Correspondence of electroencephalography and near-infrared spectroscopy sensitivities to the cerebral cortex using a high-density layout

Abstract. This study investigates the correspondence of the cortical sensitivity of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). EEG forward model sensitivity to the cerebral cortex was calculated for 329 EEG electrodes following the 10-5 EEG positioning system using a segmented structural magnetic resonance imaging scan of a human subject. NIRS forward model sensitivity was calculated for the same subject using 156 NIRS source-detector pairs selected from 32 source and 32 detector optodes positioned on the scalp using a subset of the 10-5 EEG positioning system. Sensitivity correlations between colocalized NIRS source-detector pair groups and EEG channels yielded R=0.46±0.08. Groups of NIRS source-detector pairs with maximum correlations to EEG electrode sensitivities are tabulated. The mean correlation between the point spread functions for EEG and NIRS regions of interest (ROI) was R=0.43±0.07. Spherical ROIs with radii of 26 mm yielded the maximum correlation between EEG and NIRS averaged across all cortical mesh nodes. These sensitivity correlations between EEG and NIRS should be taken into account when designing multimodal studies of neurovascular coupling and when using NIRS as a statistical prior for EEG source localization.

[1]  Paolo Giacometti,et al.  Compliant head probe for positioning electroencephalography electrodes and near-infrared spectroscopy optodes , 2013, Journal of biomedical optics.

[2]  David A. Boas,et al.  Dynamic physiological modeling for functional diffuse optical tomography , 2006, NeuroImage.

[3]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

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

[5]  M. Schweiger,et al.  Theoretical and experimental investigation of near-infrared light propagation in a model of the adult head. , 1997, Applied optics.

[6]  H. Shibasaki Human brain mapping: Hemodynamic response and electrophysiology , 2008, Clinical Neurophysiology.

[7]  Bruce Fischl,et al.  Within-subject template estimation for unbiased longitudinal image analysis , 2012, NeuroImage.

[8]  Y. Hoshi Functional near-infrared spectroscopy: potential and limitations in neuroimaging studies. , 2005, International review of neurobiology.

[9]  S. Arridge Optical tomography in medical imaging , 1999 .

[10]  Olivier D. Faugeras,et al.  A common formalism for the Integral formulations of the forward EEG problem , 2005, IEEE Transactions on Medical Imaging.

[11]  C. Iadecola Neurovascular regulation in the normal brain and in Alzheimer's disease , 2004, Nature Reviews Neuroscience.

[12]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[13]  K. K. Tan,et al.  The spatial location of EEG electrodes: locating the best-fitting sphere relative to cortical anatomy. , 1993, Electroencephalography and clinical neurophysiology.

[14]  M. Clerc,et al.  Cortical mapping by Laplace–Cauchy transmission using a boundary element method , 2007 .

[15]  Solomon G. Diamond,et al.  Diffuse Optical Tomography for Brain Imaging: Continuous Wave Instrumentation and Linear Analysis Methods , 2013 .

[16]  C. Iadecola,et al.  Neurovascular coupling in the normal brain and in hypertension, stroke, and Alzheimer disease. , 2006, Journal of applied physiology.

[17]  Qianqian Fang,et al.  Mesh-based Monte Carlo method using fast ray-tracing in Plücker coordinates , 2010, Biomedical optics express.

[18]  Y. Okada,et al.  Contributions of principal neocortical neurons to magnetoencephalography and electroencephalography signals , 2006, The Journal of physiology.

[19]  D. Delpy,et al.  Near-infrared light propagation in an adult head model. II. Effect of superficial tissue thickness on the sensitivity of the near-infrared spectroscopy signal. , 2003, Applied optics.

[20]  T. Musha,et al.  Forward and inverse problems of EEG dipole localization. , 1999, Critical reviews in biomedical engineering.

[21]  B. Zlokovic The Blood-Brain Barrier in Health and Chronic Neurodegenerative Disorders , 2008, Neuron.

[22]  Gabriel Curio,et al.  Neurovascular coupling analyzed non-invasively in the human brain , 2004, Neuroreport.

[23]  A. Dale,et al.  Robust inference of baseline optical properties of the human head with three-dimensional segmentation from magnetic resonance imaging. , 2003, Applied optics.

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

[25]  Olivier D. Faugeras,et al.  Symmetric BEM Formulation for the M/EEG Forward Problem , 2003, IPMI.

[26]  Rong Zhang,et al.  Cerebral autoregulation in Alzheimer's disease , 2011, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[27]  Martin Wolf,et al.  Reproducibility and sensitivity of detecting brain activity by simultaneous electroencephalography and near-infrared spectroscopy , 2012, Experimental Brain Research.

[28]  Hamid Dehghani,et al.  Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction. , 2009, Communications in numerical methods in engineering.

[29]  D. Boas,et al.  Improving the diffuse optical imaging spatial resolution of the cerebral hemodynamic response to brain activation in humans. , 2004, Optics letters.

[30]  Hamid Dehghani,et al.  Image Quality Analysis of High-Density Diffuse Optical Tomography Incorporating a Subject-Specific Head Model , 2012, Front. Neuroenerg..

[31]  Manfred Kaps,et al.  Neurovascular coupling in Alzheimer patients: Effect of acetylcholine-esterase inhibitors , 2009, Neurobiology of Aging.

[32]  Bin He,et al.  Estimating cortical potentials from scalp EEG's in a realistically shaped inhomogeneous head model by means of the boundary element method. , 1999, IEEE transactions on bio-medical engineering.

[33]  R. Leahy,et al.  EEG and MEG: forward solutions for inverse methods , 1999, IEEE Transactions on Biomedical Engineering.

[34]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[35]  Valer Jurcak,et al.  10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems , 2007, NeuroImage.

[36]  Helmut Laufs,et al.  A personalized history of EEG–fMRI integration , 2012, NeuroImage.

[37]  D. Louis Collins,et al.  Twenty New Digital Brain Phantoms for Creation of Validation Image Data Bases , 2006, IEEE Transactions on Medical Imaging.

[38]  Théodore Papadopoulo,et al.  OpenMEEG: opensource software for quasistatic bioelectromagnetics , 2010, Biomedical engineering online.

[39]  C. E. Acar,et al.  Sensitivity of EEG and MEG measurements to tissue conductivity , 2004, Physics in medicine and biology.

[40]  Bin He,et al.  Electrophysiological Imaging of Brain Activity and Connectivity—Challenges and Opportunities , 2011, IEEE Transactions on Biomedical Engineering.

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

[42]  Maureen Clerc,et al.  A Global Sensitivity Analysis of Three- and Four-Layer EEG Conductivity Models , 2009, IEEE Transactions on Biomedical Engineering.

[43]  Jinlan Guan,et al.  Optical tomography reconstruction algorithm based on the radiative transfer equation considering refractive index - Part 1: Forward model , 2013, Comput. Medical Imaging Graph..

[44]  A. Eke,et al.  The modified Beer–Lambert law revisited , 2006, Physics in medicine and biology.

[45]  David A. Boas,et al.  Calibrating the BOLD signal during a motor task using an extended fusion model incorporating DOT, BOLD and ASL data , 2012, NeuroImage.

[46]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[47]  D. Delpy,et al.  Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer. , 2003, Applied optics.

[48]  D. Boas,et al.  Diffuse optical tomography system to image brain activation with improved spatial resolution and validation with functional magnetic resonance imaging. , 2006, Applied optics.

[49]  Katherine L. Perdue,et al.  Effects of Spatial Pattern Scale of Brain Activity on the Sensitivity of DOT, fMRI, EEG and MEG , 2013, PloS one.

[50]  Solomon G. Diamond,et al.  Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain , 2014, Journal of Neuroscience Methods.

[51]  Jinlan Guan,et al.  Optical tomography reconstruction algorithm based on the radiative transfer equation considering refractive index: Part 2. Inverse model , 2013, Comput. Medical Imaging Graph..

[52]  Masa-aki Sato,et al.  Cortical current source estimation from electroencephalography in combination with near-infrared spectroscopy as a hierarchical prior , 2012, NeuroImage.

[53]  M. Copet,et al.  A Monte Carlo investigation of optical pathlength in inhomogeneous tissue and its application to near-infrared spectroscopy , 1993 .

[54]  Qianqian Fang,et al.  Quantitative assessment of diffuse optical tomography sensitivity to the cerebral cortex using a whole-head probe. , 2012, Physics in medicine and biology.

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

[56]  David A. Boas,et al.  Tetrahedral mesh generation from volumetric binary and grayscale images , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.