Background: Problematic school attendance in adolescence is a heterogeneous phenomenon with academic, emotional, and developmental consequences. Although relational factors are increasingly recognised, the literature remains conceptually fragmented, with overlapping constructs such as social isolation, loneliness, social withdrawal, and peer difficulties examined under partially distinct frameworks. Objective: This systematic review aimed to synthesize empirical evidence on the association between social isolation, related constructs, and problematic school attendance during adolescence. Methods: Following PRISMA 2020 guidelines, searches were conducted in PubMed, Scopus, and Embase (final search: 20 January 2026). Eligible studies were observational or qualitative empirical investigations involving adolescents aged 11-18 years and examining at least one relational construct (objective/subjective social isolation, loneliness, social withdrawal, peer rejection) together with at least one school attendance outcome (school refusal, withdrawal, truancy, avoidance, dropout). Risk of bias was assessed with design-specific JBI tools and findings were synthesized narratively. Results: Eleven studies met the inclusion criteria: six cross-sectional, four longitudinal/cohort, and one qualitative. Loneliness, bullying, school alienation, and social isolation at school were most consistently associated with school refusal and absenteeism, whereas peer acceptance, reciprocal friendships, teacher emotional support, and school belonging were associated with reduced dropout intentions, higher graduation rates, and better school transition. Longitudinally, loneliness and low teacher support preceded increases in intention to quit, while supportive relationships appeared to protect against later disengagement. Conclusions: Social isolation and related constructs are meaningfully associated with problematic school attendance in adolescence. Social isolation and related constructs appear to be meaningfully associated with problematic school attendance in adolescence, although the evidence base remains limited and heterogeneous. These findings suggest that a relational perspective may usefully complement existing approaches to attendance difficulties, and point to the need for longitudinal, multi-informant studies disentangling subjective, interpersonal, and institutional disconnection across distinct attendance profiles.

This brief research report explored the relationships between hikikomori symptom severity (extreme social withdrawal), basic psychological needs of Competence, Autonomy, and Relatedness, and perceived parental bonding in Italian adults experiencing social isolation. Participants (N = 33; Mage = 27.83, SD = 7.46; 42.9% women) were individuals recruited online who scored above the high-risk cutoff for hikikomori on the Hikikomori Questionnaire-25 (HQ-25). They completed the Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS) and the Parental Bonding Instrument (PBI). Competence Frustration accounted for substantial variability in hikikomori symptom severity in this high-risk sample, explaining approximately 31% of the variance. Regarding perceived parental bonding, maternal Care was positively associated with Autonomy Satisfaction and negatively with Competence Frustration, whereas maternal Control was positively related to frustration of all three needs. Paternal Care was negatively related to Autonomy and Competence Frustration, while paternal Control was positively associated with Relatedness Frustration. Over 30% of participants perceived maternal bonding as Affectionless Control and paternal bonding as either Affectionless Control or Neglectful. No gender differences emerged. Findings suggest that Competence Frustration may represent a key psychological correlate of hikikomori symptom severity in this high-risk group. Moreover, distinct maternal and paternal patterns of perceived Care and Control were associated with need frustration and satisfaction, as well as with the hikikomori dimension of perceived lack of Emotional Support. Study limitations include small sample size, cross-sectional design, reliance on self-report measures, and potential selection bias toward help-seeking individuals. Replication in larger longitudinal samples is warranted to confirm these preliminary results.

Background:Large-scale T1-weighted MRI studies have established grey-matter abnormalities in bipolar disorder (BD), with our group contributing to consensus findings. However, structural connectivity, particularly within emotion- and reward-related circuits, remains poorly understood. Diffusion-weighted MRI (dMRI) enables investigation of white-matter pathways, yet prior work is constrained by small samples, methodological heterogeneity, and unclear medication effects. We conducted the largest dMRI network analysis in BD, relating symptom burden and polypharmacy to tractography-derived connectivity and graph-theoretic metrics. Methods:Cross-sectional structural and diffusion MRI scans from 449 individuals with BD (35.7±12.6 years) and 510 controls (33.3±12.6 years), aged 18-65, were analyzed across 16 ENIGMA-BD sites. Standardized segmentation/parcellation and constrained spherical deconvolution tractography generated individual structural connectivity matrices. Graph-theoretic metrics of global and subnetwork organization were related to symptom severity and medications. Results:BD showed widespread network alterations (lower density and efficiency, longer path length, and higher betweenness centrality), altered microstructural organization in a limbic-basal ganglia circuit, and abnormal streamline counts in a default-mode/salience/fronto-limbic-basal ganglia network. Longer illness duration, later onset, and psychosis history were associated with greater abnormalities in network architecture, whereas more manic episodes were associated with greater fronto-limbic connectivity. Antidepressant (particularly SSRI), anticonvulsant, and antipsychotic use related to poorer global and fronto-limbic connectivity; no clear lithium effects emerged. Conclusions:As the largest structural connectivity study in BD, we reveal widespread disruption in reward and emotion-regulation networks influenced by illness severity and medication use. Results show that multisite harmonization is feasible and highlight ENIGMA-BD as a scalable framework for identifying reproducible neurobiological markers.

Affective and non-affective psychoses exhibit heterogeneous clinical presentation, neurobiological underpinnings, and treatment response. This special issue integrates multimodal findings to examine overlapping and distinct neural substrates in the psychosis spectrum. In this context, longitudinal designs appear essential to observe the developmental trajectory of patients and advance real-world precision psychiatry. Interestingly, gray matter changes in psychotic patients point toward the possibility of differential developmental trajectories in insular subregions between affective and non-affective psychosis. Moreover, white matter abnormalities shared by schizophrenia and bipolar disorder, as revealed by diffusion tensor imaging studies, might indicate common neural mechanisms affecting cognitive and behavioral functions across both conditions. Beyond these neuroimaging findings, stratification approaches based on immune-inflammatory profiles may further characterize biologically distinct subgroups, with immune-neurobiological interactions potentially contributing to illness trajectories. Finally, machine learning techniques, integrating clinical and neuroimaging data in a multimodal approach, emerged as promising tools for predicting critical outcomes, such as suicide, enabling earlier and more targeted interventions. The evidence presented in this special issue underscores the complexity of psychotic disorders and the need for multimodal approaches. Future research will be essential to improve predictive tools and refine biomarker integration.

Abstract Brain similarity networks (BSNs), extracted from structural magnetic resonance imaging, provide a validated framework for studying brain network organization and encode neurodevelopmental information relevant for psychiatric disorders. Recently, a neurodevelopmental hypothesis has been proposed for bipolar disorder (BD), where evidence demonstrates neuroprogression phenotypes differing from controls. BSNs offer a promising framework for investigating BD’s neural correlates but remain largely underexplored. Parallelly, graph neural networks (GNNs) have emerged as suitable deep learning models for exploiting network-level information. This study aimed to investigate BSNs for discriminating subjects with BD from controls within a GNN framework using the multi-site StratiBip network, composed of 605 controls and 501 subjects with BD. Leveraging advanced analysis tools, we developed a multi-site classification framework including: i) the state-of-the-art MIND algorithm for computing morphometric similarity (MS) networks based on gray matter volumes (GMV), ii) MS integration with age, sex, and GMV, iii) a leave-one-site-out cross-validation for multi-site model generalizability evaluation. The best model achieved a mean multi-site accuracy of 68%. Explainability analyses revealed meaningful MS patterns in the basal ganglia, frontal and temporal lobes, and a particularly relevant integration with age. This study provides interpretable insights into the role of MS in BD and unveils evidence supporting ageing-related processes as a significant component of BD pathophysiology.

This chapter explores the phenomenon of social isolation, focusing on its defining characteristics, prevalence across populations, and its association with various psychopathological conditions, including depression, anxiety, and psychotic symptoms. Drawing on recent empirical findings, we highlight how social isolation has emerged as a significant public health concern, particularly in vulnerable and underserved groups. We then introduce a telematic psychotherapeutic intervention developed by our research group, designed specifically for adults experiencing chronic social isolation, who face barriers in accessing traditional, face-to-face mental health care through national health systems. The intervention leverages digital platforms to provide structured psychological support remotely. Finally, we present preliminary findings from an ongoing randomized controlled trial conducted at our laboratory and affiliated research center in Italy, aimed at assessing the clinical efficacy and feasibility of such intervention. These findings offer early insights into the potential of digital treatments to bridge existing gaps in mental health service provision.

Major depressive disorder (MDD) affects approximately 4.4% of the global population. Its prevalence is increasing among adolescents and has led to the psychosocial condition known as hikikomori. MDD is typically assessed by self-report questionnaires, which, although informative, are subject to evaluator bias and subjectivity. To address these limitations, recent studies have explored machine learning (ML) for automated MDD detection. Among the input data used, speech signals stand out due to their low cost and minimal intrusiveness. However, many speech-based approaches lack integration with cognitive behavioral therapy (CBT) and adherence to evidence-based, patient-centered care—often aiming to replace rather than support clinical monitoring. In this context, we propose ML models to assess MDD in hikikomori patients using speech data from a real-world clinical trial. The trial is conducted in Italy, supervised by physicians, and comprises an eight-session CBT plan that is clinical evidence-based and follows patient-centered practices. Patients’ speech is recorded during therapy, and the Mel-Frequency Cepstral Coefficients (MFCCs) and wav2vec 2.0 embedding are extracted to train the models. The results show that the Multi-Layer Perceptron (MLP) predicted depression outcomes with a Root Mean Squared Error (RMSE) of 0.064 using only MFCCs from the first session, suggesting that early-session speech may be valuable for outcome prediction. When considering the entire CBT treatment (i.e., all sessions), the MLP achieved an RMSE of 0.063 using MFCCs and a lower RMSE of 0.057 with wav2vec 2.0, indicating approximately a 9.5% performance improvement. To aid the interpretability of the treatment outcomes, a binary task was conducted, where Logistic Regression (LR) achieved 70% recall in predicting depression improvement among young adults using wav2vec 2.0. These findings position speech as a valuable predictive tool in clinical informatics, potentially supporting clinicians in anticipating treatment response.

Background: Hikikomori, or prolonged social withdrawal, is a clinical condition usually emerging during adolescence or young adulthood, characterized by severe self-isolation in one's home, and often associated with other psychiatric disorders and symptoms. Objective: The study summarized evidence of hikikomori diagnostic criteria, clinical manifestations, and comorbidity with psychiatric disorders and symptoms in adolescents and young adults. Methods: A scoping review was conducted following PRISMA guidelines, with four electronic databases searched for original works in English, French, and Italian published since 2010. Results: A total of 15 studies were selected, 7 involved adolescents, 4 young adults, and 4 participants from both age groups. Most studies relied on the diagnostic criteria proposed for hikikomori inclusion in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Differences in hikikomori and comorbidity profiles were identified between adolescents and young adults. Conclusions: Heterogeneity characterized hikikomori diagnostic criteria, comorbidity, demographic and clinical features of the study samples. Studies adopting more homogeneous populations, shared diagnostic criteria, consistent assessment tools and longitudinal designs are needed to better clarify the clinical features of hikikomori in young people.

Il dibattito sul suicidio assistito in pazienti affetti da depressione grave solleva questioni complesse di natura clinica, etica, scientifica, medico-legale e culturale. Il presente contributo si propone di affermare che tale opzione non è accettabile nel contesto della malattia depressiva, anche nelle sue forme più gravi e resistenti ai trattamenti. La depressione non rappresenta una condizione irreversibile o terminale: esistono molteplici possibilità di trattamento, sono documentate remissioni anche spontanee e tardive, e l’ideazione suicidaria deve essere considerata un sintomo cardinale della malattia, non il frutto di una decisione ponderata. Sotto il profilo scientifico, mancano biomarcatori affidabili per definire l’“incurabilità” della depressione e la prognosi del disturbo è spesso incerta. Eticamente, il principio di non maleficenza impone al medico di non contribuire alla morte del paziente, mentre la vulnerabilità di chi soffre di depressione grave ne compromette la capacità di autodeterminazione. Dal punto di vista medico-legale, è estremamente difficile valutare con certezza la capacità di “intendere e volere” in tali condizioni. Infine, sul piano simbolico e culturale, la psichiatria deve riaffermare il proprio mandato di cura e di contrasto alla disperazione, evitando derive pericolose che potrebbero legittimare lo stigma e il contagio suicidario. In conclusione, lo psichiatra non può e non deve assumere il ruolo di facilitatore di morte, ma deve continuare a offrire cura, speranza e protezione anche nei contesti clinici più complessi.

The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We used deep autoencoders in an anomaly detection framework, combined for the first time with a confounder removal step that integrates training and external validation.The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus, and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs.Our generalizable framework enabled the identification of brain deviating patterns differing between the subject and the group levels, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry.

Raising trends of neuroimaging data sharing among different research centers, including resting-state functional magnetic resonance imaging (rs-fMRI) measurements, have driven to accessible large-scale sample and improvement of reliability and consistency of downstream analyses. However, in this context several concerns arise for non-biological confounding factors mainly related to differences in magnetic resonance scanners and imaging parameters among sites. Until now, there is limited knowledge of the impact of site-to-site variations in rsfMRI functional connectivity (FC) measures and the most suitable harmonization approach for mitigating such impact. In this study, we aimed to quantitatively evaluate the site-to-site variations in rs-fMRI FC patterns and how the widely used ComBat harmonization performs in removing them. A multi-scale analytical approach was adopted, from single pairs of regions to resting-state networks (RSNs) and to the entire brain. Our findings show that ComBat removes unwanted site effects from rs-fMRI FC measures while improving signal-to-noise ratio (SNR) in the data and RSNs identifiability. Further, we identify and visualized specific FC links highly affected by site, highlighting differences in such effects among RSNs. Overall, our findings demonstrate that ComBat is effective in harmonizing rs-fMRI FC measures, emphasizing also the overall RSNs identifiability and the enhancement of the majority of single RSNs in the entire brain connectome.

INTRODUCTION: Social anxiety disorder (SAD) is a psychiatric condition severely impacting patients' daily lives and potentially leading to isolation. Among all anxiety disorders, SAD has the lowest remission rate and an estimated lifetime prevalence of 12% constituting a significant burden on patients and health care institutions. Cognitive impairments in psychiatric disorders are known to impact clinical interventions and are supposed to constitute an important maintenance factor that should be treated. However, to date, the exact neuropsychological profile of SAD remains unclear. Therefore, we conducted the present review aimed at summarizing the cognitive functioning of SAD patients and their differences from healthy controls.EVIDENCE ACQUISITION: Web of Science and PubMed databases were searched to identify studies that used neuropsychological tools to assess non-affective cognition in SAD adult patients. After thorough research, we included 14 studies examining the cognitive status of SAD patients. A wide variety of cognitive tests were used to evaluate patients' cognitive functioning.EVIDENCE SYNTHESIS: Results indicate that patients with SAD exhibit significant alterations in memory, executive functioning, and attention, possibly constituting maintenance factors. However, several limitations should be considered when interpreting the results of our review including the absence of longitudinal studies, the heterogeneity of the tests and the absence of secondary variables that could enlighten the nature of the cognitive deficits of SAD (i.e., neurobiological markers, clinical and quality of life variables).CONCLUSIONS: Our review suggests that SAD patients are characterized by memory and executive functioning alterations, possibly constituting maintenance factors of the disorder. Future studies are warranted, exploring the impact of these alterations on patients' daily lives and developing cognitive enhancing interventions tailored for this condition.

Data aggregation across multiple research centers is gaining importance in the context of MRI research, driving diverse high-dimensional datasets to form large-scale heterogeneous sample, increasing statistical power and relevance of machine learning and deep learning algorithm. Site-related effects have been demonstrated to introduce bias in MRI features and confound subsequent analyses. Although Combating Batch (ComBat) technique has been recently reported to successfully harmonize multi-scale neuroimaging features, its performance assessments are still limited and largely based on qualitative visualizations and statistical analyses. In this study, we stand out by using a robust cross-validation approach to assess ComBat performances applied on volume- and surface-based measures acquired across three sites. A machine learning approach based on Multi-Class Gaussian Process Classifier was applied to predict imaging site based on raw and harmonized brain features, providing quantitative insights into ComBat effectiveness, and verifying the association between biological covariates and harmonized brain features. Our findings showed differences in terms of ComBat performances across measures of regional brain morphology, demonstrating tissue specific site effect modeling. ComBat adjustment of site effects also varied across regional level of each specific volume-based and surface-based measures. ComBat effectively eliminates unwanted data site-related variability, by maintaining or even enhancing data association with biological factors. Of note, ComBat has demonstrated flexibility and robustness of application on unseen independent gray matter volume data from the same sites.

The treatment of bipolar disorder (BD) often involves administering multiple psychotropic medications, yet little research has examined how these medications, especially when used together, affect the brain's white matter in BD. We investigate how polypharmacy and the severity of symptoms are associated with white matter connectivity in BD, in the largest neuroimaging study of its kind.

ObjectiveThe present study aims to present a novel cognitive-behavioral intervention protocol focused on treating social isolation through telematic interaction, thus overcoming common barriers characteristic of face-to-face interventions.MethodsWe examined current literature about face-to-face and telematic psychotherapeutic interventions for the treatment of social isolation in early adulthood. Current evidence is mixed, suggesting the need to develop novel interventions focused on patients’ cognitive functioning. Moreover, telematic interventions are promising candidates for overcoming common barriers intrinsic to the condition of social isolation.ResultsThe present 8-session model inspired by cognitive behavioral theoretical models and cognitive interventions currently present in the literature is thought to help socially isolated adult patients reduce clinical symptoms associated with the condition and lead to a reduction in the avoidance of social situations, leading to an improvement of the quality of life.ConclusionWe presented a telematic psychotherapeutic intervention aimed at helping adult patients suffering from social isolation who are unable to seek help from national health systems and face-to-face interventions, thus overcoming barriers intrinsic to social isolation. The present cognitive-behavioral treatment protocol has been developed in the context of a randomized clinical trial ongoing in Italy, aimed at implementing and testing the feasibility and effectiveness of multimodal digital interventions for treating social isolation.

Cognitive Behavioral Therapy (CBT) is among the gold-standard psychotherapeutic interventions for the treatment of psychiatric disorders, including bipolar disorder (BD). While the clinical response of CBT in patients with BD has been widely investigated, its neural correlates remain poorly explored. Therefore, this scoping review aimed to discuss neuroimaging studies on CBT-based interventions in bipolar populations. Particular attention has been paid to similarities and differences between studies to inform future research. The literature search was conducted on PubMed, PsycINFO, and Web of Science databases in June 2023, identifying 307 de-duplicated records. Six studies fulfilled the inclusion criteria and were reviewed. All of them analyzed functional brain activity data. Four studies showed that the clinical response to CBT was associated with changes in the functional activity and/or connectivity of prefrontal and posterior cingulate cortices, temporal parietal junction, amygdala, precuneus, and insula. In two additional studies, a peculiar pattern of baseline activations in the prefrontal cortex, hippocampus, amygdala, and insula predicted post-treatment improvements in depressive symptoms, emotion dysregulation, and psychosocial functioning, although CBT-specific effects were not shown. These results suggest, at the very preliminary level, the potential of CBT-based interventions in modulating neural activity and connectivity of patients with BD, especially in regions ascribed to emotional processing. Nonetheless, the discrepancies between studies concerning aims, design, sample characteristics, and CBT and fMRI protocols do not allow conclusions to be drawn. Further research using multimodal imaging techniques, better-characterized BD samples, and standardized CBT-based interventions is needed.

The current biologically uninformed psychiatric taxonomy complicates optimal diagnosis and treatment. Neuroimaging-based machine learning methods hold promise for tackling these issues, but large-scale, representative cohorts are required for building robust and generalizable models. The European College of Neuropsychopharmacology Neuroimaging Network Accessible Data Repository (ECNP-NNADR) addresses this need by collating multi-site, multi-modal, multi-diagnosis datasets that enable collaborative research. The newly established ECNP-NNADR includes 4,829 participants across 21 cohorts and 11 distinct psychiatric diagnoses, available via the Virtual Pooling and Analysis of Research data (ViPAR) software. The repository includes demographic and clinical information, including diagnosis and questionnaires evaluating psychiatric symptomatology, as well as multi-atlas grey matter volume regions of interest (ROI). To illustrate the opportunities offered by the repository, two proof-of-concept analyses were performed: (1) multivariate classification of 498 patients with schizophrenia (SZ) and 498 matched healthy control (HC) individuals, and (2) normative age prediction using 1170 HC individuals with subsequent application of this model to study abnormal brain maturational processes in patients with SZ. In the SZ classification task, we observed varying balanced accuracies, reaching a maximum of 71.13% across sites and atlases. The normative-age model demonstrated a mean absolute error (MAE) of 6.95 years [coefficient of determination (R2)=0.77, P<.001] across sites and atlases. The model demonstrated robust generalization on a separate HC left-out sample achieving a MAE of 7.16 years [R2=0.74,P<.001]. When applied to the SZ group, the model exhibited a MAE of 7.79 years [R2=0.79, P<.001], with patients displaying accelerated brain-aging with a brain age gap (BrainAGE) of 4.49 (8.90) years. Conclusively, this novel multi-site, multi-modal, transdiagnostic data repository offers unique opportunities for systematically tackling existing challenges around the generalizability and validity of imaging-based machine learning applications for psychiatry.

Background: Males and females who consume cannabis can experience different mental health and cognitive problems. Neuroscientific theories of addiction postulate that dependence is underscored by neuroadaptations, but do not account for the contribution of distinct sexes. Further, there is little evidence for sex differences in the neurobiology of cannabis dependence as most neuroimaging studies have been conducted in largely male samples in which cannabis dependence, as opposed to use, is often not ascertained. Methods: We examined subregional hippocampus and amygdala volumetry in a sample of 206 people recruited from the ENIGMA Addiction Working Group. They included 59 people with cannabis dependence (17 females), 49 cannabis users without cannabis dependence (20 females), and 98 controls (33 females). Results: We found no group-by-sex effect on subregional volumetry. The left hippocampal cornu ammonis subfield 1 (CA1) volumes were lower in dependent cannabis users compared with non-dependent cannabis users (p<0.001, d=0.32) and with controls (p=0.022, d=0.18). Further, the left cornu ammonis subfield 3 (CA3) and left dentate gyrus volumes were lower in dependent versus non-dependent cannabis users but not versus controls (p=0.002, d=0.37, and p=0.002, d=0.31, respectively). All models controlled for age, intelligence quotient (IQ), alcohol and tobacco use, and intracranial volume. Amygdala volumetry was not affected by group or group-by-sex, but was smaller in females than males. Conclusions: Our findings suggest that the relationship between cannabis dependence and subregional volumetry was not moderated by sex. Specifically, dependent (rather than non-dependent) cannabis use may be associated with alterations in selected hippocampus subfields high in cannabinoid type 1 (CB1) receptors and implicated in addictive behavior. As these data are cross-sectional, it is plausible that differences predate cannabis dependence onset and contribute to the initiation of cannabis dependence. Longitudinal neuroimaging work is required to examine the time-course of the onset of subregional hippocampal alterations in cannabis dependence, and their progression as cannabis dependence exacerbates or recovers over time.