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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.
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.
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).
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.
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.
Early-life adverse events or childhood adversities (CAs) are stressors and harmful experiences severely impacting on a child's wellbeing and development. Examples of CAs include parental neglect, emotional and physical abuse and bullying. Even though the prevalence of CAs and their psychological effects in both healthy and psychiatric populations is established, only a paucity of studies have investigated the neurobiological firms associated with CAs in bipolar disorder (BD). In particular, the exact neural mechanisms and trajectories of biopsychosocial models integrating both environmental and genetic effects are still debated. Considering the potential impact of CAs on BD, including its clinical manifestations, we reviewed existing literature discussing the association between CAs and brain alterations in BD patients. Results showed that CAs are associated with volume alterations of several grey matter regions including the hippocampus, thalamus, amygdala and frontal cortex. A handful of studies suggest the presence of alterations in the corpus callosum and the pre-fronto-limbic connectivity at rest. Alterations in these regions of the brain of patients with BD are possibly due to the effect of stress produced by CAs, being hippocampus part of the hypothalamus–pituitary–adrenal axis and thalamus together with amygdala filtering sensory information and regulating emotional responses. However, results are mixed possibly due to the heterogeneity of methods and study design. Future neuroimaging studies disentangling between different types of CAs or differentiating between BD sub-types are needed in order to understand the link between CAs and BD.
Personalised prediction of functional outcomes is a promising approach for targeted early intervention in psychiatry. However, generalisability and resource efficiency of such prognostic models represent challenges. In the PRONIA study (German Clinical Trials Register: DRKS00005042), we demonstrate excellent generalisability of prognostic models in individuals at clinical high-risk for psychosis or with recent-onset depression, and substantial contributions of detailed clinical phenotyping, particularly to the prediction of role functioning. These results indicate that it is possible that functioning prediction models based only on clinical data could be effectively applied in diverse healthcare settings, so that neuroimaging data may not be needed at early assessment stages.
The European impact of the clinical high risk for psychosis (CHR-P) paradigm is constrained by the lack of critical mass (detection) to power prognostic and preventive interventions.
An ITAlian partnership for psychosis prevention (ITAPP) was created across CHR-P centers, which were surveyed to describe: (a) service, catchment area, and outreach; (b) service users; and (c) interventions and outcomes. Descriptive statistics and Kaplan–Meier failure function complemented the analyses.
The ITAPP included five CHR-P clinical academic centers established from 2007 to 2018, serving about 13 million inhabitants, with a recruitment capacity of 277 CHR-P individuals (mean age: 18.7 years, SD: 4.8, range: 12–39 years; 53.1% females; 85.7% meeting attenuated psychotic symptoms; 85.8% without any substance abuse). All centers were multidisciplinary and included adolescents and young adults (transitional) primarily recruited through healthcare services. The comprehensive assessment of at-risk mental state was the most widely used instrument, while the duration of follow-up, type of outreach, and preventive interventions were heterogeneous. Across 205 CHR-P individuals with follow up (663.7 days ± 551.7), the cumulative risk of psychosis increased from 8.7% (95% CI 5.3–14.1) at 1 year to 15.9% (95% CI 10.6–23.3) at 2 years, 21.8% (95% CI 14.9–31.3) at 3 years, 34.8% (95% CI 24.5–47.9) at 4 years, and 51.9% (95% CI 36.3–69.6) at 5 years.
The ITAPP is one of the few CHR-P clinical research partnerships in Europe for fostering detection, prognosis, and preventive care, as well as for translating research innovations into practice.
Childhood trauma (CT) is associated with an increased risk of mental health disorders; however, it is unknown whether this represents a diagnosis-specific risk factor for specific psychopathology mediated by structural brain changes. Our aim was to explore whether (i) a predictive CT pattern for transdiagnostic psychopathology exists, and whether (ii) CT can differentiate between distinct diagnosis-dependent psychopathology. Furthermore, we aimed to identify the association between CT, psychopathology and brain structure.
We used multivariate pattern analysis in data from 643 participants of the Personalised Prognostic Tools for Early Psychosis Management study (PRONIA), including healthy controls (HC), recent onset psychosis (ROP), recent onset depression (ROD), and patients clinically at high-risk for psychosis (CHR). Participants completed structured interviews and self-report measures including the Childhood Trauma Questionnaire, SCID diagnostic interview, BDI-II, PANSS, Schizophrenia Proneness Instrument, Structured Interview for Prodromal Symptoms and structural MRI, analyzed by voxel-based morphometry.
(i) Patients and HC could be distinguished by their CT pattern with a reasonable precision [balanced accuracy of 71.2% (sensitivity = 72.1%, specificity = 70.4%, p ≤ 0.001]. (ii) Subdomains ‘emotional neglect’ and ‘emotional abuse’ were most predictive for CHR and ROP, while in ROD ‘physical abuse’ and ‘sexual abuse’ were most important. The CT pattern was significantly associated with the severity of depressive symptoms in ROD, ROP, and CHR, as well as with the PANSS total and negative domain scores in the CHR patients. No associations between group-separating CT patterns and brain structure were found.
These results indicate that CT poses a transdiagnostic risk factor for mental health disorders, possibly related to depressive symptoms. While differences in the quality of CT exposure exist, diagnostic differentiation was not possible suggesting a multi-factorial pathogenesis.
Diogenes syndrome is a neurobehavioural syndrome characterised by domestic squalor, hoarding and lack of insight. It is an uncommon but high-mortality condition, often associated with dementia.
To describe the clinical features and treatment of Diogenes syndrome secondary to behavioural variant frontotemporal dementia (bvFTD).
We describe a case of bvFTD in a 77-year-old man presenting with Diogenes syndrome.
The patient's medical and psychiatric histories were unremarkable, but in recent years he had begun packing his flat with ‘art pieces’. Mental state examination revealed confabulation and more structured delusions. Neuropsychological evaluation outlined an impairment in selective attention and letter verbal fluency, but no semantic impairment, in the context of an overall preserved mental functioning. Brain magnetic resonance imaging and positron emission tomography (PET) with fluorodeoxyglucose showed mild bilateral temporo-insular atrophy and hypometabolism in the left-superior temporal gyrus respectively. An amyloid PET scan and genetic analysis covering the dementia spectrum were normal. A diagnosis of bvFTD was made.
The clinical framing of behavioural symptoms of dementia such as hoarding poses a diagnostic challenge. This case illustrates the importance of a deeper understanding of Diogenes syndrome, leading to timelier diagnosis and effective therapeutic strategies.
Mood disorders are the most common mental illnesses with a lifetime prevalence of up to 20% worldwide (1). Major depressive disorder (MDD) and bipolar disorder (BD) are significant health problems in the United States and worldwide (2). In the United States alone, the lifetime prevalence of MDD is up to 17%, and that of BD about 2.1% (2) that can go up to 4% of individuals with mood episodes not meeting episodic criteria included. Both are chronic illnesses characterized by recurrent episodes of depression and mania and depression in MDD and BD, respectively. Severe and disabling forms of BD and MDD are associated with increased risk of suicide, decline of physical health, and reduced productivity, and both conditions are associated with high rates of completed suicide of up to 8% (3).
Mood disorders such as depression and bipolar disorder are common mental illnesses, affecting millions of patients worldwide. The application of newly available brain imaging methods to the study of mood disorders holds substantial promise in uncovering the brain mechanisms affected in these illnesses. This comprehensive and authoritative text features contributions from leading international experts, providing easily accessible information on the study of the brain mechanisms involved in the causation of mood disorders and the available treatments. Topics covered include the potential of magnetoencephalography (MEG), neuroimaging brain inflammation in depression, electrophysiology studies in mood disorders, and the applications of machine learning, filling an important gap in available neuropsychiatric literature and highlighting new developments. An invaluable resource for practitioners in the fields of psychiatry, neurology, primary care medicine, and related mental health professions, as well as researchers, students, graduate and post-graduate trainees.