Migrationsanalysen an den humanen Glioblastomzelllinien U-373 MG und U-87 MG unter Anwendung von Medikamenten des klinischen Alltags

Das Glioblastoma multiforme ist einer der häufigsten hirneigenen Tumoren des Menschen und die Prognose für die daran erkrankten Patienten trotz multidisziplinärer Therapieschemata noch immer infaust. Insofern verwundert es kaum, dass diese Tumorentität unter anderem in der experimentellen Forschung in Bezug auf die molekularen Stoffwechselmechanismen und die Interaktion der Zellen mit der Umwelt weit verbreitet ist. Und obwohl immer mehr dieser Signalwege entschlüsselt werden, hat sich bis jetzt noch kein bahnbrechender Therapieansatz offenbart. Gegenstand dieser Arbeit ist es, die Wirkung von Medikamenten der alltäglichen, klinischen Praxis auf die Migrationsrate kultivierter humaner Glioblastomzellen zu untersuchen. Hintergrund dessen ist vor allem die Erkenntnis, dass infolge der malignen Tumorbiologie des Zellverbandes in vivo eine Störung der Blut-Hirn-Schranke resultiert (Long 1970), sodass letztlich jedes systemisch verabreichte Medikament auf sie einwirken kann. Daher wurde ein breites Spektrum häufig angeordneter Wirkstoffe in ihrer handelsüblichen Zubereitungsform (für die intravenöse Gabe) ausgewählt, auch im Hinblick auf eine ähnliche Studie über eine intrazelluläre Kalziumantwort unter einer erstaunlichen Vielzahl solcher Medikamente (Kuhn et al. 2009). Diese wurden Glioblastomzellen der Linien U-373 MG und U-87 MG in verschiedenen Konzentrationen zugesetzt und deren Migrationsrate in drei unterschiedlichen Transwell-Verfahren erfasst. Die statistische Auswertung der damit erhobenen Ergebnisse kann mit multiplen signifikanten Effekten der Medikamente auf die Wanderungsbestrebungen der beiden Zelllinien aufwarten. (…

Hippocampal volume as a putative marker of resilience or compensation to minor depressive symptoms in a nonclinical sample

Case-control studies in major depression have established patterns of regional gray matter loss, including the hippocampus, which might show state-related effects dependent on disease stage. However, there is still limited knowledge on compensation effects that might occur in people resilient to depression showing only subclinical symptoms. We used voxel-based morphometry on a multicenter data set of 409 healthy nonclinical subjects to test the hypothesis that local hippocampal volume would be inversely correlated with subclinical depressive symptoms [Symptom Checklist 90-Revised (SCL-90-R) depression scores]. Our region-of-interest results show a significant (p = 0.042, FWE cluster-level corrected) positive correlation of SCL-90-R scores for depression and a left hippocampus cluster. Additionally, we provide an exploratory finding of gyrification, a surface-based morphometric marker, correlating with a right postcentral gyrus cluster [p = 0.031, family-wise error (FWE) cluster-level corrected]. Our findings provide first preliminary evidence of an inverse relationship for subjects in the absence of clinical depression and might thus point to processes related to compensation. Similar effects have been observed in remission from major depression and thus deserve further study to evaluate hippocampal volume not only as a state-dependent marker of disease but also of resilience

Subclinical Agoraphobia Symptoms and Regional Brain Volumes in Non-clinical Subjects: Between Compensation and Resilience?

Background: Symptoms of anxiety are present not only in panic disorder or other anxiety disorders, but are highly prevalent in the general population. Despite increasing biological research on anxiety disorders, there is little research on understanding subclinical or sub-threshold symptoms relating to anxiety in non-clinical community samples, which could give clues to factors relating to resilience or compensatory changes.Aims:This study focused on brain structural correlates of subclinical anxiety/agoraphobia symptoms from a multi-center imaging study.Methods: We obtained high-resolution structural T1 MRI scans of 409 healthy young participants and used the CAT12 toolbox for voxel-based morphometry (VBM) analysis. Subjects provided self-ratings of anxiety using the SCL-90-R, from which we used the phobia subscale, covering anxiety symptoms related to those of panic and agoraphobia spectrum.Results: We found significant (p < 0.05, FDR-corrected) correlations (mostly positive) of cortical volume with symptom severity, including the right lingual gyrus and calcarine sulcus, as well as left calcarine sulcus, superior, middle, and inferior temporal gyri. Uncorrected exploratory analysis also revealed positive correlations with GMV in orbitofrontal cortex, precuneus, and insula.Conclusions: Our findings show brain structural associations of subclinical symptoms of anxiety, which overlap with those seen in panic disorder or agoraphobia. This is consistent with a dimensional model of anxiety, which is reflected not only functionally but also on the structural level

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects

Das Münchhausen-by-Proxy-Syndrom als eine Form der Kindesmisshandlung

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Twin studies of brain structure and cognition in schizophrenia

Twin studies in schizophrenia have been crucial in establishing estimates for the heritability and thus providing evidence for a genetic component in this disorder. Recent years have seen the application of the twin study paradigm to both putative intermediate phenotypes and biomarkers of disease as well as a diversification of its use in schizophrenia research. This review addressed studies of brain structure (T1 morphometry) and cognition in schizophrenia using twin study designs. We review major findings such as the overlap of genetic variance between schizophrenia and cognition as a model for the emergence of psychopathology. The use of novel hybrid models integrating molecular genetic risk markers, as well as the use of twin studies in epigenetics might prove to significantly enhance schizophrenia research in the post-GWAS era

Human time perspective and its structural associations with voxel-based morphometry and gyrification

Time perspective refers to humans' concept of integrating and evaluating temporal position and evaluation of memories, emotions, and experiences. We tested the hypothesis that different aspects of time perspective, as assessed with the Zimbardo Time Perspective Inventory (ZTPI) are related to variation of brain structure in non-clinical subjects. Analysing data from n = 177 psychiatrically healthy subjects using voxel-based morphometry with the CAT12 software package, we identified several significant (p < 0.05 FWE, cluster-level corrected) associations. The factors past negative, reflecting a negative attitude towards past events and present fatalistic, measuring a hopeless and fatalistic attitude towards future life, were both negatively associated with grey matter volumes of the anterior insula. The ZTPI factor future was negatively associated with precuneus grey matter. There was no association of ZTPI scores with gyrification using an absolute mean curvature method, a marker of early brain development. These findings provide a link between a general psychological construct of time perspective and brain structural variations in key areas related to time keeping (anterior insula) and the default mode network (precuneus), both of which overlap with variation in behavioral aspects and psychopathology