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- ItemComparison of Multiscale Imaging Methods for Brain Research(Basel : MDPI, 2020) Tröger, Jessica; Hoischen, Christian; Perner, Birgit; Monajembashi, Shamci; Barbotin, Aurélien; Löschberger, Anna; Eggeling, Christian; Kessels, Michael M.; Qualmann, Britta; Hemmerich, PeterA major challenge in neuroscience is how to study structural alterations in the brain. Even small changes in synaptic composition could have severe outcomes for body functions. Many neuropathological diseases are attributable to disorganization of particular synaptic proteins. Yet, to detect and comprehensively describe and evaluate such often rather subtle deviations from the normal physiological status in a detailed and quantitative manner is very challenging. Here, we have compared side-by-side several commercially available light microscopes for their suitability in visualizing synaptic components in larger parts of the brain at low resolution, at extended resolution as well as at super-resolution. Microscopic technologies included stereo, widefield, deconvolution, confocal, and super-resolution set-ups. We also analyzed the impact of adaptive optics, a motorized objective correction collar and CUDA graphics card technology on imaging quality and acquisition speed. Our observations evaluate a basic set of techniques, which allow for multi-color brain imaging from centimeter to nanometer scales. The comparative multi-modal strategy we established can be used as a guide for researchers to select the most appropriate light microscopy method in addressing specific questions in brain research, and we also give insights into recent developments such as optical aberration corrections.
- ItemImproving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis(San Francisco, California, US : PLOS, 2016) Deliano, Matthias; Tabelow, Karsten; König, Reinhard; Polzehl, JörgEstimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors in the analysis of single-subject data as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning.
- ItemGivinostat-Liposomes: Anti-Tumor Effect on 2D and 3D Glioblastoma Models and Pharmacokinetics(Basel : MDPI, 2022) Taiarol, Lorenzo; Bigogno, Chiara; Sesana, Silvia; Kravicz, Marcelo; Viale, Francesca; Pozzi, Eleonora; Monza, Laura; Carozzi, Valentina Alda; Meregalli, Cristina; Valtorta, Silvia; Moresco, Rosa Maria; Koch, Marcus; Barbugian, Federica; Russo, Laura; Dondio, Giulio; Steinkühler, Christian; Re, FrancescaGlioblastoma is the most common and aggressive brain tumor, associated with poor prognosis and survival, representing a challenging medical issue for neurooncologists. Dysregulation of histone-modifying enzymes (HDACs) is commonly identified in many tumors and has been linked to cancer proliferation, changes in metabolism, and drug resistance. These findings led to the development of HDAC inhibitors, which are limited by their narrow therapeutic index. In this work, we provide the proof of concept for a delivery system that can improve the in vivo half-life and increase the brain delivery of Givinostat, a pan-HDAC inhibitor. Here, 150-nm-sized liposomes composed of cholesterol and sphingomyelin with or without surface decoration with mApoE peptide, inhibited human glioblastoma cell growth in 2D and 3D models by inducing a time-and dose-dependent reduction in cell viability, reduction in the receptors involved in cholesterol metabolism (from −25% to −75% of protein levels), and reduction in HDAC activity (−25% within 30 min). In addition, liposome-Givinostat formulations showed a 2.5-fold increase in the drug half-life in the bloodstream and a 6-fold increase in the amount of drug entering the brain in healthy mice, without any signs of overt toxicity. These features make liposomes loaded with Givinostat valuable as potential candidates for glioblastoma therapy.
- ItemMore Specific Signal Detection in Functional Magnetic Resonance Imaging by False Discovery Rate Control for Hierarchically Structured Systems of Hypotheses(San Francisco, California, US : PLOS, 2016) Schildknecht, Konstantin; Tabelow, Karsten; Dickhaus, ThorstenSignal detection in functional magnetic resonance imaging (fMRI) inherently involves the problem of testing a large number of hypotheses. A popular strategy to address this multiplicity is the control of the false discovery rate (FDR). In this work we consider the case where prior knowledge is available to partition the set of all hypotheses into disjoint subsets or families, e. g., by a-priori knowledge on the functionality of certain regions of interest. If the proportion of true null hypotheses differs between families, this structural information can be used to increase statistical power. We propose a two-stage multiple test procedure which first excludes those families from the analysis for which there is no strong evidence for containing true alternatives. We show control of the family-wise error rate at this first stage of testing. Then, at the second stage, we proceed to test the hypotheses within each non-excluded family and obtain asymptotic control of the FDR within each family at this second stage. Our main mathematical result is that this two-stage strategy implies asymptotic control of the FDR with respect to all hypotheses. In simulations we demonstrate the increased power of this new procedure in comparison with established procedures in situations with highly unbalanced families. Finally, we apply the proposed method to simulated and to real fMRI data.
- ItemStatistical Properties and Predictability of Extreme Epileptic Events([London] : Macmillan Publishers Limited, part of Springer Nature, 2019) Frolov, Nikita S.; Grubov, Vadim V.; Maksimenko, Vladimir A.; Lüttjohann, Annika; Makarov, Vladimir V.; Pavlov, Alexey N.; Sitnikova, Evgenia; Pisarchik, Alexander N.; Kurths, Jürgen; Hramov, Alexander E.The use of extreme events theory for the analysis of spontaneous epileptic brain activity is a relevant multidisciplinary problem. It allows deeper understanding of pathological brain functioning and unraveling mechanisms underlying the epileptic seizure emergence along with its predictability. The latter is a desired goal in epileptology which might open the way for new therapies to control and prevent epileptic attacks. With this goal in mind, we applied the extreme event theory for studying statistical properties of electroencephalographic (EEG) recordings of WAG/Rij rats with genetic predisposition to absence epilepsy. Our approach allowed us to reveal extreme events inherent in this pathological spiking activity, highly pronounced in a particular frequency range. The return interval analysis showed that the epileptic seizures exhibit a highly-structural behavior during the active phase of the spiking activity. Obtained results evidenced a possibility for early (up to 7 s) prediction of epileptic seizures based on consideration of EEG statistical properties.