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Adult gyrification provides a window into coordinated early neurodevelopment when disruptions predispose individuals to psychiatric illness. We hypothesized that the echoes of such disruptions should be observed within structural gyrification networks in early psychiatric illness that would demonstrate associations with developmentally relevant variables rather than specific psychiatric symptoms. We employed a new data-driven method (Orthogonal Projective Non-Negative Matrix Factorization) to delineate novel gyrification-based networks of structural covariance in 308 healthy controls. Gyrification within the networks was then compared to 713 patients with recent onset psychosis or depression, and at clinical high-risk. Associations with diagnosis, symptoms, cognition, and functioning were investigated using linear models. Results demonstrated 18 novel gyrification networks in controls as verified by internal and external validation. Gyrification was reduced in patients in temporal-insular, lateral occipital, and lateral fronto-parietal networks (pFDR

New treatment approaches for opioid-dependent patients include injectable opioid agonist treatment with diacetylmorphine. While evidence has shown beneficial clinical effects of diacetylmorphine, it is still not clear how long-term diacetylmorphine treatment affects the brain and whether functional brain changes are accompanied by clinical improvements. Therefore, this prospective case-control study focuses on long-term effects of diacetylmorphine on resting-state functional connectivity. We included opioid-dependent patients (N = 22, age range 33–58, 16 males) treated with diacetylmorphine and healthy controls (N = 9, age range 27–55, 5 males) that underwent two MRI assessments approximately nine years apart. For the patients, the assessments took part shortly after the diacetylmorphine intake to be able to explore changes in resting-state functional connectivity in brain regions related to the stage of binge and intoxication (caudate, putamen, nucleus accumbens). A cluster in the right superior frontal gyrus was detected, showing over nine years an increase in functional connectivity originating from the left caudate and the left accumbens in patients but not in healthy controls. These connectivity changes in patients were related to the duration of the diacetylmorphine treatment at the follow-up, indicating smaller increases in functional connectivity with longer treatment duration (r = 0.63, P < 0.01). These results suggest that long-term diacetylmorphine treatment in opioid-dependent patients increases fronto-striatal connections, an effect that is linked to the duration of the treatment duration. Future research needs to further address the wide-ranging effects of diacetylmorphine on brain functioning and deepen the understanding of their clinical relevance.

Background Negative symptoms are core contributors to social deficits in psychosis. However, currently available interventions do not significantly ameliorate negative symptoms or social outcomes in individuals at Clinical High Risk of Psychosis (CHR-P). Given its critical role in human social behaviour and cognition, the oxytocin (OT) system is a promising target for the treatment of social impairments in CHR-P subjects. Methods In a double-blind, placebo-controlled, crossover design, 30 CHR-P males were studied using functional magnetic resonance imaging (fMRI) on two occasions, once after 40IU intranasal OT and once after placebo. A modified version of the Sally-Anne task was used to record brain activation during inferring others’ beliefs (cognitive empathy) and social emotions (emotional empathy). The Reading the Mind in the Eyes Test was acquired to test whether OT effects were mediated by baseline social-emotional abilities. Results OT did not modulate behavioural task performance but reduced activation in the bilateral inferior frontal gyrus compared with placebo during emotional empathy. Furthermore, the relationship between brain activation and task performance after OT administration was mediated by baseline social-emotional abilities; while task accuracy during emotional empathy increased with decreasing activation in the left inferior frontal gyrus in CHR-P individuals with low social-emotional abilities, there was no such relationship in CHR-P individual with high social-emotional abilities. Discussion These findings suggest that OT enhances neural efficiency in inferring others’ social emotions in those people at clinical high risk for psychosis with attenuated emotional empathy.

Background Individuals at clinical high risk (CHR) of psychosis have an approximately 20% probability of developing psychosis within 2 years, as well as an associated risk of non-psychotic disorders and functional impairment. People with subclinical psychotic experiences (PEs) are also at risk of future psychotic and non-psychotic disorders and decreased functioning. It is difficult to accurately predict outcomes in individuals at risk of psychosis on the basis of symptoms alone. Biomarkers for accurate prediction of outcomes could inform the clinical management of this group. Methods We conducted two nested case-control studies. We employed discovery-based proteomic methods to analyse protein expression in baseline plasma samples in EU-GEI and age 12 plasma samples in ALSPAC using liquid chromatography mass spectrometry. Differential expression of quantified proteomic markers was determined by analyses of covariance (with false discovery rate of 5%) comparing expression levels for each marker between those who did not and did not develop psychosis in Study 1 (adjusting for age, gender, body mass index and years in education), and between those who did and did not develop PEs in Study 2 (adjusting for gender, body mass index and maternal social class). Support vector machine algorithms were used to develop models for prediction of transition vs. non-transition (as determined by the Comprehensive Assessment of At Risk Mental States) and poor vs. good functional outcome at 2 years in Study 1 (General Assessment of Functioning: Disability subscale score 60). Similar algorithms were used to develop a model for prediction of PEs vs. no PEs at age 18 in Study 2 (as determined by the Psychosis Like Symptoms Interview). Results In Study 1, 35 of 166 quantified proteins were significantly differentially expressed between CHR participants who did and did not develop psychosis. Functional enrichment analysis provided evidence for particular implication of the complement and coagulation cascade (false discovery rate-adjusted Fisher’s exact test p=2.23E-21). Using 65 clinical and 166 proteomic features a model demonstrated excellent performance for prediction of transition status (area under the receiver-operating curve [AUC] 0.96, positive predictive value [PPV] 83.0%, negative predictive value [NPV] 93.8%). A model based on the ten most predictive proteins accurately predicted transition status in training (AUC 0.96, PPV 87.5%, NPV 95.8%) and withheld data (AUC 0.92, PPV 88.9%, NPV 91.4%). A model using the same 65 clinical and 166 proteomic features predicted 2-year functional outcome with AUC 0.72 (PPV 67.6%, NPV 47.6%). In Study 2, 5 of 265 quantified proteins were significantly differentially expressed between participants who did and did not report PEs at age 18. A model using 265 proteomic features predicted PEs at age 18 with AUC 0.76 (PPV 69.1%, NPV 74.2%). Discussion With external validation, models incorporating proteomic data may contribute to improved prediction of clinical outcomes in individuals at risk of psychosis.

Background Functional deficits associated with the Clinical High Risk (CHR) status very often lead to inability to attend school, unemployment, as well as social isolation, thus calling for predictors of individual functional outcomes which may facilitate the identification of people requiring care irrespective of transition to psychosis. Studies have revealed that a pattern of cortical and subcortical gray matter volumes (GMV) anomalies measured at baseline in CHR individuals could predict their functional abilities at follow up. Furthermore, literature is consistent in revealing the crucial role of several environmental adverse events in increasing the risk of developing either transition to psychosis, or a worse overall personal functioning. Therefore, the aim of this study is to employ machine learning to test the individual and combined ability of baseline GMV data and of history of environmental adverse events in predicting good vs. poor social and occupational outcome in CHR individuals at follow up. Methods 92 CHR individuals recruited from the 7 discovery PRONIA sites were included in this project. Social and occupational impairment at follow up (9–12 months) were respectively measured through the Global Functioning: Social (GF:S) and Role (GF:R) scale, and CHR with a follow up rating of 7 or below were labeled as having a poor functional outcome. This way, we could separate our cohort in 52 poor outcome CHR and 40 good outcome CHR. GMV data were preprocessed following published procedures which allowed also to correct for site effects. The environmental classifier was built based on Childhood Trauma Questionnaire, Bullying Scale, and Premorbid Adjustment Scale (childhood, early adolescence, late adolescence and adulthood) scores. Raw scores have been normalized according to the psychometric properties of the healthy samples used for validating these questionnaires and scale, in order to obtain individual scores of deviation from the normative occurrence of adverse environmental events. GMV and environmental-based predictive models were independently trained and tested within a leave-site-out cross validation framework using a Support Vector Machine algorithm (LIBSVM) through the NeuroMiner software, and their predictions were subsequently combined through stacked generalization procedures. Results Our GMV-based model could predict follow up social outcome with 67.4% Balanced Accuracy (BAC) and significance (p=0.01), while it could not predict occupational outcome (46.6% BAC). On the other hand, our environmental-based model could discriminate both poor vs. good social and occupational outcomes at follow up with, respectively, 71% and 66.4% BACs, and significance (both p=0.0001). Specifically, the most reliable features in the environmental classifier were scores reflecting deviations from the normative values in childhood trauma and adult premorbid adjustment, for social outcome prediction, and in bullying experiences and late adolescence premorbid adjustment, for occupational outcome prediction. Only for social outcome prediction, stacked models outperformed individual classifiers’ predictions (74.3% BAC, p=0.0001). Discussion Environmental features seem to be more accurate than GMV in predicting both social and occupational outcomes in CHR. Interestingly, the predictions of follow up social and occupational outcomes rely on different patterns of occurrence of specific environmental adverse events, thus providing novel insights about how environmental adjustment disabilities, bullying and traumatic premorbid experiences may impact on different bad outcomes associated with the CHR status.

Background Formal thought disorder (FThD) has been associated with more severe illness courses and functional deficits in psychosis patients. Given these associations, it remains unclear whether the presence of FThD accounts for the heterogeneous presentation of psychoses, and whether it characterises a specific subgroup of patients showing prominent differential illness severity, neurocognitive and functional impairments already in the early stages of psychosis. Thus, our aim is 1) to evaluate whether there are stable subtypes of patients with Recent-Onset Psychosis (ROP) that are characterized by distinct FThD patterns, 2) to investigate whether this FThD-related stratification is associated with clinical, and neurocognitive phenotypes at an early stage of the disease, and 3) to explore correlation patterns among the FThD-related symptoms, functioning and neurocognition through network analysis. Methods 279 individuals experiencing ROP were recruited for this project as part of multi-site European PRONIA study. In the present study, FThD was assessed with conceptual disorganization, difficulty in abstract thinking, poverty of content of speech, increased latency of response and poverty of speech items from the Positive and Negative Symptom Scale (PANSS) and the Scale for the Assessment of Negative Symptoms (SANS). We first applied a multi-step clustering protocol comparing three clustering algorithms: (i) k-means, (ii) hierarchical clustering, and (iii) partitioning around medoids with the number of clusters ranging from 2 to 10. Our protocol runs following four checkpoints; (i) validity [ClValid package], (ii) re-evaluation of validity results and unbiased determination of the winning algorithm [NbClust package], (iii) stability test [ClusterStability package] and (iv) generalizability [predict.strength package] testing for the most optimal clustering solution. Thereafter, we investigated whether the identified FThD subgrouping solution was associated with neurocognitive performance, social and occupational functioning by using Welch’s two-sample t-test or Mann-Whitney-U test based on the distribution of data, and explored the interrelation of these domains with network analysis by using qgraph package with the spearman correlation matrix among variables. All analyses and univariate statistical comparisons were conducted with R version 3.5.2. We used the False Discovery Rate (FDR)37 to correct all P-values for the multiple comparisons. Results The k-means algorithm-based on two-cluster solution (FThD high vs. low) surviving these validity, stability and generalizability tests was chosen for further association tests and network analysis with core disease phenotypes. Patients in FThD high subgroup had lower scores in global (pfdr = 0.0001), social (pfdr < 0.0001) and role (pfdr < 0.0001) functioning, in semantic (pfdr < 0.0001) and phonological verbal fluency (pfdr = 0.0004), verbal short-term memory (pfdr = 0.0018) and abstract thinking (pfdr = 0.0099). Cluster assignment was not informed by the global disease severity (pfdr = 0.7786) but was associated with more pronounced negative symptoms (pfdr = 0.0001) in the FThD high subgroup. Discussion Our findings highlight how the combination of unsupervised machine learning algorithms with network analysis techniques may provide novel insight about the mappings between psychopathology, neurocognition and functioning. Furthermore, they point how FThD may represent a target variable for individualized psycho-, socio-, logotherapeutic interventions aimed at improving neurocognition abilities and functioning. Prospective studies should further test this promising perspective.

Background Evidence exists that cannabis consumption is associated with the development of psychosis. Further, continued cannabis use in individuals with recent onset psychosis (ROP) increases the risk for rehospitalization, high symptom severity and low general functioning. Clear inter-individual differences in the vulnerability to the harmful effects of the drug have been pointed out. These findings emphasize the importance of investigating the inter-individual variability in the role of cannabis use in ROP and to understand how cannabis use relates to subclinical conditions that predate the full-blown disease in clinical high-risk (CHR). Specific symptoms have been linked with continued cannabis consume, still research is lacking on how different factors contribute together to an elevated risk of cannabis relapse. Multivariate techniques have the capacity to extract complex patterns from high dimensional data and apply generalized rules to unseen cases. The aim of the study is therefore to assess the predictability of cannabis relapse in ROP and CHR by applying machine learning to clinical and environmental data. Methods All participants were recruited within the multi-site, longitudinal PRONIA study (www.pronia.eu). 112 individuals (58 ROP and 54 CHR) from 8 different European research centres reported lifetime cannabis consume at baseline and were abstinent for at least 4 weeks. We defined cannabis relapse as any cannabis consume between baseline and 9 months follow-up reported by the individual. To predict cannabis relapse, we trained a random forest algorithm implemented in the mlr package, R version 3.5.2. on 183 baseline variables including clinical symptoms, general functioning, demographics and consume patterns within a repeated-nested cross-validation framework. The data underwent pre-processing through pruning of non-informative variables and median-imputation for missing values. The number of trees was set to 500, while the number of nodes, sample fraction and mtry were optimized. All hyperparameters were tuned with the model-based optimization implemented in the mlrMBO R package. Results After 9 months 50 individuals (48 % ROP, 52 % CHR) have relapsed on cannabis use. Relapse was over all timepoints associated with more severe psychotic symptoms measured by PANSS positive and PANSS general (p Discussion Our results using a state-of-the-art machine learning approach suggest that the multivariate signature of baseline demographic and clinical data could predict follow up cannabis relapse above chance level in CHR and ROP. Our findings revealing that cannabis relapse is associated with more severe symptoms is in line with previous literature and emphasizes the need for targeted treatment towards abstinence from cannabis. The information of demographic and clinical patterns might be useful in order to specifically address therapeutic strategies in individuals at higher risk for relapse. This might include special programs for younger patients and taking into account the place of living, like urban areas. Further research is needed in order to validate our model in an independent sample.

Background Childhood maltreatment (CM) is a major psychiatric risk factor and leads to long-lasting physical and mental health implications throughout the affected individual’s lifespan. Nonetheless, the neuroanatomical correlates of CM and their specific clinical impact remain elusive. This might be attributed to the complex, multidimensional nature of CM as well as to the restrictions of traditional analysis pipelines using nosological grouping, univariate analysis and region-of-interest approaches. To overcome these issues, we present a novel transdiagnostic and naturalistic machine learning approach towards a better and more comprehensive understanding of the clinical and neuroanatomical complexity of CM. Methods We acquired our dataset from the multi-center European PRONIA cohort (www.pronia.eu). Specifically, we selected 649 male and female individuals, comprising young, minimally medicated patients with clinical high-risk states for psychosis as well as recent-onset of depression or psychosis and healthy volunteers. As part of our analysis approach, we created a new Matlab Toolbox, which performs multivariate Sparse Partial Least Squares Analysis in a robust machine learning framework. We employed this algorithm to detect multi-layered associations between combinations of items from the Childhood Trauma Questionnaire (CTQ) and grey matter volume (GMV) and assessed their generalizability via nested cross-validation. The clinical relevance of these CM signatures was assessed by correlating them to a wide range of clinical measurements, including current functioning (GAF, GF), depressivity (BDI), quality of life (WHOQOL-BREF) and personality traits (NEO-FFI). Results Overall, we detected three distinct signatures of sexual, physical and emotional maltreatment. The first signature consisted of an age-dependent sexual abuse pattern and a corresponding GMV pattern along the prefronto-thalamo-cerebellar axis. The second signature yielded a sex-dependent physical and sexual abuse pattern with a corresponding GMV pattern in parietal, occipital and subcortical regions. The third signature was a global emotional trauma signature, independent of age or sex, and projected to a brain structural pattern in sensory and limbic brain regions. Regarding the clinical impact of these signatures, the emotional trauma signature was most strongly associated with massively impaired state- and trait-level characteristics. Both on a phenomenological and on a brain structural level, the emotional trauma pattern was significantly correlated with lower levels of functioning, higher depression scores, decreased quality of life and maladaptive personality traits. Discussion Our findings deliver multimodal, data-driven evidence for a differential impact of sexual, physical and emotional trauma on brain structure and clinical state- and trait-level phenotypes. They also highlight the multidimensional nature of CM, which consists of multiple layers of highly complex trauma-brain patterns. In broader terms, our study emphasizes the potential of machine learning approaches in generating novel insights into long-standing psychiatric topics.

Background Childhood trauma (CT) is associated with an increased risk for psychiatric disorders like major depression and psychosis. However, the pathophysiological relationship between CT, psychiatric disease and structural brain alterations is still unknown. Methods PRONIA (‘Personalized Prognostic Tools for Early Psychosis Mangement’) is a prospective collaboration project funded by the European Union under the 7th Framework Programme (grant agreement n° 602152). Considering a broad set of variables (sMRI, rsMRI, DTI, psychopathological, life event related and sociobiographic data, neurocognition, genomics and other blood derived parameters) as well as advanced statistical methods, PRONIA aims at developing an innovative multivariate prognostic tool enabling an individualized prediction of illness trajectories and outcome. Seven clinical centers in five European countries and in Australia participate in the evaluation of three clinical groups (subjects clinically at high risk of developing a psychosis (CHR), patients with a recent onset psychosis (ROP) and patients with a recent onset depression (ROD)) as well as healthy controls (HC). To investigate the high-dimensional patterns of CT experience, measured by the childhood trauma questionnaire (CTQ), in HC and our three patient groups (PAT) (n=643), we used a Support Vector Machine (SVM). Furthermore, we tested whether patient-specific CT exposure is associated with structural brain changes by VBM analyses. Results We found that patients and HC could be separated very well by their CTQ pattern, whereas the different patient groups showed no specific CTQ pattern. Furthermore, an association with extensive grey matter changes suggests an impact on brain maturation which may put individuals at increased risk for mental disease. Discussion We have demonstrated in this large multi-center cohort that adverse experiences in childhood contribute transdiagnostically to the riskr for developing a psychiatric disease. The observed association between CTQ scores and structural changes suggests an impact of adverse childhood experiences on brain development. Resulting alterations may add to a neurobiological vulnerability for depression and psychosis. A role of both features for other mental disorders could be assumed and warrants further investigation.

To date, the MRI-based individualized prediction of psychosis has only been demonstrated in single-site studies. It remains unclear if MRI biomarkers generalize across different centers and MR scanners and represent accurate surrogates of the risk for developing this devastating illness. Therefore, we assessed whether a MRI-based prediction system identified patients with a later disease transition among 73 clinically defined high-risk persons recruited at two different early recognition centers. Prognostic performance was measured using cross-validation, independent test validation, and Kaplan-Meier survival analysis. Transition outcomes were correctly predicted in 80% of test cases (sensitivity: 76%, specificity: 85%, positive likelihood ratio: 5.1). Thus, given a 54-month transition risk of 45% across both centers, MRI-based predictors provided a 36%-increase of prognostic certainty. After stratifying individuals into low-, intermediate-, and high-risk groups using the predictor’s decision score, the high- vs low-risk groups had median psychosis-free survival times of 5 vs 51 months and transition rates of 88% vs 8%. The predictor’s decision function involved gray matter volume alterations in prefrontal, perisylvian, and subcortical structures. Our results support the existence of a cross-center neuroanatomical signature of emerging psychosis enabling individualized risk staging across different high-risk populations. Supplementary results revealed that (1) potentially confounding between-site differences were effectively mitigated using statistical correction methods, and (2) the detection of the prodromal signature considerably depended on the available sample sizes. These observations pave the way for future multicenter studies, which may ultimately facilitate the neurobiological refinement of risk criteria and personalized preventive therapies based on individualized risk profiling tools.

Background There is urgent need to improve the limited prognostic accuracy of psychopathology-based classifications to predict the onset of psychosis in clinical high-risk (CHR) subjects for psychosis. However, as yet no reliable biological marker has been established to differentiate CHR subjects who will develop psychosis from those who will not. This study investigated abnormalities in graph-based gyrification connectome in CHR subjects and patients with first-episode psychosis (FEP) and tested the accuracy of this systems-based approach to predict the transition to psychosis among CHR individuals. Methods 44 healthy controls (HC), 63 at-risk mental state (ARMS) subjects without later transition to psychosis (ARMS-NT), 16 ARMS subjects with later transition (ARMS-T), and 38 antipsychotic-free patients with FEP were recruited from the specialized clinic for the early detection of psychosis at the Department of Psychiatry, University of Basel, Basel, Switzerland. Gyrification-based structural covariance networks (connectomes) were constructed to quantify global integration, segregation and small-worldness. Extremely randomized trees with repeated, nested cross-validation was performed to differentiate ARMS-T from ARMS-NT individuals. Permutation testing was used to assess the significance of classification performance measures. Results Small-worldness is reduced in both ARMS-T and FEP patients, secondary to reduced integration and increased segregation in both groups. In addition, we also found that transitivity (segregation) was significantly higher in ARMS-T and FEP groups compared to both ARMS-NT and healthy controls. Using the connectome properties as features, we obtained a high classification accuracy of 90% (balanced accuracy: 81%, positive predictive value: 85%, negative predictive value: 92%.) All performance measures were highly significant as indicated by permutation tests (all p < 0.01). Discussion Our findings suggest that there is poor integration in the coordinated development of cortical folding in patients who develop psychosis. This study further indicates that gyrification-based connectomes might be a promising means to generate systems-based measures from anatomical data that improves individual prediction of psychosis transition in CHR subjects.

Background Neuro-cognitive deficits are a core feature of psychosis. In the clinical high risk stages of psychosis, neuro-cognitive deficits qualitatively affect the same functions while being quantitatively less marked compared to those in full-blown disorder. Therefore, cognitive impairments are considered to be an important intermediate phenotype for transition to psychosis. Partially overlapping deficits were also reported in depressive disorders, so it is important to identify deficits specifically associated to psychotic symptoms from those common to other conditions. We aimed to identify and differentiate cognitive deficits specifically associated to [i] psychopathology in general (i.e., presence of clinical diagnosis); [ii] psychotic symptoms; [iii] sub- and threshold levels of psychotic symptoms. Methods We compared four groups of participants within the project Personalised Prognostic Tools for Early Psychosis Management (PRONIA; www.pronia.eu). The PRONIA Cognitive Battery (PCB) includes 10 tests selected as reliable measures of neuropsychological difficulties in patients at high-risk of psychosis. The scores were obtained from the PRONIA Discovery Sample, which included 707 participants: 278 healthy controls (HC); 138 recent-onset depression (ROD); 139 clinical high-risk (CHR); 152 recent-onset psychosis (ROP), tested in seven sites across Europe. At first the norms were calculated correcting the HC’s raw scores by sex, age, cognitive level, education, and mother language (English, Finnish, German, Italian, or other). Then, univariate analyses of variance with a priori contrasts were used for directly comparing [i] HC vs ROD/CHR/ROP; [ii] ROD vs CHR/ROP; [iii] CHR vs ROP. Results The difference in cognitive performance between the clinical groups (ROD, CHR, ROP) as compared to the HC [i], was shown in measures of: speed of execution (ωP2 range 0.016–0.123; all p≤0.035); sustained attention (ωP2: 0.024–0.080; p≤0.022); verbal fluency (ωP2: 0.020–0.031; p≤0.002); emotion recognition (ωP2=0.026; p=0.001); visuo-spatial (ωP2: 0.018–0.049; p≤0.006) and verbal (ωP2: 0.038–0.075; p

Structural brain abnormalities are central to schizophrenia (SZ), but it remains unknown whether they are linked to dysmaturational processes crossing diagnostic boundaries, aggravating across disease stages, and driving the neurodiagnostic signature of the illness. Therefore, we investigated whether patients with SZ (N = 141), major depression (MD; N = 104), borderline personality disorder (BPD; N = 57), and individuals in at-risk mental states for psychosis (ARMS; N = 89) deviated from the trajectory of normal brain maturation. This deviation was measured as difference between chronological and the neuroanatomical age (brain age gap estimation [BrainAGE]). Neuroanatomical age was determined by a machine learning system trained to individually estimate age from the structural magnetic resonance imagings of 800 healthy controls. Group-level analyses showed that BrainAGE was highest in SZ (+5.5 y) group, followed by MD (+4.0), BPD (+3.1), and the ARMS (+1.7) groups. Earlier disease onset in MD and BPD groups correlated with more pronounced BrainAGE, reaching effect sizes of the SZ group. Second, BrainAGE increased across at-risk, recent onset, and recurrent states of SZ. Finally, BrainAGE predicted both patient status as well as negative and disorganized symptoms. These findings suggest that an individually quantifiable "accelerated aging" effect may particularly impact on the neuroanatomical signature of SZ but may extend also to other mental disorders.

Background: Despite impressive advancements in early interventions in psychosis, there is an urgent need of robust neurobiological markers to improve the predictive value of psychosis transition. Available structural imaging literature in the field is undermined by several methodological caveats and a number of confounders such as exposure to antipsychotic treatment. Methods: Fourteen voxel-based morphometry studies of antipsychotic-naive subjects at enhanced clinical risk for psychosis (high risk [HR]) or experiencing a first-episode psychosis (FEP) were included. Formal meta-analysis of effect sizes and “signed differential mapping” voxel-based meta-analysis were combined to control the results for sample sizes, strength of individual findings, and confounding variables. Results: Formal effect size meta-analysis indicated consistent gray matter (GM) reductions both in subjects at enhanced clinical risk for psychosis and in first-episode subjects when compared with control groups. Voxel-based meta-analysis showed GM reductions in the temporal, limbic prefrontal cortex within the HR group and in the temporal insular cortex and cerebellum within the FEP group. Psychosis onset was characterized by GM decreases in temporal, anterior cingulate, cerebellar, and insular regions. GM alterations in the temporal regions directly related to severity of psychotic symptoms. There was no publication bias. Heterogeneity across studies was low. Sensitivity analyses confirmed robustness of the above results. Conclusions: Vulnerability to psychosis is associated with consistent GM decreases in prefrontal and temporolimbic areas. The onset of full disease is accompanied by temporoinsular, anterior cingulate, and cerebellar GM reductions. Neuroanatomical alterations in temporal regions may underlie the clinical onset of psychotic symptoms.

Background Schizotypy traits range from odd behaviors to symptoms that resemble full schizophrenia, although less severe. Previous studies associated different degrees of positive and negative schizotypal traits to variations in the persons’ cognitive profiles while others related them to the risk to develop psychosis. We hypothesize that similar pattern of positive and negative schyzoypy traits characterize individuals at risk of psychosis and patients meeting the criteria for recent onset psychosis, although with different degrees of severity. Also, both should differ from depressed patients. Moreover, specific combinations of schizotypy traits and neurocognitive alterations should be associated to the different psychopathological profiles. The final goal of the study is to identify candidate predictors of risk of psychosis that will be used as features in next machine learning analyses. Methods The present is a multi-centric study that was conducted as part of the project titled ‘Personalised Prognostic Tools for Early Psychosis Management’ (PRONIA). 115 participants at high-risk for psychosis (CHR), 114 recent onset psychosis (ROP), 123 recent onset depression (ROD) and 252 healthy controls (HC) took part in the study. All were aged between 15 and 40 years. The participants filled the Wisconsin Schizotypy questionnaire, measuring positive (Magical Ideation Scale - MIS; Perceptual Aberration Scale - PAS) and negative schizotypy traits (Social Anhedonia Scale - SAnS; Physical Anhedonia Scale - PAnS). Moreover, they were administered the PRONIA Cognitive Battery (PCB), comprising measures of visuo-spatial dexterity and memory (Rey Figure, copy and delayed drawing), short-term memory (Digit Span - DS), Verbal Learning, Verbal Fluency, Attention (Continuous Performance Test - CPT, Digit Symbol Substitution Test - DSST), Emotions’ Recognition, General Intelligence (WAIS Vocabulary, Matrix Reasoning). Results We run i) a Multivariate Analysis of Covariance with ‘WSS subscales’ as dependent variable; ‘Group’ as between subject factor; ‘Age’ and ‘Gender’ as covariates; ii) a Multinomial logistic regression with ‘Group’ as dependent variable; HC ‘Group’ as reference parameter; ‘WSS subscales’ and scores at the PCB’s tests as predictors; ‘Age’ and ‘Gender’ as covariates. ROP and CHR reported both positive and negative schizotypy traits, although only the negative symptoms involving social aspects were clearly evident in CHR. Also, ROP and CHR differed for the positive symptoms, as they were present but at a lower level in CHR than in ROP. ROD instead scored high at the negative symptoms. Interestingly, ROP, CHR and ROD did not differ between each other for the negative symptoms, probably reflecting the effect of the psychopathology on the patients’ general motivation to life. The regressions analysis highlighted different patterns of associations of WSS and neurocognitive scores with the clinical status. In particular, the scores at the MIS, PAS and SanS combined with the Rey Figure (delayed drawing), predicted that the participants were CHR; the MIS, PAS and SAnS with measures of attention (CPT, DSST) predicted that the participant were ROP; the PAS; SAnS and short-term memory (DS) predicted to being ROD. Discussion Coherently with the hypotheses, different schizotypy traits or grade of severity characterized patients with distinct psychopathology profiles. Also, the association of WSS subscales with the cognitive measures differentiated between groups, with visuo-spatial long-term memory being associated to CHR, measures of attention relating to ROP and verbal short term memory relating to ROD. This makes these measures good candidates for the upcoming machine learning analyses.