Psychotic symptoms in adolescence are associated with social adversity and genetic risk for schizophrenia. This gene–environment interplay may be mediated by personality, which also develops during adolescence. We hypothesized that (i) personality development predicts later Psychosis Proneness Signs (PPS), and (ii) personality traits mediate the association between genetic risk for schizophrenia, social adversities, and psychosis.

A total of 784 individuals were selected within the IMAGEN cohort (Discovery Sample-DS: 526; Validation Sample-VS: 258); personality was assessed at baseline (13–15 years), follow-up-1 (FU1, 16–17 years), and FU2 (18–20 years). Latent growth curve models served to compute coefficients of individual change across 14 personality variables. A support vector machine algorithm employed these coefficients to predict PPS at FU3 (21–24 years). We computed mediation analyses, including personality-based predictions and self-reported bullying victimization as serial mediators along the pathway between polygenic risk score (PRS) for schizophrenia and FU3 PPS. We replicated the main findings also on 1132 adolescents recruited within the TRAILS cohort.

Growth scores in neuroticism and openness predicted PPS with 65.6% balanced accuracy in the DS, and 69.5% in the VS Mediations revealed a significant positive direct effect of PRS on PPS (confidence interval [CI] 0.01–0.15), and an indirect effect, serially mediated by personality-based predictions and victimization (CI 0.006–0.01), replicated in the TRAILS cohort (CI 0.0004–0.004).

Adolescent personality changes may predate future experiences associated with psychosis susceptibility. PPS personality-based predictions mediate the relationship between PRS and victimization toward adult PPS, suggesting that gene–environment correlations proposed for psychosis are partly mediated by personality.

Psychiatric drugs, including antipsychotics and antidepressants, are widely prescribed, even in young and adolescent populations at early or subthreshold disease stages. However, their impact on brain structure remains elusive. Elucidating the relationship between psychotropic medication and structural brain changes could enhance the understanding of the potential benefits and risks associated with such treatment.

Investigation of the associations between psychiatric drug intake and longitudinal grey matter volume (GMV) changes in a transdiagnostic sample of young individuals at early stages of psychosis or depression using an unbiased data-driven approach.

The study sample comprised 247 participants (mean [SD] age = 25.06 [6.13] years, 50.61% male), consisting of young, minimally medicated individuals at clinical high-risk states for psychosis, individuals with recent-onset depression or psychosis, and healthy control individuals. Structural magnetic resonance imaging was used to obtain whole-brain voxel-wise GMV for all participants at two timepoints (mean [SD] time between scans = 11.15 [4.93] months). The multivariate sparse partial least squares (SPLS) algorithm (Monteiro et al. JNMEDT 2016; 271:182-194) was embedded in a nested cross-validation framework to identify parsimonious associations between the cumulative intake of psychiatric drugs, including commonly prescribed antipsychotics and antidepressants, and change in GMV between both timepoints, while additionally factoring in age, sex, and diagnosis. Furthermore, we correlated the retrieved SPLS results to personality domains (NEO-FFI) and childhood trauma (CTQ).

SPLS analysis revealed significant associations between the antipsychotic classes of benzamides, butyrophenones and thioxanthenes and longitudinal GMV decreases in cortical regions including the insula, posterior superior temporal sulcus as well as cingulate, postcentral, precentral, orbital and frontal gyri (Figure 1A-C). These brain regions corresponded most closely to the dorsal and ventral attention, somatomotor, salience and default network (Figure 1D). Furthermore, the medication signature was negatively associated with the personality domains extraversion, agreeableness and conscientiousness and positively associated with the CTQ domains emotional and physical neglect.

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Psychiatric drug intake over a period of one year was linked to distinct GMV reductions in key cortical hubs. These patterns were already visible in young individuals at early or subthreshold stages of mental illness and were further linked to childhood neglect and personality traits. Hence, a better and more in-depth understanding of the structural brain implications of medicating young and adolescent individuals might lead to more cautious, sustainable and targeted treatment strategies.

None Declared

The clinical high-risk state for psychosis (CHR) is associated with alterations in grey matter volume (GMV) in various regions such as the hippocampus (Vissink et al. BP:GOS 2022; 2(2) 147-152). Within the scope of the North American Prodrome Longitudinal Study (NAPLS-2; Cannon et al. AM J Psychiatry 2016; 173(10), 980-988), a publicly available risk calculator based on clinical variables was developed to assess the likelihood of individuals to transition to psychosis within a 2-year period.

In the current study, we aim to examine the association between GMV and NAPLS-2 risk scores calculated for individuals with CHR and recent-onset depression (ROD), taking a transdiagnostic approach on the transition to psychosis.

The sample consisted of 315 CHR (M = 23.85, SD = ± 5.64; female: 164) and 295 ROD (M = 25.11, SD = ± 6.21; female: 144) patients from the multi-site Personalised Prognostic Tools for Early Psychosis Management (PRONIA) Study (Koutsouleris et al. JAMA Psychiatry 2018; 57(11), 1156-1172). Risk scores were calculated using the six clinical and neurocognitive variables included in the NAPLS-2 risk calculator that were significant for predicting psychosis. Further, we derived smoothed GMV maps from T1-weighted structural magnetic resonance imaging using a full width at half maximum kernel size of 8 mm. We employed a multiple regression design in SPM12 to examine associations between risk scores and GMV. On the whole-brain level, we calculated permutation-based threshold-free cluster enhancement (TFCE) contrasts using the TFCE toolbox. Additionally, we calculated t-contrasts within a region-of-interest (ROI) analysis encompassing the hippocampus. All results were thresholded at p < 0.05 with family wise error correction to address multiple comparisons.

Our analysis revealed that linear GMV increases in the right middle and superior frontal gyrus (kE= 2726 voxels) were significantly associated with higher risk for psychosis transition within two years (see figure 1, highlighted in blue). In the ROI analysis, we found a significant negative linear association between GMV decreases in the left hippocampus (kE = 353 voxels) and higher risk for psychosis transition (see figure 1, highlighted in red).

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GMV reductions in the hippocampus have frequently been observed in CHR and psychosis patients (Vissink et al. BP:GOS 2022; 2(2) 147-152), therefore our results further highlight the crucial role of this region in the progression of the disease. There is limited evidence on GMV increases in CHR patients. However, the GMV increase we found in the frontal pole may reflect compensatory mechanisms of the brain in the development of psychosis. In addition, we were able to provide biological validation of the NAPLS-2 risk calculator and its assessment of risk for transition to psychosis.

None Declared

Previous evidence suggests that early life complications (ELCs) interact with polygenic risk for schizophrenia (SCZ) in increasing risk for the disease. However, no studies have investigated this interaction on neurobiological phenotypes. Among those, anomalous emotion-related brain activity has been reported in SCZ, even if evidence of its link with SCZ-related genetic risk is not solid. Indeed, it is possible this relationship is influenced by non-genetic risk factors. Thus, this study investigated the interaction between SCZ-related polygenic risk and ELCs on emotion-related brain activity.

169 healthy participants (HP) in a discovery and 113 HP in a replication sample underwent functional magnetic resonance imaging (fMRI) during emotion processing, were categorized for history of ELCs and genome-wide genotyped. Polygenic risk scores (PRSs) were computed using SCZ-associated variants considering the most recent genome-wide association study. Furthermore, 75 patients with SCZ also underwent fMRI during emotion processing to verify consistency of their brain activity patterns with those associated with risk factors for SCZ in HP.

Results in the discovery and replication samples indicated no effect of PRSs, but an interaction between PRS and ELCs in left ventrolateral prefrontal cortex (VLPFC), where the greater the activity, the greater PRS only in presence of ELCs. Moreover, SCZ had greater VLPFC response than HP.

These results suggest that emotion-related VLPFC response lies in the path from genetic and non-genetic risk factors to the clinical presentation of SCZ, and may implicate an updated concept of intermediate phenotype considering early non-genetic factors of risk for SCZ.

The conceptualization of negative symptoms (NS) in schizophrenia is still controversial. Recent confirmatory factor-analytic studies suggested that the bi-dimensional model (motivational deficit [MAP] and expressive deficit [EXP]) may not capture the complexity of NS structure, which could be better defined by a five-factor (five NS domains) or a hierarchical model (five NS domains as first-order factors, and MAP and EXP, as second-order factors). A validation of these models is needed to define the structure of NS. To evaluate the validity and temporal stability of the five-factor or the hierarchical structure of the brief negative symptom scale (BNSS) in individuals with schizophrenia (SCZ), exploring associations between these models with cognition, social cognition, functional capacity, and functioning at baseline and at 4 years follow-up.

Clinical variables were assessed using state-of-the-art tools in 612 SCZ at two-time points. The validity of the five-factor and the hierarchical models was analyzed through structural equation models.

The two models had both a good fit and showed a similar pattern of associations with external validators at the two-time points, with minor variations. The five-factor solution had a slightly better fit. The associations with external validators favored the five-factor structure.

Our findings suggest that both five-factor and hierarchical models provide a valid conceptualization of NS in relation to external variables and that five-factor solution provides the best balance between parsimony and granularity to summarize the BNSS structure. This finding has important implications for the study of pathophysiological mechanisms and the development of new treatments.

Computational models offer promising potential for personalised treatment of psychiatric diseases. For their clinical deployment, fairness must be evaluated alongside accuracy. Fairness requires predictive models to not unfairly disadvantage specific demographic groups. Failure to assess model fairness prior to use risks perpetuating healthcare inequalities. Despite its importance, empirical investigation of fairness in predictive models for psychiatry remains scarce.

To evaluate fairness in prediction models for development of psychosis and functional outcome.

Using data from the PRONIA study, we examined fairness in 13 published models for prediction of transition to psychosis (n = 11) and functional outcome (n = 2) in people at clinical high risk for psychosis or with recent-onset depression. Using accuracy equality, predictive parity, false-positive error rate balance and false-negative error rate balance, we evaluated relevant fairness aspects for the demographic attributes ‘gender’ and ‘educational attainment’ and compared them with the fairness of clinicians’ judgements.

Our findings indicate systematic bias towards assigning less favourable outcomes to individuals with lower educational attainment in both prediction models and clinicians’ judgements, resulting in higher false-positive rates in 7 of 11 models for transition to psychosis. Interestingly, the bias patterns observed in algorithmic predictions were not significantly more pronounced than those in clinicians’ predictions.

Educational bias was present in algorithmic and clinicians’ predictions, assuming more favourable outcomes for individuals with higher educational level (years of education). This bias might lead to increased stigma and psychosocial burden in patients with lower educational attainment and suboptimal psychosis prevention in those with higher educational attainment.

Autistic symptoms represent a frequent feature in schizophrenia spectrum disorders (SSD). However, the prevalence and the cognitive and functional correlates of autistic symptoms in unaffected first-degree relatives of people with SSD remain to be assessed.

A total of 342 unaffected first-degree relatives related to 247 outpatients with schizophrenia were recruited as part of the multicenter study of the Italian Network for Research on Psychoses (NIRP). Autistic features were measured with the PANSS Autism Severity Scale. Three groups of participants, defined on the presence and severity of autistic symptoms, were compared on a wide array of cognitive and functional measures.

Of the total sample, 44.9% presented autistic symptoms; 22.8% showed moderate levels of autistic symptoms, which can be observed in the majority of people with SSD. Participants with higher levels of autistic symptoms showed worse performance on Working Memory (p = 0.014) and Social Cognition (p = 0.025) domains and in the Global Cognition composite score (p = 0.008), as well as worse on functional capacity (p = 0.001), global psychosocial functioning (p < 0.001), real-world interpersonal relationships (p < 0.001), participation in community activities (p = 0.017), and work skills (p = 0.006).

A high prevalence of autistic symptoms was observed in first-degree relatives of people with SSD. Autistic symptoms severity showed a negative correlation with cognitive performance and functional outcomes also in this population and may represent a diagnostic and treatment target of considerable scientific and clinical interest in both patients and their first-degree relatives.

Neurocognitive deficits are a core feature of psychosis and depression. Despite commonalities in cognitive alterations, it remains unclear if and how the cognitive deficits in patients at clinical high risk for psychosis (CHR) and those with recent-onset psychosis (ROP) are distinct from those seen in recent-onset depression (ROD).

This study was carried out within the European project ‘Personalized Prognostic Tools for Early Psychosis Management’, and aimed to characterise the cognitive profiles of patients with psychosis or depression.

We examined cognitive profiles for patients with ROP (n = 105), patients with ROD (n = 123), patients at CHR (n = 116) and healthy controls (n = 372) across seven sites in five European countries. Confirmatory factor analysis identified four cognitive factors independent of gender, education and site: speed of processing, attention and working memory, verbal learning and spatial learning.

Patients with ROP performed worse than healthy controls in all four domains (P < 0.001), whereas performance of patients with ROD was not affected (P > 0.05). Patients at CHR performed worse than healthy controls in speed of processing (P = 0.001) and spatial learning (P = 0.003), but better than patients with ROP across all cognitive domains (all P ≤ 0.01). CHR and ROD groups did not significantly differ in any cognitive domain. These findings were independent of comorbid depressive symptoms, substance consumption and illness duration.

These results show that neurocognitive abilities are affected in CHR and ROP, whereas ROD seems spared. Although our findings may support the notion that those at CHR have a specific vulnerability to psychosis, future studies investigating broader transdiagnostic risk cohorts in longitudinal designs are needed.

The structure of negative symptoms of schizophrenia is still a matter of controversy. Although a two-dimensional model (comprising the expressive deficit dimension and the motivation and pleasure dimension) has gained a large consensus, it has been questioned by recent investigations.

To investigate the latent structure of negative symptoms and its stability over time in people with schizophrenia using network analysis.

Negative symptoms were assessed in 612 people with schizophrenia using the Brief Negative Symptom Scale (BNSS) at baseline and at 4-year follow-up. A network invariance analysis was conducted to investigate changes in the network structure and strength of connections between the two time points.

The network analysis carried out at baseline and follow-up, supported by community detection analysis, indicated that the BNSS's items aggregate to form four or five distinct domains (avolition/asociality, anhedonia, blunted affect and alogia). The network invariance test indicated that the network structure remained unchanged over time (network invariance test score 0.13; P = 0.169), although its overall strength decreased (6.28 at baseline, 5.79 at follow-up; global strength invariance test score 0.48; P = 0.016).

The results lend support to a four- or five-factor model of negative symptoms and indicate overall stability over time. These data have implications for the study of pathophysiological mechanisms and the development of targeted treatments for negative symptoms.

Negative symptoms represent a fundamental aspect of schizophrenia: they have a substantial impact on patients’ real-life functioning and do not respond satisfactorily to currently available treatments. Therefore, a better understanding of the pathophysiological mechanisms underlying these symptoms could favor the development of new treatments.

To date, the most validated pathophysiological hypothesis indicates an association between the Motivational domain (consisting of avolition, anhedonia and asociality) and alterations in the neuronal circuits involved in motivation. The Expressive Deficit domain (consisting of blunted affect and alogia) would be subtended by widespread alterations of cortical connectivity and associated with impaired neurocognition, social cognition, and the presence of neurological soft signs.

The aim of the present study is to examine the neurobiological correlates of the two domains of negative symptoms, starting from the brain areas that have been most commonly found in the literature to be associated with negative symptoms.

Resting-state (rs) fMRI data were acquired in 62 subjects with schizophrenia (SZ) and 46 healthy controls (HC). The two negative symptom domains were assessed using the Brief Negative Symptom Scale. In addition, the following assessment tools were used: the Positive and Negative Syndrome Scale for the assessment of positive symptoms and disorganization, the Calgary Depression Scale for Schizophrenia for depression and the St. Hans Rating Scale for extrapyramidal symptoms. The study of the possible relationships between rs-brain activity and the negative symptoms domains was conducted through partial correlations, checking for possible confounding factors (positive, depressive, extrapyramidal symptoms and disorganization).

The SZ, compared to the HC, showed higher rs-brain activity of the right inferior parietal lobule and of the right temporoparietal junction and lower rs-brain activity of the right dorsolateral prefrontal cortex, bilateral anterior dorsal cingulate cortex, bilateral ventral caudate and bilateral dorsal caudate. Furthermore, in the group of patients, the rs-brain activity of the left ventral caudate showed a moderate negative correlation with the Expressive deficit domain (r = -0.401; p = 0.003), but not with the Motivational domain.

The results of the present study, in line with the literature, demonstrated how the two domains of negative symptomatology are subtended by different pathophysiological mechanisms. Given the role played by the ventral caudate in neurocognitive processes, these results are in line with the hypothesis that Expressive deficit may have a common etiopathogenesis with cognitive deficits. A better understanding of the neurobiology of negative symptoms could foster the development of innovative treatment strategies targeting the two negative symptom domains.

None Declared

Negative symptoms (NS) represent a heterogeneous construct of schizophrenia, whose conceptualization is still to be clarified. In the last decade, the conceptualization model that has received the most support from the literature has described 2 NS domains: the expressive deficit (EXP), which includes blunted affect and alogia, and the motivational deficit (MAP), which includes avolition, asociality, and anhedonia. However, different confirmatory factor-analytic studies suggest that the bi-dimensional model may not capture the complexity of this construct, which could be better defined by a 5-factor model (5 individual negative symptoms) or a hierarchical model (5 individual negative symptoms as first-order factors, and the 2 domains, MAP and EXP domains, as second-order factors). However, to our knowledge, no study has investigated associations between negative symptom models with social cognition and functional capacity, which are largely documented to correlate with negative symptoms, nor the associations with external validators over time, looking at the potential stability of negative symptom models validity through the course of the illness.

In the light of this observations, we investigated, the external validity of the five-factor model and the hierarchical model of the BNSS in subjects with schizophrenia, looking at associations with cognition, social cognition, functioning and functional capacity at baseline and at four years follow-up.

NS were assessed in 612 subjects with schizophrenia using the Brief Negative Symptom Scale at the baseline and after 4-year follow-up. State of the art assessment instruments were used to assess cognitive and functioning related variables. Structural equation models (SEM) that included the NS models and 4 external variables were used to our aim.

According to recent multicenter studies, our results confirmed the validity of the 5-factor- and the hierarchical-model of negative symptoms. In particular, these 2 models proved to be equivalent in terms of fit to the data at baseline and follow-up. As regard to the relationship of the two BNSS models with external variables, we found that there was a similar pattern of associations at the two time points despite minor variations.

The five factor and the hierarchical models provide an optimal conceptualization of negative symptoms in relation to external variables. The similar pattern of associations with external variables of the two models at the two time points despite minor variations, suggests that the simple and widely used 5-factor solution provides the best balance between parsimony and granularity to summarize BNSS structure. This data is of important relevance with consequent implications in the study of pathophysiological mechanisms and the development of targeted treatments for NS.

None Declared

The social defeat hypothesis (SDH) suggests that a chronic experience of social defeat increases the likelihood of the development of psychosis. The SDH indicates that a negative experience of exclusion leads to an increase in the baseline activity of the mesolimbic dopamine system (MDS), which in turn leads to the onset of psychosis. Social defeat models have previously been produced using animal models and preclinical literature; however, these theories have not fully been tested in human clinical samples. There have been studies implying changes in brain structure due to social defeat interactions; however, research evidence is varied.

This study aims to uncover whether exposure to SoDe has an impact on brain structure. Furthermore, we hope to understand if these changes are relevant to other mental health disorders.

698 (506 no SoDe, 191 SoDe) participants between the ages of 15-41 were recruited from the PRONIA-FP7 study. SoDe was measured from the self-reported questionnaires’ Bullying Scale’ and ‘The Everyday Discrimination Scale’. T1-weighted structural MRI data were processed; five 2 sample t-test analyses were carried out to compare the GMV differences in the entire sample and between the four groups.

The VBM analysis showed significant group interactions in the right thalamus proper when comparing participants who had experience SoDe to participants who had not experienced SoDe including all 4 groups along with left cerebral white matter differences. In the ROP subgroup, significant group interactions in the left cerebellum white matter were found along with right cerebral white matter, left cerebral white matter and right Thalamus proper.

The findings suggest that there are significant group interactions in thalamus and cerebral white matter. This is in keeping with some previous research suggesting volumetric changes in the thalamus due to stress and psychosis. Similarly for white matter there is some evidence suggesting differences due to SoDe and psychosis. However, there is a scarcity of research in this area with different research suggesting distinctive findings and therefore the evidence is inconclusive. In the ROP group analysis significant group interactions were present in the cerebellum due to SoDe experience. There is research suggesting the cerebellum’s role in multiple different aspects like social interaction, higher-order cognition, working memory, cognitive flexibility, and psychotic symptoms, with every research suggesting multiple different things the role of the cerebellum in SoDe in the ROP population is in question. Nonetheless this large-scale research presents some interesting novel finding and leads the way to a new area of research. Further analysis will explore the relationship between groups on markers of stress (CRP) and neuroinflammation as potential mediation of the environmental effects of SoDe.

None Declared

Negative symptoms (NS) represent an unmet need of treatment in schizophrenia (SCZ). As a result, these symptoms pose a significant burden on patients, their families, and the health care system. In the last decade, the conceptualization model that has received the most support from the literature has described 2 domains of NS: the expressive deficit (EXP), which includes blunted affect and alogia, and the motivational deficit (MAP), which includes avolition, asociality, and anhedonia. However, different confirmatory factor-analytic studies suggest that the bi-dimensional model may not capture the complexity of this construct, which could be better defined by the 5-factor model. To date no study exploiting innovative tools and state of the art assessment instruments has yet been conducted to evaluate the NS structure stability over time.

The aim of this study was to investigate the stability of the latent structure of NS in subjects with SCZ.

NS were assessed in 612 subjects with SCZ using the Brief Negative Symptom Scale (BNSS) at the baseline and after 4-year follow-up. A network invariance analysis was conducted for the data collected longitudinally.

Results showed that the BNSS’ items aggregated to form 5 distinct domains (avolition, asociality, blunted affect, alogia and anhedonia). The result of the network invariance test indicated that the network structure remained unchanged over time (network invariance test = 0.13; p = 0.169) while its overall strength decreased significantly (6.28 baseline, 5.79 at follow-up; global strength invariance test = 0.48; p = 0.016).

The results of this study show how the construct of NS can be better explained by the 5 individual negative symptoms and that this model is almost stable over time. Therefore the 2-dimensional model may be insufficient to describe the characteristics of NS. This data is of important relevance with consequent implications in the study of pathophysiological mechanisms and the development of targeted treatments for NS.

None Declared

Studies investigating cognitive impairments in psychosis and depression have typically compared the average performance of the clinical group against healthy controls (HC), and do not report on the actual prevalence of cognitive impairments or strengths within these clinical groups. This information is essential so that clinical services can provide adequate resources to supporting cognitive functioning. Thus, we investigated this prevalence in individuals in the early course of psychosis or depression.

A comprehensive cognitive test battery comprising 12 tests was completed by 1286 individuals aged 15–41 (mean age 25.07, s.d. 5.88) from the PRONIA study at baseline: HC (N = 454), clinical high risk for psychosis (CHR; N = 270), recent-onset depression (ROD; N = 267), and recent-onset psychosis (ROP; N = 295). Z-scores were calculated to estimate the prevalence of moderate or severe deficits or strengths (>2 s.d. or 1–2 s.d. below or above HC, respectively) for each cognitive test.

Impairment in at least two cognitive tests was as follows: ROP (88.3% moderately, 45.1% severely impaired), CHR (71.2% moderately, 22.4% severely impaired), ROD (61.6% moderately, 16.2% severely impaired). Across clinical groups, impairments were most prevalent in tests of working memory, processing speed, and verbal learning. Above average performance (>1 s.d.) in at least two tests was present for 40.5% ROD, 36.1% CHR, 16.1% ROP, and was >2 SDs in 1.8% ROD, 1.4% CHR, and 0% ROP.

These findings suggest that interventions should be tailored to the individual, with working memory, processing speed, and verbal learning likely to be important transdiagnostic targets.

Deficits in social cognition (SC) are significantly related to community functioning in schizophrenia (SZ). Few studies investigated longitudinal changes in SC and its impact on recovery. In the present study, we aimed: (a) to estimate the magnitude and clinical significance of SC change in outpatients with stable SZ who were assessed at baseline and after 4 years, (b) to identify predictors of reliable and clinically significant change (RCSC), and (c) to determine whether changes in SC over 4 years predicted patient recovery at follow-up.

The reliable change index was used to estimate the proportion of true change in SC, not attributable to measurement error. Stepwise multiple logistic regression models were used to identify the predictors of RCSC in a SC domain (The Awareness of Social Inference Test [TASIT]) and the effect of change in TASIT on recovery at follow-up.

In 548 participants, statistically significant improvements were found for the simple and paradoxical sarcasm of TASIT scale, and for the total score of section 2. The reliable change index was 9.8. A cut-off of 45 identified patients showing clinically significant change. Reliable change was achieved by 12.6% and RCSC by 8% of participants. Lower baseline TASIT sect. 2 score predicted reliable improvement on TASIT sect. 2. Improvement in TASIT sect. 2 scores predicted functional recovery, with a 10-point change predicting 40% increase in the probability of recovery.

The RCSC index provides a conservative way to assess the improvement in the ability to grasp sarcasm in SZ, and is associated with recovery.

Abnormal auditory processing of deviant stimuli, as reflected by mismatch negativity (MMN), is often reported in schizophrenia (SCZ). At present, it is still under debate whether this dysfunctional response is specific to the full-blown SCZ diagnosis or rather a marker of psychosis in general. The present study tested MMN in patients with SCZ, bipolar disorder (BD), first episode of psychosis (FEP), and in people at clinical high risk for psychosis (CHR).

Source-based MEG activity evoked during a passive auditory oddball task was recorded from 135 patients grouped according to diagnosis (SCZ, BD, FEP, and CHR) and 135 healthy controls also divided into four subgroups, age- and gender-matched with diagnostic subgroups. The magnetic MMN (mMMN) was analyzed as event-related field (ERF), Theta power, and Theta inter-trial phase coherence (ITPC).

The clinical group as a whole showed reduced mMMN ERF amplitude, Theta power, and Theta ITPC, without any statistically significant interaction between diagnosis and mMMN reductions. The mMMN subgroup contrasts showed lower ERF amplitude in all the diagnostic subgroups. In the analysis of Theta frequency, SCZ showed significant power and ITPC reductions, while only indications of diminished ITPC were observed in CHR, but no significant decreases characterized BD and FEP.

Significant mMMN alterations in people experiencing psychosis, also for diagnoses other than SCZ, suggest that this neurophysiological response may be a feature shared across psychotic disorders. Additionally, reduced Theta ITPC may be associated with risk for psychosis.

Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR).

SCZ from the Italian Network for Research on Psychoses (N = 598 in the Discovery sample, N = 298 in the Validation sample) underwent historical, clinical, psychological, environmental and resilience assessments. A Support Vector Machine algorithm (based on 85 variables extracted from the above-mentioned assessments) was built in the Discovery sample, and replicated in the Validation sample, to classify between HR and LR patients, within a nested, Leave-Site-Out Cross-Validation framework. We then investigated whether algorithm decision scores were associated with the cognitive and clinical characteristics of patients.

The algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% (p < 0.0001) in the Discovery sample, and 80.2% in the Validation sample. Higher self-esteem, larger social network and use of adaptive coping strategies were the variables most frequently chosen by the algorithm to generate decisions. Correlations between algorithm decision scores, socio-cognitive abilities, and symptom severity were significant (pFDR < 0.05).

We identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia.