Psychometric properties of the Kessler psychological scales in a Swiss young‐adult community sample indicate poor suitability for community screening for mental disorders

Aim The Kessler psychological distress scales (K10 and K6) are used as screening tools to assess psychological distress related to the likely presence of a mental disorder. Thus, we studied the psychometric properties of their German versions in a Swiss community sample to evaluate their potential usefulness to screen for mental disorders or relevant mental problems in the community and, relatedly, for low threshold transdiagnostic German-speaking services. Methods The sample consisted of 829 citizens of the Swiss canton Bern of age 19–43 years. K10/K6 were validated against mini-international neuropsychiatric interview (M.I.N.I.) diagnoses, questionnaires about health status and quality of life. Receiver operating characteristic (ROC) curve analyses were used to test for general discriminative ability and to select optimal cut-offs of the K10 and K6 for non-psychotic full-blown and subthreshold mental disorders. Results Cronbach's alphas were 0.81 (K10) and 0.70 (K6). ROC analyses indicated much lower optimal thresholds than earlier suggested; 10 for K10 and 6 for K6. At these thresholds, against M.I.N.I. diagnoses, Cohen's κ (≤0.173) and correspondence rates (≤58.14%) were insufficient throughout. Values were higher at the earlier suggested threshold, yet, at the cost of sensitivity that was below 0.5 in all but three, and below 0.3 in all but six cases. Conclusions For the lack of sufficient validity and sensitivity, respectively, our findings suggest that both K10 and K6 would only be of limited use in a low-threshold transdiagnostic mental health service for young adults in Switzerland and likely other German-speaking countries

Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers

MRI-based markers can distinguish patients with schizophrenia from healthy controls. Koutsouleris et al. now report a diagnostic signature that distinguishes major depression/bipolar disorder from schizophrenia in 80%/74% of cases. Classification accuracy generalizes to early phases of psychosis, and is moderated by disease stage, age of onset and accelerated brain agein

Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers

Magnetic resonance imaging-based markers of schizophrenia have been repeatedly shown to separate patients from healthy controls at the single-subject level, but it remains unclear whether these markers reliably distinguish schizophrenia from mood disorders across the life span and generalize to new patients as well as to early stages of these illnesses. The current study used structural MRI-based multivariate pattern classification to (i) identify and cross-validate a differential diagnostic signature separating patients with first-episode and recurrent stages of schizophrenia (n = 158) from patients with major depression (n = 104); and (ii) quantify the impact of major clinical variables, including disease stage, age of disease onset and accelerated brain ageing on the signature's classification performance. This diagnostic magnetic resonance imaging signature was then evaluated in an independent patient cohort from two different centres to test its generalizability to individuals with bipolar disorder (n = 35), first-episode psychosis (n = 23) and clinically defined at-risk mental states for psychosis (n = 89). Neuroanatomical diagnosis was correct in 80% and 72% of patients with major depression and schizophrenia, respectively, and involved a pattern of prefronto-temporo-limbic volume reductions and premotor, somatosensory and subcortical increments in schizophrenia versus major depression. Diagnostic performance was not influenced by the presence of depressive symptoms in schizophrenia or psychotic symptoms in major depression, but earlier disease onset and accelerated brain ageing promoted misclassification in major depression due to an increased neuroanatomical schizophrenia likeness of these patients. Furthermore, disease stage significantly moderated neuroanatomical diagnosis as recurrently-ill patients had higher misclassification rates (major depression: 23%; schizophrenia: 29%) than first-episode patients (major depression: 15%; schizophrenia: 12%). Finally, the trained biomarker assigned 74% of the bipolar patients to the major depression group, while 83% of the first-episode psychosis patients and 77% and 61% of the individuals with an ultra-high risk and low-risk state, respectively, were labelled with schizophrenia. Our findings suggest that neuroanatomical information may provide generalizable diagnostic tools distinguishing schizophrenia from mood disorders early in the course of psychosis. Disease course-related variables such as age of disease onset and disease stage as well alterations of structural brain maturation may strongly impact on the neuroanatomical separability of major depression and schizophrenia

Detecting the Psychosis Prodrome Across High-Risk Populations Using Neuroanatomical Biomarkers

To date, the MRI-based individualized prediction of psychosis has only been demonstrated in single-site studies. It remains unclear if MRI biomarkers generalize across different centers and MR scanners and represent accurate surrogates of the risk for developing this devastating illness. Therefore, we assessed whether a MRI-based prediction system identified patients with a later disease transition among 73 clinically defined high-risk persons recruited at two different early recognition centers. Prognostic performance was measured using cross-validation, independent test validation, and Kaplan-Meier survival analysis. Transition outcomes were correctly predicted in 80% of test cases (sensitivity: 76%, specificity: 85%, positive likelihood ratio: 5.1). Thus, given a 54-month transition risk of 45% across both centers, MRI-based predictors provided a 36%-increase of prognostic certainty. After stratifying individuals into low-, intermediate-, and high-risk groups using the predictor's decision score, the high- vs low-risk groups had median psychosis-free survival times of 5 vs 51 months and transition rates of 88% vs 8%. The predictor's decision function involved gray matter volume alterations in prefrontal, perisylvian, and subcortical structures. Our results support the existence of a cross-center neuroanatomical signature of emerging psychosis enabling individualized risk staging across different high-risk populations. Supplementary results revealed that (1) potentially confounding between-site differences were effectively mitigated using statistical correction methods, and (2) the detection of the prodromal signature considerably depended on the available sample sizes. These observations pave the way for future multicenter studies, which may ultimately facilitate the neurobiological refinement of risk criteria and personalized preventive therapies based on individualized risk profiling tool

(Attenuated) hallucinations join basic symptoms in a transdiagnostic network cluster analysis

Producción CientíficaHallucinations are considered characteristic symptoms of psychosis and part of the ‘psychosis superspectrum’ of the Hierarchical Taxonomy Of Psychopathology (HiTOP) initiative. To gain insight into their psychopathological relevance, we studied their dimensional placement within a single dense transdiagnostic network constituting of basic symptoms as well as of attenuated and frank psychotic, and related symptoms. Newman's modularity analysis was used to detect symptom clusters in an earlier generated network (Jimeno, N., et al., 2020. Main symptomatic treatment targets in suspected and early psychosis: New insights from network analysis. Schizophr. Bull. 46, 884–895. https://doi.org/10.1093/schbul/sbz140). The constituting 86 symptoms were assessed with the Schizophrenia Proneness Instrument, Adult version (SPI-A), the Structured Interview for Psychosis-Risk Syndromes (SIPS), and the Positive And Negative Syndrome Scale (PANSS) in three adult samples of an early detection service: clinical high-risk (n = 203), first-episode psychosis (n = 153), and major depression (n = 104). Three clusters were detected: “subjective disturbances”, “positive symptoms and behaviors”, and “negative and anxious-depressive symptoms”. The predominately attenuated hallucinations of both SIPS and PANSS joined the basic symptoms in “subjective disturbances”, whereas other positive symptoms entered “positive symptoms and behaviors”. Our results underline the importance of insight in separating true psychotic hallucinations from other hallucinatory experiences that, albeit phenomenologically similar are still experienced with some insight, i.e., are present in an attenuated form. We conclude that, strictly, hallucinations held with any degree of insight should not be used to diagnose transition to or presence of frank psychoses and, relatedly, to justify antipsychotic medication.Deutsche Forschungsgemeinschaft (grants KL970/3-1 and KL970/3-2)Koeln Fortune Program / Faculty of Medicine of the University of Cologne (projects 8/2005 and 27/2006)Ministerio de Ciencia e Innovación - Fondo Europeo de Desarrollo Regional (projects PGC2018-098214-A-I00 and DPI2017-84280-R)Unión Europea (grant 602152)German Research Foundation (grant KA 4413/1-1

Distinguishing prodromal from first-episode psychosis using neuroanatomical single-subject pattern recognition

BACKGROUND: The at-risk mental state for psychosis (ARMS) and the first episode of psychosis have been associated with structural brain abnormalities that could aid in the individualized early recognition of psychosis. However, it is unknown whether the development of these brain alterations predates the clinical deterioration of at-risk individuals, or alternatively, whether it parallels the transition to psychosis at the single-subject level. METHODS: We evaluated the performance of an magnetic resonance imaging (MRI)-based classification system in classifying disease stages from at-risk individuals with subsequent transition to psychosis (ARMS-T) and patients with first-episode psychosis (FE). Pairwise and multigroup biomarkers were constructed using the structural MRI data of 22 healthy controls (HC), 16 ARMS-T and 23 FE subjects. The performance of these biomarkers was measured in unseen test cases using repeated nested cross-validation. RESULTS: The classification accuracies in the HC vs FE, HC vs ARMS-T, and ARMS-T vs FE analyses were 86.7%, 80.7%, and 80.0%, respectively. The neuroanatomical decision functions underlying these discriminative results particularly involved the frontotemporal, cingulate, cerebellar, and subcortical brain structures. CONCLUSIONS: Our findings suggest that structural brain alterations accumulate at the onset of psychosis and occur even before transition to psychosis allowing for the single-subject differentiation of the prodromal and first-episode stages of the disease. Pattern regression techniques facilitate an accurate prediction of these structural brain dynamics at the early stage of psychosis, potentially allowing for the early recognition of individuals at risk of developing psychosis

Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression

The large number of multicollinear regional features that are provided by resting state (rs) fMRI data requires robust feature selection to uncover consistent networks of functional disconnection in Alzheimer's disease (AD). Here, we compared elastic net regularized and classical stepwise logistic regression in respect to consistency of feature selection and diagnostic accuracy using rs-fMRI data from four centers of the German resting-state initiative for diagnostic biomarkers (psymri.org), comprising 53 AD patients and 118 age and sex matched healthy controls. Using all possible pairs of correlations between the time series of rs-fMRI signal from 84 functionally defined brain regions as the initial set of predictor variables, we calculated accuracy of group discrimination and consistency of feature selection with bootstrap cross-validation. Mean areas under the receiver operating characteristic curves as measure of diagnostic accuracy were 0.70 in unregularized and 0.80 in regularized regression. Elastic net regression was insensitive to scanner effects and recovered a consistent network of functional connectivity decline in AD that encompassed parts of the dorsal default mode as well as brain regions involved in attention, executive control, and language processing. Stepwise logistic regression found no consistent network of AD related functional connectivity decline. Regularized regression has high potential to increase diagnostic accuracy and consistency of feature selection from multicollinear functional neuroimaging data in AD. Our findings suggest an extended network of functional alterations in AD, but the diagnostic accuracy of rs-fMRI in this multicenter setting did not reach the benchmark defined for a useful biomarker of AD

Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

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

The heterogeneity of attenuated and brief limited psychotic symptoms: association of contents with age, sex, country, religion, comorbidities, and functioning

INTRODUCTION The Attenuated Psychosis Symptoms (APS) syndrome mostly represents the ultra-high-risk state of psychosis but, as does the Brief Intermittent Psychotic Symptoms (BIPS) syndrome, shows a large variance in conversion rates. This may be due to the heterogeneity of APS/BIPS that may be related to the effects of culture, sex, age, and other psychiatric morbidities. Thus, we investigated the different thematic contents of APS and their association with sex, age, country, religion, comorbidity, and functioning to gain a better understanding of the psychosis-risk syndrome. METHOD A sample of 232 clinical high-risk subjects according to the ultra-high risk and basic symptom criteria was recruited as part of a European study conducted in Germany, Italy, Switzerland, and Finland. Case vignettes, originally used for supervision of inclusion criteria, were investigated for APS/BIPS contents, which were compared for sex, age, country, religion, functioning, and comorbidities using chi-squared tests and regression analyses. RESULT We extracted 109 different contents, mainly of APS (96.8%): 63 delusional, 29 hallucinatory, and 17 speech-disorganized contents. Only 20 contents (18.3%) were present in at least 5% of the sample, with paranoid and referential ideas being the most frequent. Thirty-one (28.5%) contents, in particular, bizarre ideas and perceptual abnormalities, demonstrated an association with age, country, comorbidity, or functioning, with regression models of country and obsessive-compulsive disorders explaining most of the variance: 55.8 and 38.3%, respectively. Contents did not differ between religious groups. CONCLUSION Psychosis-risk patients report a wide range of different contents of APS/BIPS, underlining the psychopathological heterogeneity of this group but also revealing a potential core set of contents. Compared to earlier reports on North-American samples, our maximum prevalence rates of contents were considerably lower; this likely being related to a stricter rating of APS/BIPS and cultural influences, in particular, higher schizotypy reported in North-America. The various associations of some APS/BIPS contents with country, age, comorbidities, and functioning might moderate their clinical severity and, consequently, the related risk for psychosis and/or persistent functional disability