sLORETA allows reliable distributed source reconstruction based on subdural strip and grid recordings

Source localization based on invasive recordings by subdural strip and grid electrodes is a topic of increasing interest. This simulation study addresses the question, which factors are relevant for reliable source reconstruction based on sLORETA. MRI and electrode positions of a patient undergoing invasive presurgical epilepsy diagnostics were the basis of sLORETA simulations. A boundary element head model derived from the MRI was used for the simulation of electrical potentials and source reconstruction. Focal dipolar sources distributed on a regular three‐dimensional lattice and spatiotemporal distributed patches served as input for simulation. In addition to the distance between original and reconstructed source maxima, the activation volume of the reconstruction and the correlation of time courses between the original and reconstructed sources were investigated. Simulations were supplemented by the localization of the patient's spike activity. For noise‐free simulated data, sLORETA achieved results with zero localization error. Added noise diminished the percentage of reliable source localizations with a localization error ≤15 mm to 67.8%. Only for source positions close to the electrode contacts the activation volume correctly represented focal generators. Time‐courses of original and reconstructed sources were significantly correlated. The case study results showed accurate localization. sLORETA is a distributed source model, which can be applied for reliable grid and strip based source localization. For distant source positions, overestimation of the extent of the generator has to be taken into account. sLORETA‐based source reconstruction has the potential to improve the localization of distributed generators in presurgical epilepsy diagnostics and cognitive neuroscience. Hum Brain Mapp , 2011. © 2011 Wiley‐Liss, Inc.

[1]  Andreas Schulze-Bonhage,et al.  Signal quality of simultaneously recorded invasive and non-invasive EEG , 2009, NeuroImage.

[2]  S. Gonzalez-Andino,et al.  A critical analysis of linear inverse solutions to the neuroelectromagnetic inverse problem , 1998, IEEE Transactions on Biomedical Engineering.

[3]  Thomas R. Knösche,et al.  ASA-Advanced Source Analysis of Continuous and Event-Related EEG/MEG Signals , 2003, Brain Topography.

[4]  J Gotman,et al.  Reliability of dipole models of epileptic spikes , 1999, Clinical Neurophysiology.

[5]  J.C. Mosher,et al.  Multiple dipole modeling and localization from spatio-temporal MEG data , 1992, IEEE Transactions on Biomedical Engineering.

[6]  Matthias Dümpelmann,et al.  3D source localization derived from subdural strip and grid electrodes: A simulation study , 2009, Clinical Neurophysiology.

[7]  H. Lüders,et al.  Presurgical evaluation of epilepsy. , 2001, Brain : a journal of neurology.

[8]  M. Murray,et al.  EEG source imaging , 2004, Clinical Neurophysiology.

[9]  A. Schulze-Bonhage,et al.  Functional organization of the human anterior insular cortex , 2009, Neuroscience Letters.

[10]  M. Fuchs,et al.  Development of Volume Conductor and Source Models to Localize Epileptic Foci , 2007, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[11]  John W Belliveau,et al.  Monte Carlo simulation studies of EEG and MEG localization accuracy , 2002, Human brain mapping.

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

[13]  W. Drongelen,et al.  Localization of brain electrical activity via linearly constrained minimum variance spatial filtering , 1997, IEEE Transactions on Biomedical Engineering.

[14]  M. Cook,et al.  EEG source localization in focal epilepsy: Where are we now? , 2008, Epilepsia.

[15]  Simon K. Warfield,et al.  EEG source analysis of epileptiform activity using a 1 mm anisotropic hexahedra finite element head model , 2009, NeuroImage.

[16]  Michael Wagner,et al.  From ECoG near fields to EEG and MEG far fields , 2007 .

[17]  Christian Vollmar,et al.  Usefulness of 3-D reconstructed images of the human cerebral cortex for localization of subdural electrodes in epilepsy surgery , 2000, Epilepsy Research.

[18]  M. Fuchs,et al.  Linear and nonlinear current density reconstructions. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[19]  R M Leahy,et al.  EEG source localization and imaging using multiple signal classification approaches. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[20]  Michael X. Cohen,et al.  Intracranial EEG Correlates of Expectancy and Memory Formation in the Human Hippocampus and Nucleus Accumbens , 2010, Neuron.

[21]  M. Matousek,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA) in the prediction of response to cholinesterase inhibitors in patients with Alzheimer's disease , 2008 .

[22]  A. Palmini,et al.  Terminology and classification of the cortical dysplasias , 2004, Neurology.

[23]  D. Kovalev,et al.  Rapid and fully automated visualization of subdural electrodes in the presurgical evaluation of epilepsy patients. , 2005, AJNR. American journal of neuroradiology.

[24]  E. Halgren,et al.  Cancellation of EEG and MEG signals generated by extended and distributed sources , 2009, Human brain mapping.

[25]  C. Michel,et al.  EEG Source Imaging in Pediatric Epilepsy Surgery: A New Perspective in Presurgical Workup , 2006, Epilepsia.

[26]  L. Wilkins January 13 Highlights , 2004, Neurology.

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

[28]  P Praamstra,et al.  Linear estimation discriminates midline sources and a motor cortex contribution to the readiness potential. , 1996, Electroencephalography and clinical neurophysiology.

[29]  Bin He,et al.  Three-dimensional brain current source reconstruction from intra-cranial ECoG recordings , 2008, NeuroImage.

[30]  Olaf Hauk,et al.  Comparison of noise-normalized minimum norm estimates for MEG analysis using multiple resolution metrics , 2011, NeuroImage.

[31]  G. Barkley,et al.  MEG and EEG in Epilepsy , 2003, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[32]  J. Gotman,et al.  Systematic source estimation of spikes by a combination of independent component analysis and RAP-MUSIC I: Principles and simulation study , 2002, Clinical Neurophysiology.

[33]  J. Spreer,et al.  Visualization of subdural strip and grid electrodes using curvilinear reformatting of 3D MR imaging data sets. , 2002, AJNR. American journal of neuroradiology.

[34]  Rolando Grave de Peralta Menendez,et al.  The Neuroelectromagnetic Inverse Problem and the Zero Dipole Localization Error , 2009, Comput. Intell. Neurosci..

[35]  Nathalie Chang,et al.  Dipole localization using simulated intracerebral EEG , 2005, Clinical Neurophysiology.

[36]  Jean Gotman,et al.  Systematic source estimation of spikes by a combination of independent component analysis and RAP-MUSIC II: Preliminary clinical application , 2002, Clinical Neurophysiology.

[37]  K Lehnertz,et al.  Real-time tracking of memory formation in the human rhinal cortex and hippocampus. , 1999, Science.

[38]  Oleg Korzyukov,et al.  Generators of the intracranial P50 response in auditory sensory gating , 2007, NeuroImage.

[39]  T Ball,et al.  Overlap of Musical and Linguistic Syntax Processing: Intracranial ERP Evidence , 2009, Annals of the New York Academy of Sciences.