Proof-of-concept evidence for high-density EEG investigation of sleep slow wave traveling in First-Episode Psychosis.

Schizophrenia is thought to reflect aberrant connectivity within cortico-cortical and reentrant thalamo-cortical loops, which physiologically integrate and coordinate the function of multiple cortical and subcortical structures. Despite extensive research, reliable biomarkers of such "dys-connectivity" remain to be identified at the onset of psychosis, and before exposure to antipsychotic drugs. Because slow waves travel across the brain during sleep, they represent an ideal paradigm to study pathological conditions affecting brain connectivity. Here, we provide proof-of-concept evidence for a novel approach to investigate slow wave traveling properties in First-Episode Psychosis (FEP) with high-density electroencephalography (EEG). Whole-night sleep recordings of 5 drug-naïve FEP and 5 age- and gender-matched healthy control subjects were obtained with a 256-channel EEG system. One patient was re-recorded after 6 months and 3 years of continuous clozapine treatment. Slow wave detection and traveling properties were obtained with an open-source toolbox. Slow wave density and slow wave traveled distance (measured as the line of longest displacement) were significantly lower in patients (p < 0.05). In the patient who was tested longitudinally during effective clozapine treatment, slow wave density normalized, while traveling distance only partially recovered. These preliminary findings suggest that slow wave traveling could be employed in larger samples to detect cortical "dys-connectivity" at psychosis onset.

The Italian version of the 16-item Prodromal Questionnaire (PQ-16) and its psychometric features in help-seeking ultra-high-risk subjects and in the general population.

BACKGROUND: Increasing attention to the early stages of psychosis and the identification of symptomatic prodromal states have led to the development of a growing number of screening tools. The 16-item version of the Prodromal Questionnaire (PQ-16) is a worldwide used self-administered tool for this purpose. However, to date, fundamental psychometric properties of PQ-16 were not thoroughly investigated. This study aimed to examine the structural validity, measurement invariance, reliability and other psychometrical properties of the Italian version of the PQ-16 (iPQ-16) in help-seeking individuals and in the general population. METHODS: The iPQ-16 was administered to 449 young outpatients attending six community mental health services and to 318 control participants enrolled in educational environment. Confirmatory factor analyses (CFAs), measurement invariance (MI) between the help-seeking group and the general population sample, convergent validity, test-retest reliability, internal consistency, and prevalence analyses were performed. Lastly, the validity of the adopted PQ-16 cut-offs through Receiver Operating Characteristic (ROC) curves plotted against CAARMS diagnoses was also tested. RESULTS: CFAs confirmed the single-factor structure for the iPQ-16 and scalar MI was reached. The iPQ-16 showed high internal consistency, test-retest reliability, convergent validity, and acceptable diagnostic accuracy. ROC analysis suggested a score of ≥4 as best cut-off. CONCLUSIONS: The iPQ-16 represents a valid and reliable questionnaire for the assessment of high mental risk in both Italian outpatients and general student population. It has good psychometric properties and is easy to implement as UHR screening for clinical as well as research purposes.

Machine learning methods to predict outcomes of pharmacological treatment in psychosis.

In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.

Oral and long-acting antipsychotics for relapse prevention in schizophrenia-spectrum disorders: a network meta-analysis of 92 randomized trials including 22,645 participants.

According to current evidence and guidelines, continued antipsychotic treatment is key for preventing relapse in people with schizophrenia-spectrum disorders, but evidence-based recommendations for the choice of the individual antipsychotic for maintenance treatment are lacking. Although oral antipsychotics are often prescribed first line for practical reasons, long-acting injectable antipsychotics (LAIs) are a valuable resource to tackle adherence issues since the earliest phase of disease. Medline, EMBASE, PsycINFO, CENTRAL and CINAHL databases and online registers were searched to identify randomized controlled trials comparing LAIs or oral antipsychotics head-to-head or against placebo, published until June 2021. Relative risks and standardized mean differences were pooled using random-effects pairwise and network meta-analysis. The primary outcomes were relapse and dropout due to adverse events. We used the Cochrane Risk of Bias tool to assess study quality, and the CINeMA approach to assess the confidence of pooled estimates. Of 100 eligible trials, 92 (N=22,645) provided usable data for meta-analyses. Regarding relapse prevention, the vast majority of the 31 included treatments outperformed placebo. Compared to placebo, "high" confidence in the results was found for (in descending order of effect magnitude) amisulpride-oral (OS), olanzapine-OS, aripiprazole-LAI, olanzapine-LAI, aripiprazole-OS, paliperidone-OS, and ziprasidone-OS. "Moderate" confidence in the results was found for paliperidone-LAI 1-monthly, iloperidone-OS, fluphenazine-OS, brexpiprazole-OS, paliperidone-LAI 1-monthly, asenapine-OS, haloperidol-OS, quetiapine-OS, cariprazine-OS, and lurasidone-OS. Regarding tolerability, none of the antipsychotics was significantly worse than placebo, but confidence was poor, with only aripiprazole (both LAI and OS) showing "moderate" confidence levels. Based on these findings, olanzapine, aripiprazole and paliperidone are the best choices for the maintenance treatment of schizophrenia-spectrum disorders, considering that both LAI and oral formulations of these antipsychotics are among the best-performing treatments and have the highest confidence of evidence for relapse prevention. This finding is of particular relevance for low- and middle-income countries and constrained-resource settings, where few medications may be selected. Results from this network meta-analysis can inform clinical guidelines and national and international drug regulation policies.

The Impact of Lithium on Brain Function in Bipolar Disorder: An Updated Review of Functional Magnetic Resonance Imaging Studies.

Lithium remains a gold standard treatment for bipolar disorder (BD), and functional magnetic resonance imaging (fMRI) studies have contributed to clarifying its impact on neural circuitries in affected individuals. However, the specific neurobiological mechanisms through which lithium exerts its effects on brain function are not fully understood. In this review, we aimed to summarize the results of recent fMRI studies evaluating the impact of lithium on brain functional activity and connectivity in patients diagnosed with BD. We performed a literature search of available sources found in the PubMed database reported in English since 2016, when the last available review on this topic was published. Five fMRI studies in resting-state condition and six studies performed during the execution of emotional tasks met the inclusion criteria. Overall, the available evidence supports normalizing effects of lithium on brain activity and connectivity. Most of these studies reported a normalization in prefrontal regions and interconnected areas involved in emotion regulation and processing, regardless of the task employed. Importantly, lithium treatment showed distinct patterns of activity/connectivity changes compared with other treatments. Finally, lithium modulation of neural circuitries was found to be associated with clinical improvement in BD. These results are consistent with the hypothesis that selective abnormalities in neural circuitries supporting emotion processing and regulation improve during lithium treatment in BD. However, the heterogeneity of the examined studies regarding study design, sample selection, and analysis methods might limit the generalizability of the findings and lead to difficulties in comparing the results. Therefore, in future studies, larger cohorts and homogeneous experimental tasks are needed to further corroborate these findings.

Functional brain network dysfunctions in subjects at high-risk for psychosis: A meta-analysis of resting-state functional connectivity.

Although emerging evidence suggests that altered functional connectivity (FC) of large-scale neural networks is associated with disturbances in individuals at high-risk for psychosis, the findings are still far to be conclusive. We conducted a meta-analysis of seed-based resting-state functional magnetic resonance imaging studies that compared individuals at clinical high-risk for psychosis (CHR), first-degree relatives of patients with schizophrenia, or subjects who reported psychotic-like experiences with healthy controls. Twenty-nine studies met the inclusion criteria. The MetaNSUE method was used to analyze connectivity comparisons and symptom correlations. Our results showed a significant hypo-connectivity within the salience network (p = 0.012, uncorrected) in the sample of CHR individuals (n = 810). Additionally, we found a positive correlation between negative symptom severity and FC between the default mode network and both the salience network (p < 0.001, r = 0.298) and the central executive network (p = 0.003, r = 0.23) in the CHR group. This meta-analysis lends support for the hypothesis that large-scale network dysfunctions represent a core neural deficit underlying psychosis development.

Gastrointestinal side effects associated with antidepressant treatments in patients with major depressive disorder: A systematic review and meta-analysis.

Gastrointestinal side effects (SEs) are frequently observed in patients with major depressive disorder (MDD) while taking antidepressants and may lead to treatment discontinuation. The aim of this meta-analysis is to provide quantitative measures on short-term rates of gastrointestinal SEs in MDD patients treated with second-generation antidepressants. An electronic search of the literature was conducted by using MEDLINE, ISI Web of Science - Web of Science Core Collection, and Cochrane Library databases. Eligible studies had to focus on the use of at least one of 15 antidepressants commonly used in MDD (i.e., agomelatine, bupropion, citalopram, desvenlafaxine, duloxetine, escitalopram, fluoxetine, fluvoxamine, levomilnacipran, mirtazapine, paroxetine, reboxetine, sertraline, venlafaxine, and vortioxetine) and report data on treatment-emergent gastrointestinal SEs (i.e. nausea/vomiting, diarrhoea, constipation, abdominal pain, dyspepsia, anorexia, increased appetite and dry mouth) within 12 weeks of treatment. Overall, 304 studies were included in the meta-analyses. All the considered antidepressants showed higher rates of gastrointestinal SEs than placebo. Escitalopram and sertraline were shown to be the least tolerated antidepressants on the gastrointestinal tract, being associated with all the considered SEs with the exception of constipation and increased appetite, while mirtazapine was shown to be the antidepressant with fewer side effects on the gut, being only associated with increased appetite. In conclusion, commonly used antidepressants showed different profiles of gastrointestinal SEs, possibly related to their mechanisms of action. The specific tolerability profile of each compound should be considered by clinicians when prescribing antidepressants in order to improve adherence to treatment and increase positive outcomes in patients with MDD.

Effects of olanzapine during cognitive and emotional processing in schizophrenia: A review of functional magnetic resonance imaging findings.

OBJECTIVE: Olanzapine is an atypical antipsychotic that is widely used in the treatment of schizophrenia and has shown some degree of efficacy on negative and cognitive symptoms. We aimed to review the effects of olanzapine treatment on brain regions that are directly involved in cognitive and emotional processing. METHODS: We used the PubMed database to perform a bibliographic search on functional magnetic resonance imaging studies that investigated the effects of olanzapine treatment on neural activity in patients with schizophrenia during cognitive and emotional tasks. RESULTS: Despite the high variability of tasks and analysis methods employed, the weight of the evidence was consistent with the hypothesis that olanzapine treatment is associated with a normalization of brain activity in schizophrenia. Distinctive functional changes were found in frontal cortex and cingulate cortex activity during both cognitive and emotional tasks. During emotional processing, olanzapine treatment seems to specifically regulate the activity of the striatum and limbic system. CONCLUSIONS: The results of the reviewed studies suggest that in patients with schizophrenia, olanzapine treatment might lead to a more physiological brain activity coupled with regulation of dopamine release. Future studies should further corroborate these hypotheses using larger samples and homogeneous experimental tasks.