Effects of C1-INH Treatment on Neurobehavioral Sequelae and Late Seizures After Traumatic Brain Injury in a Mouse Model of Controlled Cortical Impact.

C1 human-derived C1 esterase inhibitor (C1-INH) is a U.S. Food and Drig Administration-approved drug with anti-inflammatory actions. In the present study, we investigated the therapeutic effects of C1-INH on acute and chronic neurobehavioral outcomes and on seizures in the chronic stage in a mouse traumatic brain injury (TBI) model. Adult male CD1 mice were subjected to controlled cortical impact and randomly allocated to receive C1-INH or vehicle solution 1 h post-TBI. Effects of C1-INH treatment on inflammatory responses and brain damage after TBI were examined using the Cytometric Bead Array, C5a enzyme-linked immunosorbent assay, Fluoro-Jade C staining, and Nissl staining. Neurobehavioral outcomes after TBI were assessed with modified neurological severity scores, the rotarod and open field tests, and the active place avoidance task. Video-electroencephalographic monitoring was performed in the 15th and 16th weeks after TBI to document epileptic seizures. We found that C1-INH treatment reduced TNFα expression and alleviated brain damage. Treatment with C1-INH improved neurological functions, increased locomotor activity, alleviated anxiety-like behavior, and exhibited an effect on seizures in the chronic stage after TBI. These findings suggest that C1-INH has beneficial effects on the treatment of TBI.

GABAa receptor density alterations revealed in a mouse model of early moderate prenatal ethanol exposure using [18F]AH114726.

INTRODUCTION: Prenatal ethanol exposure (PEE) has been shown to alter the level and function of receptors in the brain, one of which is GABAa receptors (GABAaR), the major inhibitory ligand gated ion channels that mediate neuronal inhibition. High dose PEE in animals resulted in the upregulation of GABAaR, but the effects of low and moderate dose PEE at early gestation have not been investigated. This study aimed at examining GABAaR density in the adult mouse brain following PEE during a period equivalent to the first 3 to 4 weeks in human gestation. It was hypothesized that early moderate PEE would cause alterations in brain GABAaR levels in the adult offspring. METHODS: C57BL/6J mice were given 10% v/v ethanol during the first 8 gestational days. Male offspring were studied using in-vivo Positron Emission Tomography (PET)/Magnetic Resonance Imaging (MRI), biodistribution, in-vitro autoradiography using [18F]AH114726, a novel flumazenil analogue with a high affinity for the benzodiazepine-binding site, and validated using immunohistochemistry. RESULTS: In vivo PET and biodistribution did not detect alteration in brain tracer uptake. In vitro radiotracer studies detected significantly reduced GABAaR in the olfactory bulbs. Immunohistochemistry detected reduced GABAaR in the cerebral cortex, cerebellum and hippocampus, while Nissl staining showed that cell density was significantly higher in the striatum following PEE. CONCLUSION: Early moderate PEE may induce long-term alterations in the GABAaR system that persisted into adulthood.

Magnetic Resonance Imaging and Micro-Computed Tomography reveal brain morphological abnormalities in a mouse model of early moderate prenatal ethanol exposure.

BACKGROUND: This study investigated the effects of early moderate prenatal ethanol exposure (PEE) on the brain in a mouse model that mimics a scenario in humans, whereby moderate daily drinking ceases after a woman becomes aware of her pregnancy. METHODS: C57BL/6J pregnant mice were given 10% v/v ethanol from gestational day 0-8 in the drinking water. The male offspring were used for imaging. Anatomical and diffusion Magnetic Resonance Imaging were performed in vivo at postnatal day 28 (P28, adolescence) and P80 (adulthood). Micro-Computed Tomography was performed on fixed whole heads at P80. Tensor-based morphometry (TBM) was applied to detect alterations in brain structure and voxel-based morphometry (VBM) for skull morphology. Diffusion tensor and neurite orientation dispersion and density imaging models were used to detect microstructural changes. Neurofilament (NF) immunohistochemistry was used to validate findings by in vivo diffusion MRI. RESULTS: TBM showed that PEE mice exhibited a significantly smaller third ventricle at P28 (family-wise error rate (FWE), p < 0.05). All other macro-structural alterations did not survive FWE corrections but when displayed with an uncorrected p < 0.005 showed multiple regional volume reductions and expansions, more prominently in the right hemisphere. PEE-induced gross volume changes included a bigger thalamus, hypothalamus and ventricles at P28, and bigger total brain volumes at both P28 and P80 (2-sample t-tests). Disproportionately smaller olfactory bulbs following PEE were revealed at both time-points. No alterations in diffusion parameters were detected, but PEE animals exhibited reduced NF positive staining in the thalamus and striatum and greater bone density in various skull regions. CONCLUSION: Our results show that early moderate PEE can cause alterations in the brain that are detectable during development and adulthood.

Optimization of Distributions Differences for Classification.

In this paper, we introduce a new classification algorithm called the optimization of distribution differences (ODD). The algorithm aims to find a transformation from the feature space to a new space where the instances in the same class are as close as possible to one another, whereas the gravity centers of these classes are as far as possible from one another. This aim is formulated as a multiobjective optimization problem that is solved by a hybrid of an evolutionary strategy and the quasi-Newton method. The choice of the transformation function is flexible and could be any continuous space function. We experiment with a linear and a nonlinear transformation in this paper. We show that the algorithm can outperform eight other classification methods, namely naive Bayes, support vector machines, linear discriminant analysis, multilayer perceptrons, decision trees, and k -nearest neighbors, and two recently proposed classification methods, in 12 standard classification data sets. Our results show that the method is less sensitive to the imbalanced number of instances compared with these methods. We also show that ODD maintains its performance better than other classification methods in these data sets and hence offers a better generalization ability.

Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique.

OBJECTIVE: Epilepsy is a common neurological disorder characterized by recurrent, unprovoked seizures. The search for new treatments for seizures and epilepsy relies upon studies in animal models of epilepsy. To capture data on seizures, many applications require prolonged electroencephalography (EEG) with recordings that generate voluminous data. The desire for efficient evaluation of these recordings motivates the development of automated seizure detection algorithms. APPROACH: A new seizure detection method is proposed, based on multiple features and a simple thresholding technique. The features are derived from chaos theory, information theory and the power spectrum of EEG recordings and optimally exploit both linear and nonlinear characteristics of EEG data. MAIN RESULT: The proposed method was tested with real EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and specificity. SIGNIFICANCE: The proposed approach introduces two new features: negative logarithm of adaptive correlation integral and power spectral coherence ratio. The combination of these new features with two previously described features, entropy and phase coherence, improved seizure detection accuracy significantly. Negative logarithm of adaptive correlation integral can also be used to compute the duration of automatically detected seizures.

Radiological studies of fetal alcohol spectrum disorders in humans and animal models: An updated comprehensive review.

Fetal Alcohol Spectrum Disorders encompass a wide range of birth defects in children born to mothers who consumed alcohol during pregnancy. Typical mental impairments in FASD include difficulties in life adaptation and learning and memory, deficits in attention, visuospatial skills, language and speech disabilities, mood disorders and motor disabilities. Multimodal imaging methods have enabled in vivo studies of the teratogenic effects of alcohol on the central nervous system, giving more insight into the FASD phenotype. This paper offers an up-to-date comprehensive review of radiological findings in the central nervous system in studies of prenatal alcohol exposure in both humans and translational animal models, including Magnetic Resonance Imaging, Computed Tomography, Positron Emission Tomography, Single Photon Emission Tomography and Ultrasonography.

Disease-modifying effect of intravenous immunoglobulin in an experimental model of epilepsy.

Novel therapies that prevent or modify the development of epilepsy following an initiating brain insult could significantly reduce the burden of this disease. In light of evidence that immune mechanisms play an important role in generating and maintaining the epileptic condition, we evaluated the effect of a well-established immunomodulatory treatment, intravenous immunoglobulin (IVIg), on the development of epilepsy in an experimental model of epileptogenesis. In separate experiments, IVIg was administered either before (pre-treatment) or after (post-treatment) the onset of pilocarpine status epilepticus (SE). Our results show that both pre- and post-treatment with IVIg attenuated acute inflammation in the SE model. Specifically, IVIg reduced local activation of glial cells, complement system activation, and blood-brain barrier damage (BBB), which are all thought to play important roles in the development of epilepsy. Importantly, post-treatment with IVIg was also found to reduce the frequency and duration of subsequent spontaneous recurrent seizures as detected by chronic video-electroencephalographic (video-EEG) recordings. This finding supports a novel application for IVIg, specifically its repurposing as a disease-modifying therapy in epilepsy.

Crowdsourcing reproducible seizure forecasting in human and canine epilepsy.

SEE MORMANN AND ANDRZEJAK DOI101093/BRAIN/AWW091 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE : Accurate forecasting of epileptic seizures has the potential to transform clinical epilepsy care. However, progress toward reliable seizure forecasting has been hampered by lack of open access to long duration recordings with an adequate number of seizures for investigators to rigorously compare algorithms and results. A seizure forecasting competition was conducted on kaggle.com using open access chronic ambulatory intracranial electroencephalography from five canines with naturally occurring epilepsy and two humans undergoing prolonged wide bandwidth intracranial electroencephalographic monitoring. Data were provided to participants as 10-min interictal and preictal clips, with approximately half of the 60 GB data bundle labelled (interictal/preictal) for algorithm training and half unlabelled for evaluation. The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leader board. The contest ran from August to November 2014, and 654 participants submitted 17 856 classifications of the unlabelled test data. The top performing entry scored 0.84 area under the classification curve. Following the contest, additional held-out unlabelled data clips were provided to the top 10 participants and they submitted classifications for the new unseen data. The resulting area under the classification curves were well above chance forecasting, but did show a mean 6.54 ± 2.45% (min, max: 0.30, 20.2) decline in performance. The kaggle.com model using open access data and algorithms generated reproducible research that advanced seizure forecasting. The overall performance from multiple contestants on unseen data was better than a random predictor, and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.media-1vid110.1093/brain/aww045_video_abstractaww045_video_abstract.

Mouse EEG spike detection based on the adapted continuous wavelet transform.

OBJECTIVE: Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Interictal spikes on EEG are used to monitor the development of epilepsy and the effects of drug therapy. EEG recordings are generally long and the data voluminous. Thus developing a sensitive and reliable automated algorithm for analyzing EEG data is necessary. APPROACH: A new algorithm for detecting and classifying interictal spikes in mouse EEG recordings is proposed, based on the adapted continuous wavelet transform (CWT). The construction of the adapted mother wavelet is founded on a template obtained from a sample comprising the first few minutes of an EEG data set. MAIN RESULT: The algorithm was tested with EEG data from a mouse model of epilepsy and experimental results showed that the algorithm could distinguish EEG spikes from other transient waveforms with a high degree of sensitivity and specificity. SIGNIFICANCE: Differing from existing approaches, the proposed approach combines wavelet denoising, to isolate transient signals, with adapted CWT-based template matching, to detect true interictal spikes. Using the adapted wavelet constructed from a predefined template, the adapted CWT is calculated on small EEG segments to fit dynamical changes in the EEG recording.

lop-DWI: A Novel Scheme for Pre-Processing of Diffusion-Weighted Images in the Gradient Direction Domain.

We describe and evaluate a pre-processing method based on a periodic spiral sampling of diffusion-gradient directions for high angular resolution diffusion magnetic resonance imaging. Our pre-processing method incorporates prior knowledge about the acquired diffusion-weighted signal, facilitating noise reduction. Periodic spiral sampling of gradient direction encodings results in an acquired signal in each voxel that is pseudo-periodic with characteristics that allow separation of low-frequency signal from high frequency noise. Consequently, it enhances local reconstruction of the orientation distribution function used to define fiber tracks in the brain. Denoising with periodic spiral sampling was tested using synthetic data and in vivo human brain images. The level of improvement in signal-to-noise ratio and in the accuracy of local reconstruction of fiber tracks was significantly improved using our method.

MRI resolution enhancement: how useful are shifted images obtained by changing the demodulation frequency?

Super-resolution reconstruction is a process by which a set of different low resolution images of the same object are used to create an enhanced, higher resolution image of that object. Recently there has been debate amongst researchers whether it is possible to obtain in-plane image enhancement using a set of low resolution magnetic resonance images, acquired by making small, independent changes to the demodulation frequency. We show that shifted low-resolution images contain different information that can be used to obtain denser sampling, leading to image enhancement. We conclude this from specific phantom experiments, applying signal processing sampling theory and taking into consideration the relative sampling of the point spread function with respect to the location of signal sources. Furthermore, the maximum achievable resolution for Fourier encoded MRI data at a boundary or object feature is governed by the effective width of the point spread function or the Fourier pixel size determined by the extent of k-space; this is verified experimentally.

Globally optimal, minimum stored energy, double-doughnut superconducting magnets.

The use of the minimum stored energy current density map-based methodology of designing closed-bore symmetric superconducting magnets was described recently. The technique is further developed to cater for the design of interventional-type MRI systems, and in particular open symmetric magnets of the double-doughnut configuration. This extends the work to multiple magnet domain configurations. The use of double-doughnut magnets in MRI scanners has previously been hindered by the ability to deliver strong magnetic fields over a sufficiently large volume appropriate for imaging, essentially limiting spatial resolution, signal-to-noise ratio, and field of view. The requirement of dedicated interventional space restricts the manner in which the coils can be arranged and placed. The minimum stored energy optimal coil arrangement ensures that the field strength is maximized over a specific region of imaging. The design method yields open, dual-domain magnets capable of delivering greater field strengths than those used prior to this work, and at the same time it provides an increase in the field-of-view volume. Simulation results are provided for 1-T double-doughnut magnets with at least a 50-cm 1-ppm (parts per million) field of view and 0.7-m gap between the two doughnuts.

Globally optimal superconducting magnets part I: minimum stored energy (MSE) current density map.

An optimal current density map is crucial in magnet design to provide the initial values within search spaces in an optimization process for determining the final coil arrangement of the magnet. A strategy for obtaining globally optimal current density maps for the purpose of designing magnets with coaxial cylindrical coils in which the stored energy is minimized within a constrained domain is outlined. The current density maps obtained utilising the proposed method suggests that peak current densities occur around the perimeter of the magnet domain, where the adjacent peaks have alternating current directions for the most compact designs. As the dimensions of the domain are increased, the current density maps yield traditional magnet designs of positive current alone. These unique current density maps are obtained by minimizing the stored magnetic energy cost function and therefore suggest magnet coil designs of minimal system energy. Current density maps are provided for a number of different domain arrangements to illustrate the flexibility of the method and the quality of the achievable designs.

Globally optimal superconducting magnets part II: symmetric MSE coil arrangement.

A globally optimal superconducting magnet coil design procedure based on the Minimum Stored Energy (MSE) current density map is outlined. The method has the ability to arrange coils in a manner that generates a strong and homogeneous axial magnetic field over a predefined region, and ensures the stray field external to the assembly and peak magnetic field at the wires are in acceptable ranges. The outlined strategy of allocating coils within a given domain suggests that coils should be placed around the perimeter of the domain with adjacent coils possessing alternating winding directions for optimum performance. The underlying current density maps from which the coils themselves are derived are unique, and optimized to possess minimal stored energy. Therefore, the method produces magnet designs with the lowest possible overall stored energy. Optimal coil layouts are provided for unshielded and shielded short bore symmetric superconducting magnets.