Source localization of epileptic spikes using Multiple Sparse Priors

Objective: To evaluate epileptic source estimation using multiple sparse priors (MSP) inverse method and high-resolution, individual electrical head models. Methods: Accurate source localization is dependent on accurate electrical head models and appropriate inverse solvers. Using high-resolution, individual electrical head models in fifteen epilepsy patients, with surgical resection and clinical outcome as criteria for accuracy, performance of MSP method was compared against standardized low-resolution brain electromagnetic tomography (sLORETA) and coherent maximum entropy on the mean (cMEM) methods. Results: The MSP method performed similarly to the sLORETA method and slightly better than the cMEM method in terms of success rate. The MSP and cMEM methods were more focal than sLORETA with the advantage of not requiring an arbitrary selection of a hyperparameter or thresholding of reconstructed current density values to determine focus. MSP and cMEM methods were better than sLORETA in terms of spatial dispersion. Conclusions: Results suggest that the three methods are complementary and could be used together. In practice, the MSP method will be easier to use and interpret compared to sLORETA, and slightly more accurate and faster than the cMEM method. Significance: Source localization of interictal spikes from dense-array electroencephalography data has been shown to be a reliable marker of epileptic foci and useful for pre-surgical planning. The advantages of MSP make it a useful complement to other inverse solvers in clinical practice.

[1]  Manfred Fuchs,et al.  Evaluation of sLORETA in the Presence of Noise and Multiple Sources , 2003, Brain Topography.

[2]  Sergei Turovets,et al.  Skull Modeling Effects in Conductivity Estimates Using Parametric Electrical Impedance Tomography , 2018, IEEE Transactions on Biomedical Engineering.

[3]  Karl J. Friston,et al.  Statistical parametric mapping for event-related potentials (II): a hierarchical temporal model , 2004, NeuroImage.

[4]  Jan C. de Munck,et al.  The boundary element method in the forward and inverse problem of electrical impedance tomography , 2000, IEEE Transactions on Biomedical Engineering.

[5]  Bart Vanrumste,et al.  Review on solving the forward problem in EEG source analysis , 2007, Journal of NeuroEngineering and Rehabilitation.

[6]  Allen D. Malony,et al.  A 3D Finite-Difference BiCG Iterative Solver with the Fourier-Jacobi Preconditioner for the Anisotropic EIT/EEG Forward Problem , 2014, Comput. Math. Methods Medicine.

[7]  Andreas Schulze-Bonhage,et al.  The role of blood vessels in high-resolution volume conductor head modeling of EEG , 2015, NeuroImage.

[8]  Carlos H. Muravchik,et al.  General bounds for electrode mislocation on the EEG inverse problem , 2011, Comput. Methods Programs Biomed..

[9]  Karl J. Friston,et al.  A Parametric Empirical Bayesian framework for fMRI‐constrained MEG/EEG source reconstruction , 2010, Human brain mapping.

[10]  D. Lehmann,et al.  Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[11]  S. Kochen,et al.  Association between equivalent current dipole source localization and focal cortical dysplasia in epilepsy patients , 2012, Epilepsy Research.

[12]  Pieter van Mierlo,et al.  Improved Localization of Seizure Onset Zones Using Spatiotemporal Constraints and Time-Varying Source Connectivity , 2017, Front. Neurosci..

[13]  M. Fuchs,et al.  Clinical utility of distributed source modelling of interictal scalp EEG in focal epilepsy , 2010, Clinical Neurophysiology.

[14]  Onno W. Weier,et al.  On the numerical accuracy of the boundary element method (EEG application) , 1989, IEEE Transactions on Biomedical Engineering.

[15]  David R. Wozny,et al.  The electrical conductivity of human cerebrospinal fluid at body temperature , 1997, IEEE Transactions on Biomedical Engineering.

[16]  Xavier Tricoche,et al.  Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling , 2006, NeuroImage.

[17]  P Kahane,et al.  Rapport 2008 : Traitements chirurgicaux de l ’ épilepsie Évolution des concepts La zone épileptogène The epileptogenic zone , 2008 .

[18]  Jerome Engel,et al.  A Proposed Diagnostic Scheme for People with Epileptic Seizures and with Epilepsy: Report of the ILAE Task Force on Classification and Terminology , 2001, Epilepsia.

[19]  T. Oostendorp,et al.  Interpolation on a triangulated 3D surface , 1989 .

[20]  R. Turner,et al.  Microstructural Parcellation of the Human Cerebral Cortex – From Brodmann's Post-Mortem Map to in vivo Mapping with High-Field Magnetic Resonance Imaging , 2011, Front. Hum. Neurosci..

[21]  Bart Vanrumste,et al.  Journal of Neuroengineering and Rehabilitation Open Access Review on Solving the Inverse Problem in Eeg Source Analysis , 2022 .

[22]  J Silny,et al.  A model of the electrical volume conductor in the region of the eye in the ELF range. , 2001, Physics in medicine and biology.

[23]  Robert Plonsey,et al.  Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields , 1995 .

[24]  F Wendling,et al.  Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG data , 2016, NeuroImage.

[25]  Fetsje Bijma,et al.  In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head , 2003, IEEE Transactions on Biomedical Engineering.

[26]  H. Lüders,et al.  The epileptogenic zone: general principles. , 2006, Epileptic disorders : international epilepsy journal with videotape.

[27]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[28]  Carlos E. M. Tassinari,et al.  Glossary of Descriptive Terminology for Ictal Semiology: Report of the ILAE Task Force on Classification and Terminology , 2001, Epilepsia.

[29]  Jean Daunizeau,et al.  Bayesian multi-modal model comparison: A case study on the generators of the spike and the wave in generalized spike–wave complexes , 2010, NeuroImage.

[30]  Karl J. Friston,et al.  Multiple sparse priors for the M/EEG inverse problem , 2008, NeuroImage.

[31]  Don M. Tucker,et al.  Regional head tissue conductivity estimation for improved EEG analysis , 2000, IEEE Transactions on Biomedical Engineering.

[32]  Jinsong Wu,et al.  Accurate source imaging based on high resolution scalp electroencephalography and individualized finite difference head models in epilepsy pre-surgical workup , 2018, Seizure.

[33]  R. W. Lau,et al.  The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. , 1996, Physics in medicine and biology.

[34]  R. W. Lau,et al.  The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues. , 1996, Physics in medicine and biology.

[35]  François Dubeau,et al.  Localization Accuracy of Distributed Inverse Solutions for Electric and Magnetic Source Imaging of Interictal Epileptic Discharges in Patients with Focal Epilepsy , 2015, Brain Topography.

[36]  Jean Gotman,et al.  Evaluation of EEG localization methods using realistic simulations of interictal spikes , 2006, NeuroImage.

[37]  Lara Jehi,et al.  The Epileptogenic Zone: Concept and Definition , 2018, Epilepsy currents.

[38]  J. Haueisen,et al.  Influence of head models on EEG simulations and inverse source localizations , 2006, Biomedical engineering online.

[39]  Richard M. Leahy,et al.  Brainstorm: A User-Friendly Application for MEG/EEG Analysis , 2011, Comput. Intell. Neurosci..

[40]  D. A. Driscoll,et al.  Current Distribution in the Brain From Surface Electrodes , 1968, Anesthesia and analgesia.

[41]  Claudio Pollo,et al.  Electroencephalographic source imaging: a prospective study of 152 operated epileptic patients , 2011, Brain : a journal of neurology.

[42]  Kai Li,et al.  BrainK for Structural Image Processing: Creating Electrical Models of the Human Head , 2016, Comput. Intell. Neurosci..

[43]  Josemir W Sander The epidemiology of epilepsy revisited , 2003, Current opinion in neurology.

[44]  I. Lemahieu,et al.  Dipole location errors in electroencephalogram source analysis due to volume conductor model errors , 2000, Medical and Biological Engineering and Computing.

[45]  A Schulze-Bonhage,et al.  A realistic multimodal modeling approach for the evaluation of distributed source analysis: application to sLORETA , 2017, Journal of neural engineering.

[46]  R. Ilmoniemi,et al.  Interpreting magnetic fields of the brain: minimum norm estimates , 2006, Medical and Biological Engineering and Computing.

[47]  関原 謙介,et al.  Adaptive Spatial Filters for Electromagnetic Brain Imaging , 2008 .

[48]  S. Spencer Neural Networks in Human Epilepsy: Evidence of and Implications for Treatment , 2002, Epilepsia.

[49]  Thom F. Oostendorp,et al.  The conductivity of the human skull: results of in vivo and in vitro measurements , 2000, IEEE Transactions on Biomedical Engineering.