Electrical source imaging of interictal spikes using multiple sparse volumetric priors for presurgical epileptogenic focus localization

ABSTRACT: Electrical source imaging of interictal spikes observed in EEG recordings of patients with refractory epilepsy provides useful information to localize the epileptogenic focus during the presurgical evaluation. However, the selection of the time points or time epochs of the spikes in order to estimate the origin of the activity remains a challenge. In this study, we consider a Bayesian EEG source imaging technique for distributed sources, i.e. the multiple volumetric sparse priors (MSVP) approach. The approach allows to estimate the time courses of the intensity of the sources corresponding with a specific time epoch of the spike. Based on presurgical averaged interictal spikes in six patients who were successfully treated with surgery, we estimated the time courses of the source intensities for three different time epochs: (i) an epoch starting 50 ms before the spike peak and ending at 50% of the spike peak during the rising phase of the spike, (ii) an epoch starting 50 ms before the spike peak and ending at the spike peak and (iii) an epoch containing the full spike time period starting 50 ms before the spike peak and ending 230 ms after the spike peak. To identify the primary source of the spike activity, the source with the maximum energy from 50 ms before the spike peak till 50% of the spike peak was subsequently selected for each of the time windows. For comparison, the activity at the spike peaks and at 50% of the peaks was localized using the LORETA inversion technique and an ECD approach. Both patient-specific spherical forward models and patient-specific 5-layered finite difference models were considered to evaluate the influence of the forward model. Based on the resected zones in each of the patients, extracted from post-operative MR images, we compared the distances to the resection border of the estimated activity. Using the spherical models, the distances to the resection border for the MSVP approach and each of the different time epochs were in the same range as the LORETA and ECD techniques. We found distances smaller than 23 mm, with robust results for all the patients. For the finite difference models, we found that the distances to the resection border for the MSVP inversions of the full spike time epochs were generally smaller compared to the MSVP inversions of the time epochs before the spike peak. The results also suggest that the inversions using the finite difference models resulted in slightly smaller distances to the resection border compared to the spherical models. The results we obtained are promising because the MSVP approach allows to study the network of the estimated source-intensities and allows to characterize the spatial extent of the underlying sources

Improved localization of seizure onset zones using spatiotemporal constraints and time-varying source connectivity

Presurgical evaluation of brain neural activity is commonly carried out in refractory epilepsy patients to delineate as accurately as possible the seizure onset zone (SOZ) before epilepsy surgery. In practice, any subjective interpretation of electroencephalographic (EEG) recordings is hindered mainly because of the highly stochastic behavior of the epileptic activity. We propose a new method for dynamic source connectivity analysis that aims to accurately localize the seizure onset zones by explicitly including temporal, spectral, and spatial information of the brain neural activity extracted from EEG recordings. In particular, we encode the source nonstationarities in three critical stages of processing: Inverse problem solution, estimation of the time courses extracted from the regions of interest, and connectivity assessment. With the aim to correctly encode all temporal dynamics of the seizure-related neural network, a directed functional connectivity measure is employed to quantify the information flow variations over the time window of interest. Obtained results on simulated and real EEG data confirm that the proposed approach improves the accuracy of SOZ localization

Forward volumetric modeling framework for realistic head models towards accurate EEG source localization

Synergetic effects connecting spatial and functional neuroimaging techniques allows reduction of the weakness for single method analysis. Specifically, Electroencephalographic (EEG) Source Imaging (ESI) relating structural head models and distributed source localization techniques improves the time and spatial resolution of single MRI or EEG analysis. The construction of more accurate forward models for ESI solutions, holding better precision and less computational burden is an important task for investigative purposes, but also for surgery planning and disorder treatments. In this regard, we present a novel finite-difference EEG forward problem solution that we called ghost-filling finite difference anisotropic reciprocity method (GFDARM). First, we introduce a finite difference numerical solution for the conservative form of the Poisson equation, using an asymmetric volumetric stencil, together with the transition layer technique to formulate finite differences that properly deal with the considered Newman and Dirichlet boundary conditions. Later, we formulate a solution for an irregular free-form boundary domain, based on a second-order accuracy ghost-filling approximation for the homogeneous Newman flux condition, allowing us to solve the discretized finite differences volume only for the significant potential unknowns. Then we analyze the linear equation system solution and the considerations for a reciprocity solution over the electrodes space. Further, we test our method using a multilayer spherical head model that can include anisotropy and can admit an analytical solution of the Poisson equation. Finally, we analyze a noisy linear equation system to study the numerical stability of the technique in the presence of perturbations. Our results show stability and super-linear convergence. Moreover, validation against an analytical solution shows high correspondence in the potential distribution for a wide range of dipole positions and orientations. As a final stage, we introduce a realistic patient-specific EEG forward modeling pipeline, including anisotropy in the skull and the white matter; MRI segmentation; electrode co-register; voxelwise conductivity definitions; reciprocity space solution; and GFDARM numeric EEG forward solver. Our results using Bayesian model selection for group studies in a random fixed effect analysis show strong evidence in favor of more complex head models, including anisotropic skull and white matter modelingResumen: Los efectos conjuntos conectando técnicas espaciales y funcionales de neuro-imagen permiten el mejoramiento de las características de un solo método. Específicamente, la generación de imágenes de fuentes de activación (ESI) mediante electroencefalografía (EEG) que relaciona modelos estructurales de conductividad y técnicas de localización de fuentes distribuidas, permite un mejoramiento en la resolución espacial, conservando la resolución temporal del EEG. La construcción de modelos de conductividad más precisos, con una mayor precisión y menos carga computacional es una tarea importante para soluciones que emplean ESI, así como para fines de investigación, planificación de cirugía y/o los tratamientos de trastornos neurológicos en general. En este trabajo presentamos una nueva solución del problema directo empleando diferencias finitas, a la que llamamos método de diferencias finitas empleando llenado-fantasma, reciprocidad y anisotropía (GFDARM). Primero, nosotros presentamos una solución numérica de diferencias finitas para la forma conservativa de la ecuación de Poisson, utilizando una plantilla volumétrica asimétrica, junto con la técnica de transición de capas, para formular diferencias finitas que aborden adecuadamente las condiciones de contorno de Newman y Dirichlet. Más adelante, formulamos la solución para una frontera irregular y de forma libre basada en una aproximación de segundo orden de llenado-fantasma que permite cumplir la condición de flujo homogéneo de Newman, lo que nos permite resolver el volumen discretizado solo para las incógnitas de potencial diferentes de cero (significativas). Posteriormente se analiza la solución del sistema de ecuaciones lineales y las consideraciones para una solución de reciprocidad sobre el espacio de los electrodos. Además, realizamos pruebas utilizando un modelo de cabeza esférico multicapa que puede incluir anisotropía y del cual se puede obtener una solución analítica. Finalmente, se analiza la solución del sistema lineal de ecuaciones en presencia de ruido estudiando la estabilidad numérica de la técnica. Nuestros resultados muestran estabilidad y convergencia súper lineal y una alta correspondencia en la distribución de potenciales para una amplia gama de posiciones y orientaciones de dipolos comparando contra una solución analítica esférica. Finalmente se una metodología para el modelado directo de EEG empleando modelos realistas y paciente-específicos, que incluye anisotropía en el cráneo y la materia blanca; segmentación de MRI; co-registro de electrodos; definiciones de conductividad voxel a voxel; solución de espacio de reciprocidad; y solución numérica del problema directo en EEG empleando GFDARM. El desempeño de la técnica y la influencia de los modelos directos realísticos son analizados empleando selección de modelos para estudios de grupos en un marco Bayesiano, los cuales muestran fuerte evidencia a favor de modelos de conductividad más complejos, que incluyan modelado anisótropo del cráneo y la materia blancaDoctorad

Source reconstruction of the P300 event-related potential as a biomarker for the efficacy of vagus nerve stimulation in patients with epilepsy

Abstract The working mechanism of VNS remains to be fully understood, making it impossible to predict a patient’s response to the treatment. In the present study, we explore whether EEG source reconstruction of the P300 event-related potential can provide information about the working mechanism and efficacy of VNS. 1. Introduction Vagus Nerve Stimulation (VNS) is a neurostimulation treatment for refractory epilepsy that reduces seizures with more than 50% in one third of the treated patients. The working mechanism of VNS is currently unknown. This makes it impossible to predict whether a patient will benefit from VNS treatment or not prior to implantation. Therefore, we want to further investigate the working mechanism of VNS and find biomarkers that indicate the efficacy of the treatment. 2. Data and methods In this study, the P300 component of the event-related potential during the auditory oddball task was investigated in VNS responders (R) and non-responders (NR) under two conditions: VNS turned ON vs. OFF. The P300 component is modulated by the norepinephrine level in the brain, which has been linked to the anti-epileptic effect of VNS [1]. 60-channel EEG was recorded in 10R and 10NR of VNS. The sources of the P300 wave were reconstructed using the multiple volumetric sparse priors algorithm [2]. For 14 patients (6R + 8NR), individual head models including scalp, skull, cerebrospinal fluid, gray and white matter, were constructed as a good quality MR image was available. For the other 6 patients, a template head model, including scalp, skull, CSF and brain, was used. Second level analysis was performed in the statistical parametric mapping software to find significant differences between the R and NR. 3. Results Significant differences in brain activity for R vs. NR were found in the left hippocampus, fusiform gyrus and insular lobe (pcorr<0.001), indicating a possible biomarker for the efficacy of VNS. Significant differences in brain activity were found for VNS OFF vs. ON in the left and right hippocampus and amygdala (p¬uncorr<0.02), indicating that the lymbic system is involved in the mechanism of action of VNS. If we look at the difference between VNS OFF and ON in each group separately, there is an indication that the right hippocampus is more influenced by VNS in R than in NR, while the opposite holds for the left middle orbital gyrus. However, no significance was reached. 4. Conclusion Although more research is needed, we showed the potential of EEG source reconstruction as a means to provide information on the working mechanism of VNS and as a biomarker for the efficacy of VNS. References [1] Raedt, R. et al. Increased hippocampal noradrenaline is a biomarker for efficacy of vagus nerve stimulation in a limbic seizure model. Journal of neurochemistry, 117(3), 461-469, 2011. [2] Strobbe, G. et al. Multiple sparse volumetric priors for distributed EEG source reconstruction. NeuroImage, 100, 715-724, 2014

Influence of atlas-based and patient dependent forward models in EEG source reconstruction

Abstract: Electroencephalography Source Imaging (ESI) techniques have become the most attractive alternative to support the estimation of neuronal activity through the mapping of electrical potentials measured over the scalp. It takes advantage of the low implementation cost, the high temporal resolution, and non-invasiveness in the patient. ESI techniques require a volumetric conductor model (commonly named Electroencephalography (EEG) Forward Model), including information about the physiological and geometrical properties of the head, and modeling the electromagnetic field propagation of the neuronal activity throughout the head tissues to reach the scalp. In this regard, the accuracy of ESI solutions depends partially on the capabilities of the forward model to correctly describe the structural information provided by a Magnetic Resonance Image (MRI). However, acquiring MRIs for generating personalized head models is expensive, slow, and in some cases unpractical. In this work, we investigate how the head model influences the source reconstruction based on EEG when progressively including different levels of prior structural information. Hence, we evaluate two approaches to enhance the model of brain structure in the EEG forward problem formulation. First, the incorporation of different brain tissue morphology, mainly, based on a Generic MRI, based on a target population Atlas, or based on a patient-specific MRI. Second, the variation of the tissue model complexity in the number of segmented brain layers. All the head models are build using the Finite Difference Reciprocity Method (FDRM). Model comparison is carried out under a Parametric Empirical Bayesian (PEB) framework using Event-Related Potentials (ERPs) taken from the studied population. Obtained results show that the more realistic and subject dependent model, the better performance of the ESI solutionResumen: Las técnicas de reconstrucción de fuentes basadas en Electroencefalografía (ESI) son la alternativa más interesante para la estimación de fuentes mediante los potenciales eléctricos medidos sobre el cuero cabelludo, aprovechando el bajo costo de implementación, la alta resolución temporal, y la poca invasión que requiere en el paciente. Es por esto, que estas técnicas requieren un modelo de conducción volumétrico (comúnmente llamado modelo directo), que incluye información de las propiedades físicas y geométricas de la cabeza, además de modelar la propagación del campo electromagnético generado por la actividad neuronal a través de los diferentes tejidos de la cabeza hasta alcanzar el cuero cabelludo. En este sentido, el correcto desempeño de las técnicas ESI depende directamente de las capacidades del modelo directo para describir de manera apropiada la información estructural aportada por una Imagen por Resonancia Magnética (MRI). Sin embargo, adquirir MRIs para generar modelos de la cabeza personalizados, es costoso, lento, y en algunos casos poco práctico. En este trabajo, se investiga la manera en que el modelo directo influencia la tarea de reconstrucción de fuentes basada en EEG, incluyendo de manera progresiva diferentes niveles de información estructural relacionada al paciente. Así, se evaluan dos enfoques específicos para mejorar el modelo de la estructura cerebral en la formulación del problema directo de EEG. El primer enfoque está relacionado con la incorporación de diferentes morfologías de tejido cerebral, principalmente, basadas en un MRI genérico, un atlas de la población estudiada, ó en el MRI específico del paciente. El segundo enfoque es la variación de la complejidad del modelo en términos de el número de tejidos segmentados en el cerebro. En este trabajo el modelo directo se soluciona usando el Método de Diferencias Finitas con Reciprocidad (FDRM). La comparación de modelos se realiza bajo un framework Bayesiano Empírico Paramétrico (PEB), que permite contrastar los diferentes enfoques del modelo directo, usando datos reales. En general, los resultados obtenidos muestran que usar modelos más realistas y más dependientes de la población de estudio, mejora significativamente el desempeño de las técnicas ESIMaestrí

A Novel Noninvasive Approach Based on SPECT and EEG for the Location of the Epileptogenic Zone in Pharmacoresistant Non-Lesional Epilepsy

Background and objectives: The aim of this study is to propose a methodology that combines non-invasive functional modalities electroencephalography (EEG) and single photon emission computed tomography (SPECT) to estimate the location of the epileptogenic zone (EZ) for the presurgical evaluation of patients with drug-resistant non-lesional epilepsy. Materials and Methods: This methodology consists of: (i) Estimation of ictal EEG source imaging (ESI); (ii) application of the subtraction of ictal and interictal SPECT co-registered with MRI (SISCOM) methodology; and (iii) estimation of ESI but using the output of the SISCOM as a priori information for the estimation of the sources. The methodology was implemented in a case series as an example of the application of this novel approach for the presurgical evaluation. A gold standard and a coincidence analysis based on measures of sensitivity and specificity were used as a preliminary assessment of the proposed methodology to localize EZ. Results: In patients with good postoperative evolution, the estimated EZ presented a spatial coincidence with the resection site represented by high values of sensitivity and specificity. For the patient with poor postoperative evolution, the methodology showed a partial incoherence between the estimated EZ and the resection site. In cases of multifocal epilepsy, the method proposed spatially extensive epileptogenic zones. Conclusions: The results of the case series provide preliminary evidence of the methodology's potential to epileptogenic zone localization in non-lesion drug-resistant epilepsy. The novelty of the article consists in estimating the sources of ictal EEG using SISCOM result as a prior for the inverse solution. Future studies are necessary in order to validate the described methodology. The results constitute a starting point for further studies in order to support the clinical reliability of the proposed methodology and advocate for their implementation in the presurgical evaluation of patients with intractable non-lesional epilepsy.</p