The Burden of Psychiatric Manifestations in Inflammatory Bowel Diseases: A Systematic Review With Meta-analysis.

BACKGROUND: Psychiatric disorders in patients with inflammatory bowel disease (IBD) represent a significant but uncertain facet of the disease, with unsolved questions regarding their overall magnitude, their impact on intestinal disease, and the whole burden of psychiatric manifestations. AIM: This systematic review summarizes the evidence on the prevalence and impact of psychiatric disorders, including depression, anxiety, bipolar disorder (BD), and schizophrenia, among patients with IBD. METHODS: A systematic search across PubMed/MEDLINE, Embase, and Scopus databases from January 2010 to January 2023 was performed to identify relevant studies. The focus was on studies exploring the prevalence of specific psychiatric disorders in IBD patients compared to the general population and that reported specific outcome measures. A subsequent meta-analysis (MA) assessed the strength of the association between IBD and these psychiatric disorders, with data reliability ensured through rigorous extraction and quality assessment. RESULTS: Out of 3,209 articles, 193 met the inclusion criteria and only 26 provided complete data for comprehensive analysis. These studies showed a significantly higher overall prevalence of psychiatric comorbidities in IBD patients compared to the general population. The MA showed a significant association between IBD and depression (pooled OR 1.42, 95% CI = 1.33-1.52, P < .0001) and anxiety (pooled OR 1.3, 95% CI = 1.22-1.44, P < .0001). The association between IBD and BD was significant (pooled OR 1.64, 95% CI = 1.20-2.24, P < .0001) but showed considerable heterogeneity (I2 = 94.01%). Only 3 studies examined the association between schizophrenia and IBD, providing widely heterogeneous results, with an inconclusive OR, estimated at 0.93 (95% CI = 0.62-1.39, P = .73). CONCLUSIONS: This MA highlights the high prevalence of psychiatric disorders, particularly depression and anxiety, in IBD patients, which exceeds rates in the general population. BD in IBD is proving to be an important but under-researched area. The sparse and contradictory data on schizophrenia requires further investigation. These findings highlight the need for better understanding, early detection, and tailored mental health interventions in the management of IBD to significantly improve patients' quality of life.

This systematic review with meta-analysis establishes a substantial association between inflammatory bowel diseases (IBD) and psychiatric disorders, primarily depression and anxiety. The study emphasizes the need for comprehensive mental health care in IBD management for improved patient outcomes.

Resting-state functional magnetic resonance imaging alterations in first-degree relatives of individuals with bipolar disorder: A systematic review.

BACKGROUND: Relatives of individuals with bipolar disorder (BD) are at higher risk of developing the disorder. Identifying brain alterations associated with familial vulnerability in BD can help discover endophenotypes, which are quantifiable biological traits more prevalent in unaffected relatives of BD (BD-RELs) than the general population. This review aimed at expanding our knowledge on endophenotypes of BD by providing an overview of resting-state functional magnetic resonance imaging (rs-fMRI) alterations in BD-RELs. METHODS: A systematic search of PubMed, Scopus, and Web of Science was performed to identify all available rs-fMRI studies conducted in BD-RELs up to January 2024. A total of 18 studies were selected. Six included BD-RELs with no history of psychiatric disorders and 10 included BD-RELs that presented psychiatric disorders. Two investigations examined rs-fMRI alterations in BD-RELs with and without subthreshold symptoms for BD. RESULTS: BD-RELs presented rs-fMRI alterations in the cortico-limbic network, fronto-thalamic-striatal circuit, fronto-occipital network, and, to a lesser extent, in the default mode network. This was true both for BD-RELs with no history of psychopathology and for BD-RELs that presented psychiatric disorders. The direct comparison of rs-fMRI alterations in BD-RELs with and without psychiatric symptoms displayed largely non-overlapping patterns of rs-fMRI abnormalities. LIMITATIONS: Small sample sizes and the clinical heterogeneity of BD-RELs limit the generalizability of our findings. CONCLUSIONS: The current literature suggests that first-degree BD-RELs exhibit rs-fMRI alterations in brain circuits involved in emotion regulation, cognition, reward processing, and psychosis susceptibility. Future studies are needed to validate these findings and to explore their potential as biomarkers for early detection and intervention.

Exploring the effects of family and life events on genetic and environmental architecture of schizotypal and hypomanic dimensions: Insights from a twin study.

BACKGROUND: Strategies of prevention for psychiatric disorders need a deep understanding of the aetiological factors involved in the psychopathological processes. Our twin study aims at disentangling the contributions of genes and environment to schizotypal and hypomanic dimensions, considering the role of stressful life events (LEs) and the quality of family relationships. METHODS: The Magical Ideation Scale (MIS) and Perceptual Aberration Scale (PAS) were used to assess positive schizotypy, while Hypomanic Personality Scale (HPS) and its sub-scales were used to investigate proneness to affective disorders. 268 twins (54.5 % female; aged 18.0 ± 6.68) were included. Participants filled out a questionnaire on LEs and their parents provided an evaluation of intra-family relationship (Relationship Quality Index, RQI). Classic univariate twin models for quantitative traits were fitted for scales, and the effects of covariates (LEs and RQI) were assessed. RESULTS: For MIS, HPS and its sub-scales, significant common and unique environmental effects were detected, with genetic factors affecting only HPS Social Vitality sub-scale. Unique environment was the only source of variance of PAS score. The number of recent LEs influenced MIS and PAS models, while RQI score affected MIS model. LIMITATIONS: The main limitation of the study is the small sample size, which reduces statistical power and may potentially lead to an underestimation of heritability. Additionally, the cross-sectional design limits the possibility to draw causal considerations. CONCLUSIONS: Findings provide preliminary evidence for a significant environmental role in modulating states of vulnerability. Moreover, the expression of positive schizotypy resulted influenced by recent stressors and intra-family relationships.

Clinical investigation of former workers exposed to asbestos: the health surveillance experience of an Italian University Hospital.

Background: The need for health surveillance of former workers exposed to asbestos was provided by law in Italy after the asbestos ban in 1992. Objectives: We describe the results of the health surveillance of former workers exposed to asbestos, conducted over 27 years, from 1994 to 2020, at the Operative Unit of Occupational Medicine of the University Hospital of Bari. Materials and methods: We adopted the health surveillance protocol, which was validated at the national level in 2018. Results: A total of 1,405 former workers exposed to asbestos were examined. We proceeded with diagnosing pathologies in 339 cases (24% of the cohort subjected to surveillance), with diagnoses of some cases involving multiple pathologies. Specifically, pleural plaques were diagnosed in 49.2% of the 339 cases, asbestosis in 35.9%, malignant pleural mesothelioma (MPM) in 20.3%, mesothelioma of the vaginal tunic of the testis (MTVT) in 9.1%, lung cancer in 5.8%, and laryngeal cancer in 0.8%. Conclusion: Despite the 1992 asbestos ban, asbestos-related diseases remain a serious public health issue. It is important to establish criteria that ensure the health surveillance of formerly exposed workers minimizes costs, reduces the number of invasive examinations, and optimizes achievable results.

Heritability of white matter in twins: A diffusion neuroimaging review.

Diffusion neuroimaging has emerged as an essential non-invasive technique to explore in vivo microstructural characteristics of white matter (WM), whose integrity allows complex behaviors and cognitive abilities. Studying the factors contributing to inter-individual variability in WM microstructure can provide valuable insight into structural and functional differences of brain among individuals. Genetic influence on this variation has been largely investigated in twin studies employing different measures derived from diffusion neuroimaging. In this context, we performed a comprehensive literature search across PubMed, Scopus and Web of Science of original twin studies focused on the heritability of WM. Overall, our results highlighted a consistent heritability of diffusion indices (i.e., fractional anisotropy, mean, axial and radial diffusivity), and network topology among twins. The genetic influence resulted prominent in frontal and occipital regions, in the limbic system, and in commissural fibers. To enhance the understanding of genetic influence on WM microstructure further studies in less heterogeneous experimental settings, encompassing all diffusion indices, are warranted.

Machine learning applied to the prediction of relapse, hospitalization, and suicide in bipolar disorder using neuroimaging and clinical data: A systematic review.

BACKGROUND: Bipolar disorder (BD) is associated with increased morbidity/mortality. Adverse outcome prediction might help with the management of patients with BD. METHODS: We systematically reviewed the performance of machine learning (ML) studies in predicting adverse outcomes (relapse or recurrence, hospital admission, and suicide-related events) in patients with BD. Demographic, clinical, and neuroimaging-related poor outcome predictors were also reviewed. Three databases (PubMed, Scopus, and Web of Science) were explored from inception to July 2023. RESULTS: Eighteen studies, accounting for >30,000 patients, were included. Support vector machine, decision trees, random forest, and logistic regression were the most frequently used ML algorithms. ML models' area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity ranged from 0.71 to 0.98, 72.7-92.8 %, and 59.0-95.2 % for relapse/recurrence prediction (4 studies (3 on relapses and 1 on recurrences). The corresponding values were 0.78-0.88, 21.4-100 %, and 77.0-99.7 % for hospital admissions (3 studies, 21,266 patients), and 0.71-0.99, 44.4-97.9 %, and 38.9-95.0 % for suicide-related events (10 studies, 5558 patients). Also, one study addressed a combination of the interest outcomes. Adverse outcome predictors included early onset BD, BD type I, comorbid psychiatric or substance use disorder, circadian rhythm disruption, hospitalization characteristics, and neuroimaging parameters, including increased dynamic amplitude of low-frequency fluctuation, decreased frontolimbic functional connectivity and aberrant dynamic functional connectivity in corticostriatal circuitry. CONCLUSIONS: ML models can predict adverse outcomes of BD with relatively acceptable performance measures. Future studies with larger samples and nested cross-validation validation should be conducted to reach more reliable results.

Perioperative esketamine administration for prevention of postpartum depression after the cesarean section: A systematic review and meta-analysis.

BACKGROUND: Postpartum Depression (PPD) exerts a substantial negative effect on maternal well-being post-delivery, particularly among Cesarean Section (C/S) recipients. In this study, we aimed to review the efficacy of perioperative esketamine, the S-enantiomer of ketamine, in preventing PPD incidence and depressive symptoms as measured with the Edinburgh Postnatal Depression Scale (EPDS) after C/S. METHODS: A systematic search for relevant articles was conducted in Scopus, PubMed, Web of Sciences, and PsycINFO until April 6, 2024. Meta-analyses were conducted using random-effect models to compare the PPD incidence and EPDS scores via log odds ratio and Hedge's g, respectively, during the first week post-C/S and at 42 days post-C/S in the esketamine and control group. RESULTS: Fourteen studies, including 12 randomized controlled trials and 2 retrospective cohorts, were reviewed. Our meta-analyses found lower PPD incidence during the first week (log odds ratio: -0.956 [95 % confidence interval: -1.420, -0.491]) and at day 42 post-C/S (log odds ratio: -0.989 [95 % confidence interval: -1.707, -0.272]) among patients administered esketamine compared to controls. Additionally, EPDS scores for the esketamine group were significantly lower than controls during the first week (Hedge's g: -0.682 [95 % confidence interval: -1.088, -0.276]) and at day 42 post-C/S (Hedge's g: -0.614 [95 % confidence interval: -1.098, -0.129]). LIMITATIONS: Presence of various concomitant medications and heterogeneous study designs. CONCLUSION: Our review highlights the potential impact of esketamine in PPD prevention, as well as in alleviating depressive symptoms post-C/S, regardless of PPD occurrence, therefore suggesting the benefits of adding esketamine to peri-C/S analgesic regimen.

Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review.

Background: Schizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB. Methods: We performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients. Results: We included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors. Conclusion: ML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field.

Machine learning and the prediction of suicide in psychiatric populations: a systematic review.

Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric populations, a formal psychiatric diagnosis emerged as a strong predictor of suicidal risk, overshadowing more subtle risk factors specific to distinct populations. To overcome this limitation, we conducted a systematic review of ML studies evaluating suicidal behaviors exclusively in psychiatric clinical populations. A systematic literature search was performed from inception through November 17, 2022 on PubMed, EMBASE, and Scopus following the PRISMA guidelines. Original research using ML techniques to assess the risk of suicide or predict suicide attempts in the psychiatric population were included. An assessment for bias risk was performed using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. About 1032 studies were retrieved, and 81 satisfied the inclusion criteria and were included for qualitative synthesis. Clinical and demographic features were the most frequently employed and random forest, support vector machine, and convolutional neural network performed better in terms of accuracy than other algorithms when directly compared. Despite heterogeneity in procedures, most studies reported an accuracy of 70% or greater based on features such as previous attempts, severity of the disorder, and pharmacological treatments. Although the evidence reported is promising, ML algorithms for suicidal prediction still present limitations, including the lack of neurobiological and imaging data and the lack of external validation samples. Overcoming these issues may lead to the development of models to adopt in clinical practice. Further research is warranted to boost a field that holds the potential to critically impact suicide mortality.

Right frontal cingulate cortex mediates the effect of prenatal complications on youth internalizing behaviors.

Prenatal and perinatal complications represent well-known risk factors for the future development of psychiatric disorders. Such influence might become manifested during childhood and adolescence, as key periods for brain and behavioral changes. Internalizing and externalizing behaviors in adolescence have been associated with the risk of psychiatric onset later in life. Both brain morphology and behavior seem to be affected by obstetric complications, but a clear link among these three aspects is missing. Here, we aimed at analyzing the association between prenatal and perinatal complications, behavioral issues, and brain volumes in a group of children and adolescents. Eighty-two children and adolescents with emotional-behavioral problems underwent clinical and 3 T brain magnetic resonance imaging (MRI) assessments. The former included information on behavior, through the Child Behavior Checklist/6-18 (CBCL/6-18), and on the occurrence of obstetric complications. The relationships between clinical and gray matter volume (GMV) measures were investigated through multiple generalized linear models and mediation models. We found a mutual link between prenatal complications, GMV alterations in the frontal gyrus, and withdrawn problems. Specifically, complications during pregnancy were associated with higher CBCL/6-18 withdrawn scores and GMV reductions in the right superior frontal gyrus and anterior cingulate cortex. Finally, a mediation effect of these GMV measures on the association between prenatal complications and the withdrawn dimension was identified. Our findings suggest a key role of obstetric complications in affecting brain structure and behavior. For the first time, a mediator role of frontal GMV in the relationship between prenatal complications and internalizing symptoms was suggested. Once replicated on independent cohorts, this evidence will have relevant implications for planning preventive interventions.

Microstructural white matter alterations associated with social anxiety disorders: A systematic review.

BACKGROUND: Social anxiety disorder (SAD) is a psychiatric condition characterized by impaired social functioning that negatively impacts individuals' quality of life. Previous neuroimaging studies have revealed morphological and functional changes in various brain regions associated with SAD. Recent advances in diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI) have enabled the investigation of microstructural white matter (WM) alterations in SAD. This study aims to provide an overview of DTI/DWI studies exploring WM microstructure changes in SAD. METHODS: A systematic search on PubMed, Scopus, Web of Science, and PsycINFO was conducted for relevant records on July 8, 2023. An exploratory meta-analysis was also performed. RESULTS: Eight studies were reviewed. Consistent findings indicated reduced fractional anisotropy and increased diffusivity measures in different WM tracts in SAD patients compared to healthy controls. These alterations were mostly observed within regions of the fronto-limbic network, like uncinate fasciculus (UF) and superior and inferior longitudinal fasciculi (SLF and ILF). Finally, our exploratory meta-analysis on four studies utilizing a voxel-wise analytic approach yielded no significant differences between SAD patients and controls. LIMITATIONS: Limited number of studies, small sample sizes, and heterogeneity in analysis methods. CONCLUSIONS: Patients with SAD exhibited altered WM integrity, particularly in the UF, SLF, and ILF, compared to healthy controls. However, due to the limited number of included studies, our meta-analysis yielded no significant differences between SAD patients and controls. Therefore, future research is crucial to unravel the link between altered WM structure and the involvement of other limbic and cortical structures in SAD pathogenesis.

Case report and systematic review of cerebellar vermis alterations in psychosis.

INTRODUCTION: Cerebellar alterations, including both volumetric changes in the cerebellar vermis and dysfunctions of the corticocerebellar connections, have been documented in psychotic disorders. Starting from the clinical observation of a bipolar patient with cerebellar hypoplasia, the purpose of this review is to summarize the data in the literature about the association between hypoplasia of the cerebellar vermis and psychotic disorders [schizophrenia (SCZ) and bipolar disorder (BD)]. METHODS: A bibliographic search on PubMed has been conducted, and 18 articles were finally included in the review: five used patients with BD, 12 patients with SCZ and one subject at psychotic risk. RESULTS: For SCZ patients and subjects at psychotic risk, the results of most of the reviewed studies seem to suggest a gray matter volume reduction coupled with an increase in white matter volumes in the cerebellar vermis, compared to healthy controls. Instead, the results of the studies on BD patients are more heterogeneous with evidence showing a reduction, no difference or even an increase in cerebellar vermis volume compared to healthy controls. CONCLUSIONS: From the results of the reviewed studies, a possible correlation emerged between cerebellar vermis hypoplasia and psychotic disorders, especially SCZ, ultimately supporting the hypothesis of psychotic disorders as neurodevelopmental disorders.

SARS-CoV-2 Infections, Re-Infections and Clinical Characteristics: A Two-Year Retrospective Study in a Large University Hospital Cohort of Vaccinated Healthcare Workers.

At the University Hospital of Bari, during the first year after the start of the mandatory vaccination campaign with BNT162b2 mRNA COVID-19 vaccine, the preliminary results of an observational study showed a significant prevalence of SARS-CoV-2 breakthrough infections (BIs) among healthcare workers (HCWs), but no hospitalization or deaths. In the present study, we extended the observation period (January 2021-January 2023) with the aim of determining the incidence, characteristics and clinical course of SARS-CoV-2 BIs among 6213 HCWs. All HCWs were regularly monitored and screened. To allow return to work after BI, the protocol required one negative nasopharyngeal swab test followed by a medical examination certifying complete clinical recovery. We observed an overall incidence rate of SARS-CoV-2 BIs of 20.2%. Females were most affected, especially in the nurse group compared with doctors and other HCWs (p < 0.0001). Cardiovascular diseases were the most frequent comorbidity (n = 140; 11.4%). The source of infection was non-occupational in 52.4% of cases. Most cases (96.9%) showed minor symptoms and only two cases of hospitalization (one in intensive care unit), 13 cases of re-infection and no deaths were recorded. Our results confirm that SARS-CoV-2 infection can break vaccination protection but the clinical course is favorable.

Mesothelioma Risk Among Maritime Workers According to Job Title: Data From the Italian Mesothelioma Register (ReNaM).

The study describes the 466 cases of malignant mesotheliomas (MM) collected by the National Mesothelioma Register (ReNaM) in Italy in the period 1993-2018 relating to subjects with exclusive asbestos exposure in merchant or military navy. The cases among maritime workers represent 1.8% of the total cases with defined exposure registred in the ReNaM, of which 212 cases (45.4%) among merchant maritime workers and 254 cases (54.5%) among navy. The distribution by site of mesothelioma showed 453 (97.2%) MM cases of the pleura, 11 (2.3%) of the peritoneum and 2 (0.4%) of the tunica vaginalis of the testis. With regard to occupational exposure, it was classified as certain in 318 (68.2%) cases, probable in 69 (14.8%) cases and possible in 79 (16.9%) cases. Among the 23 classified jobs, the highest percentages of certain exposures are among naval engineers, motor mechanics, machine captains and sailors. Machine crew accounted for 49.3% of the cases, deck crew for 27.6%. All cases began exposure on board between 1926 and 1988. Seamen were exposed to asbestos while at sea by virtue of living onboard ships and from continual release of asbestos fibers due to the motion of a vessel. Epidemiological surveillance through the ReNaM has allowed us to verify among cases in the maritime, navy and merchant marine sectors, that in the past, subjects were exposed regardless of the ship's department where have provided service therefore all these cases must be considered as occupational diseases.

Resting-state functional magnetic resonance imaging alterations in borderline personality disorder: A systematic review.

BACKGROUND: Borderline personality disorder (BPD) is a severe psychiatric disorder characterized by emotion dysregulation, impulsivity, and interpersonal disturbances. Several structural and functional neuroimaging abnormalities have been described in BPD. In particular, resting-state functional magnetic resonance imaging (rs-fMRI) studies have recently suggested various connectivity alterations within and between large-scale brain networks in BPD. This review aimed at providing an updated summary of the evidence reported by the available rs-fMRI studies in BPD individuals. METHODS: A search on PubMed, Scopus, and Web of Science was performed to identify rs-fMRI alterations in BPD. A total of 15 studies met our inclusion criteria. RESULTS: Overall, aberrant resting-state functional connectivity (rs-FC) within and between default mode network (DMN), salience network (SN), and central executive network (CEN) were observed in BPD compared to healthy controls, as well as selective functional impairments in bilateral amygdala, anterior and posterior cingulate cortex, hippocampus, and prefrontal cortex. LIMITATIONS: The observational design, small sample size, prevalence of females, high rates of concurrent comorbidities and medications, and heterogeneity across imaging methodologies limit the generalizability of the results. CONCLUSIONS: The identification of altered patterns of rs-FC within and between selective brain networks, including DMN, SN, and CEN, could further our knowledge of the clinical symptoms of BPD, and therefore, future studies with multimodal methodologies and longitudinal designs are warranted to further explore the neural correlates of this disorder.

Machine learning applied to functional magnetic resonance imaging in anxiety disorders.

BACKGROUND: Brain functional abnormalities have been commonly reported in anxiety disorders, including generalized anxiety disorder, social anxiety disorder, panic disorder, agoraphobia, and specific phobias. The role of functional abnormalities in the discrimination of these disorders can be tested with machine learning (ML) techniques. Here, we aim to provide a comprehensive overview of ML studies exploring the potential discriminating role of functional brain alterations identified by functional magnetic resonance imaging (fMRI) in anxiety disorders. METHODS: We conducted a search on PubMed, Web of Science, and Scopus of ML investigations using fMRI as features in patients with anxiety disorders. A total of 12 studies (resting-state fMRI n = 5, task-based fMRI n = 6, resting-state and task-based fMRI n=1) met our inclusion criteria. RESULTS: Overall, the studies showed that, regardless of the classifiers, alterations in functional connectivity and aberrant neural activation involving the amygdala, anterior cingulate cortex, hippocampus, insula, orbitofrontal cortex, temporal pole, cerebellum, default mode network, dorsal attention network, sensory network, and affective network were able to discriminate patients with anxiety from controls, with accuracies spanning from 36 % to 94 %. LIMITATIONS: The small sample size, different ML approaches and heterogeneity in the selection of regions included in the multivariate pattern analyses limit the conclusions of the present review. CONCLUSIONS: ML methods using fMRI as features can distinguish patients with anxiety disorders from healthy controls, indicating that these techniques could be used as a helpful tool for the diagnosis and the development of more targeted treatments for these disorders.

Association between cannabis use and symptom dimensions in schizophrenia spectrum disorders: an individual participant data meta-analysis on 3053 individuals.

Background: The association between cannabis use and positive symptoms in schizophrenia spectrum disorders is well documented, especially via meta-analyses. Yet, findings are inconsistent regarding negative symptoms, while other dimensions such as disorganization, depression, and excitement, have not been investigated. In addition, meta-analyses use aggregated data discarding important confounding variables which is a source of bias. Methods: PubMed, ScienceDirect and PsycINFO were used to search for publications from inception to September 27, 2022. We contacted the authors of relevant studies to extract raw datasets and perform an Individual Participant Data meta-analysis (IPDMA). Inclusion criteria were: psychopathology of individuals with schizophrenia spectrum disorders assessed by the Positive and Negative Syndrome Scale (PANSS); cannabis-users had to either have a diagnosis of cannabis use disorder or use cannabis at least twice a week. The main outcomes were the PANSS subscores extracted via the 3-factor (positive, negative and general) and 5-factor (positive, negative, disorganization, depression, excitement) structures. Preregistration is accessible via Prospero: ID CRD42022329172. Findings: Among the 1149 identified studies, 65 were eligible and 21 datasets were shared, totaling 3677 IPD and 3053 complete cases. The adjusted multivariate analysis revealed that relative to non-use, cannabis use was associated with higher severity of positive dimension (3-factor: Adjusted Mean Difference, aMD = 0.34, 95% Confidence Interval, CI = [0.03; 0.66]; 5-factor: aMD = 0.38, 95% CI = [0.08; 0.63]), lower severity of negative dimension (3-factor: aMD = -0.49, 95% CI [-0.90; -0.09]; 5-factor: aMD = -0.50, 95% CI = [-0.91; -0.08]), higher severity of excitement dimension (aMD = 0.16, 95% CI = [0.03; 0.28]). No association was found between cannabis use and disorganization (aMD = -0.13, 95% CI = [-0.42; 0.17]) or depression (aMD = -0.14, 95% CI = [-0.34; 0.06]). Interpretation: No causal relationship can be inferred from the current results. The findings could be in favor of both a detrimental and beneficial effect of cannabis on positive and negative symptoms, respectively. Longitudinal designs are needed to understand the role of cannabis is this association. The reported effect sizes are small and CIs are wide, the interpretation of findings should be taken with caution. Funding: This research did not receive any specific grant or funding. Primary financial support for authors was provided by Le Vinatier Psychiatric Hospital.

Classification of suicidality by training supervised machine learning models with brain MRI findings: A systematic review.

BACKGROUND: Suicide is a global public health issue causing around 700,000 deaths worldwide each year. Therefore, identifying suicidal thoughts and behaviors in patients can help lower the suicide-related mortality rate. This review aimed to investigate the feasibility of suicidality identification by applying supervised Machine Learning (ML) methods to Magnetic Resonance Imaging (MRI) data. METHODS: We conducted a systematic search on PubMed, Scopus, and Web of Science to identify studies examining suicidality by applying ML methods to MRI features. Also, the Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed for the quality assessment. RESULTS: 23 studies met the inclusion criteria. Of these, 20 developed prediction models without external validation and 3 developed prediction models with external validation. The performance of ML models varied among the reviewed studies, with the highest reported values of accuracies and Area Under the Curve (AUC) ranging from 51.7 % to 100 % and 0.52 to 1, respectively. Over half of the studies that reported accuracy (12/21) or AUC (13/16) achieved values of ≥0.8. Our comparative analysis indicated that deep learning exhibited the highest predictive performance compared to other ML models. The most commonly identified discriminative imaging features were resting-state functional connectivity and grey matter volume within prefrontal-limbic structures. LIMITATIONS: Small sample sizes, lack of external validation, heterogeneous study designs, and ML model development. CONCLUSIONS: Most of the studies developed ML models capable of ML-based suicide identification, although ML models' predictive performance varied across the reviewed studies. Thus, further well-designed is necessary to uncover the true potential of different ML models in this field.

Neuroimaging alterations associated with medication use in early-onset bipolar disorder: An updated review.

BACKGROUND: Pediatric bipolar disorder (PBD) is a severe disorder characterized by mood fluctuations starting at a young age. Several neuroimaging studies revealed a specific biological signature of PBD involving alterations in the amygdala and prefrontal regions. Considering the growing concerns regarding the effects of PBD treatments on developing brains, this review aims to provide an overview of the studies investigating the effect of mood stabilizers, antipsychotics, and anticonvulsants on neuroimaging findings in PBD. METHODS: We searched PubMed, Scopus, and Web of Science to identify all structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) studies exploring the effects of medications on neuroimaging findings in PBD. A total of 18 studies met our inclusion criteria (fMRI n = 11, sMRI n = 6, DTI n = 1). RESULTS: Although the findings varied highly across the studies, some investigations consistently indicated that medications primarily affect the prefrontal cortex and the amygdala. Moreover, despite some exceptions, the reported medication effects predominantly lean towards structural and functional normalization. LIMITATIONS: The reviewed studies differ in methods, medications, and fMRI paradigms. Furthermore, most studies used observational approaches with small sample sizes, minimizing the statistical power. CONCLUSIONS: Evidence suggests the potential of antipsychotics and mood stabilizers to modulate the neuroimaging findings in PBD patients, mostly normalizing brain structure and function in key mood-regulating regions.

How does feeling pain look like in depression: A review of functional neuroimaging studies.

INTRODUCTION: Major Depression Disorder (MDD) and pain appear to be reciprocal risk factors and sharing common neuroanatomical pathways and biological substrates. However, the role of MDD on pain processing remains still unclear. Therefore, this review aims to focus on the effect of depression on pain anticipation, and perception, before and after treatment, through functional magnetic resonance imaging (fMRI). METHODS: A bibliographic search was conducted on PubMed, Scopus and Web of Science, looking for fMRI studies exploring pain processing in MDD patients. RESULTS: Amongst the 602 studies retrieved, 12 met the inclusion criteria. In terms of pain perception, studies evidenced that MDD patients generally presented increased activation in brain regions within the prefrontal cortex, insula and in the limbic system (such as amygdala, hippocampus) and occipital cortex. The studies investigating the effect of antidepressant treatment evidenced a reduced activation in areas such as insula, anterior cingulate, and prefrontal cortices. In terms of pain anticipation, contrasting results were evidenced in MDD patients, which presented both increased and decreased activity in the prefrontal cortex, the insula and the temporal lobe, alongside with increased activity in the anterior cingulate cortex, the frontal gyrus and occipital lobes. LIMITATIONS: The small number of included studies, the heterogeneous approaches of the studies might limit the conclusions of this review. CONCLUSIONS: Acute pain processing in MDD patients seems to involve numerous and different brain areas. However, more specific fMRI studies with a more homogeneous population and rigorous approach should be conducted to better highlight the effect of depression on pain processing.