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 We found a mutual link between prenatal complications, GMV alterations in the frontal gyrus, and withdrawn problems. Specifically, complications during pregnancy were associated with higher CBCL/6-18 withdrawn scores and GMV reductions in the right superior frontal gyrus and anterior cingulate cortex. Finally, a mediation effect of these GMV measures on the association between prenatal complications and the withdrawn dimension was identified.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.

A machine learning approach on whole blood immunomarkers to identify an inflammation-associated psychosis onset subgroup.

Psychosis onset is a transdiagnostic event that leads to a range of psychiatric disorders, which are currently diagnosed through clinical observation. The integration of multimodal biological data could reveal different subtypes of psychosis onset to target for the personalization of care. In this study, we tested the existence of subgroups of patients affected by first-episode psychosis (FEP) with a possible immunopathogenic basis. To do this, we designed a data-driven unsupervised machine learning model to cluster a sample of 127 FEP patients and 117 healthy controls (HC), based on the peripheral blood expression levels of 12 psychosis-related immune gene transcripts. To validate the model, we applied a resampling strategy based on the half-splitting of the total sample with random allocation of the cases. Further, we performed a post-hoc univariate analysis to verify the clinical, cognitive, and structural brain correlates of the subgroups identified. The model identified and validated two distinct clusters: 1) a FEP cluster characterized by the high expression of inflammatory and immune-activating genes (IL1B, CCR7, IL12A and CXCR3); 2) a cluster consisting of an equal number of FEP and HC subjects, which did not show a relative over or under expression of any immune marker (balanced subgroup). None of the subgroups was related to specific symptoms dimensions or longitudinal diagnosis of affective vs non-affective psychosis. FEP patients included in the balanced immune subgroup showed a thinning of the left supramarginal and superiorfrontal cortex (FDR-adjusted p-values < 0.05). Our results demonstrated the existence of a FEP patients' subgroup identified by a multivariate pattern of immunomarkers involved in inflammatory activation. This evidence may pave the way to sample stratification in clinical studies aiming to develop diagnostic tools and therapies targeting specific immunopathogenic pathways of psychosis.

Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium.

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.

Using combined environmental-clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression.

BACKGROUND: 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. AIMS: 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. METHOD: 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). RESULTS: 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. CONCLUSIONS: 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.

Association between formal thought disorders, neurocognition and functioning in the early stages of psychosis: a systematic review of the last half-century studies.

Recent review articles provided an extensive collection of studies covering many aspects of format thought disorders (FTD) among their epidemiology and phenomenology, their neurobiological underpinnings, genetics as well as their transdiagnostic prevalence. However, less attention has been paid to the association of FTD with neurocognitive and functioning deficits in the early stages of evolving psychosis. Therefore, this systematic review aims to investigate the state of the art regarding the association between FTD, neurocognition and functioning in the early stages of evolving psychotic disorders in adolescents and young adults, by following the PRISMA flowchart. A total of 106 studies were screened. We included 8 studies due to their reports of associations between FTD measures and functioning outcomes measured with different scales and 7 studies due to their reports of associations between FTD measures and neurocognition. In summary, the main findings of the included studies for functioning outcomes showed that FTD severity predicted poor social functioning, unemployment, relapses, re-hospitalisations, whereas the main findings of the included studies for neurocognition showed correlations between attentional deficits, executive functions and FTD, and highlighted the predictive potential of executive dysfunctions for sustained FTD. Further studies in upcoming years taking advantage of the acceleration in computational psychiatry would allow researchers to re-investigate the clinical importance of FTD and their role in the transition from at-risk to full-blown psychosis conditions. Employing automated computer-assisted diagnostic tools in the early stages of psychosis might open new avenues to develop targeted neuropsychotherapeutics specific to FTD.

The clinical relevance of formal thought disorder in the early stages of psychosis: results from the PRONIA study.

BACKGROUND: Formal thought disorder (FTD) has been associated with more severe illness courses and functional deficits in patients with psychotic disorders. However, it remains unclear whether the presence of FTD characterises a specific subgroup of patients showing more prominent illness severity, neurocognitive and functional impairments. This study aimed to identify stable and generalizable FTD-subgroups of patients with recent-onset psychosis (ROP) by applying a comprehensive data-driven clustering approach and to test the validity of these subgroups by assessing associations between this FTD-related stratification, social and occupational functioning, and neurocognition. METHODS: 279 patients with ROP were recruited as part of the multi-site European PRONIA study (Personalised Prognostic Tools for Early Psychosis Management; www.pronia.eu). Five FTD-related symptoms (conceptual disorganization, poverty of content of speech, difficulty in abstract thinking, increased latency of response and poverty of speech) were assessed with Positive and Negative Symptom Scale (PANSS) and the Scale for the Assessment of Negative Symptoms (SANS). RESULTS: The results with two patient subgroups showing different levels of FTD were the most stable and generalizable clustering solution (predicted clustering strength value = 0.86). FTD-High subgroup had lower scores in social (pfdr < 0.001) and role (pfdr < 0.001) functioning, as well as worse neurocognitive performance in semantic (pfdr < 0.001) and phonological verbal fluency (pfdr < 0.001), short-term verbal memory (pfdr = 0.002) and abstract thinking (pfdr = 0.010), in comparison to FTD-Low group. CONCLUSIONS: Clustering techniques allowed us to identify patients with more pronounced FTD showing more severe deficits in functioning and neurocognition, thus suggesting that FTD may be a relevant marker of illness severity in the early psychosis pathway.

Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.

Neurobiological heterogeneity in schizophrenia is poorly understood and confounds current analyses. We investigated neuroanatomical subtypes in a multi-institutional multi-ethnic cohort, using novel semi-supervised machine learning methods designed to discover patterns associated with disease rather than normal anatomical variation. Structural MRI and clinical measures in established schizophrenia (n = 307) and healthy controls (n = 364) were analysed across three sites of PHENOM (Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging) consortium. Regional volumetric measures of grey matter, white matter, and CSF were used to identify distinct and reproducible neuroanatomical subtypes of schizophrenia. Two distinct neuroanatomical subtypes were found. Subtype 1 showed widespread lower grey matter volumes, most prominent in thalamus, nucleus accumbens, medial temporal, medial prefrontal/frontal and insular cortices. Subtype 2 showed increased volume in the basal ganglia and internal capsule, and otherwise normal brain volumes. Grey matter volume correlated negatively with illness duration in Subtype 1 (r = -0.201, P = 0.016) but not in Subtype 2 (r = -0.045, P = 0.652), potentially indicating different underlying neuropathological processes. The subtypes did not differ in age (t = -1.603, df = 305, P = 0.109), sex (chi-square = 0.013, df = 1, P = 0.910), illness duration (t = -0.167, df = 277, P = 0.868), antipsychotic dose (t = -0.439, df = 210, P = 0.521), age of illness onset (t = -1.355, df = 277, P = 0.177), positive symptoms (t = 0.249, df = 289, P = 0.803), negative symptoms (t = 0.151, df = 289, P = 0.879), or antipsychotic type (chi-square = 6.670, df = 3, P = 0.083). Subtype 1 had lower educational attainment than Subtype 2 (chi-square = 6.389, df = 2, P = 0.041). In conclusion, we discovered two distinct and highly reproducible neuroanatomical subtypes. Subtype 1 displayed widespread volume reduction correlating with illness duration, and worse premorbid functioning. Subtype 2 had normal and stable anatomy, except for larger basal ganglia and internal capsule, not explained by antipsychotic dose. These subtypes challenge the notion that brain volume loss is a general feature of schizophrenia and suggest differential aetiologies. They can facilitate strategies for clinical trial enrichment and stratification, and precision diagnostics.

The metabolic basis of psychosis in bipolar disorder: A positron emission tomography study.

OBJECTIVES: Psychotic symptoms are a common feature in bipolar disorder (BD), especially during manic phases, and are associated with a more severe course of illness. However, not all bipolar subjects experience psychosis during the course of their illness, and this difference often guides assessment and pharmacological treatment. The aim of the present study is to elucidate, for the first time, the FDG uptake dysfunctions associated with psychosis in BD patients with and without a history of past psychotic symptoms, through a positron emission tomography (PET) approach. METHODS: Fifty BD patients with lifetime psychotic symptoms, 40 BD patients without lifetime psychotic symptoms and 27 healthy controls (HC) were recruited and underwent an 18F-FDG-PET session. RESULTS: Compared to HC, BD subjects shared common FDG uptake deficits in several brain areas, including insula, inferior temporal gyrus and middle occipital gyrus. Moreover, we found that BD patients with a history of past psychotic symptoms had a unique FDG uptake alteration in the right fusiform gyrus compared to both BD patients without lifetime psychotic symptoms and HC (all P < 0.01, cFWE corrected). CONCLUSIONS: Overall, our results suggest that FDG uptake alterations in brain regions involved in emotion regulation are a key feature of BD, regardless the presence of past psychosis. Finally, we demonstrated that the FDG uptake reduction in fusiform gyrus is associated with the presence of past psychotic symptoms in BD, ultimately leading towards the idea that the fusiform gyrus might be considered a putative biomarker of psychosis.

Exploring predictors of work competence in schizophrenia: The role of theory of mind.

In schizophrenia employment rate is dramatically low, also among patients receiving job support interventions. Recent studies showed a direct relationship between neurocognitive deficits and work functioning, as well as proving the benefits of combined neurocognitive and work interventions. Current evidence also supports a role of Theory of Mind (ToM), on work functioning. However, the effect of integrated rehabilitation programmes including a social cognitive training on job outcome is still less explored. The aim of this pilot study is to investigate the relationship between work competence and clinical factors, neurocognitive and ToM abilities, as well as to explore the effect of neurocognitive and ToM treatments combined with work therapy. Thirty-seven outpatients with schizophrenia were assigned to either a Computer-assisted Cognitive Remediation (CACR) plus work therapy group (WTG) or to CACR and WTG added to ToM Intervention, both followed by a job support programme. All patients were assessed for psychopathology, neurocognition, ToM and work functioning. Work outcome was significantly predicted by age at onset, neurocognitive abilities and the degree of ToM improvement after the specific intervention. This study provides preliminary insight on predictors of work competence in schizophrenia, highlighting the importance of ToM abilities.

Visual and audio emotion processing training for outpatients with schizophrenia: an integrated multisensory approach.

Deficits in emotion processing (EP) represent a target of rehabilitation in schizophrenia, as they have been related to poor personal and social functioning. To date neither the relationship between these deficits and the generalised cognitive impairment, nor the involvement of specific mechanisms of perception (visual or auditory) are fully comprehended. We developed two treatments targeting EP, through visual or auditory channels, with the aim of disentangling possible differences and/or interactions between the two modalities in schizophrenia-related impairments, also taking into account the role of cognition and social functioning. Thirty five outpatients with schizophrenia were assessed for neurocognition, social functioning and EP (visual and auditory channel) and participated in either visual or auditory EP training or in an active control group. Results showed a significant improvement in EP through the specific channel trained for both groups, with an extended effect also on vocal stimuli for the visual training group. Positive correlations were found between working memory, social functioning and EP. Our findings help to shed light on the possible different involvement of perceptual channels in schizophrenia, as well as supporting previous evidence that emotion recognition may be inter-related but does not overlap with neurocognition and can be specifically trained.

A diffusion weighted imaging study of basal ganglia in schizophrenia.

OBJECTIVES: Several magnetic resonance imaging (MRI) studies provided evidence of selective brain abnormalities in schizophrenia, both in cortical and subcortical structures. Basal ganglia are of particular interest, given not only the high concentration of dopaminergic neurons and receptors, but also for their crucial role in cognitive functions, commonly impaired in schizophrenia. To date, very few studies explored basal ganglia using diffusion imaging, which is sensitive to microstructural organization in brain tissues. The aim of our study is to explore basal ganglia structures with diffusion imaging in a sizeable sample of patients affected by schizophrenia and healthy controls. METHODS: We enrolled 52 subjects affected by schizophrenia according to DMS-IV-R criteria and 46 healthy controls. Diffusion weighted images were obtained using a 1.5 Tesla scanner and apparent diffusion coefficient (ADC) values were determined in axial and coronal sections at the level of basal ganglia. RESULTS: Patients affected by schizophrenia showed a significantly higher ADC compared to healthy controls in the left anterior lenticular nucleus (F = 3.9, p = .05). A significant positive correlation between right anterior lenticular nucleus and psychotropic dosages was found (r = 0.4, p = .01). CONCLUSIONS: Our study provides evidence of lenticular nucleus microstructure alterations in schizophrenia, potentially sustaining cognitive and motor deficits in schizophrenia. Key points The basal ganglia structures was explored with diffusion imaging in a sizeable sample of patients affected by schizophrenia and healthy controls. Patients affected by schizophrenia showed a significantly higher ADC compared to healthy controls in the left anterior lenticular nucleus. Our study provides evidence of lenticular nucleus microstructure alterations in schizophrenia, potentially sustaining cognitive and motor deficits in schizophrenia.

Temperament and Character Inventory in Bipolar Disorder versus Healthy Controls and Modulatory Effects of 3 Key Functional Gene Variants.

BACKGROUND: Bipolar disorder (BD) has been associated with temperamental and personality traits, although the relationship is still to be fully elucidated. Several studies investigated the genetic basis of temperament and character, identifying catechol-O-methyltransferase (COMT), brain derived neurotrophic factor (BDNF), and serotonin transporter (5-HTT) gene variants as strong candidates. METHODS: In the GECO-BIP study, 125 BD patients and 173 HC were recruited. Subjects underwent to a detailed assessment and the temperament and character inventory 125 items (TCI) was administrated. Three functional genetic variants within key candidate genes (COMT rs4680, BDNF rs6265, and the serotonin-transporter-linked polymorphic region (5-HTTLPR)) were genotyped. Univariate and multivariate analyses were performed. RESULTS: Compared to HC, BD patients showed higher scores in novelty seeking (NS; p = 0.001), harm avoidance (HA; p < 0.001), and self transcendence (St; p < 0.001), and lower scores in self directness (p < 0.001) and cooperativeness (p < 0.001) TCI dimensions. Concerning the genetic analyses, COMT rs4680 was associated with NS in the total sample (p = 0.007) and in the male subsample (p = 0.022). When performing the analysis in the HC and BD samples, the association was confirmed only in HC (p = 0.012), and in the HC male subgroup in particular (p = 0.004). BDNF rs6265 was associated with St in the BD group (p = 0.017). CONCLUSION: COMT rs4680 may modulate NS in males in the general population. This effect was not detected in BD patients, probably because BD alters the neurobiological basis of some TCI dimensions. BDNF rs6265 seems to modulate St TCI dimension only in BD patients, possibly modulating the previously reported association between rs6265 and BD treatment response. Further studies are needed to confirm our findings.

Sexual dimorphism of the planum temporale in schizophrenia: A MRI study.

OBJECTIVE: Anatomical alterations in the superior temporal gyrus have been consistently reported in patients with schizophrenia, and they have mostly been linked to positive symptoms, including hallucinations and thought disorders. The superior temporal gyrus is considered one of the most asymmetric and lateralized structure of the human brain, and the process of lateralization seems to vary according to gender in the normal population. However, although it has been consistently suggested that patients with schizophrenia did not show normal brain lateralization in several regions, only few studies investigated it in the superior temporal gyrus and its sub-regions considering the effects of gender. In this context, the aim of this study was to evaluate sexual dimorphism in superior temporal gyrus volumes in a sample of patients with schizophrenia compared to age- and gender-matched healthy controls. METHODS: A total of 72 right/left-handed males (40 schizophrenia patients and 32 healthy controls) and 45 right/left-handed females (18 schizophrenia patients and 27 healthy controls) underwent clinical evaluation and a 1.5T magnetic resonance imaging scan. Gray and white matter volumes of regions of interest within the superior temporal gyrus were manually detected, including the Heschl's gyrus and the planum temporale. RESULTS: Female patients with schizophrenia presented a reduction in left planum temporale gray matter volumes ( F = 4.58, p = 0.03) and a lack of the normal planum temporale asymmetry index ( t = 0.27; p = 0.79) compared to female controls ( t = 5.47; p = 0.001). No differences were found between males for any volumes or laterality indices. Finally, in female patients with schizophrenia, Heschl's gyrus gray and white matter volumes negatively correlated with positive symptoms ( r = -0.56, p = 0.01). CONCLUSION: Our results showed that sexual dimorphism plays a key role on planum temporale in schizophrenia, underlining the importance of gender as a modulator of brain morphology and lateralization of schizophrenia.

COMT and STH polymorphisms interaction on cognition in schizophrenia.

Catechol-O-methyltransferase (COMT) gene, a key regulator of prefrontal cortex (PFC) dopamine (DA) availability, has been extensively studied in relation to cognitive domains, mainly executive functions, that are impaired in schizophrenia, but results are still controversial. Since recent studies in patients affected by neurodegenerative and psychiatric disorders suggested a role of saitohin (STH) gene as a concurring factor in hypofrontality, we hypothesize that STH and COMT polymorphisms could have an additive effect on cognition in schizophrenia. Three forty three clinically stabilized patients with schizophrenia were assessed with a broad neuropsychological battery including the Brief Assessment of Cognition in Schizophrenia, the Wisconsin Card Sorting Test and the Continuous Performance Test and were genotyped for COMT Val108/158Met and STH Q7R polymorphisms. We observed the effects of COMT on speed of processing and executive functions, as well as a significant effect of STH on executive functions performances. Moreover, a significant interaction between COMT and STH polymorphisms was found on executive functions, with COMT Val/Val and STH R carriers performing worse. Our results showed a significant interaction effect of COMT and STH polymorphisms on cognitive performances, strengthening the involvement of STH in cognitive impairments, especially in the domains commonly impaired in schizophrenia.

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.

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 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.

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.

Association between testosterone levels and mood disorders: A minireview.

BACKGROUND: Although many studies reported the neuropsychiatric involvement of testosterone (T) levels in the development of mood disorders, its role in this disabling disorder is still not well understood. Therefore, in this review, we aim to summarize the current literature exploring serum testosterone levels in both major depressive disorder (MDD) and bipolar disorder (BD), with particular attention given to the possible causal relationship between pathological mood alterations and T levels. METHODS: We selected 9 original studies from a bibliographic search on PubMed, excluding studies on hormonal therapy and other psychiatric disorders other than mood disorders. RESULTS: The results reported by the reviewed studies were conflicting especially with regards to the presence of dysfunctional levels of T in patients with BD. Specifically, while MDD was found to be associated with low levels of T compared to healthy controls (HC), in BD the results were highly heterogeneous, with a mixed picture of reduced, increased or no difference in T levels in BD patients compared to HC. LIMITATIONS: Studies were highly heterogeneous in terms of samples employed, psychometric scales used for assessing depressive symptoms, T assay methods and therapeutic regimens. CONCLUSIONS: Overall, T levels were shown to be reduced in both MDD and BD patients, ultimately suggesting that T could be useful as a biomarker in mood disorders and provide guidance for future research.

Geometric Reliability of Super-Resolution Reconstructed Images from Clinical Fetal MRI in the Second Trimester.

Fetal Magnetic Resonance Imaging (MRI) is an important noninvasive diagnostic tool to characterize the central nervous system (CNS) development, significantly contributing to pregnancy management. In clinical practice, fetal MRI of the brain includes the acquisition of fast anatomical sequences over different planes on which several biometric measurements are manually extracted. Recently, modern toolkits use the acquired two-dimensional (2D) images to reconstruct a Super-Resolution (SR) isotropic volume of the brain, enabling three-dimensional (3D) analysis of the fetal CNS.We analyzed 17 fetal MR exams performed in the second trimester, including orthogonal T2-weighted (T2w) Turbo Spin Echo (TSE) and balanced Fast Field Echo (b-FFE) sequences. For each subject and type of sequence, three distinct high-resolution volumes were reconstructed via NiftyMIC, MIALSRTK, and SVRTK toolkits. Fifteen biometric measurements were assessed both on the acquired 2D images and SR reconstructed volumes, and compared using Passing-Bablok regression, Bland-Altman plot analysis, and statistical tests.Results indicate that NiftyMIC and MIALSRTK provide reliable SR reconstructed volumes, suitable for biometric assessments. NiftyMIC also improves the operator intraclass correlation coefficient on the quantitative biometric measures with respect to the acquired 2D images. In addition, TSE sequences lead to more robust fetal brain reconstructions against intensity artifacts compared to b-FFE sequences, despite the latter exhibiting more defined anatomical details.Our findings strengthen the adoption of automatic toolkits for fetal brain reconstructions to perform biometry evaluations of fetal brain development over common clinical MR at an early pregnancy stage.