellated brain structural volume (204 gray, white, and cerebrospinal fluid regions) data across CHR-P, ROP, ROD, and healthy controls study groups. Stability criteria determined cluster number using nested cross-validation. Validation targets were compared across subgroup solutions (premorbid, longitudinal, and schizophrenia polygenic risk scores). Multiclass supervised machine learning produced a transferable solution to the validation sample. Results: There were a total of 749 individuals in the discovery group and 610 individuals in the validation group. Individuals included those with CHR-P (n = 287), ROP (n = 323), ROD (n = 285), and healthy controls (n = 464), The mean (SD) age was 25.1 (5.9) years, and 702 (51.7%) were female. A clinical 4-dimensional solution separated individuals based on positive symptoms, negative symptoms, depression, and functioning, demonstrating associations with all validation targets. Brain clustering revealed a subgroup with distributed brain volume reductions associated with negative symptoms, reduced performance IQ, and increased schizophrenia polygenic risk scores. Multilevel results distinguished between normative and illness-related brain differences. Subgroup results were largely validated in the external sample. Conclusions and Relevance: The results of this longitudinal cohort study provide stratifications beyond the expression of positive symptoms that cut across illness stages and diagnoses. Clinical results suggest the importance of negative symptoms, depression, and functioning. Brain results suggest substantial overlap across illness stages and normative variation, which may highlight a vulnerability signature independent from specific presentations. Premorbid, longitudinal, and genetic risk validation suggested clinical importance of the subgroups to preventive treatments.

Longitudinal reciprocal associations between depression, anxiety, and substance use disorders over three decades of life.

BACKGROUND AND OBJECTIVE: Studies exploring longitudinal reciprocal associations between depressive, anxiety, and substance use disorders (DD, AD and SUD, respectively) over long periods of time are mainly lacking. Therefore, the aim of the present study is to test longitudinal associations (i.e. temporal dynamics) between DD, AD and SUD from young adulthood to middle adulthood. METHODS: A stratified community sample of 591 participants from the canton of Zurich, Switzerland, was interviewed with the Structured Psychopathological Interview and Rating of the Social Consequences of Psychological Disturbances for Epidemiology over seven interview waves from ages 20/21 to 49/50. Diagnostic and Statistical Manual of Mental Disorders criteria were used to evaluate the presence of DD, AD and SUD. We fitted an auto-regressive cross-lagged path analysis within a Bayesian structural equation model to test longitudinal associations. RESULTS: Regarding autoregressive effects, AD (except during young adulthood) and SUD predicted themselves over the entire time period, while DD recurrently predicted itself not consistently over time. Regarding cross-lagged effects, DD predicted SUD at different time points, and vice versa. DD predicted subsequent AD in adulthood, whereas the reverse did not happen. Female gender was associated with DD and AD at all ages while male gender was associated with SUD only in young adulthood. CONCLUSIONS: Reciprocal longitudinal associations were found between DD and SUD and DD usually preceded AD. Our results further confirm an increased risk of DD and AD in women and a higher risk of SUD in young men. Early treatment and broad psychosocial interventions should be provided in order to prevent chronicity and further maladjustment as well as interrupting the cycle of mutual reinforcement between DD and SUD.

Cognitive Patterns and Conversion in a Representative Sample of Individuals at Risk for Psychosis.

ABSTRACT: Clinical high-risk (CHR) individuals belong to a heterogeneous group, of which only a few will cross the threshold for a clinical diagnosis. Cognitive disturbances are present in CHR subjects and may be indicative of transition. Our study aims to identify such deficits in a representative CHR for psychosis sample. Our sample comprised 92 CHR individuals and 54 controls from a representative cohort of the general population. They were followed up for a mean of 2.5 years, with 15 individuals converting to schizophrenia or other Diagnostic and Statistical Manual of Mental Disorders, 5th Edition diagnoses. Neurocognitive assessment was performed with the University of Pennsylvania Computerized Neuropsychological Testing, and CHR status was assessed with the Structured Interview for Prodromal Syndromes (SIPS). Baseline scores were entered in a latent profile analysis model. Our study brought forward a four-class model on cognitive performance. One class displayed better performance, whereas the other three performed worse, all compared with controls. The class with lower executive function also had the highest score on disorganized communication (SIPS P5 = 1.36, p < 0.05), although unrelated to conversion. Among the low performers, the class significantly related to conversion (p = 0.023) had the highest score in decreased expression of emotion (SIPS N3 = 0.85, p < 0.05). Our study brings new and relevant data on non-help-seeking CHR individuals and the relationship between cognitive patterns and conversion. We have highlighted a specific cognitive signature, associated with negative symptoms, which represents a stable trait with presumed lower conversion to a psychiatric illness.

Use of a Bayesian Network Model to predict psychiatric illness in individuals with 'at risk mental states' from a general population cohort.

The 'at risk mental state' (ARMS) paradigm has been introduced in psychiatry to study prodromal phases of schizophrenia. With time it was seen that the ARMS state can also precede mental disorders other than schizophrenia, such as depression and anxiety. However, several problems hamper the paradigm's use in preventative medicine, such as varying transition rates across studies, the use of non-naturalistic samples, and the multifactorial nature of psychiatric disorders. To strengthen ARMS predictive power, there is a need for a holistic model incorporating-in an unbiased fashion-the small-effect factors that cause mental disorders. Bayesian networks, a probabilistic graphical model, was used in a populational cohort of 83 ARMS individuals to predict conversion to psychiatric illness. Nine predictors-including state, trait, biological and environmental factors-were inputted. Dopamine receptor 2 polymorphism, high private religiosity, and childhood trauma remained in the final model, which reached an 85.51% (SD = 0.1190) accuracy level in predicting conversion. This is the first time a robust model was produced with Bayesian networks to predict psychiatric illness among at risk individuals from the general population. This could be an important tool to strengthen predictive measures in psychiatry which should be replicated in larger samples to provide the model further learning.

Cortical and subcortical neuroanatomical signatures of schizotypy in 3004 individuals assessed in a worldwide ENIGMA study.

Neuroanatomical abnormalities have been reported along a continuum from at-risk stages, including high schizotypy, to early and chronic psychosis. However, a comprehensive neuroanatomical mapping of schizotypy remains to be established. The authors conducted the first large-scale meta-analyses of cortical and subcortical morphometric patterns of schizotypy in healthy individuals, and compared these patterns with neuroanatomical abnormalities observed in major psychiatric disorders. The sample comprised 3004 unmedicated healthy individuals (12-68 years, 46.5% male) from 29 cohorts of the worldwide ENIGMA Schizotypy working group. Cortical and subcortical effect size maps with schizotypy scores were generated using standardized methods. Pattern similarities were assessed between the schizotypy-related cortical and subcortical maps and effect size maps from comparisons of schizophrenia (SZ), bipolar disorder (BD) and major depression (MDD) patients with controls. Thicker right medial orbitofrontal/ventromedial prefrontal cortex (mOFC/vmPFC) was associated with higher schizotypy scores (r = 0.067, pFDR = 0.02). The cortical thickness profile in schizotypy was positively correlated with cortical abnormalities in SZ (r = 0.285, pspin = 0.024), but not BD (r = 0.166, pspin = 0.205) or MDD (r = -0.274, pspin = 0.073). The schizotypy-related subcortical volume pattern was negatively correlated with subcortical abnormalities in SZ (rho = -0.690, pspin = 0.006), BD (rho = -0.672, pspin = 0.009), and MDD (rho = -0.692, pspin = 0.004). Comprehensive mapping of schizotypy-related brain morphometry in the general population revealed a significant relationship between higher schizotypy and thicker mOFC/vmPFC, in the absence of confounding effects due to antipsychotic medication or disease chronicity. The cortical pattern similarity between schizotypy and schizophrenia yields new insights into a dimensional neurobiological continuity across the extended psychosis phenotype.

Impact of clinical decision-making participation and satisfaction on outcomes in mental health practice: results from the CEDAR European longitudinal study.

The present study aimed to assess: (1) whether a more active involvement of patients is associated with an improvement of clinical symptoms, global functioning, and quality-of-life; and (2) how patients' satisfaction with clinical decisions can lead to better outcome after 1 year. Data were collected as part of the study 'Clinical decision-making and outcome in routine care for people with severe mental illness (CEDAR)', a longitudinal observational study, funded by the European Commission and carried out in six European countries. Patients' inclusion criteria were: (a) aged between 18 and 60 years; (b) diagnosis of a severe mental illness of any kind according to the Threshold Assessment Grid (TAG) ≥ 5 and duration of illness ≥ 2 years; (c) expected contact with the local mental health service during the 12-month observation period; (d) adequate skills in the language of the host countries; and (e) the ability to provide written informed consent. The clinical decision-making styles of clinicians and the patient satisfaction with decisions were assessed using the Clinical Decision Making Style and the Clinical Decision Making Involvement and Satisfaction scales, respectively. Patients were assessed at baseline and 1 year after the recruitment. The sample consisted of 588 patients with severe mental illness, mainly female, with a mean age of 41.69 (±10.74) and a mean duration of illness of 12.5 (±9.27) years. The majority of patients were diagnosed with psychotic (45.75%) or affective disorders (34.01%). At baseline, a shared CDM style was preferred by 70.6% of clinicians and about 40% of patients indicated a high level of satisfaction with the decision and 31% a medium level of satisfaction. Higher participation in clinical decisions was associated with improved social functioning and quality-of-life, and reduced interpersonal conflicts, sense of loneliness, feelings of inadequacy, and withdrawal in friendships after 1 year (p < 0.05). Moreover, a higher satisfaction with decisions was associated with a better quality-of-life (p < 0.0001), reduced symptom severity (p < 0.0001), and a significantly lower illness burden associated with symptoms of distress (p < 0.0001), interpersonal difficulties (p < 0.0001), and problems in social roles (p < 0.05). Our findings clearly show that a higher involvement in and satisfaction of patients with clinical decision-making was associated with better outcomes. More efforts have to be made to increase the involvement of patients in clinical decision-making in routine care settings.

Influence of migration on the thought process of individuals at ultra-high risk for psychosis

Objective: To assess the influence of migration on the psychopathological presentation of individuals at ultra-high risk for psychosis (UHR) in São Paulo, Brazil. Methods: This study is part of the Subclinical Symptoms and Prodromal Psychosis (SSAPP) project, a cohort study in São Paulo, Brazil, designed to follow individuals at UHR. After screening with the Prodromal Questionnaire (PQ) and a clinical interview, the Global Assessment of Functioning (GAF) was administered, a neuropsychological assessment was performed, sociodemographic and migration data were obtained. We then analyzed UHR individuals who had migration data to see if migration had any effect on their cognition and psychopathology. Chi-square tests were used for categorical variables, and Student's t test or analysis of variance (ANOVA) were used for nonparametric and parametric distributions, respectively. Results: The sample was composed of 42 at-risk subjects, of whom 5 had a migration history in the past two generations. Those with migration history showed significantly more formal thought disturbances (p = 0.012) and sleeping problems (p = 0.033) compared to those without. Conclusions: Our data reinforce migration as a risk factor for psychosis in developing countries as well, and highlights the importance of studying the specific effect of this factor in UHR psychopathology.

The Interplay Between Postsynaptic Striatal D2/3 Receptor Availability, Adversity Exposure and Odd Beliefs: A [11C]-Raclopride PET Study.

BACKGROUND: Between unaffected mental health and diagnosable psychiatric disorders, there is a vast continuum of functioning. The hypothesized link between striatal dopamine signaling and psychosis has guided a prolific body of research. However, it has been understudied in the context of multiple interacting factors, subclinical phenotypes, and pre-postsynaptic dynamics. METHOD: This work investigated psychotic-like experiences and D2/3 dopamine postsynaptic receptor availability in the dorsal striatum, quantified by in vivo [11C]-raclopride positron emission tomography, in a sample of 24 healthy male individuals. Additional mediation and moderation effects with childhood trauma and key dopamine-regulating genes were examined. RESULTS: An inverse relationship between nondisplaceable binding potential and subclinical symptoms was identified. D2/3 receptor availability in the left putamen fully mediated the association between traumatic childhood experiences and odd beliefs, that is, inclinations to see meaning in randomness and unfounded interpretations. Moreover, the effect of early adversity was moderated by a DRD2 functional variant (rs1076560). The results link environmental and neurobiological influences in the striatum to the origination of psychosis spectrum symptomology, consistent with the social defeat and diathesis-stress models. CONCLUSIONS: Adversity exposure may affect the dopamine system as in association with biases in probabilistic reasoning, attributional style, and salience processing. The inverse relationship between D2/3 availability and symptomology may be explained by endogenous dopamine occupying the receptor, postsynaptic compensatory mechanisms, and/or altered receptor sensitivity. This may also reflect a cognitively stabilizing mechanism in non-help-seeking individuals. Future research should comprehensively characterize molecular parameters of dopamine neurotransmission along the psychosis spectrum and according to subtype profiling.

Mental Health Consequences for Healthcare Workers During the COVID-19 Pandemic: A Scoping Review to Draw Lessons for LMICs.

Background: The COVID-19 pandemic has had a significant impact on the mental health of healthcare workers (HCWs) particularly in low and middle-income countries (LMICs). This scoping review provides a summary of current evidence on the mental health consequences of COVID on HCWs. Methods: A scoping review was conducted searching PubMed and Embase for articles relevant to mental health conditions among HCWs during COVID-19. Relevant articles were screened and extracted to summarize key outcomes and findings. Results: A total of fifty-one studies were included in this review. Depressive symptoms, anxiety symptoms, psychological trauma, insomnia and sleep quality, workplace burnout and fatigue, and distress were the main outcomes reviewed. Most studies found a high number of symptoms endorsed for depression, anxiety, and other conditions. We found differences in symptoms by sex, age, and HCW role, with female, younger-aged, frontline workers, and non-physician workers being affected more than other subgroups. Conclusion: This review highlights the existing burden of mental health conditions reported by HCWs during COVID-19. It also demonstrates emerging disparities among affected HCW subgroups. This scoping review emphasizes the importance of generating high quality evidence and developing informed interventions for HCW mental health with a focus on LMICs.