CK2 Inhibition Prior to Status Epilepticus Persistently Enhances KCa2 Function in CA1 Which Slows Down Disease Progression.

PURPOSE: Epilepsy therapy is currently based on anti-seizure drugs that do not modify the course of the disease, i.e., they are not anti-epileptogenic in nature. Previously, we observed that in vivo casein kinase 2 (CK2) inhibition with 4,5,6,7-tetrabromotriazole (TBB) had anti-epileptogenic effects in the acute epilepsy slice model. METHODS: Here, we pretreated rats with TBB in vivo prior to the establishment of a pilocarpine-induced status epilepticus (SE) in order to analyze the long-term sequelae of such a preventive TBB administration. RESULTS: We found that TBB pretreatment delayed onset of seizures after pilocarpine and slowed down disease progression during epileptogenesis. This was accompanied with a reduced proportion of burst firing neurons in the CA1 area. Western blot analyses demonstrated that CA1 tissue from TBB-pretreated epileptic animals contained significantly less CK2 than TBB-pretreated controls. On the transcriptional level, TBB pretreatment led to differential gene expression changes of KCa2.2, but also of HCN1 and HCN3 channels. Thus, in the presence of the HCN channel blocker ZD7288, pretreatment with TBB rescued the afterhyperpolarizing potential (AHP) as well as spike frequency adaptation in epileptic animals, both of which are prominent functions of KCa2 channels. CONCLUSION: These data indicate that TBB pretreatment prior to SE slows down disease progression during epileptogenesis involving increased KCa2 function, probably due to a persistently decreased CK2 protein expression.

Beyond fractional anisotropy: extraction of bundle-specific structural metrics from crossing fiber models.

Diffusion MRI (dMRI) measurements are used for inferring the microstructural properties of white matter and to reconstruct fiber pathways. Very often voxels contain complex fiber configurations comprising multiple bundles, rendering the simple diffusion tensor model unsuitable. Multi-compartment models deliver a convenient parameterization of the underlying complex fiber architecture, but pose challenges for fitting and model selection. Spherical deconvolution, in contrast, very economically produces a fiber orientation density function (fODF) without any explicit model assumptions. Since, however, the fODF is represented by spherical harmonics, a direct interpretation of the model parameters is impossible. Based on the fact that the fODF can often be interpreted as superposition of multiple peaks, each associated to one relatively coherent fiber population (bundle), we offer a solution that seeks to combine the advantages of both approaches: first the fiber configuration is modeled as fODF represented by spherical harmonics and then each of the peaks is parameterized separately in order to characterize the underlying bundle. In this work, the fODF peaks are approximated by Bingham distributions, capturing first and second-order statistics of the fiber orientations, from which we derive metrics for the parametric quantification of fiber bundles. We propose meaningful relationships between these measures and the underlying microstructural properties. We focus on metrics derived directly from properties of the Bingham distribution, such as peak length, peak direction, peak spread, integral over the peak, as well as a metric derived from the comparison of the largest peaks, which probes the complexity of the underlying microstructure. We compare these metrics to the conventionally used fractional anisotropy (FA) and show how they may help to increase the specificity of the characterization of microstructural properties. While metrics relying on the first moments of the Bingham distributions provide relatively robust results, second-order metrics representing the peak spread are only meaningful, if the SNR is very high and no fiber crossings are present in the voxel.

The Gini coefficient: a methodological pilot study to assess fetal brain development employing postmortem diffusion MRI.

BACKGROUND: Diffusion-weighted imaging (DWI) is important in the assessment of fetal brain development. However, it is clinically challenging and time-consuming to prepare neuromorphological examinations to assess real brain age and to detect abnormalities. OBJECTIVE: To demonstrate that the Gini coefficient can be a simple, intuitive parameter for modelling fetal brain development. MATERIALS AND METHODS: Postmortem fetal specimens(n = 28) were evaluated by diffusion-weighted imaging (DWI) on a 3-T MRI scanner using 60 directions, 0.7-mm isotropic voxels and b-values of 0, 150, 1,600 s/mm(2). Constrained spherical deconvolution (CSD) was used as the local diffusion model. Fractional anisotropy (FA), apparent diffusion coefficient (ADC) and complexity (CX) maps were generated. CX was defined as a novel diffusion metric. On the basis of those three parameters, the Gini coefficient was calculated. RESULTS: Study of fetal brain development in postmortem specimens was feasible using DWI. The Gini coefficient could be calculated for the combination of the three diffusion parameters. This multidimensional Gini coefficient correlated well with age (Adjusted R(2) = 0.59) between the ages of 17 and 26 gestational weeks. CONCLUSIONS: We propose a new method that uses an economics concept, the Gini coefficient, to describe the whole brain with one simple and intuitive measure, which can be used to assess the brain's developmental state.

Plausibility Tracking: a method to evaluate anatomical connectivity and microstructural properties along fiber pathways.

Diffusion MRI is a non-invasive method that potentially gives insight into the brain's white matter structure regarding the pathway of connections and properties of the axons. Here, we propose a novel global tractography method named Plausibility Tracking that provides the most plausible pathway, modeled as a smooth spline curve, between two locations in the brain. Compared to other tractography methods, plausibility tracking combines the more complete connectivity pattern of probabilistic tractography with smooth tracks that are globally optimized using the fiber orientation density function and hence is relatively robust against local noise and error propagation. It has been tested on phantom and biological data and compared to other methods of tractography. Plausibility tracking provides reliable local directions all along the fiber pathways which makes it especially interesting for tract-based analysis in combination with direction dependent indices of diffusion MRI. In order to demonstrate this potential of plausibility tracking, we propose a framework for the assessment and comparison of diffusion derived tissue properties. This framework comprises atlas-guided parameterization of tract representation and advanced bundle-specific indices describing fiber density, fiber spread and white matter complexity. We explore the new method using real data and show that it allows for a more specific interpretation of the white matter's microstructure compared to rotationally invariant indices derived from the diffusion tensor.

The CONNECT project: Combining macro- and micro-structure.

In recent years, diffusion MRI has become an extremely important tool for studying the morphology of living brain tissue, as it provides unique insights into both its macrostructure and microstructure. Recent applications of diffusion MRI aimed to characterize the structural connectome using tractography to infer connectivity between brain regions. In parallel to the development of tractography, additional diffusion MRI based frameworks (CHARMED, AxCaliber, ActiveAx) were developed enabling the extraction of a multitude of micro-structural parameters (axon diameter distribution, mean axonal diameter and axonal density). This unique insight into both tissue microstructure and connectivity has enormous potential value in understanding the structure and organization of the brain as well as providing unique insights to abnormalities that underpin disease states. The CONNECT (Consortium Of Neuroimagers for the Non-invasive Exploration of brain Connectivity and Tracts) project aimed to combine tractography and micro-structural measures of the living human brain in order to obtain a better estimate of the connectome, while also striving to extend validation of these measurements. This paper summarizes the project and describes the perspective of using micro-structural measures to study the connectome.