Linda Antonella Antonucci

Predicting individual mental-health trajectories requires models that capture cross-domain dynamics rather than only statistical associations. We introduce the Coupled Stochastic Dynamical System (CSDS), a forward generative model of interacting digital behaviour, wearable-derived physiology and psychiatric burden. In the multi-year GLOBEM student cohort (N=496), Bayesian inversion recovered four behavioural phenotypes, reproducing 80–83% of cluster targets through distinct trait-vulnerability, physical-activity and affective-response parameter combinations. These phenotypes were primarily differentiated by digital-media overuse, physical activity, sleep and affective burden. Participant-specific inversion then generated virtual twins whose synthetic trajectories preserved observed behavioural traces: hidden Markov models trained on virtual-twin data reproduced state occupancy and transition patterns ( r =0.84–0.87 and 0.93–0.94). Forecasts outperformed causal mean and persistence baselines for short-horizon magnitude prediction and predicted affective-state changes with 66–78% balanced accuracy over 21 days. These findings establish CSDS as an interpretable framework for simulating individual behavioural dynamics and advancing personalised mental-health modelling.

In-scanner head motion is a recognized source of bias in structural magnetic resonance imaging (sMRI), yet it remains under-addressed in psychiatric neuroimaging, where structural differences in patient populations are considered foundational. We examined motion-related bias in grey matter volume estimates across eight independent cohorts comprising 9,664 individuals, including 8,979 neurotypical controls (NC), 497 patients with schizophrenia (SCZ), and 188 patients with bipolar disorder (BD). Motion estimates were derived from multiple fMRI scans acquired within the same scanning session and summarized using principal component analysis. In NC, motion accounted for 1-6% of regional grey matter variance, a magnitude comparable to reported psychiatric case-control effect sizes. Adjusting for motion attenuated SCZ-NC group differences, reducing effect sizes in 85% of brain regions and yielding 5% fewer significant ROIs (pFDR<0.05). In BD, motion correction reduced effect sizes in 97% of regions, with a 24% reduction in significant ROIs. Cross-diagnostic spatial patterns were significantly correlated (r = 0.63), explaining a sizable portion of the commonalities between SCZ and BD. Critically, a falsification analysis in UK Biobank (N=5,123) showed that stratifying NC by motion alone produced grey matter differences accounting for 45-62% of SCZ case-control effect magnitude, underscoring how difficult it is to interpret SCZ-like morphometric differences as tissue properties rather than as motion-driven patterns. These findings urge caution in interpretations of sMRI differences in patient-control comparisons and use of systematic fMRI based motion control as standard practice in sMRI analyses.

Background: Identifying neuroanatomical correlates of clinical prediction models may offer pathophysiological insights into the clinical high-risk states for psychosis (CHR-P) and unveil new therapeutic targets for early intervention. Study Design: We used the North American Prodrome Longitudinal Study (NAPLS-2) risk calculator to obtain psychosis risk scores for 315 CHR-P (M = 23.85, SD = ± 5.64; female: 164) and 295 recent-onset depression (M = 25.11, SD = ± 6.21; female: 144) patients from the Personalized Prognostic Tools for Early Psychosis Management (PRONIA) cohort. Voxel-based morphometry was employed to examine associations between risk scores, gray matter volume (GMV), and white matter volume (WMV). Post-hoc, we used eigenvariate extraction to explore network-level alterations associated with significant regions. Moderation analyses were conducted to understand the influence of individual NAPLS-2 risk variables on these networks (False Discovery Rate-corrected). Study Results: Reduced hippocampal GMV (${k}_E$ = 847 voxels) and cerebellar WMV (${k}_E$ = 10 423 voxels) were associated with higher risk scores. Post-hoc analyses revealed parallel structural alterations between these regions and the entorhinal cortex, anterior cingulate cortex, thalamus, anterior limb of the internal capsule, and pons. Moderation analyses showed that family risk (first-degree relative with psychotic disorder), verbal memory, and social functioning significantly influenced structural patterns. Conclusions: Our results provide evidence for neuroanatomical correlates of the NAPLS-2 model, with alterations in hippocampal circuits suggesting a key prognostic role in the development of neurocognitive and psychosocial deficits across diagnostic boundaries. Future longitudinal studies incorporating multimodal imaging techniques should validate these findings as potential biomarkers for psychosis risk.

Early exposure to interpersonal adverse events, such as traumatic childhood events or exposure to bullying, abuse, or harassment, can impact an adult’s psychological functioning. The negative effects on quality of life and the increased risk of psychopathological outcomes may underscore long-lasting vulnerabilities. Psychological flexibility is defined as the ability to be present in the moment and to pursue one’s values, and it can facilitate coping with adverse events occurring in the present. An open question is whether psychological flexibility may contribute to reducing the long-term consequences of exposure to early adversities. The relevance of this research is that psychological flexibility is modifiable through intervention. The present study investigated the potential role of psychological flexibility in modulating the relationship between early exposure to interpersonal adverse events and two outcomes in young adults: quality of life and psychological symptomatology. We recruited one hundred and nine women aged 19–21 years. Exposure to adverse interpersonal events was assessed with the Childhood Trauma Questionnaire and the Bullying Scale; quality of life and psychological symptomatology were measured with the World Health Organization Quality of Life Scale and the Symptom checklist-90, respectively; psychological flexibility was assessed with the Multidimensional Psychological Flexibility Inventory. The frequency of adverse interpersonal events was associated with lower quality of life and greater psychological symptoms. Psychological flexibility mediated the relationship between the frequency of interpersonal adverse events and measures of quality of life, with an indirect effect explaining 18–42% of the total effect. Similarly, psychological flexibility mediated between the frequency of interpersonal adverse events and psychological symptomatology, with an indirect effect explaining 21–22% of the total effect. The present study evaluates the impact of exposure to interpersonal adverse events on different aspects of quality of life and psychological symptomatology. The results suggest that psychological flexibility mediates the long-term repercussions of interpersonal adversity. As psychological flexibility is a modifiable skill, interventions aimed at enhancing it in individuals with early exposure to adverse interpersonal events, such as traumatic childhood events, bullying, abuse, or harassment, could be used to reduce the long-term effects of past adversities.

Understanding the genetic architecture of schizophrenia (SCZ) is invaluable for the development of personalized treatment. In three independent cohorts, using an automated pipeline, we calculated 413 psychiatry-relevant polygenic scores. Using these scores, machine learning was applied to stratify SCZ patients into two biotypes in FinnGen (n_SCZ=7486), and to validate these results in the PsyCourse Study (n_SCZ=421) and Bari (n_SCZ=531). While the two biotypes showed comparable polygenic SCZ risk, they were primarily distinguished by a greater predisposition for neuroticism, depression-related traits, and lower cognitive performance in Biotype 1. The genetic prediction of Biotype 1 was phenotypically characterized by an increased prevalence of SCZ and a more severe and complex clinical manifestation. This illustrates that the penetrance of genetic SCZ risk might partially depend on the predisposition for the aforementioned traits through pleiotropic variants. Our results provide novel, replicable insights into the genetic architecture of SCZ and might inform future personalized treatments.

Alterations in cognitive and neuroimaging measures in psychosis may reflect altered brain‐behavior interactions patterns accompanying the symptomatic manifestation of the disease. Using graph connectivity‐based approaches, we tested the brain‐behavior association between cognitive functioning and functional connectivity at different stages of psychosis. We collected resting‐state fMRI of 204 neurotypical controls (NC) in two independent cohorts, 43 patients with chronic psychosis (PSY), and 22 subjects with subthreshold psychotic symptoms (STPS). In NC, we calculated graph connectivity metrics and tested their associations with neuropsychological scores. Replicable associations were tested in PSY and STPS and externally validated in three cohorts of 331, 371, and 232 individuals, respectively. NC showed a positive correlation between the degree centrality of a right prefrontal‐cingulum‐striatal circuit and total errors on Wisconsin Card Sorting Test. Conversely, PSY and STPS showed negative correlations. External replications confirmed both associations while highlighting the heterogeneity of STPS. Group differences in either centrality or cognition alone were not equally replicable. In four independent cohorts totaling 1,203 participants, we identified a replicable alteration of the brain‐behavior association in different stages of psychosis. These results highlight the high replicability of multimodal markers and suggest the opportunity for longitudinal investigations that may test this marker for early risk identification.

Cognition and social cognition anomalies in patients with bipolar disorder (BD) and schizophrenia (SCZ) have been largely documented, but the degree of overlap between the two disorders remains unclear in this regard. We used machine learning to generate and combine two classifiers based on cognitive and socio-cognitive variables, thus delivering unimodal and multimodal signatures aimed at discriminating BD and SCZ from two independent groups of Healthy Controls (HC1 and HC2 respectively). Multimodal signatures discriminated well between patients and controls in both the HC1-BD and HC2-SCZ cohorts. Although specific disease-related deficits were characterized, the HC1 vs. BD signature successfully discriminated HC2 from SCZ, and vice-versa. Such combined signatures allowed to identify also individuals at First Episode of Psychosis (FEP), but not subjects at Clinical High Risk (CHR), which were classified neither as patients nor as HC. These findings suggest that both trans-diagnostic and disease-specific cognitive and socio-cognitive deficits characterize SCZ and BD. Anomalous patterns in these domains are also relevant to early stages of disease and offer novel insights for personalized rehabilitative programs.

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.

Background: Psycho-oncology literature pointed out that individual health outcomes may depend on patients’ propensity to adopt approach or, conversely, avoidant coping strategies. Nevertheless, coping factors associated with postoperative distress remain unclear, unfolding the lack of tailored procedures to help breast cancer patients manage the psychological burden of scheduled surgery. In view of this, the present study aimed at investigating: 1. pre-/post-surgery distress variations occurring among women diagnosed with breast cancer; 2. the predictivity of approach and avoidant coping strategies and factors in affecting post-surgery perceived distress. Methods: N = 150 patients (mean age = 59.37; SD = ± 13.23) scheduled for breast cancer surgery were administered a screening protocol consisting of the Distress Thermometer (DT) and the Brief-COPE. The DT was used to monitor patients’ distress levels before and after surgery (± 7 days), whereas the Brief-COPE was adopted only preoperatively to evaluate patients’ coping responses to the forthcoming surgical intervention. Non-parametric tests allowed for the detection of pre-/post-surgery variations in patients’ perceived distress. Factor analysis involved the extraction and rotation of principal components derived from the Brief-COPE strategies. The predictivity of such coping factors was investigated through multiple regression (Backward Elimination). Results: The Wilcoxon Signed-Rank Test yielded a significant variation in DT mean scores (TW = -5,68 < -zα/2 = -1,96; p < .001) indicative of lower perceived distress following surgery. The four coping factors extracted and Varimax-rotated were, respectively: 1. cognitive processing (i.e., planning + acceptance + active coping + positive reframing); 2. support provision (i.e., instrumental + emotional support); 3. emotion-oriented detachment (i.e., self-blame + behavioral disengagement + humor + denial); 4. goal-oriented detachment (i.e., self-distraction). Among these factors, support provision (B = .458; β = − .174; t = − 2.03; p = .045), encompassing two approach coping strategies, and goal-oriented detachment (B = .446; β = − .176; t = − 2.06; p = .042), consisting of one avoidant strategy, were strongly related to post-surgery distress reduction. Conclusion: The present investigation revealed that the pre-surgery adoption of supportive and goal-oriented strategies led to postoperative distress reduction among breast cancer patients. These findings highlight the importance of timely psychosocial screening and proactive interventions in order to improve patients’ recovery and prognosis.