Deep transcranial magnetic stimulation (TMS) is a technique of neuromodulation and neurostimulation based on the principle of electromagnetic induction of an electric field in the brain. The coil (H-coil) used in deep TMS is able to modulate cortical excitability up to a maximum depth of 6 cm and is therefore able not only to modulate the activity of the cerebral cortex but also the activity of deeper neural circuits. Deep TMS is largely used for the treatment of drug-resistant major depressive disorder (MDD) and is being tested to treat a very wide range of neurological, psychiatric and medical conditions. The aim of this review is to illustrate the biophysical principles of deep TMS, to explain the pathophysiological basis for its utilization in each psychiatric disorder (major depression, autism, bipolar depression, auditory hallucinations, negative symptoms of schizophrenia), to summarize the results presented thus far in the international scientific literature regarding the use of deep TMS in psychiatry, its side effects and its effects on cognitive functions.

Reduced gut-microbial diversity (“gut dysbiosis”) has been associated with an anhedonic/amotivational syndrome (“sickness behavior”) that manifests across severe mental disorders and represent the key clinical feature of chronic fatigue. In this systematic review and meta-analysis, we investigated differences in proxy biomarkers of gut dysbiosis in patients with severe mental illness and chronic fatigue vs. controls and the association of these biomarkers with sickness behavior across diagnostic categories. Following PRISMA guidelines, we searched from inception to April 2020 for all the studies investigating proxy biomarkers of gut dysbiosis in patients with severe mental illness and chronic fatigue. Data were independently extracted by multiple observers, and a random-mixed model was used for the analysis. Heterogeneity was assessed with the I² index. Thirty-three studies were included in the systematic review; nineteen in the meta-analysis (N = 2758 patients and N = 1847 healthy controls). When compared to controls, patients showed increased levels of zonulin (four studies reporting data on bipolar disorder and depression, SMD = 0.97; 95% Cl = 0.10–1.85; P = 0.03, I² = 86.61%), lipopolysaccharide (two studies reporting data on chronic fatigue and depression, SMD = 0.77; 95% Cl = 0.42–1.12; P < 0.01; I² = 0%), antibodies against endotoxin (seven studies reporting data on bipolar disorder, depression, schizophrenia, and chronic fatigue, SMD = 0.99; 95% CI = 0.27–1.70; P < 0.01, I² = 97.14%), sCD14 (six studies reporting data on bipolar disorder, depression, schizophrenia, and chronic fatigue, SMD = 0.54; 95% Cl 0.16–0.81; P < 0.01, I² = 90.68%), LBP (LBP, two studies reporting data on chronic fatigue and depression, SMD = 0.87; 95% Cl = 0.25–1.48; P < 0.01; I² = 56.80%), alpha-1-antitripsin (six studies reporting data on bipolar disorder, depression, and schizophrenia, SMD = 1.23; 95% Cl = 0.57–1.88; P < 0.01, I²: 89.25%). Elevated levels of gut dysbiosis markers positively correlated with severity of sickness behavior in patients with severe mental illness and chronic fatigue. Our findings suggest that gut dysbiosis may underlie symptoms of sickness behavior across traditional diagnostic boundaries. Future investigations should validate these findings comparing the performances of the trans-diagnostic vs. categorical approach. This will facilitate treatment breakthrough in an area of unmet clinical need.

The endocannabinoid system (ECS) is an endogenous biological signaling system that encompasses lipid-based mediators, their synthesizing and degrading enzymes, and 2 main receptors (Figure 1).^1,2 The ECS regulates a series of physiological functions throughout the body, including cognition,³ sleep,⁴ energy metabolism,⁵ and inflammation.⁶ In the brain, the ECS modulates different neurotransmitter systems, including dopamine,⁷ glutamate,⁸ and γ-aminobutyric acid.¹ The ECS mainly uses 2 major lipid-based endogenous mediators, anandamide and arachidonoyl-sn-glycerol, that act through type 1 and type 2 cannabinoid (CB1 and CB2)² receptors (Figure 1). The main endocannabinoid-synthesizing enzymes are the N-acyl phosphatidylethanolamine phospholipase D and the diacylglycerol lipase.² Catabolism is primarily regulated by the fatty acid amide hydrolase and the monoacylglycerol lipase.²

Tobacco use is strongly associated with a variety of psychiatric disorders. Smokers are more likely than non-smokers to meet current criteria for mental health conditions, such as mood disorders, anxiety disorders and psychosis. Evidence also suggest that smokers with psychiatric disorders may have more difficulty quitting, offering at least a partial explanation for why smoking rates are higher in this population. The mechanisms linking mental health conditions and cigarette smoking are complex and likely differ across each of the various disorders. The most commonly held view is that patients with mental health conditions smoke in an effort to regulate the symptoms associated with their disorder. However some recent evidence suggests that quitting smoking may actually improve mental health symptoms. This is particularly true if the tobacco cessation intervention is integrated into the context of ongoing mental health treatment. In this paper we reviewed and summarized the most relevant knowledge about the relationship between tobacco use and dependence and psychiatric disorders. We also reviewed the most effective smoking cessation strategies available for patients with psychiatric comorbidity and the impact of smoking behavior on psychiatric medication.

Recently published large-scale randomised controlled trials (RCTs) have questioned the efficacy of preventive interventions in individuals at clinical high risk for psychosis (CHR-P). We conducted a systematic review and meta-analysis to include this new evidence and provide future directions for the field. We followed the PRISMA guidelines and a pre-registered protocol, with a literature search conducted from inception to November 2023. We included RCTs that collected data on psychosis transition (the primary outcome) in CHR-P. Secondary outcomes were symptoms severity and functioning. Investigated time points were 6,12,24,36, and +36 months. We used odd ratios (ORs) and standardised mean differences (SMD) as summary outcomes. Heterogeneity was estimated with the Higgins I². Twenty-four RCTs, involving 3236 CHR-P individuals, were included. Active interventions were Cognitive Behavioural Therapy (CBT), family-focused therapy, Integrated Psychological Therapy, antipsychotics, omega-3 fatty acids, CBT plus risperidone, minocycline, and other non-pharmacological approaches (cognitive remediation, sleep-targeted therapy, brain stimulation). Results showed no evidence that any of the investigated active interventions had a sustained and robust effect on any of the investigated outcomes in CHR-P, when compared to control interventions, including CBT on transition to psychosis at 12 months (9 RCTs; OR: 0.64; 95% CI: 0.39–1.06; I²: 21%; P = 0.08). These results highlight the need for novel treatment approaches in CHR-P. Future studies should consider the heterogeneity of this clinical population and prioritise stratification strategies and bespoke treatments.

Primary indicated prevention is reliant on accurate tools to predict the onset of psychosis. The gold standard assessment for detecting individuals at clinical high risk (CHR-P) for psychosis in the UK and many other countries is the Comprehensive Assessment for At Risk Mental States (CAARMS). While the prognostic accuracy of CHR-P instruments has been assessed in general, this is the first study to specifically analyse that of the CAARMS. As such, the CAARMS was used as the index test, with the reference index being psychosis onset within 2 years. Six independent studies were analysed using MIDAS (STATA 14), with a total of 1876 help-seeking subjects referred to high risk services (CHR-P+: n = 892; CHR-P–: n = 984). Area under the curve (AUC), summary receiver operating characteristic curves (SROC), quality assessment, likelihood ratios, and probability modified plots were computed, along with sensitivity analyses and meta-regressions. The current meta-analysis confirmed that the 2-year prognostic accuracy of the CAARMS is only acceptable (AUC = 0.79 95% CI: 0.75–0.83) and not outstanding as previously reported. In particular, specificity was poor. Sensitivity of the CAARMS is inferior compared to the SIPS, while specificity is comparably low. However, due to the difficulties in performing these types of studies, power in this meta-analysis was low. These results indicate that refining and improving the prognostic accuracy of the CAARMS should be the mainstream area of research for the next era. Avenues of prediction improvement are critically discussed and presented to better benefit patients and improve outcomes of first episode psychosis.