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  • 1
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    Online Resource
    Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis
    Springer Science and Business Media LLC ; 2021
    In:  Neuropsychopharmacology Vol. 46, No. 8 ( 2021-07), p. 1484-1493
    Details
    In: Neuropsychopharmacology, Springer Science and Business Media LLC, Vol. 46, No. 8 ( 2021-07), p. 1484-1493
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 0893-133X , 1740-634X
    URL: Article
    DOI: 10.1038/s41386-021-00977-9
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
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  • 2
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    Cognitive subtypes in recent onset psychosis: distinct neurobiological fingerprints?
    Springer Science and Business Media LLC ; 2021
    In:  Neuropsychopharmacology Vol. 46, No. 8 ( 2021-07), p. 1475-1483
    Details
    In: Neuropsychopharmacology, Springer Science and Business Media LLC, Vol. 46, No. 8 ( 2021-07), p. 1475-1483
    Abstract: 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 ( N spared  = 40, N impaired  = 13). Impaired patients showed significantly increased negative symptomatology ( p fdr  = 0.003), reduced cognitive performance ( p fdr   〈  0.001) and general functioning ( p fdr   〈  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.
    Type of Medium: Online Resource
    ISSN: 0893-133X , 1740-634X
    URL: Article
    DOI: 10.1038/s41386-021-00963-1
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2008300-2
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  • 3
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    S219. SINGLE-SUBJECT PREDICTION OF FUNCTIONAL OUTCOMES ACROSS DIAGNOSTIC GROUPS USING CLINICAL DATA
    Oxford University Press (OUP) ; 2020
    In:  Schizophrenia Bulletin Vol. 46, No. Supplement_1 ( 2020-05-18), p. S122-S122
    Details
    In: Schizophrenia Bulletin, Oxford University Press (OUP), Vol. 46, No. Supplement_1 ( 2020-05-18), p. S122-S122
    Abstract: Psychotic disorders are associated with serious deterioration in functioning even before the first psychotic episode. Also on clinical high risk (CHR) states of developing a first psychotic episode, several studies reported a decreased global functioning. In a considerable proportion of CHR individuals, functional deterioration remains even after (transient) remission of symptomatic risk indicators. Furthermore, deficits in functioning cause immense costs for the health care system and are often more debilitating for individuals than positive symptoms. However in the past, CHR research has mostly focused on clinical outcomes like transition. Prediction of functioning in CHR populations has received less attention. Therefore, the current study aims at predicting functioning in CHR individuals at a single subject level applying multi pattern recognition to clinical data. Patients with a first depressive episode who frequently have persistent functional deficits comparable to patients in the CHR state were investigated in addition. Methods PRONIA (‘Personalized Prognostic Tools for Early Psychosis Management’) is a prospective collaboration project funded by the European Union under the 7th Framework Programme (grant agreement n°602152). Considering a broad set of variables (MRI, clinical data, neurocognition, genomics and other blood derived parameters) as well as advanced statistical methods, PRONIA aims at developing an innovative multivariate prognostic tool enabling an individualized prediction of illness trajectories and outcome. 11 university centers in five European countries and in Australia (Munich, Basel, Birmingham, Cologne, Düsseldorf, Münster, Melbourne, Milan, Udine, Bari, Turku) participate in the evaluation of three clinical groups (subjects clinically at high risk of developing a psychosis [CHR], patients with a recent onset psychosis [ROP] and patients with a recent onset depression [ROD]) as well as healthy controls. In the current study, we analysed data of 114 CHR and 106 ROD patients. Functioning was measured by the ‘Global Functioning: Social and Role’ Scales (GF S/R). In a repeated, nested cross validation framework we trained a l1-regularized SVM to predict good versus bad outcome. Multivariate pattern recognition analysis allowed to identify most predictive variables from a multitude of clinical, environmental as well as sociodemographic potential predictors assessed in PRONIA. Results Based on the 5 to 20 identified most predictive features, prediction models revealed a balanced accuracy (BAC) up to 77/72 for social functioning in CHR/ROD patients and up to 73/69 for role functioning. These models showed satisfying performance of BACs up to 69/63 for social functioning and 67/60 for role functioning in an independent test sample. As expected, prior functioning levels were identified as main predictive factor but also distinct protective and risk factors were selected into the prediction models. Discussion Results suggest that especially prediction of the multi-faceted construct of role functioning could benefit from inclusion of a rich set of clinical variables. To the best of our knowledge this is the first study that has validated clinical prediction models of functioning in an independent test sample. Identification of predictive variables enables a much more efficient prognostic process. Moreover, understanding the mechanisms underlying functional decline and its illness related pattern might enable an improved definition of targets for intervention. Future research should aim at further maximisation of prediction accuracy and cross-centre generalisation capacity. In addition, other functioning outcomes as well as clinical outcomes need to be focused on.
    Type of Medium: Online Resource
    ISSN: 0586-7614 , 1745-1701
    URL: Article
    DOI: 10.1093/schbul/sbaa031.285
    RVK:
    XA 10000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
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  • 4
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    Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach
    Oxford University Press (OUP) ; 2021
    In:  Schizophrenia Bulletin Vol. 47, No. 4 ( 2021-07-08), p. 1130-1140
    Details
    In: Schizophrenia Bulletin, Oxford University Press (OUP), Vol. 47, No. 4 ( 2021-07-08), p. 1130-1140
    Abstract: 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 & lt; .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.
    Type of Medium: Online Resource
    ISSN: 0586-7614 , 1745-1701
    URL: Article
    DOI: 10.1093/schbul/sbaa185
    RVK:
    XA 10000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
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  • 5
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    O8.5. SIGNS OF ADVERSITY - A NOVEL MACHINE LEARNING APPROACH TO CHILDHOOD TRAUMA, BRAIN STRUCTURE AND CLINICAL PROFILES
    Oxford University Press (OUP) ; 2020
    In:  Schizophrenia Bulletin Vol. 46, No. Supplement_1 ( 2020-05-18), p. S20-S20
    Details
    In: Schizophrenia Bulletin, Oxford University Press (OUP), Vol. 46, No. Supplement_1 ( 2020-05-18), p. S20-S20
    Abstract: Childhood maltreatment (CM) is a major psychiatric risk factor and leads to long-lasting physical and mental health implications throughout the affected individual’s lifespan. Nonetheless, the neuroanatomical correlates of CM and their specific clinical impact remain elusive. This might be attributed to the complex, multidimensional nature of CM as well as to the restrictions of traditional analysis pipelines using nosological grouping, univariate analysis and region-of-interest approaches. To overcome these issues, we present a novel transdiagnostic and naturalistic machine learning approach towards a better and more comprehensive understanding of the clinical and neuroanatomical complexity of CM. Methods We acquired our dataset from the multi-center European PRONIA cohort (www.pronia.eu). Specifically, we selected 649 male and female individuals, comprising young, minimally medicated patients with clinical high-risk states for psychosis as well as recent-onset of depression or psychosis and healthy volunteers. As part of our analysis approach, we created a new Matlab Toolbox, which performs multivariate Sparse Partial Least Squares Analysis in a robust machine learning framework. We employed this algorithm to detect multi-layered associations between combinations of items from the Childhood Trauma Questionnaire (CTQ) and grey matter volume (GMV) and assessed their generalizability via nested cross-validation. The clinical relevance of these CM signatures was assessed by correlating them to a wide range of clinical measurements, including current functioning (GAF, GF), depressivity (BDI), quality of life (WHOQOL-BREF) and personality traits (NEO-FFI). Results Overall, we detected three distinct signatures of sexual, physical and emotional maltreatment. The first signature consisted of an age-dependent sexual abuse pattern and a corresponding GMV pattern along the prefronto-thalamo-cerebellar axis. The second signature yielded a sex-dependent physical and sexual abuse pattern with a corresponding GMV pattern in parietal, occipital and subcortical regions. The third signature was a global emotional trauma signature, independent of age or sex, and projected to a brain structural pattern in sensory and limbic brain regions. Regarding the clinical impact of these signatures, the emotional trauma signature was most strongly associated with massively impaired state- and trait-level characteristics. Both on a phenomenological and on a brain structural level, the emotional trauma pattern was significantly correlated with lower levels of functioning, higher depression scores, decreased quality of life and maladaptive personality traits. Discussion Our findings deliver multimodal, data-driven evidence for a differential impact of sexual, physical and emotional trauma on brain structure and clinical state- and trait-level phenotypes. They also highlight the multidimensional nature of CM, which consists of multiple layers of highly complex trauma-brain patterns. In broader terms, our study emphasizes the potential of machine learning approaches in generating novel insights into long-standing psychiatric topics.
    Type of Medium: Online Resource
    ISSN: 0586-7614 , 1745-1701
    URL: Article
    DOI: 10.1093/schbul/sbaa028.046
    RVK:
    XA 10000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
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  • 6
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    29.4 IMPLEMENTING PRECISION PSYCHIATRY FOR THE EARLY RECOGNITION OF ADVERSE OUTCOMES IN PSYCHOSES: FINDINGS FROM THE PRONIA STUDY
    Oxford University Press (OUP) ; 2019
    In:  Schizophrenia Bulletin Vol. 45, No. Supplement_2 ( 2019-04-09), p. S137-S137
    Details
    In: Schizophrenia Bulletin, Oxford University Press (OUP), Vol. 45, No. Supplement_2 ( 2019-04-09), p. S137-S137
    Type of Medium: Online Resource
    ISSN: 0586-7614 , 1745-1701
    URL: Article
    DOI: 10.1093/schbul/sbz022.120
    RVK:
    XA 10000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
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  • 7
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    M121. CLINICAL PREDICTION MODELS FOR TRANSITION TO PSYCHOSIS: AN EXTERNAL VALIDATION STUDY IN THE PRONIA SAMPLE
    Oxford University Press (OUP) ; 2020
    In:  Schizophrenia Bulletin Vol. 46, No. Supplement_1 ( 2020-05-18), p. S181-S181
    Details
    In: Schizophrenia Bulletin, Oxford University Press (OUP), Vol. 46, No. Supplement_1 ( 2020-05-18), p. S181-S181
    Abstract: A multitude of clinical models to predict transition to psychosis in individuals at clinical high risk (CHR) have been proposed. However, only limited efforts have been made to systematically compare these models and to validate their performance in independent samples. Therefore, in this study we identified psychosis risk models based on information readily obtainable in general clinical settings, such as clinical and neuropsychological data, and compared their performance in the PRONIA study (Personalised Prognostic Tools for Early Psychosis Management, www.pronia.eu) as an independent sample. Methods Of the 278 CHR participants in the PRONIA sample, 150 had available data until month 18 and were included in the validation of eleven psychosis prediction models identified through systematic literature search. Discrimination performance was assessed with the area under the receiver operating characteristic curve (AUC), and compared to the performance of the prognosis of clinical raters. Psychosocial functioning was explored as an alternative outcome. Results Discrimination performance varied considerably across models (AUC ranging from 0.42 to 0.79). High model performance was associated with the inclusion of neurocognitive variables as predictors. Low model performance was associated with predictors based on dichotomized variables. Clinical raters performed comparable to the best data-driven models (AUC = 0.75). Combining raters’ prognosis and model-based predictions improved discrimination performance (AUC = 0.84), particularly for less experienced raters. One of the tested models predicted transition to psychosis and psychosocial outcomes comparably well. Discussion The present external validation study highlights the benefit of enriching clinical information with neuropsychological data in predicting transition to psychosis satisfactorily and with good generalizability across samples. Integration of data-driven risk models and clinical expertise may improve clinical decision-making in CHR for psychosis, particularly for less experienced raters. This external validation study provides an important step toward early intervention and the personalized treatment of psychotic disorders.
    Type of Medium: Online Resource
    ISSN: 0586-7614 , 1745-1701
    URL: Article
    DOI: 10.1093/schbul/sbaa030.433
    RVK:
    XA 10000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2180196-4
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  • 8
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    The Psychopathology and Neuroanatomical Markers of Depression in Early Psychosis
    Oxford University Press (OUP) ; 2021
    In:  Schizophrenia Bulletin Vol. 47, No. 1 ( 2021-01-23), p. 249-258
    Details
    In: Schizophrenia Bulletin, Oxford University Press (OUP), Vol. 47, No. 1 ( 2021-01-23), p. 249-258
    Abstract: Depression frequently occurs in first-episode psychosis (FEP) and predicts longer-term negative outcomes. It is possible that this depression is seen primarily in a distinct subgroup, which if identified could allow targeted treatments. We hypothesize that patients with recent-onset psychosis (ROP) and comorbid depression would be identifiable by symptoms and neuroanatomical features similar to those seen in recent-onset depression (ROD). Data were extracted from the multisite PRONIA study: 154 ROP patients (FEP within 3 months of treatment onset), of whom 83 were depressed (ROP+D) and 71 who were not depressed (ROP−D), 146 ROD patients, and 265 healthy controls (HC). Analyses included a (1) principal component analysis that established the similar symptom structure of depression in ROD and ROP+D, (2) supervised machine learning (ML) classification with repeated nested cross-validation based on depressive symptoms separating ROD vs ROP+D, which achieved a balanced accuracy (BAC) of 51%, and (3) neuroanatomical ML-based classification, using regions of interest generated from ROD subjects, which identified BAC of 50% (no better than chance) for separation of ROP+D vs ROP−D. We conclude that depression at a symptom level is broadly similar with or without psychosis status in recent-onset disorders; however, this is not driven by a separable depressed subgroup in FEP. Depression may be intrinsic to early stages of psychotic disorder, and thus treating depression could produce widespread benefit.
    Type of Medium: Online Resource
    ISSN: 0586-7614 , 1745-1701
    URL: Article
    DOI: 10.1093/schbul/sbaa094
    RVK:
    XA 10000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2180196-4
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  • 9
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    The impact of visual dysfunctions in recent-onset psychosis and clinical high-risk state for psychosis
    Springer Science and Business Media LLC ; 2022
    In:  Neuropsychopharmacology Vol. 47, No. 12 ( 2022-11), p. 2051-2060
    Details
    In: Neuropsychopharmacology, Springer Science and Business Media LLC, Vol. 47, No. 12 ( 2022-11), p. 2051-2060
    Abstract: Subtle subjective visual dysfunctions (VisDys) are reported by about 50% of patients with schizophrenia and are suggested to predict psychosis states. Deeper insight into VisDys, particularly in early psychosis states, could foster the understanding of basic disease mechanisms mediating susceptibility to psychosis, and thereby inform preventive interventions. We systematically investigated the relationship between VisDys and core clinical measures across three early phase psychiatric conditions. Second, we used a novel multivariate pattern analysis approach to predict VisDys by resting-state functional connectivity within relevant brain systems. VisDys assessed with the Schizophrenia Proneness Instrument (SPI-A), clinical measures, and resting-state fMRI data were examined in recent-onset psychosis (ROP, n  = 147), clinical high-risk states of psychosis (CHR, n  = 143), recent-onset depression (ROD, n  = 151), and healthy controls (HC, n  = 280). Our multivariate pattern analysis approach used pairwise functional connectivity within occipital (ON) and frontoparietal (FPN) networks implicated in visual information processing to predict VisDys. VisDys were reported more often in ROP (50.34%), and CHR (55.94%) than in ROD (16.56%), and HC (4.28%). Higher severity of VisDys was associated with less functional remission in both CHR and ROP, and, in CHR specifically, lower quality of life (Qol), higher depressiveness, and more severe impairment of visuospatial constructability. ON functional connectivity predicted presence of VisDys in ROP (balanced accuracy 60.17%, p  = 0.0001) and CHR (67.38%, p  = 0.029), while in the combined ROP + CHR sample VisDys were predicted by FPN (61.11%, p  = 0.006). These large-sample study findings suggest that VisDys are clinically highly relevant not only in ROP but especially in CHR, being closely related to aspects of functional outcome, depressiveness, and Qol. Findings from multivariate pattern analysis support a model of functional integrity within ON and FPN driving the VisDys phenomenon and being implicated in core disease mechanisms of early psychosis states.
    Type of Medium: Online Resource
    ISSN: 0893-133X , 1740-634X
    URL: Article
    DOI: 10.1038/s41386-022-01385-3
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
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  • 10
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    Transdiagnostic subgroups of cognitive impairment in early affective and psychotic illness
    Springer Science and Business Media LLC ; 2023
    In:  Neuropsychopharmacology
    Details
    In: Neuropsychopharmacology, Springer Science and Business Media LLC
    Abstract: Cognitively impaired and spared patient subgroups were identified in psychosis and depression, and in clinical high-risk for psychosis (CHR). Studies suggest differences in underlying brain structural and functional characteristics. It is unclear whether cognitive subgroups are transdiagnostic phenomena in early stages of psychotic and affective disorder which can be validated on the neural level. Patients with recent-onset psychosis (ROP; N  = 140; female = 54), recent-onset depression (ROD; N  = 130; female = 73), CHR ( N  = 128; female = 61) and healthy controls (HC; N  = 270; female = 165) were recruited through the multi-site study PRONIA. The transdiagnostic sample and individual study groups were clustered into subgroups based on their performance in eight cognitive domains and characterized by gray matter volume (sMRI) and resting-state functional connectivity (rsFC) using support vector machine (SVM) classification. We identified an impaired subgroup ( N ROP  = 79, N ROD  = 30, N CHR  = 37) showing cognitive impairment in executive functioning, working memory, processing speed and verbal learning (all p   〈  0.001). A spared subgroup ( N ROP  = 61, N ROD  = 100, N CHR  = 91) performed comparable to HC. Single-disease subgroups indicated that cognitive impairment is stronger pronounced in impaired ROP compared to impaired ROD and CHR. Subgroups in ROP and ROD showed specific symptom- and functioning-patterns. rsFC showed superior accuracy compared to sMRI in differentiating transdiagnostic subgroups from HC (BAC impaired  = 58.5%; BAC spared  = 61.7%, both: p   〈  0.01). Cognitive findings were validated in the PRONIA replication sample ( N  = 409). Individual cognitive subgroups in ROP, ROD and CHR are more informative than transdiagnostic subgroups as they map onto individual cognitive impairment and specific functioning- and symptom-patterns which show limited overlap in sMRI and rsFC. Clinical trial registry name German Clinical Trials Register (DRKS). Clinical trial registry URL: https://www.drks.de/drks_web/ . Clinical trial registry number: DRKS00005042.
    Type of Medium: Online Resource
    ISSN: 0893-133X , 1740-634X
    URL: Article
    DOI: 10.1038/s41386-023-01729-7
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2008300-2
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