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Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

BACKGROUND: Schizophrenia is associated with an increased risk of aggressive behaviour, which may partly be explained by illness-related changes in brain structure. However, previous studies have been limited by group-level analyses, small and selective samples of inpatients and long time lags between exposure and outcome. METHODS: This cross-sectional study pooled data from 20 sites participating in the international ENIGMA-Schizophrenia Working Group. Sites acquired T1-weighted and diffusion-weighted magnetic resonance imaging scans in a total of 2095 patients with schizophrenia and 2861 healthy controls. Measures of grey matter volume and white matter microstructural integrity were extracted from the scans using harmonised protocols. For each measure, normative modelling was used to calculate how much patients deviated (in z-scores) from healthy controls at the individual level. Ordinal regression models were used to estimate the associations of these deviations with concurrent aggressive behaviour (as odds ratios [ORs] with 99% confidence intervals [CIs]). Mediation analyses were performed for positive symptoms (i.e., delusions, hallucinations and disorganised thinking), impulse control and illness insight. Aggression and potential mediators were assessed with the Positive and Negative Syndrome Scale, Scale for the Assessment of Positive Symptoms or Brief Psychiatric Rating Scale. RESULTS: Aggressive behaviour was significantly associated with reductions in total cortical volume (OR [99% CI] = 0.88 [0.78, 0.98], p = .003) and global white matter integrity (OR [99% CI] = 0.72 [0.59, 0.88], p = 3.50 × 10-5) and additional reductions in dorsolateral prefrontal cortex volume (OR [99% CI] = 0.85 [0.74, 0.97], p =.002), inferior parietal lobule volume (OR [99% CI] = 0.76 [0.66, 0.87], p = 2.20 × 10-7) and internal capsule integrity (OR [99% CI] = 0.76 [0.63, 0.92], p = 2.90 × 10-4). Except for inferior parietal lobule volume, these associations were largely mediated by increased severity of positive symptoms and reduced impulse control. CONCLUSIONS: This study provides evidence that the co-occurrence of positive symptoms, poor impulse control and aggressive behaviour in schizophrenia has a neurobiological basis, which may inform the development of therapeutic interventions.

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Background and Hypothesis Treatment resistant schizophrenia (TRS) affects almost 30% of patients with schizophrenia and has been considered a different phenotype of the disease. In vivo characterization of brain metabolic patterns associated with treatment response could contribute to elucidate the neurobiological underpinnings of TRS. Here, we used 2-[18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) to provide the first head-to-head comparative analysis of cerebral glucose metabolism in TRS patients compared to schizophrenia responder patients (nTRS), and controls. Additionally, we investigated, for the first time, the differences between clozapine responders (Clz-R) and non-responders (Clz-nR). Study Design 53 participants underwent FDG-PET studies (41 patients and 12 controls). Response to conventional antipsychotics and to clozapine was evaluated using a standardized prospective procedure based on PANSS score changes. Maps of relative brain glucose metabolism were processed for voxel-based analysis using Statistical Parametric Mapping software. Study Results Restricted areas of significant bilateral relative hypometabolism in the superior frontal gyrus characterized TRS compared to nTRS. Moreover, reduced parietal and frontal metabolism was associated with high PANSS disorganization factor scores in TRS (P <.001 voxel level uncorrected, P <.05 cluster level FWE-corrected). Only TRS compared to controls showed significant bilateral prefrontal relative hypometabolism, more extensive in CLZ-nR than in CLZ-R (P <.05 voxel level FWE-corrected). Relative significant hypermetabolism was observed in the temporo-occipital regions in TRS compared to nTRS and controls. Conclusions These data indicate that, in TRS patients, altered metabolism involved discrete brain regions not found affected in nTRS, possibly indicating a more severe disrupted functional brain network associated with disorganization symptoms. [ABSTRACT FROM AUTHOR]

Few longitudinal studies have so far investigated the impact of sustained COVID‐19 among people with pre‐existing psychiatric disorders. We conducted a prospective study involving people with serious mental illness (n = 114) and healthy controls (n = 41) to assess changes in the Perceived Stress Scale, Generalized Anxiety Disorder Scale, Patient Health Questionnaire, and Specific Psychotic Experiences Questionnaire scores 18 months after the COVID‐19 pandemic outset. Subjects underwent interviews with a mental health professional in April 2020 and at the end of the local third wave (October 2021). A significant increase in perceived stress was found in healthy controls, especially females. Psychiatric patients showed a significant worsening of anxiety symptoms compared to baseline records (t = −2.3, p = 0.036). Patients who rejected vaccination had significantly higher paranoia scores compared to those willing to get vaccinated (U = 649.5, z = −2.02, p = 0.04). These findings indicate that COVID‐19's sustained emergency may cause enduring consequences on mental health, soliciting further investigations. [ABSTRACT FROM AUTHOR]

Aim: Early age at schizophrenia onset (EOS) has been associated with a worse clinical course, although previous studies reported substantial heterogeneity. Despite the relevance of the subject, the relationship between the age of onset and treatment resistant schizophrenia (TRS) is less clear. Methods: We screened 197 non‐affective psychotic patients. Of these, 99 suffered from schizophrenia and were putative TRS and were included in a prospective 4‐to‐8‐week trial to assess their response to antipsychotics. According to status (TRS/nonTRS) and age‐at‐onset (early: ≤18 years, EOS; adult: >18 years, adult onset schizophrenia [AOS]) patients were subdivided in EOS‐TRS, EOS‐nonTRS, AOS‐TRS, AOS‐nonTRS. Multiple clinical variables were measured and compared by analysis of covariance (ANCOVA), using age as a covariate. Two‐way analysis of variance (ANOVA) was used to assess whether significant differences were attributable to TRS status or age‐at‐onset. Results: The rate of TRS patients was significantly higher in EOS compared to AOS. At the ANCOVA, EOS‐TRS had significantly worse clinical, cognitive, and psychosocial outcomes compared to the other groups. Overall, EOS‐TRS were more impaired than EOS‐nonTRS, while significant differences with AOS‐TRS were less consistent, albeit appreciable. Two‐way ANOVA demonstrated that, in the majority of the investigated variables, the significant differences among groups were attributable to the TRS status effect rather than to age‐at‐onset or combined effects. Conclusions: These results suggest that refractoriness to antipsychotics may be strongly linked to the early onset of psychotic symptoms, possibly as a result of common neurobiology. [ABSTRACT FROM AUTHOR]

Background: Schizophrenia (SCZ) is a debilitating mental illness characterized by a highly complex, heterogeneous, non-mendelian genetic background. Recent progress in dissecting genetic architecture of SCZ has accelerated over the last decade due to new advanced technologies. Genome-Wide Association Studies (GWAS) on extremely large samples of patients identified and replicated hundreds of Single-Nucleotide Polymorphism (SNPs), each exhibiting only a modest effect. The analysis of genomic Copy Number Variations (CNVs) clarified the role of rare structural variants conferring significant risk by disrupting multiple genes involved in neurodevelopmental pathways, and linked to SCZ. In this scenario, the aim of our study is to carry out a genetic characterization of a cohort of patients affected by SCZ, in order to assess the risk of recurrence, to elucidate putative pathogenetic mechanisms and, whenever possible, to conceive tailored interventions and therapies. Methods: 34 patients (8 women and 26 men) affected by SCZ and admitted to Day Hospital at Psychiatric Division for Treatment Resistant Psychosis of the University of Naples Federico II were recruited, and underwent: i) psychopathological evaluation and assessment of clinical response to antipsychotics; ii) genetic counseling; iii) further diagnostic investigation by using Comparative Genomic Hybridization (CGH) + Single Nucleotide Polymorphism (SNP) microarray with 2x400k Agilent’s platform “GenetiSure” for detecting unbalanced chromosomal abnormalities and regions of homozygosity (ROHs). Results: Structural pathogenetic rearrangements resulted in 9 (27%) patients. Those identified were the following: 15q13.3 deletion, 16p13.11 duplication, 22q11.22 deletion (TOP3B), 22q11.22 (PRODH, DGCR5, DGCR6), RBFOX1 deletion, TCF4 deletion, derivative X chromosome (X;Y translocation). Potentially pathogenic rearrangements, involving genes associated with psychiatric disorders or implicated in neurodevelopment, resulted in 15 patients (44%). No relevant CNVs were detected in 10 patients (29%), although they showed the presence of ROHs that may contain susceptibility loci, since many neurodevelopmental genes map onto or near these specific regions. Certain of these rearrangements occur in many patients, and certain patients showed likewise multiple rearrangements. Discussion: The analysis of CNVs and SNPs allowed us to characterize the genetic disease structure in the whole cohort of patients and helped to refine the diagnosis in a few cases, thereby ascertaining an underlying specific genetic condition. A further extension of the study, in terms of sample size and more accurate investigations (i.e genetic mapping of ROHs) is underway. According to literature, rare risk-associated CNVs account for 2% of SCZ cases, but their higher prevalence (27%) in our sample may be influenced by a larger percentage of Treatment Resistant and more severely ill patients (since they were recruited in a highly specialized Unit for Treatment Resistant Psychosis). Therefore, our future purpose is to demonstrate a robust genetic modulation of Treatment Resistant endophenotypes of SCZ. Moreover, we believe that the role of genetic counseling in psychiatric services should be emphasized, and that genetic testing in this field should not be restricted to suspected childhood neuropsychiatric disorders. According to the neurodevelopmental hypothesis of SCZ, that suggests a brain development disruption in early life (due to genetic and early environmental factors), prompting to a subsequent later emergence of the disease in adulthood, even chronic complex adult mental illness, such as SCZ, deserves detailed investigations and a more exhaustive genetic evaluation [ABSTRACT FROM AUTHOR]

Background: Schizophrenia is a disabling psychiatric disease with a lifetime prevalence of 1%, which usually starts in late adolescence or initial adulthood and has a rare possibility of recovery. According to the extent of antipsychotic response, schizophrenia individuals can be divided into treatment resistant schizophrenia (TRS) and Treatment responders (non-TRS). Currently, it is unclear whether these categories belong to different biological and prognostic groups. Therefore, in order to deepen the knowledge of these conditions, we analyzed the cerebral metabolism of a sample of stabilized but still actively symptomatic TRS and non-TRS patients with substantial cognitive deficits. Methods: We recruited 35 schizophrenia patients (18 TRS and 17 non-TRS) among approximately 80 consecutive non-affective psychotic patients, that were referred to our academic outpatient unit for supposed resistance to antipsychotics. The diagnosis of TRS was made by a structured diagnostic flowchart, according to published guidelines. Patients underwent a wide set of clinical and cognitive evaluations by trained raters. All patients who exhibited substantial cognitive deficits (at least two BACS/MATRICS items < than 1 - adjusted scores) and met inclusion/not met exclusion criteria were included in the neuroimaging study by 18F-fluorodeoxyglucose PET scans and structural 3T Magnetic Resonance Imaging. There were no statistically significant differences in age, chlorpromazine equivalents, and duration of illness between the TRS/non-TRS groups. Patients groups were matched with a control group. PET images were normalized on SPM template and then examined in visual and voxel-based analyses. Results: Globally, the visual analysis of the images showed a diffuse relative metabolic reduction in the cerebral cortex with prevalent frontal involvement in the majority of the patients of the TRS group and to a lesser extent in the non-TRS group. The voxel-per-voxel analysis revealed an extensive cluster of significant relative metabolic reduction in TRS patients compared to controls and to non-TRS patients (13653 voxels) localized bilaterally in the frontal cortex with a mild left prevalence. In particular, the metabolic reduction peaks involved the upper right and left medial frontal girder (BA6 and BA8, respectively), left opercular frontal (BA44), middle frontal girth (BA10) bilaterally, left lower frontal girth (BA47). Notably, at least three patients from the TRS group were excluded from the initial pool due to the observation of previously unrecognized structural brain anomalies. Discussion: In this study, we observed that TRS patients had a relative hypometabolic state in dorsal and medial prefrontal cortex, including frontobasal areas, compared to controls and to non-TRS patients, while the striatum and the occipital cortex appeared relatively preserved. In non-TRS patients, brain distribution of 18F-FDG was more homogeneous and only slightly reduced in the cerebral cortex, as compared to controls. These patterns of brain metabolism cannot be associated with more severe cognitive dysfunctions in TRS patients since all patients included in the study had substantial cognitive impairments, irrespective of being TRS or non-TRS. Although it cannot be excluded that these differential patterns may stem from the lack of response to antipsychotics, our findings suggest distinct neurobiological alterations in TRS vs. non-TRS patients, which may include more relevant brain disconnections in non-responder ones. [ABSTRACT FROM AUTHOR]

Background: Clozapine is the gold standard for Treatment Resistant Schizophrenia (TRS) and its use is strongly recommended after failure of two/three antipsychotics. Nonetheless, prescribing trends do not appear to follow the global guidelines and switching to clozapine is often delayed or improper. Reasons for clozapine underuse are quite understood, whereas little is known on clozapine misuse. In fact, more stringent operative criteria to define TRS should be used in clinical practice, taking advantage of standardized assessment tools in order to avoid false TRS positive. In these perspectives, we studied the patterns of clozapine prescription in a sample of TRS and non-TRS patients, in order to identify psychopathological variables associated with lifetime exposure to clozapine. Methods: 198 consecutive patients have been screened for eligibility criteria from 2016 until 2019 at Treatment Resistant Psychosis Centre of the University of Naples “Federico II”. Only stabilized schizophrenic patients aged between 18 and 65 were enrolled. All relevant clinical-demographic data were recorded. TRS diagnosis was based on TRRIP working group operational criteria and clear pseudo-resistance factors were excluded. Patients were subdivided into two groups: those having received or currently under clozapine treatment (eCLZ) and those never treated by clozapine (nCLZ). Patients were assessed for psychotic symptoms (PANSS), cognitive performances (BACS), neurological soft signs (NES), functional capacity (UPSA), social functioning (PSP and SLOF scales), and severity of illness (CGI-S). Results: Among the patients included (92), eCLZ were 50 (54.3%), of which 20% being non-TRS. Compared to nCLZ, eCLZ had a significantly earlier age at onset, more hospitalizations, higher daily antipsychotic doses; eCLZ patients had significantly higher scores on the CGI-S, PANSS total, PANSS Negative, General Psychopathology and PAUSS subscale and, according to the Five Factor PANSS Model, higher score on Negative and Disorganization Factors; eCLZ had also more impaired Working Memory performances, higher NES Total score, lower PSP and all SLOF areas scores, except Area4. Then we use univariate and multivariate analysis to delineate predictors of clozapine exposure and to evaluate whether these factors may organize in proximal (independent) and distal (mediated) associations. We found five independently associated variables that mediate all the other ones: high antipsychotic doses, higher disease severity, suicide attempts, impaired working memory, more severe Stereotyped Thinking. Discussion: While it is expected that some TRS patients might not receive clozapine, the observation that non-TRS patients had been exposed to clozapine may surprise. A possible explanation may be the lack of systematized procedure to diagnose TRS, which is often merely coincident with a nonspecific lack of response to antipsychotics. Due to mediation analysis, we further gained insights into the hierarchy of these variables’ impact on clozapine prescription and we finally compose a complex network of proximal and distal factors predicting clozapine exposure in schizophrenic patients. The above-mentioned independent variables significantly associated with clozapine exposure let intend that eCLZ patients may belong to a subset of more severely ill ones, beyond reaching the response threshold. Earlier onset patients may suffer from a more severe disease, be more prone to suicide attempts, and less responsive to antipsychotics. This result needs to be replicated in wider samples, and this relevant therapeutic issue should be further debated in order to gather robust evidence to support or discourage clozapine off-label use and misuse. [ABSTRACT FROM AUTHOR]