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We investigated the performance of a new sparse neuroimaging method, i.e., Variation-Based Sparse Cortical Current Density (VB-SCCD) using magnetoencephalography (MEG) data to reconstruct extended cortical sources and their spatial distributions on the cortical surface. We conducted Monte Carlo simulation studies to compare the performance of the VB-SCCD method with different number of cortical sources...
Data from performance on a computerized battery of driving-related sensory-motor and cognitive tests (SMCTests™) were used to predict outcome on a blinded on-road driving assessment in 501 people with brain disorders. Six modelling approaches were assessed: discriminant analysis (DA), binary logistic regression (BLR), nonlinear causal resource analysis (NCRA), and three kernel methods (product kernel...
This work aims to analyze the reference (montage) problem in electroencephalographic (EEG) recordings. It is well accepted that EEG signals are a mixture of cerebral and extracerebral sources, and the solution to the reference problem depends on the hypothesized mixing model. We focus here on an acquisition model using a distant reference electrode and propose a method for determining and eliminating...
In this study a simple model of the postural control system is implemented and its parameters are adjusted to explain the differences between Parkinson's disease patient's measures and healthy young adult's measures by performing simulations of spontaneous sway with center-of-pressure traces. An optimization procedure was performed varying four parameters of the model. The best adjustment obtained...
Graphical models are powerful tools to infer statistical relationships between simultaneously observed random variables. Here, we used Dynamic Bayesian Networks (DBN) to infer causal relationships between simultaneously recorded neurons in the rat somatosensory (barrel) cortex in response to whisker stimulation. DBNs attempt to explain the activity of the observed neurons by searching for the best...
The problem of model falsification or model invalidation appears in several areas where we are interested in distinguishing among an eligible set of dynamic systems. In the context of fMRI studies of brain activity, modeling the haemodynamic response function (HRF) is a critical step. The estimation of the dynamic system describing a biophysical model of the HRF may leave much uncertainty on the exact...
Epilepsy is a neurological disorder characterized by recurrent seizures which affects about 1% people worldwide. During the past decades, some mechanisms involved in ictogenesis (generation of seizures) have been identified and, to some extent, partially understood. However, regarding epileptogenesis (process by which a neuronal system becomes epileptic), underlying mechanisms remain elusive. This...
This study presents a preliminary analysis of the relationship between electroencephalographic (EEG) and electrocorticographic (ECoG) event-related potentials (ERPs) recorded from from a single patient using a brain-computer interface (BCI) speller. The patient had medically intractable epilepsy and underwent temporary placement of an intracranial ECoG grid electrode array to localize seizure foci...
Glioma is the most aggressive type of brain tumor. Several mathematical models have been developed during the last two decades, towards simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. The proposed diffusive models have mainly used finite differences...
This paper investigates the applicability of multilevel macroscopic models for simulating solid tumor growth in the invasive glioblastoma multiforme (GBM) case. The continuum case approach tumor model based on the diffusion reaction equation is evaluated on a pre-segmented tomographic atlas where all tissue properties are known a priori. The atlas is further registered on a real clinical case where...
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