Validating Non-invasive EEG Source Imaging Using Optimal Electrode Configurations on a Representative Rat Head Model

The curtain of technical limitations impeding rat multichannel non-invasive electroencephalography (EEG) has risen. Given the importance of this preclinical model, development and validation of EEG source imaging (ESI) is essential. We investigate the validity of well-known human ESI methodologies in rats which individual tissue geometries have been approximated by those extracted from an MRI template, leading also to imprecision in electrode localizations. With the half and fifth sensitivity volumes we determine both the theoretical minimum electrode separation for non-redundant scalp EEG measurements and the electrode sensitivity resolution, which vary over the scalp because of the head geometry. According to our results, electrodes should be at least ~3 to 3.5 mm apart for an optimal configuration. The sensitivity resolution is generally worse for electrodes at the boundaries of the scalp measured region, though, by analogy with human montages, concentrates the sensitivity enough to localize sources. Cramér–Rao lower bounds of source localization errors indicate it is theoretically possible to achieve ESI accuracy at the level of anatomical structures, such as the stimulus-specific somatosensory areas, using the template. More validation for this approximation is provided through the comparison between the template and the individual lead field matrices, for several rats. Finally, using well-accepted inverse methods, we demonstrate that somatosensory ESI is not only expected but also allows exploring unknown phenomena related to global sensory integration. Inheriting the advantages and pitfalls of human ESI, rat ESI will boost the understanding of brain pathophysiological mechanisms and the evaluation of ESI methodologies, new pharmacological treatments and ESI-based biomarkers.

[1]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[2]  John S George,et al.  Probabilistic forward model for electroencephalography source analysis , 2007, Physics in medicine and biology.

[3]  S. Kochen,et al.  Approximate average head models for EEG source imaging , 2009, Journal of Neuroscience Methods.

[4]  Jochen Ditterich,et al.  Splash: A Software Tool for Stereotactic Planning of Recording Chamber Placement and Electrode Trajectories , 2011, Front. Neuroinform..

[5]  E. John,et al.  Conventional and quantitative electroencephalography in psychiatry. , 1999, The Journal of neuropsychiatry and clinical neurosciences.

[6]  Thomas R. Knösche,et al.  A guideline for head volume conductor modeling in EEG and MEG , 2014, NeuroImage.

[7]  Don M. Tucker,et al.  The spatial resolution of scalp EEG , 2001, Neurocomputing.

[8]  Théodore Papadopoulo,et al.  Forward Field Computation with OpenMEEG , 2011, Comput. Intell. Neurosci..

[9]  Iman Mohammad-Rezazadeh,et al.  Using quantitative and analytic EEG methods in the understanding of connectivity in autism spectrum disorders: a theory of mixed over- and under-connectivity , 2014, Front. Hum. Neurosci..

[10]  A. Gevins,et al.  Beyond topographic mapping: Towards functional-anatomical imaging with 124-channel EEGs and 3-D MRIs , 2005, Brain Topography.

[11]  A. Grinvald,et al.  Spatiotemporal Dynamics of Sensory Responses in Layer 2/3 of Rat Barrel Cortex Measured In Vivo by Voltage-Sensitive Dye Imaging Combined with Whole-Cell Voltage Recordings and Neuron Reconstructions , 2003, The Journal of Neuroscience.

[12]  J. Chapin,et al.  Corticocortical connections within the primary somatosensory cortex of the rat , 1987, The Journal of comparative neurology.

[13]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .

[14]  Jaakko Malmivuo,et al.  Effect of skull resistivity on the spatial resolutions of EEG and MEG , 2004, IEEE Transactions on Biomedical Engineering.

[15]  Daniel Pope,et al.  Study of the cortical representation of whisker directional deflection using voltage-sensitive dye optical imaging , 2010, NeuroImage.

[16]  Karl J. Friston,et al.  Canonical Source Reconstruction for MEG , 2007, Comput. Intell. Neurosci..

[17]  Roberto D. Pascual-Marqui,et al.  Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization , 2007, 0710.3341.

[18]  Michael Brecht,et al.  Organization of rat vibrissa motor cortex and adjacent areas according to cytoarchitectonics, microstimulation, and intracellular stimulation of identified cells , 2004, The Journal of comparative neurology.

[19]  Markus Rudin,et al.  Functional and Anatomical Reorganization of the Sensory-Motor Cortex after Incomplete Spinal Cord Injury in Adult Rats , 2009, The Journal of Neuroscience.

[20]  Alfred O. Hero,et al.  Robust Shrinkage Estimation of High-Dimensional Covariance Matrices , 2010, IEEE Transactions on Signal Processing.

[21]  M. Clerc,et al.  Comparison of Boundary Element and Finite Element Approaches to the EEG Forward Problem , 2012, Biomedizinische Technik. Biomedical engineering.

[22]  G. Foffani,et al.  Imaging the Spatio-Temporal Dynamics of Supragranular Activity in the Rat Somatosensory Cortex in Response to Stimulation of the Paws , 2012, PloS one.

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

[24]  M. Pletnikov,et al.  Pre-clinical models of neurodevelopmental disorders: focus on the cerebellum , 2014, Reviews in the neurosciences.

[25]  N C Fox,et al.  Diagnosis of early Alzheimer's disease. , 1999, Revue neurologique.

[26]  Felix Darvas,et al.  Generic head models for atlas‐based EEG source analysis , 2006, Human brain mapping.

[27]  Christoph Kayser,et al.  Asymmetric Multisensory Interactions of Visual and Somatosensory Responses in a Region of the Rat Parietal Cortex , 2013, PloS one.

[28]  J. Riera,et al.  Electric lead field for a piecewise homogeneous volume conductor model of the head , 1998, IEEE Transactions on Biomedical Engineering.

[29]  G. Paxinos,et al.  The Rat Brain in Stereotaxic Coordinates , 1983 .

[30]  Jihye Bae,et al.  Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings , 2015, Journal of visualized experiments : JoVE.

[31]  Katrina Wendel,et al.  The Influence of CSF on EEG Sensitivity Distributions of Multilayered Head Models , 2008, IEEE Transactions on Biomedical Engineering.

[32]  David A. Boas,et al.  Coupling between somatosensory evoked potentials and hemodynamic response in the rat , 2008, NeuroImage.

[33]  Stefan A. Carp,et al.  The effect of different anesthetics on neurovascular coupling , 2010, NeuroImage.

[34]  A. Evans,et al.  Pitfalls in the dipolar model for the neocortical EEG sources. , 2012, Journal of neurophysiology.

[35]  Wolfgang Löscher,et al.  Critical review of current animal models of seizures and epilepsy used in the discovery and development of new antiepileptic drugs , 2011, Seizure.

[36]  Michel Verleysen,et al.  Feature Selection for Interpatient Supervised Heart Beat Classification , 2011, BIOSIGNALS.

[37]  Celine Mateo,et al.  Motor Control by Sensory Cortex , 2010, Science.

[38]  Hee-Sup Shin,et al.  High resolution electroencephalography in freely moving mice. , 2010, Journal of neurophysiology.

[39]  W. Hackbusch,et al.  Efficient Computation of Lead Field Bases and Influence Matrix for the FEM-based EEG and MEG Inverse Problem. Part I: Complexity Considerations , 2003 .

[40]  Christoph M. Michel,et al.  A mouse model for studying large-scale neuronal networks using EEG mapping techniques , 2008, NeuroImage.

[41]  A. Cuello,et al.  Modeling Alzheimer’s disease in transgenic rats , 2013, Molecular Neurodegeneration.

[42]  Erzsébet Marosi,et al.  Frequency source analysis in patients with brain lesions , 2005, Brain Topography.

[43]  Petre Stoica,et al.  MUSIC, maximum likelihood and Cramer-Rao bound: further results and comparisons , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[44]  D. A. Driscoll,et al.  EEG electrode sensitivity--an application of reciprocity. , 1969, IEEE transactions on bio-medical engineering.

[45]  梅田 稔子 Evaluation of Pax6 mutant rat as a model for autism , 2011 .

[46]  I. Macrae,et al.  Preclinical stroke research – advantages and disadvantages of the most common rodent models of focal ischaemia , 2011, British journal of pharmacology.

[47]  Xiaoping P. Hu,et al.  Comparison of alpha-chloralose, medetomidine and isoflurane anesthesia for functional connectivity mapping in the rat. , 2010, Magnetic resonance imaging.

[48]  M. E. Spencer,et al.  Error bounds for EEG and MEG dipole source localization. , 1993, Electroencephalography and clinical neurophysiology.

[49]  A. Hero,et al.  Robust shrinkage estimation of high-dimensional covariance matrices , 2010 .

[50]  S. Geer,et al.  On asymptotically optimal confidence regions and tests for high-dimensional models , 2013, 1303.0518.

[51]  Carlos H. Muravchik,et al.  Effects of geometric head model perturbations on the EEG forward and inverse problems , 2006, IEEE Transactions on Biomedical Engineering.

[52]  Takeshi Ogawa,et al.  An in vivo MRI Template Set for Morphometry, Tissue Segmentation, and fMRI Localization in Rats , 2011, Front. Neuroinform..

[53]  Suparna Bharadwaj,et al.  EEG as a surrogate to brain imaging for diagnosing stroke in morbidly obese patients. , 2015, Journal of neurosurgical anesthesiology.

[54]  Petre Stoica,et al.  MUSIC, maximum likelihood, and Cramer-Rao bound , 1989, IEEE Transactions on Acoustics, Speech, and Signal Processing.

[55]  Outi Väisänen,et al.  New method for analysing sensitivity distributions of electroencephalography measurements , 2008, Medical & Biological Engineering & Computing.

[56]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[57]  Olivier Ledoit,et al.  Honey, I Shrunk the Sample Covariance Matrix , 2003 .

[58]  H. Lüders,et al.  EEG source imaging in epilepsy—practicalities and pitfalls , 2012, Nature Reviews Neurology.

[59]  Karl J. Friston,et al.  Electromagnetic source reconstruction for group studies , 2008, NeuroImage.

[60]  Cun-Hui Zhang,et al.  Confidence intervals for low dimensional parameters in high dimensional linear models , 2011, 1110.2563.

[61]  J. Malmivuo,et al.  Sensitivity distributions of EEG and MEG measurements , 1997, IEEE Transactions on Biomedical Engineering.

[62]  Roberta Adorni,et al.  Orthographic familiarity, phonological legality and number of orthographic neighbours affect the onset of ERP lexical effects , 2008, Behavioral and Brain Functions.

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

[64]  F. Haiss,et al.  Spatiotemporal Dynamics of Cortical Sensorimotor Integration in Behaving Mice , 2007, Neuron.

[65]  Pierre Mégevand,et al.  Functional Development of Large-Scale Sensorimotor Cortical Networks in the Brain , 2011, The Journal of Neuroscience.

[66]  Kevin D. Alloway,et al.  Rat whisker motor cortex is subdivided into sensory-input and motor-output areas , 2013, Front. Neural Circuits.

[67]  Yodchanan Wongsawat,et al.  EEG brain mapping and brain connectivity index for subtypes classification of attention deficit hyperactivity disorder children during the eye-opened period , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[68]  Dongwook Kim,et al.  High-density EEG Recordings of the Freely Moving Mice using Polyimide-based Microelectrode , 2011, Journal of visualized experiments : JoVE.

[69]  Takeshi Ogawa,et al.  A mini-cap for simultaneous EEG and fMRI recording in rodents , 2011, NeuroImage.

[70]  Olivier Ledoit,et al.  Honey, I Shrunk the Sample Covariance Matrix , 2003 .