RESUMEN
In this paper, we study temporal couplings between interictal events of spatially remote regions in order to localize the leading epileptic regions from intracerebral EEG (iEEG). We aim to assess whether quantitative epileptic graph analysis during interictal period may be helpful to predict the seizure onset zone of ictal iEEG. Using wavelet transform, cross-correlation coefficient, and multiple hypothesis test, we propose a differential connectivity graph (DCG) to represent the connections that change significantly between epileptic and nonepileptic states as defined by the interictal events. Postprocessings based on mutual information and multiobjective optimization are proposed to localize the leading epileptic regions through DCG. The suggested approach is applied on iEEG recordings of five patients suffering from focal epilepsy. Quantitative comparisons of the proposed epileptic regions within ictal onset zones detected by visual inspection and using electrically stimulated seizures, reveal good performance of the present method.
Asunto(s)
Potenciales de Acción/fisiología , Mapeo Encefálico/métodos , Encéfalo/fisiología , Diagnóstico por Computador/métodos , Electrocardiografía/métodos , Modelos Neurológicos , Red Nerviosa/fisiopatología , Simulación por Computador , Humanos , Vías Nerviosas/fisiología , Análisis de OndículasRESUMEN
Connectivity evaluations have been performed in a noninvasive manner by examining resting state fMRI alongside diffusion-weighted images (DWI). The spatial structures of coherent spontaneous BOLD fluctuations provided the most convincing preliminary evidence that the BOLD signal was predominantly of neuronal origin rather than non-neuronal, artifactual noise. In this study we have shown that in thalamocortical network, the results of functional connectivity analysis and DWI correspond well with each other, thereby providing cross-validation of the two techniques. We have used the resting state fMRI data of 3 subjects with 10 minute resting state functional images via a 3T Siemens scanner. we used cross correlation for functional analysis and reported thalamocortical results with p value=0.01 and cluster size=100, Then showed corresponding tracts connecting premotor cortex and thalamus. In addition, both techniques correspond well to histological delineation and invasive tract tracing, which provides a 'gold standard' validation of the two techniques. The degree of structural connectivity has been shown to correlate with the strength of functional connectivity, thereby providing a potentially straightforward structural explanation for many of the changes in functional connectivity in disease states.
Asunto(s)
Imagen de Difusión Tensora/métodos , Interpretación de Imagen Asistida por Computador/métodos , Corteza Motora/anatomía & histología , Corteza Motora/fisiología , Técnica de Sustracción , Tálamo/anatomía & histología , Tálamo/fisiología , Humanos , Imagen por Resonancia Magnética , Vías Nerviosas/anatomía & histología , Vías Nerviosas/fisiologíaRESUMEN
Directed graphs (digraphs) derived from interictal periods of intracerebral EEG (iEEG) recordings can be used to estimate the leading interictal epileptic regions for presurgery evaluations. For this purpose, quantification of the emittance contribution of each node to the rest of digraph is important. However, the usual digraph measures are not very well suited for this quantification. Here, we compare the efficiency of recently introduced local information (LI) measure and a new measure called total global efficiency with classical measures like global efficiency, local efficiency and node degree. For evaluation, the estimated leading interictal epileptic regions based on five measures are compared with seizure onset zones obtained by visual inspection of epileptologists for five patients. The comparison revealed the superior performance of the LI measure. We showed efficiency of different digraph measures for the purpose of source and sink node identification.