Mapping changes of in vivo connectivity patterns in the human mediodorsal thalamus: correlations with higher cognitive and executive functions

The mediodorsal thalamic nucleus is recognized as an association hub mediating interconnections with mainly the prefrontal cortex. Tracer studies in primates and in vivo diffusion tensor tractography findings in both humans and monkeys confirm its role in relaying networks that connect to the dorsolateral prefrontal, orbitofrontal, frontal medial and cingulate cortex. Our study was designed to use in vivo probabilistic tractography to describe the pathways emerging from or projecting to the mediodorsal nucleus; moreover, to use such information to automatically define subdivisions based on the divergence of remote structural connections. Diffusion tensor MR imaging data of 156 subjects were utilized to perform connectivity-based segmentation of the mediodorsal nucleus by employing a k-means clustering algorithm. Two domains were revealed (medial and lateral) that are separated from each other by a sagittally oriented plane. For each subject, general assessment of cognitive performance by means of the Wechsler Abbreviated Scale of Intelligence and measures of Delis-Kaplan Executive Function System (D-KEFS) test was utilized. Inter-subject variability in terms of connectivity-based cluster sizes was discovered and the relative sizes of the lateral mediodorsal domain correlated with the individuals' performance in the D-KEFS Sorting test (r = 0.232, p = 0.004). Our results show that the connectivity-based parcellation technique applied to the mediodorsal thalamic nucleus delivers a single subject level descriptor of connectional topography; furthermore, we revealed a possible weak interaction between executive performance and the size of the thalamic area from which pathways converge to the lateral prefrontal corte

Can aquatic macrophytes be biofilters for gadolinium based contrasting agents?

The use of gadolinium-based contrasting agents (GBCA) is increasing because of the intensive usage of these agents in magnetic resonance imaging (MRI). Waste-water treatment does not reduce anthropogenic Gd-concentration significantly. Anomalous Gd-concentration in surface waters have been reported worldwide. However, removal of GBCA-s by aquatic macrophytes has still hardly been investigated. Four aquatic plant species (Lemna gibba, Ceratophyllum demersum, Elodea nuttallii, E. canadensis) were investigated as potential biological filters for removal of commonly used but structurally different GBCA-s (Omniscan, Dotarem) from water. These plant species are known to accumulate heavy metals and are used for removing pollutants in constructed wetlands. The Gd uptake and release of the plants was examined under laboratory conditions. Concentration-dependent infiltration of Gd into the body of the macrophytes was measured, however significant bioaccumulation was not observed. The tissue concentration of Gd reached its maximum value between day one and four in L. gibba and C. demersum, respectively, and its volume was significantly higher in C. demersum than in L gibba. In C. demersum, the open-chain ligand Omniscan causes two-times higher tissue Gd concentration than the macrocyclic ligand Dotarem. Gadolinium was released from Gd-treated duckweeds into the water as they were grown further in Gd-free nutrient solution. Tissue Gd concentration dropped by 50% in duckweed treated by Omniscan and by Dotarem within 1.9 and 2.9 days respectively. None of the macrophytes had a significant impact on the Gd concentration of water in low and medium concentration levels (1-256 mu g L-1). Biofiltration of GBCA-s by common macrophytes could not be detected in our experiments. Therefore it seems that in constructed wetlands, aquatic plants are not able to reduce the concentration of GBCA-s in the water. Furthermore there is a low risk that these plants cause the accumulation of anthropogenic Gd in the food chain. (C) 2017 Published by Elsevier Ltd

Characterizing Network Search Algorithms Developed for Dynamic Causal Modeling

Dynamic causal modeling (DCM) is a widely used tool to estimate the effective connectivity of specified models of a brain network. Finding the model explaining measured data is one of the most important outstanding problems in Bayesian modeling. Using heuristic model search algorithms enables us to find an optimal model without having to define a model set a priori. However, the development of such methods is cumbersome in the case of large model-spaces. We aimed to utilize commonly used graph theoretical search algorithms for DCM to create a framework for characterizing them, and to investigate relevance of such methods for single-subject and group-level studies. Because of the enormous computational demand of DCM calculations, we separated the model estimation procedure from the search algorithm by providing a database containing the parameters of all models in a full model-space. For test data a publicly available fMRI dataset of 60 subjects was used. First, we reimplemented the deterministic bilinear DCM algorithm in the ReDCM R package, increasing computational speed during model estimation. Then, three network search algorithms have been adapted for DCM, and we demonstrated how modifications to these methods, based on DCM posterior parameter estimates, can enhance search performance. Comparison of the results are based on model evidence, structural similarities and the number of model estimations needed during search. An analytical approach using Bayesian model reduction (BMR) for efficient network discovery is already available for DCM. Comparing model search methods we found that topological algorithms often outperform analytical methods for single-subject analysis and achieve similar results for recovering common network properties of the winning model family, or set of models, obtained by multi-subject family-wise analysis. However, network search methods show their limitations in higher level statistical analysis of parametric empirical Bayes. Optimizing such linear modeling schemes the BMR methods are still considered the recommended approach. We envision the freely available database of estimated model-spaces to help further studies of the DCM model-space, and the ReDCM package to be a useful contribution for Bayesian inference within and beyond the field of neuroscience

Complete recovery from psychosis upon miglustat treatment in a juvenile Niemann-Pick C patient

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Cerebral micro-embolization during pulmonary vein isolation: relation to post-ablation silent cerebral ischemia

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