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Background: Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features. Methods: Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPI bipolar). Results: For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11–0.361) and a balanced accuracy of 63.1% (95% CI 55.9–70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI −0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6–67.8). BARS and EPI bipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance. Conclusions: Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features. [ABSTRACT FROM AUTHOR]

Background: While adolescent-onset schizophrenia (ADO-SCZ) and adolescent-onset bipolar disorder with psychosis (psychotic ADO-BPD) present a more severe clinical course than their adult forms, their pathophysiology is poorly understood. Here, we study potentially state- and trait-related white matter diffusion-weighted magnetic resonance imaging (dMRI) abnormalities along the adolescent-onset psychosis continuum to address this need. Methods: Forty-eight individuals with ADO-SCZ (20 female/28 male), 15 individuals with psychotic ADO-BPD (7 female/8 male), and 35 healthy controls (HCs, 18 female/17 male) underwent dMRI and clinical assessments. Maps of extracellular free-water (FW) and fractional anisotropy of cellular tissue (FAT) were compared between individuals with psychosis and HCs using tract-based spatial statistics and FSL's Randomise. FAT and FW values were extracted, averaged across all voxels that demonstrated group differences, and then utilized to test for the influence of age, medication, age of onset, duration of illness, symptom severity, and intelligence. Results: Individuals with adolescent-onset psychosis exhibited pronounced FW and FAT abnormalities compared to HCs. FAT reductions were spatially more widespread in ADO-SCZ. FW increases, however, were only present in psychotic ADO-BPD. In HCs, but not in individuals with adolescent-onset psychosis, FAT was positively related to age. Conclusions: We observe evidence for cellular (FAT) and extracellular (FW) white matter abnormalities in adolescent-onset psychosis. Although cellular white matter abnormalities were more prominent in ADO-SCZ, such alterations may reflect a shared trait, i.e. neurodevelopmental pathology, present across the psychosis spectrum. Extracellular abnormalities were evident in psychotic ADO-BPD, potentially indicating a more dynamic, state-dependent brain reaction to psychosis. [ABSTRACT FROM AUTHOR]

Background: Targeting the N-methyl-D-aspartate receptor (NMDAR) is a major translational approach for treating negative symptoms of schizophrenia. Ketamine comprehensively produces schizophrenia-like symptoms, such as positive, cognitive and negative symptoms in healthy volunteers. The amplitude of the mismatch negativity (MMN) is known to be significantly reduced not only in patients with schizophrenia, but also in healthy controls receiving ketamine. Accordingly, it was the aim of the present study to investigate whether changes of MMN amplitudes during ketamine administration are associated with the emergence of schizophrenialike negative symptoms in healthy volunteers. Methods: We examined the impact of ketamine during an MMN paradigm with 64-channel electroencephalography (EEG) and assessed the psychopathological status using the Positive and Negative Syndrome Scale (PANSS) in healthy male volunteers using a single-blind, randomized, placebo-controlled crossover design. Lowresolution brain electromagnetic tomography was used for source localization. Results: Twenty-four men were included in our analysis. Significant reductions of MMN amplitudes and an increase in all PANSS scores were identified under the ketamine condition. Smaller MMN amplitudes were specifically associated with more pronounced negative symptoms. Source analysis of MMN generators indicated a significantly reduced current source density (CSD) under the ketamine condition in the primary auditory cortex, the posterior cingulate and the middle frontal gyrus. Limitations: The sample included only men within a tight age range of 20-32 years. Conclusion: The MMN might represent a biomarker for negative symptoms in schizophrenia related to an insufficient NMDAR system and could be used to identify patients with schizophrenia with negative symptoms due to NMDAR dysfunction., Introduction Current pharmaceuticals for the treatment of schizophrenia--the typical and atypical antipsychotics--are dopamine D2 receptor antagonists with a satisfactory clinical benefit on positive symptoms, but with limited to no impact [...]

Disrupted auditory networks play an important role in the pathophysiology of psychosis, with abnormalities already observed in individuals at clinical high-risk for psychosis (CHR). Here, we examine structural and functional connectivity of an auditory network in CHR utilising state-of-the-art electroencephalography and diffusion imaging techniques. Twenty-six CHR subjects and 13 healthy controls (HC) underwent diffusion MRI and electroencephalography while performing an auditory task. We investigated structural connectivity, measured as fractional anisotropy in the Arcuate Fasciculus (AF), Cingulum Bundle, and Superior Longitudinal Fasciculus-II. Gamma-band lagged-phase synchronisation, a functional connectivity measure, was calculated between cortical regions connected by these tracts. CHR subjects showed significantly higher structural connectivity in the right AF than HC (p <.001). Although non-significant, functional connectivity between cortical areas connected by the AF was lower in CHR than HC (p =.078). Structural and functional connectivity were correlated in HC (p =.056) but not in CHR (p =.29). We observe significant differences in structural connectivity of the AF, without a concomitant significant change in functional connectivity in CHR subjects. This may suggest that the CHR state is characterised by a decoupling of structural and functional connectivity, possibly due to abnormal white matter maturation. [ABSTRACT FROM AUTHOR]