Neuronal networks underlying ictal and subclinical discharges in childhood absence epilepsy.

Childhood absence epilepsy (CAE), involves 3 Hz generalized spikes and waves discharges (GSWDs) on the electroencephalogram (EEG), associated with ictal discharges (seizures) with clinical symptoms and impairment of consciousness and subclinical discharges without any objective clinical symptoms or impairment of consciousness. This study aims to comparatively characterize neuronal networks underlying absence seizures and subclinical discharges, using source localization and functional connectivity (FC), to better understand the pathophysiological mechanism of these discharges. Routine EEG data from 12 CAE patients, consisting of 45 ictal and 42 subclinical discharges were selected.

Differentiating ictal/subclinical spikes and waves in childhood absence epilepsy by spectral and network analyses: A pilot study.

Objective: Childhood absence epilepsy (CAE) is a disease with distinct seizure semiology and electroencephalographic (EEG) features. Differentiating ictal and subclinical generalized spikes and waves discharges (GSWDs) in the EEG is challenging, since they appear to be identical upon visual inspection. Here, spectral and functional connectivity (FC) analyses were applied to routine EEG data of CAE patients, to differentiate ictal and subclinical GSWDs.

Source imaging of deep-brain activity using the regional spatiotemporal Kalman filter.

Background And Objective: The human brain displays rich and complex patterns of interaction within and among brain networks that involve both cortical and subcortical brain regions. Due to the limited spatial resolution of surface electroencephalography (EEG), EEG source imaging is used to reconstruct brain sources and investigate their spatial and temporal dynamics. The majority of EEG source imaging methods fail to detect activity from subcortical brain structures.

Pipeline for Forward Modeling and Source Imaging of Magnetocardiographic Recordings via Spatiotemporal Kalman Filtering.

the aim of this proof-of-concept work was to apply the spatiotemporal Kalman filter (STKF) algorithm to magnetocardiographic (MCG) recordings of the heart. Due to the lack of standardized software and pipelines for MCG source imaging, we needed to construct a pipeline for MCG forward modeling before we could apply the STKF method. In the forward module, the finite element method (FEM) solvers in SimBio software are used to solve the MCG forward problem.

Source reconstruction via the spatiotemporal Kalman filter and LORETA from EEG time series with 32 or fewer electrodes.

The clinical routine of non-invasive electroencephalography (EEG) is usually performed with 8-40 electrodes, especially in long-term monitoring, infants or emergency care. There is a need in clinical and scientific brain imaging to develop inverse solution methods that can reconstruct brain sources from these low-density EEG recordings. In this proof-of-principle paper we investigate the performance of the spatiotemporal Kalman filter (STKF) in EEG source reconstruction with 9-, 19- and 32- electrodes.

Spatial projection as a preprocessing step for EEG source reconstruction using spatiotemporal Kalman filtering.

The reconstruction of brain sources from non-invasive electroencephalography (EEG) or magnetoencephalography (MEG) via source imaging can be distorted by information redundancy in case of high-resolution recordings. Dimensionality reduction approaches such as spatial projection may be used to alleviate this problem. In this proof-of-principle paper we apply spatial projection to solve the problem of information redundancy in case of source reconstruction via spatiotemporal Kalman filtering (STKF), which is based on state-space modeling.

The choice of the source space and the Laplacian matrix in LORETA and the spatio-temporal Kalman filter EEG inverse methods.

The discretization of the brain and the definition of the Laplacian matrix influence the results of methods based on spatial and spatio-temporal smoothness, since the Laplacian operator is used to define the smoothness based on the neighborhood of each grid point. In this paper, the results of low resolution electromagnetic tomography (LORETA) and the spatiotemporal Kalman filter (STKF) are computed using, first, a greymatter source space with the standard definition of the Laplacian matrix and, second, using a whole-brain source space and a modified definition of the Laplacian matrix. Electroencephalographic (EEG) source imaging results of five inter-ictal spikes from a pre-surgical patient with epilepsy are used to validate the two aforementioned approaches.

The performance of the spatiotemporal Kalman filter and LORETA in seizure onset localization.

The assumption of spatial-smoothness is often used to solve the bioelectric inverse problem during electroencephalographic (EEG) source imaging, e.g., in low resolution electromagnetic tomography (LORETA).

Combining EEG and MEG for the reconstruction of epileptic activity using a calibrated realistic volume conductor model.

To increase the reliability for the non-invasive determination of the irritative zone in presurgical epilepsy diagnosis, we introduce here a new experimental and methodological source analysis pipeline that combines the complementary information in EEG and MEG, and apply it to data from a patient, suffering from refractory focal epilepsy. Skull conductivity parameters in a six compartment finite element head model with brain anisotropy, constructed from individual MRI data, are estimated in a calibration procedure using somatosensory evoked potential (SEP) and field (SEF) data. These data are measured in a single run before acquisition of further runs of spontaneous epileptic activity.

MEG-EEG fusion by Kalman filtering within a source analysis framework.

The fusion of data from multiple neuroimaging modalities may improve the temporal and spatial resolution of non-invasive brain imaging. In this paper, we present a novel method for the fusion of simultaneously recorded electroencephalograms (EEG) and magnetoencephalograms (MEG) within the framework of source analysis. This method represents an extension of a previously published spatio-temporal inverse solution method to the case of MEG or combined MEG-EEG signals.

Cholecystitis after trauma.

Background: The goal of this project was to investigate incidence, risk factors, histologic findings, and mortality rate of posttraumatic cholecystitis requiring surgical treatment.

Methods: Retrospective analysis of all patients admitted to the intensive care unit of an urban trauma center between April 1998 and January 2005. Data from the hospital and intensive care documentation systems databases and patients' charts were reviewed.

Acute acalculous cholecystitis after trauma: a prospective study.

Objective: The purpose of this study was to identify risk factors for the development of acute acalculous cholecystitis (AAC) and useful criteria to facilitate the decision to perform cholecystectomy.

Methods: This was a prospective study of patients with an Injury Severity Score (ISS) > or = 12 requiring intensive care for > 4 days (n = 255), divided into three groups by ultrasound: AAC (n = 27), hydropic gallbladder (n = 37), and normal gallbladder (n = 191). Multivariate analysis was conducted for trauma scores and laboratory and intensive care unit (ICU) data and complemented by logistic regression analysis.