Altered structural and functional thalamocortical networks in secondarily generalized extratemporal lobe seizures

Structural and functional abnormalities in the thalamocortical network in primary generalized epilepsies or mesial temporal lobe epilepsy have recently been identified by voxel-wise analyses of neuroimaging. However, evidence is needed regarding the profiles of the thalamocortical network in patients with secondarily generalized seizures from focal neocortical sources. We used high-resolution T1-weighted, diffusion-tensor and resting-state functional MR imaging (rs-fMRI) to examine 16 patients with secondarily generalized extratemporal lobe seizures and 16 healthy controls. All the patients were medically effective and MRI-negative. Using whole brain voxel-based morphometry (VBM) to compare the patients with the normal controls, we observed significantly decreased gray matter (GM) density in the thalamus and 3 frontal gyri and significantly reduced white matter (WM) fractional anisotropy (FA) in the bilateral anterior corona radiata of the patients. Alterations in the thalamocortical functional connectivity with different cortices were identified by the rs-fMRI analysis seeding of the whole thalamus. The prefrontal gyri with the greatest functional connectivity were also traced by seeding a sub-thalamic region that is demarcated in an atlas, in which the thalamic parcellation is based on the WM connectivity to the cortices. This sub-thalamic region anatomically contains the mediodorsal thalamic nucleus where, concordantly, there was a significant decrease in thalamic GM density in the VBM study. In contrast to the negative correlation between the disease duration and reduced thalamic densities and subcortical FA values, the strength of the functional thalamocortical connectivity had a paradoxical correlation. Our results conclusively indicate that generalized seizures with a focal cortical source are associated with structural and functional alterations in the thalamocortical network

Functional Connectivity of the Corpus Callosum in Epilepsy Patients with Secondarily Generalized Seizures

The corpus callosum (CC) plays an important role in generalization of seizure activity. We used resting-state function magnetic resonance imaging (rs-fMRI) to investigate the regional and interregional functional connectivity of CC in patients with magnetic resonance imaging (MRI)-negative and secondarily generalized seizures. We measured the multi-regional coherences of blood oxygen level-dependent (BOLD) signals via rs-fMRI, cortical thickness via high-resolution T1-weighted MRI, and white matter (WM) integrity via diffusion-tensor imaging in 16 epilepsy patients as well as in 16 age- and gender-matched healthy subjects. All patients had non-lesional MRI, medically well-controlled focal epilepsy and history of secondarily generalized convulsions. Individuals with epilepsy had significant differences in regional and interregional hypersynchronization of BOLD signals intrahemispherically and interhemispherically, but no difference in cortical thickness and WM integrity. The only area with increased regional hypersynchrony in WM was over the anterior CC, which also exhibited lower activation of neighboring resting-state networks. The present study revealed abnormal local and distant synchronization of spontaneous neural activities in epileptic patients with secondarily generalized seizures

s-SMOOTH: Sparsity and Smoothness Enhanced EEG Brain Tomography

EEG source imaging enables us to reconstruct current density in the brain from the electrical measurements with excellent temporal resolution (~ms). The corresponding EEG inverse problem is an ill-posed one that has infinitely many solutions. This is due to the fact that the number of EEG sensors is usually much smaller than that of the potential dipole locations, as well as noise contamination in the recorded signals. To obtain a unique solution, regularizations can be incorporated to impose additional constraints on the solution. An appropriate choice of regularization is critically important for the reconstruction accuracy of the brain image. In this paper, we propose a novel Sparsity and SMOOthness enhanced brain TomograpHy (s-SMOOTH) method to improve the reconstruction accuracy by integrating two recently proposed regularization techniques: Total Generalized Variation (TGV) regularization and l_(1-2) regularization. TGV is able to preserve the source edge and recover the spatial distribution of the source intensity with high accuracy. Compared to the relevant total variation (TV) regularization, TGV enhances the smoothness of the image and reduces staircasing artifacts. The traditional TGV defined on a 2D image has been widely used in image processing field. In order to handle 3D EEG source images, we propose a voxel-based TGV (vTGV) regularization that extends the definition of second-order TGV from 2D planar image to 3D irregular surfaces such as cortex surface. In addition, the l_(1-2) regularization is utilized to promote sparsity on the current density itself. We demonstrate that l_(1-2) regularization is able to enhance sparsity and accelerate computations than l_1 regularization. The proposed model is solved by an efficient and robust algorithm based on the difference of convex functions algorithm (DCA) and the alternating direction method of multipliers (ADMM). Numerical experiments using synthetic data demonstrate the advantages of the proposed method over other state-of-the-art methods in terms of total reconstruction accuracy, localization accuracy and focalization degree. The application to the source localization of event-related potential data further demonstrates the performance of the proposed method in real-world scenario

Adult-onset autosomal dominant myoclonic epilepsy: Report of a family with an overlooked epileptic syndrome

SummaryObjectiveMyoclonic epilepsy is a common epileptic syndrome with high genetic contribution. We described a pedigree in which 10 individuals presented with a non-progressive, adult-onset myoclonic epilepsy.Materials and methodsThe pedigree was constructed and analyzed. Six affected members were studied with clinical grounds, mental status, neurophysiology, video-electroencephalographic (EEG), brain magnetic resonance imaging (MRI) and mutational analysis of GABRA1 (GABRA1A, which endoces the α1 subunit of the γ-aminobutyric acid receptor subtype A). Clinical and EEG data were collected from six unaffected members.ResultsAutosomal dominant hereditary was shown. The age of seizure onset was approximately 40. All the individuals had myoclonic seizures and a normal cognitive level. Bilateral symmetric jerks of the shoulders, arms or legs featured the myoclonic seizure. Ictally, the consciousness was not affected. The ictal EEG demonstrated bilateral spikes-and-waves. The occurrence of myoclonic seizures was not associated with sleepiness. Rare generalized tonic-clonic seizures occurred in two individuals. No absence or accompanying involuntary movements were observed. A lower dose of valproic acid (200–500mg/D) (clonazepam 0.5mg/D in a patient) was required to stop the myoclonic seizures.ConclusionsThe clinical features of late adult-onset autosomal dominant myoclonic epilepsy are similar to juvenile myoclonic epilepsy (JME), which is a common generalized epileptic syndrome with a significant hereditary component. But the age of onset, rare association of other seizure patterns, and non-relation of seizure onset to sleepiness suggest that this may be a distinct familial epileptic syndrome different from recognized familial myoclonic epilepsies

Automatic Detection and Quantification of Acute Cerebral Infarct by Fuzzy Clustering and Histographic Characterization on Diffusion Weighted MR Imaging and Apparent Diffusion Coefficient Map

Determination of the volumes of acute cerebral infarct in the magnetic resonance imaging harbors prognostic values. However, semiautomatic method of segmentation is time-consuming and with high interrater variability. Using diffusion weighted imaging and apparent diffusion coefficient map from patients with acute infarction in 10 days, we aimed to develop a fully automatic algorithm to measure infarct volume. It includes an unsupervised classification with fuzzy C-means clustering determination of the histographic distribution, defining self-adjusted intensity thresholds. The proposed method attained high agreement with the semiautomatic method, with similarity index 89.9 ± 6.5%, in detecting cerebral infarct lesions from 22 acute stroke patients. We demonstrated the accuracy of the proposed computer-assisted prompt segmentation method, which appeared promising to replace the laborious, time-consuming, and operator-dependent semiautomatic segmentation