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.

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.

Repetitive transcranial magnetic stimulation (rTMS), a non-invasive neuromodulation therapy most widely used in depression, has shown evidence of secondary benefits for cognition in both neurologic and neuropsychiatric conditions. The recent development of more efficient stimulation protocols, such as accelerated high-dose intermittent theta burst (iTBS)-rTMS, has substantially reduced treatment burden by shortening the treatment course by >50%. This study aimed to establish the safety, feasibility, acceptability, and preliminary efficacy of iTBS-rTMS as a tool for bolstering cognition in individuals with amnestic mild cognitive impairment (aMCI).

Twenty-four patients with aMCI were enrolled in an open-label phase I trial of iTBS-rTMS; 2 withdrew prior to initiating treatment due to personal circumstances. All participants had received a diagnosis of MCI due to possible AD from a healthcare provider (i.e., neurologist or neuropsychologist) and met actuarial neuropsychological criteria for aMCI. This sample of older adults (range: 61.5-85.2 years, M = 74.1, SD = 5.71) was predominantly White/non-Hispanic (n = 23; Black/non-Hispanic: n = 1), roughly half female (n = 13), with a college education (range: 12-20 years, M = 15.9, SD = 2.5). Participants received 24 sessions of iTBS-rTMS to the left dorsolateral prefrontal cortex over 3 days (8 sessions each, lasting roughly 2 hours per day). Participants rated their perceptions and experience of common side effects during and after each treatment session as well as retrospectively at post-treatment and 4-week follow-up. They completed structural and functional brain MRI, neuropsychiatric evaluations, and neuropsychological assessments before and after treatment and were administered a subset of these measures at 4-week follow-up. MoCA scores were used to monitor for adverse neurocognitive effects, and the fluid cognition composite score from the NIH Toolbox Cognition Battery was used to test preliminary efficacy.

We achieved a high retention rate (95%), with 21 of the 22 participants completing all study procedures. There were no clinically significant adverse neuroradiological, neuropsychiatric, or neurocognitive effects of treatment. Participant reports indicated high tolerability and acceptability, with a modal rating of 0 (on a scale from 0=not at all to 10=extremely) for six common side effects (i.e., headache, pain, scalp irritation, facial twitching, fatigue, fear/anxiety), assessed both during and after each treatment session. They reported very low desire to quit despite some participants rating the treatment as moderately tiring. We observed significant, large effect-size (d = 0.98) improvements in fluid cognition from pre- to post-treatment.

Our findings support the safety, feasibility, and acceptability of iTBS-rTMS treatment in patients with aMCI. Further, although not explicitly dosed for efficacy, we provide preliminary evidence of improved fluid cognition as a function of treatment, highlighting the potential of this treatment for improving trans-domain cognitive impairment. These promising results can directly inform future trials aimed at optimizing treatment parameters, broadening the indication to other MCI subtypes, and testing the augmentation of established cognitive rehabilitation interventions when combined with rTMS.

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.

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.

Psychosis onset typically occurs during adolescence or early adulthood, coinciding with the latest stage of brain maturation. Alterations in brain functional connectivity (FC) accompany the emergence of psychiatric symptoms and cognitive impairments. Thus, age-related FC changes may be informative regarding psychosis onset.

We defined neurotypical age-related FC trajectories and hypothesized that FC of individuals at familial and clinical high risk (HR) for psychosis deviates from FC of neurotypical controls (NC).

We analyzed two independent cohorts, of (a) 356 early adult NC (yNC; age=22±2y, m:f=149:207), and 127 mature adult NC (aNC; age=38±7y, m:f=79:48), and (b) 92 yNC (age=22±2y, m:f=34:58), 33 aNC (age=36±6y, m:f=21:12), 38 early HR adults (age=20±3y, m:f=18:20). We acquired fMRI data from multiple scans (resting-state, working memory, episodic memory, and implicit emotion processing). FC was obtained by computing Pearson’s correlations between time-courses of every independent component (IC) defined by an Independent Component Analysis approach (NeuroMark). Age-varying components of interest (yNC/aNC differences on FC based on linear mixed effect regressions) were tested for differences between HR and yNC through the Wilcoxon rank-sum test.

showed age-related FC differences (yNC/aNC) in a set of 17 IC pairs (pFDR<0.05). HR showed increased FC within a network including dorsolateral and medial prefrontal cortices, and sensorimotor cortex, while decreased FC between cerebellum and the parietal and visual cortices, compared with yNC (pFDR<0.05). HR showed no significant difference compared with aNC (pFDR>0.05).

This study tested FC alterations associated with the risk for psychosis and highlighted the relationship between psychosis and potentially altered brain functional processes.

No significant relationships.

Clinical high-risk states for psychosis (CHR) are associated with functional impairments and depressive disorders. A previous PRONIA study predicted social functioning in CHR and recent-onset depression (ROD) based on structural magnetic resonance imaging (sMRI) and clinical data. However, the combination of these domains did not lead to accurate role functioning prediction, calling for the investigation of additional risk dimensions. Role functioning may be more strongly associated with environmental adverse events than social functioning.

We aimed to predict role functioning in CHR, ROD and transdiagnostically, by adding environmental adverse events-related variables to clinical and sMRI data domains within the PRONIA sample.

Baseline clinical, environmental and sMRI data collected in 92 CHR and 95 ROD samples were trained to predict lower versus higher follow-up role functioning, using support vector classification and mixed k-fold/leave-site-out cross-validation. We built separate predictions for each domain, created multimodal predictions and validated them in independent cohorts (74 CHR, 66 ROD).

Models combining clinical and environmental data predicted role outcome in discovery and replication samples of CHR (balanced accuracies: 65.4% and 67.7%, respectively), ROD (balanced accuracies: 58.9% and 62.5%, respectively), and transdiagnostically (balanced accuracies: 62.4% and 68.2%, respectively). The most reliable environmental features for role outcome prediction were adult environmental adjustment, childhood trauma in CHR and childhood environmental adjustment in ROD.

Findings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.

We assessed parents’ perceptions of and satisfaction with daily lisdexamfetamine dimesylate (LDX; Vyvanse®, US tradename, Shire US Inc.) treatment for their children with attention-deficit/hyperactivity disorder (ADHD) previously treated with osmotic-release oral system methylphenidate (OROS-MPH).

Parents of children with ADHD were surveyed via telephone or Internet about their child's experience before and approximately 6 weeks after initiating LDX treatment. Participants received a $25 coupon toward their child's next LDX prescription after survey completion. Post hoc analyses using the Wilcoxon signed rank test were performed.

School time was the most frequently reported most bothersome time of day at baseline (41%), followed by homework time (23%). At follow-up, most parents reported improvement during their child's most bothersome time of day. Significantly less interference from ADHD symptoms with school activities, family interactions, homework, and social interactions was reported (all P< .01). LDX tolerability and convenience were rated, on average, 7.1 and 8.0, respectively (1=not at all well tolerated/convenient, 9=very well tolerated/convenient). Satisfaction with LDX was significantly higher than satisfaction with OROS-MPH treatment (6.9 and 5.0, respectively, P< .01 [1=not at all satisfied, 9=very satisfied]). Eighty-four percent of parents reported that they intend for their child to continue taking LDX.

Parents of children with ADHD who were previously treated with OROS-MPH and currently taking LDX reported less interference of ADHD symptoms with daily activities after LDX treatment and high satisfaction with LDX. LDX was also well tolerated and convenient to use.

Supported by funding from Shire Development Inc.

The genetic architecture of schizophrenia is based on polygenic trajectories. Indeed, genes converge on molecular co-expression pathways, which may be associated with heritable characteristics of patients and their siblings, called intermediate phenotypes, such as prefrontal anomalies and thalamic dysconnectivity during attentional control [2].

Here, we investigated in healthy humans association between co-expression of genes with coordinated thalamo-prefrontal (THA-PFC) expression and functional connectivity during attentional control.

We used Brainspan dataset to characterize a coordinated THA-PFC expression gene list by correlating post-mortem gene expression in both areas (Kendall's Tau>.76, Bonferroni P < .05). Then, we identified a PFC co-expression network1 and tested all gene sets for THA-PFC and PGC loci [3] enrichments (P < .05). SNPs associated with the first principal component of the resulting enriched gene set were combined in a Polygenic Co-Expression Index (PCI) [1]. We conducted Independent Component Analysis (ICA) on attentional control fMRI data (n = 265) and selected Independent Components (ICs) including the thalamus and being highly correlated with an attentional control network2. Multiple regressions were conducted (predictor: PCI) using a thalamic cluster previously associated with familial risk for schizophrenia [2] as ROI (FWE P < .05).

In one of the 8 ICs of interest there was a positive effect of PCI on thalamic connectivity strength in a cluster overlapping with our ROI (Z = 4.3).

Decreased co-expression of genes included in PCI predicts thalamic dysconnectivity during attentional control, suggesting a novel co-regulated molecular pathway potentially implicated in genetic risk for schizophrenia.

The authors have not supplied their declaration of competing interest.

Previous models suggest biological and behavioral continua among healthy individuals (HC), at-risk condition, and full-blown schizophrenia (SCZ). Part of these continua may be captured by schizotypy, which shares subclinical traits and biological phenotypes with SCZ, including thalamic structural abnormalities. In this regard, previous findings have suggested that multivariate volumetric patterns of individual thalamic nuclei discriminate HC from SCZ. These results were obtained using machine learning, which allows case–control classification at the single-subject level. However, machine learning accuracy is usually unsatisfactory possibly due to phenotype heterogeneity. Indeed, a source of misclassification may be related to thalamic structural characteristics of those HC with high schizotypy, which may resemble structural abnormalities of SCZ. We hypothesized that thalamic structural heterogeneity is related to schizotypy, such that high schizotypal burden would implicate misclassification of those HC whose thalamic patterns resemble SCZ abnormalities.

Following a previous report, we used Random Forests to predict diagnosis in a case–control sample (SCZ = 131, HC = 255) based on thalamic nuclei gray matter volumes estimates. Then, we investigated whether the likelihood to be classified as SCZ (π-SCZ) was associated with schizotypy in 174 HC, evaluated with the Schizotypal Personality Questionnaire.

Prediction accuracy was 72.5%. Misclassified HC had higher positive schizotypy scores, which were correlated with π-SCZ. Results were specific to thalamic rather than whole-brain structural features.

These findings strengthen the relevance of thalamic structural abnormalities to SCZ and suggest that multivariate thalamic patterns are correlates of the continuum between schizotypy in HC and the full-blown disease.