Psychiatry - Research Publications
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ItemNo Preview AvailableHeterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach(OXFORD UNIV PRESS, 2021-07)Diagnostic heterogeneity within and across psychotic and affective disorders challenges accurate treatment selection, particularly in the early stages. Delineation of shared and distinct illness features at the phenotypic and brain levels may inform the development of more precise differential diagnostic tools. We aimed to identify prototypes of depression and psychosis to investigate their heterogeneity, with common, comorbid transdiagnostic symptoms. Analyzing clinical/neurocognitive and grey matter volume (GMV) data from the PRONIA database, we generated prototypic models of recent-onset depression (ROD) vs. recent-onset psychosis (ROP) by training support-vector machines to separate patients with ROD from patients with ROP, who were selected for absent comorbid features (pure groups). Then, models were applied to patients with comorbidity, ie, ROP with depressive symptoms (ROP+D) and ROD participants with sub-threshold psychosis-like features (ROD+P), to measure their positions within the affective-psychotic continuum. All models were independently validated in a replication sample. Comorbid patients were positioned between pure groups, with ROP+D patients being more frequently classified as ROD compared to pure ROP patients (clinical/neurocognitive model: χ2 = 14.874; P < .001; GMV model: χ2 = 4.933; P = .026). ROD+P patient classification did not differ from ROD (clinical/neurocognitive model: χ2 = 1.956; P = 0.162; GMV model: χ2 = 0.005; P = .943). Clinical/neurocognitive and neuroanatomical models demonstrated separability of prototypic depression from psychosis. The shift of comorbid patients toward the depression prototype, observed at the clinical and biological levels, suggests that psychosis with affective comorbidity aligns more strongly to depressive rather than psychotic disease processes. Future studies should assess how these quantitative measures of comorbidity predict outcomes and individual responses to stratified therapeutic interventions.
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ItemA Pilot Study of the Efficacy of the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders in Treating Posttraumatic Psychopathology: A Randomized Controlled Trial(WILEY, 2021-06)The Unified Protocol for Transdiagnostic Treatment of Emotional Disorders (UP) is an intervention that targets common mechanisms that maintain symptoms across multiple disorders. The UP has been shown to be effective across many disorders, including generalized anxiety disorder, major depressive episode (MDE), and panic disorder, that commonly codevelop following trauma exposure. The present study represented the first randomized controlled trial of the UP in the treatment of trauma-related psychopathology, including posttraumatic stress disorder (PTSD), depression, and anxiety symptoms. Adults (N = 43) who developed posttraumatic psychopathology that included PTSD, MDE, or an anxiety disorder after sustaining a severe injury were randomly assigned to receive 10-14 weekly, 60-min sessions of UP (n = 22) or usual care (n = 21). The primary treatment outcome was PTSD symptom severity, with secondary outcomes of depression and anxiety symptom severity and loss of diagnosis for any trauma-related psychiatric disorder. Assessments were conducted at intake, posttreatment, and 6-month follow-up. Posttreatment, participants who received the UP showed significantly larger reductions in PTSD, Hedges' g = 1.27; anxiety, Hedges' g = 1.20; and depression symptom severity, Hedges' g = 1.40, compared to those receiving usual care. These treatment effects were maintained at 6-month follow-up for PTSD, anxiety, and depressive symptom severity. Statistically significant posttreatment loss of PTSD, MDE, and agoraphobia diagnoses was observed for participants who received the UP but not usual care. This study provides preliminary evidence that the UP may be an effective non-trauma-focused treatment for PTSD and other trauma-related psychopathology.
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ItemCognitive subtypes in recent onset psychosis: distinct neurobiological fingerprints?(SPRINGERNATURE, 2021-07)In schizophrenia, neurocognitive subtypes can be distinguished based on cognitive performance and they are associated with neuroanatomical alterations. We investigated the existence of cognitive subtypes in shortly medicated recent onset psychosis patients, their underlying gray matter volume patterns and clinical characteristics. We used a K-means algorithm to cluster 108 psychosis patients from the multi-site EU PRONIA (Prognostic tools for early psychosis management) study based on cognitive performance and validated the solution independently (N = 53). Cognitive subgroups and healthy controls (HC; n = 195) were classified based on gray matter volume (GMV) using Support Vector Machine classification. A cognitively spared (N = 67) and impaired (N = 41) subgroup were revealed and partially independently validated (Nspared = 40, Nimpaired = 13). Impaired patients showed significantly increased negative symptomatology (pfdr = 0.003), reduced cognitive performance (pfdr < 0.001) and general functioning (pfdr < 0.035) in comparison to spared patients. Neurocognitive deficits of the impaired subgroup persist in both discovery and validation sample across several domains, including verbal memory and processing speed. A GMV pattern (balanced accuracy = 60.1%, p = 0.01) separating impaired patients from HC revealed increases and decreases across several fronto-temporal-parietal brain areas, including basal ganglia and cerebellum. Cognitive and functional disturbances alongside brain morphological changes in the impaired subgroup are consistent with a neurodevelopmental origin of psychosis. Our findings emphasize the relevance of tailored intervention early in the course of psychosis for patients suffering from the likely stronger neurodevelopmental character of the disease.
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ItemAssociation between age of cannabis initiation and gray matter covariance networks in recent onset psychosis(SPRINGERNATURE, 2021-07)Cannabis use during adolescence is associated with an increased risk of developing psychosis. According to a current hypothesis, this results from detrimental effects of early cannabis use on brain maturation during this vulnerable period. However, studies investigating the interaction between early cannabis use and brain structural alterations hitherto reported inconclusive findings. We investigated effects of age of cannabis initiation on psychosis using data from the multicentric Personalized Prognostic Tools for Early Psychosis Management (PRONIA) and the Cannabis Induced Psychosis (CIP) studies, yielding a total sample of 102 clinically-relevant cannabis users with recent onset psychosis. GM covariance underlies shared maturational processes. Therefore, we performed source-based morphometry analysis with spatial constraints on structural brain networks showing significant alterations in schizophrenia in a previous multisite study, thus testing associations of these networks with the age of cannabis initiation and with confounding factors. Earlier cannabis initiation was associated with more severe positive symptoms in our cohort. Greater gray matter volume (GMV) in the previously identified cerebellar schizophrenia-related network had a significant association with early cannabis use, independent of several possibly confounding factors. Moreover, GMV in the cerebellar network was associated with lower volume in another network previously associated with schizophrenia, comprising the insula, superior temporal, and inferior frontal gyrus. These findings are in line with previous investigations in healthy cannabis users, and suggest that early initiation of cannabis perturbs the developmental trajectory of certain structural brain networks in a manner imparting risk for psychosis later in life.
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ItemMultimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression(AMER MEDICAL ASSOC, 2021-02)IMPORTANCE: Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. OBJECTIVES: To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. DESIGN, SETTING, AND PARTICIPANTS: This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. MAIN OUTCOMES AND MEASURES: Accuracy and generalizability of prognostic systems. RESULTS: A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. CONCLUSIONS AND RELEVANCE: These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.
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ItemSleep disturbances and the At Risk Mental State: A systematic review and meta-analysis(ELSEVIER, 2021-01)AIMS: To synthesise and investigate how sleep disturbances relate to psychotic symptoms, functioning and Quality of Life (QoL) in At Risk Mental State (ARMS) youth. METHOD: A comprehensive search of six databases (MEDLINE, PsycINFO, Embase, CINAHL, Web of Science and CENTRAL) was conducted. Eligible studies provided data on sleep disturbances or disorders in ARMS patients. RESULTS: Sixteen studies met the inclusion criteria (n = 1962 ARMS patients) including 7 cross-sectional studies, 2 RCT's and 7 cohort studies. Narrative synthesis revealed that self-reported sleep (e.g., general disturbances, fragmented night time sleep and nightmares) was poorer among ARMS patients compared to healthy controls. In the limited studies (n = 4) including objective measurements of sleep disturbances, ARMS patients experienced higher levels of movement during sleep, more daytime naps and increased sleep latency compared to controls. Furthermore, sleep disturbances were associated with attenuated psychotic symptoms and functional outcomes cross-sectionally and longitudinally. Only one study investigated the relationship between sleep and QoL. The exploratory meta-analysis revealed a significant difference in self-reported sleep disturbances measured by the PSQI (mean difference in score: 3.30 (95% CI 1.87, 4.74), p < 0.00001) and SIPS (mean difference in score: 1.58 (95% CI 0.80, 2.35), p < 0.00001) of ARMS patients compared to control groups. CONCLUSIONS: ARMS individuals report impaired sleep quality and reduced sleep quantity compared to healthy controls. However, further research is needed to explore the longitudinal relationship between sleep disruptions and QoL in early psychosis. Significant variations in how sleep is measured across studies highlight a need to assess disturbances to sleep using robust and consistent approaches in this patient group.