[2,4-13C]β-hydroxybutyrate Metabolism in Astrocytes and C6 Glioblastoma Cells

This study was undertaken to determine if the ketogenic diet could be useful for glioblastoma patients. The hypothesis tested was whether glioblastoma cells can metabolize ketone bodies. Cerebellar astrocytes and C6 glioblastoma cells were incubated in glutamine and serum free medium containing [2,4-13C]β-hydroxybutyrate (BHB) with and without glucose. Furthermore, C6 cells were incubated with [1-13C]glucose in the presence and absence of BHB. Cell extracts were analyzed by mass spectrometry and media by 1H magnetic resonance spectroscopy and HPLC. Using [2,4-13C]BHB and [1-13C]glucose it could be shown that C6 cells, in analogy to astrocytes, had efficient mitochondrial activity, evidenced by 13C labeling of glutamate, glutamine and aspartate. However, in the presence of glucose, astrocytes were able to produce and release glutamine, whereas this was not accomplished by the C6 cells, suggesting lack of anaplerosis in the latter. We hypothesize that glioblastoma cells kill neurons by not supplying the necessary glutamine, and by releasing glutamate

Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the enhancing neuro Imaging genetics through meta analysis (ENIGMA) Consortium

BACKGROUND: The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group. METHODS: The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide. RESULTS: Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset. CONCLUSIONS: The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia

Psychodynamic case formulations without technical language: a reliability study

Background To bridge the gap between symptoms and treatment, constructing case formulations is essential for clinicians. Limited scientific value has been attributed to case formulations because of problems with quality, reliability, and validity. For understanding, communication, and treatment planning beyond each specific clinician-patient dyad, a case formulation must convey valid information concerning the patient, as well as being a reliable source of information regardless of the clinician’s theoretical orientation. The first aim of the present study is to explore the completeness of unstructured psychodynamic formulations, according to four components outlined in the Case Formulation Content Coding Method (CFCCM). The second aim is to estimate the reliability of independent formulations and their components, using similarity ratings of matched versus mismatched cases. Methods This study explores psychodynamic case formulations as made by two or more experienced clinicians after listening to an evaluation interview. The clinicians structured the formulations freely, with the sole constraint that technical, theory-laden terminology should be avoided. The formulations were decomposed into components after all formulations had been written. Results The results indicated that most formulations were adequately comprehensive, and that overall reliability of the formulations was high (> 0.70) for both experienced and inexperienced clinician raters, although the lower bound reliability estimate of the formulation component deemed most difficult to rate - inferred mechanisms - was marginal, 0.61. Conclusions These results were achieved on case formulations made by experienced clinicians using simple experience-near language and minimizing technical concepts, which indicate a communicative quality in the formulations that make them clinically sound. Trial registration ClinicalTrials.gov Identifier: NCT00423462. https://doi.org/10.1007/s00432-018-2781-7 ., January 18, 2007

Intracranial and subcortical volumes in adolescents with early-onset psychosis: A multisite mega-analysis from the ENIGMA consortium.

Early-onset psychosis disorders are serious mental disorders arising before the age of 18 years. Here, we investigate the largest neuroimaging dataset, to date, of patients with early-onset psychosis and healthy controls for differences in intracranial and subcortical brain volumes. The sample included 263 patients with early-onset psychosis (mean age: 16.4 ± 1.4 years, mean illness duration: 1.5 ± 1.4 years, 39.2% female) and 359 healthy controls (mean age: 15.9 ± 1.7 years, 45.4% female) with magnetic resonance imaging data, pooled from 11 clinical cohorts. Patients were diagnosed with early-onset schizophrenia (n = 183), affective psychosis (n = 39), or other psychotic disorders (n = 41). We used linear mixed-effects models to investigate differences in intracranial and subcortical volumes across the patient sample, diagnostic subgroup and antipsychotic medication, relative to controls. We observed significantly lower intracranial (Cohen's d = -0.39) and hippocampal (d = -0.25) volumes, and higher caudate (d = 0.25) and pallidum (d = 0.24) volumes in patients relative to controls. Intracranial volume was lower in both early-onset schizophrenia (d = -0.34) and affective psychosis (d = -0.42), and early-onset schizophrenia showed lower hippocampal (d = -0.24) and higher pallidum (d = 0.29) volumes. Patients who were currently treated with antipsychotic medication (n = 193) had significantly lower intracranial volume (d = -0.42). The findings demonstrate a similar pattern of brain alterations in early-onset psychosis as previously reported in adult psychosis, but with notably low intracranial volume. The low intracranial volume suggests disrupted neurodevelopment in adolescent early-onset psychosis

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

Background Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. Methods We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18–94 years of age) and applied the models to the test sets including 648 patients with SZ (18–66 years of age), 185 patients with BD (18–64 years of age), and 990 HC subjects (17–68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. Results Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen’s d = −0.29) and patients with BD (Cohen’s d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy–based models showed larger group differences than the models based on other DTI-derived metrics. Conclusions Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners

Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study

BACKGROUND: Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. METHODS: We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. RESULTS: Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics. CONCLUSIONS: Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners