Involvement of the Thalamus, Hippocampus, and Brainstem in Hypsarrhythmia of West Syndrome: Simultaneous Recordings of Electroencephalography and fMRI Study.

BACKGROUND AND PURPOSE: West syndrome is a developmental and epileptic encephalopathy characterized by epileptic spasms, neurodevelopmental regression, and a specific EEG pattern called hypsarrhythmia. Our aim was to investigate the brain activities related to hypsarrhythmia at onset and focal epileptiform discharges in the remote period in children with West syndrome using simultaneous electroencephalography and fMRI recordings. MATERIALS AND METHODS: Fourteen children with West syndrome underwent simultaneous electroencephalography and fMRI at the onset of West syndrome. Statistically significant blood oxygen level-dependent responses related to hypsarrhythmia were analyzed using an event-related design of 4 hemodynamic response functions with peaks at 3, 5, 7, and 9 seconds after the onset of each event. Six of 14 children had focal epileptiform discharges after treatment and underwent simultaneous electroencephalography and fMRI from 12 to 25 months of age. RESULTS: At onset, positive blood oxygen level-dependent responses were seen in the brainstem (14/14 patients), thalami (13/14), basal ganglia (13/14), and hippocampi (13/14), in addition to multiple cerebral cortices. Group analysis using hemodynamic response functions with peaks at 3, 5, and 7 seconds showed positive blood oxygen level-dependent responses in the brainstem, thalamus, and hippocampus, while positive blood oxygen level-dependent responses in multiple cerebral cortices were seen using hemodynamic response functions with peaks at 5 and 7 seconds. In the remote period, 3 of 6 children had focal epileptiform discharge-related positive blood oxygen level-dependent responses in the thalamus, hippocampus, and brainstem. CONCLUSIONS: Positive blood oxygen level-dependent responses with hypsarrhythmia appeared in the brainstem, thalamus, and hippocampus on earlier hemodynamic response functions than the cerebral cortices, suggesting the propagation of epileptogenic activities from the deep brain structures to the neocortices. Activation of the hippocampus, thalamus, and brainstem was still seen in half of the patients with focal epileptiform discharges after adrenocorticotropic hormone therapy.

Enabling on-demand real-time functional MRI analysis using grid technology.

OBJECTIVES: The analysis of brain imaging data such as functional MRI often requires considerable computing resources, which in most cases are not readily available in many medical imaging facilities. This lack of computing power makes it difficult for researchers and medical practitioners alike to perform on-site analysis of the generated data. This paper presents a system that is capable of analyzing functional MRI data in real time with results available within seconds after data acquisition. METHODS: The system employs remote computational servers to provide the necessary computing power. System integration is accomplished by an accompanying software package, which includes fMRI analysis tools, data transfer routines, and an easy-to-use graphical user interface. The remote analysis is transparent to the user as if all computations are performed locally. RESULTS: The use of PC clusters in the analysis of fMRI data significantly improved the performance of the system. Simulation runs fully achieved real-time performance with a total processing time of 1.089 s per image volume (64 x 64 x 30 in size), much less than the per volume acquisition time set to 3.0 s. CONCLUSIONS: The results show the feasibility of using remote computational resources to enable on-demand real-time fMRI capabilities to imaging sites. It also offers the possibility of doing more intensive analysis even if the imaging site doesn't have the necessary computing resources.

Estimation of general linear model coefficients for real-time application.

An algorithm using an orthogonalization procedure to estimate the coefficients of general linear models (GLM) for functional magnetic resonance imaging (fMRI) calculations is described. The idea is to convert the basis functions or explanatory variables of a GLM into orthogonal functions using the usual Gram-Schmidt orthogonalization procedure. The coefficients associated with the orthogonal functions, henceforth referred to as auxiliary coefficients, are then easily estimated by applying the orthogonality condition. The original GLM coefficients are computed from these estimates. With this formulation, the estimates can be updated when new image data become available, making the approach applicable for real-time estimation. Since the contribution of each image data is immediately incorporated into the estimated values, storing the data in memory during the estimation process becomes unnecessary, minimizing the memory requirements of the estimation process. By employing Cholesky decomposition, the algorithm is a factor of two faster than the standard recursive least-squares approach. Results of the analysis of an fMRI study using this approach showed the algorithm's potential for real-time application.

Variability of the electric organ discharge interval duration in resting Gymnotus carapo.

We recorded the electric organ discharges of resting Gymnotus carapo specimens. We analyzed the time series formed by the sequence of interdischarge intervals. Nonlinear prediction, false nearest neighbor analyses, and comparison between the performance of nonlinear and linear autoregressive models fitted to the data indicated that nonlinear correlations between intervals were absent, or were present to a minor extent only. Following these analyses, we showed that linear autoregressive models with combined Gaussian and shot noise reproduced the variability and correlations of the resting discharge pattern. We discuss the implications of our findings for the mechanisms underlying the timing of electric organ discharge generation. We also argue that autoregressive models can be used to evaluate the changes arising during a wide variety of behaviors, such as the modification in the discharge intervals during interaction between fish pairs.

Reconstructing bifurcation diagrams of dynamical systems using measured time series.

We present an algorithm for reconstructing the bifurcation structure of a dynamical system from time series. The method consists in finding a parameterized predictor function whose bifurcation structure is similar to that of the given system. Nonlinear autoregressive (NAR) models with polynomial terms are employed as predictor functions. The appropriate terms in the NAR models are obtained using a fast orthogonal search scheme. This scheme eliminates the problem of multiparameter optimization and makes the approach robust to noise. The algorithm is applied to the reconstruction of the bifurcation diagram (BD) of a neuron model from the simulated membrane potential waveforms. The reconstructed BD captures the different behaviors of the given system. Moreover, the algorithm also works well even for a limited number of time series.

Reconstructing bifurcation diagrams from noisy time series using nonlinear autoregressive models.

We introduce a formalism for the reconstruction of bifurcation diagrams from noisy time series. The method consists in finding a parametrized predictor function whose bifurcation structure is similar to that of the given system. The reconstruction algorithm is composed of two stages: model selection and bifurcation parameter identification. In the first stage, an appropriate model that best represents all the given time series is selected. A nonlinear autoregressive model with polynomial terms is employed in this study. The identification of the bifurcation parameters from among the many model parameters is done in the second stage. The algorithm works well even for a limited number of time series.