2015, Gabler, Carolin, Zietz, Carmen, Bieck, Richard, Göhler, Rebecca, Lindner, Tobias, Haenle, Maximilian, Finke, Birgit, Meichsner, Jürgen, Testrich, Holger, Nowottnick, Mathias, Frerich, Bernhard, Bader, Rainer

A common method to derive both qualitative and quantitative data to evaluate osseointegration of implants is histomorphometry. The present study describes a new image reconstruction algorithm comparing the results of bone-to-implant contact (BIC) evaluated by means of µCT with histomorphometry data. Custom-made conical titanium alloyed (Ti6Al4V) implants were inserted in the distal tibial bone of female Sprague-Dawley rats. Different surface configurations were examined: Ti6Al4V implants with plasma-polymerized allylamine (PPAAm) coating and plasma-polymerized ethylenediamine (PPEDA) coating as well as implants without surface coating. After six weeks postoperatively, tibiae were explanted and BIC was determined by µCT (3D) and afterwards by histomorphometry (2D). In comparison to uncoated Ti6Al4V implants demonstrating low BIC of 32.4% (histomorphometry) and 51.3% (µCT), PPAAm and PPEDA coated implants showed a nonsignificant increase in BIC (histomorphometry: 45.7% and 53.5% and µCT: 51.8% and 62.0%, resp.). Mean BIC calculated by µCT was higher for all surface configurations compared to BIC detected by histomorphometry. Overall, a high correlation coefficient of 0.70 () was found between 3D and 2D quantification of BIC. The μCT analysis seems to be suitable as a nondestructive and accurate 3D imaging method for the evaluation of the bone-implant interface.

2016, Schildknecht, Konstantin, Tabelow, Karsten, Dickhaus, Thorsten

Signal 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.

2016, Deliano, Matthias, Tabelow, Karsten, König, Reinhard, Polzehl, Jörg

Estimation 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.

2015, Jost, Aurélie, Tolstik, Elen, Feldmann, Polina, Wicker, Kai, Sentenac, Anne, Heintzmann, Rainer, Degtyar, Vadim E.

The microscope image of a thick fluorescent sample taken at a given focal plane is plagued by out-of-focus fluorescence and diffraction limited resolution. In this work, we show that a single slice of Structured Illumination Microscopy (two or three beam SIM) data can be processed to provide an image exhibiting tight sectioning and high transverse resolution. Our reconstruction algorithm is adapted from the blind-SIM technique which requires very little knowledge of the illumination patterns. It is thus able to deal with illumination distortions induced by the sample or illumination optics. We named this new algorithm thick slice blind-SIM because it models a three-dimensional sample even though only a single two-dimensional plane of focus was measured.