Study of EEGs from Somatosensory Cortex and Alzheimer's Disease Sources
This study is to investigate the electroencephalogram (EEG) differences generated from a normal and Alzheimer-s disease (AD) sources. We also investigate the effects of brain tissue distortions due to AD on EEG. We develop a realistic head model from T1 weighted magnetic resonance imaging (MRI) using finite element method (FEM) for normal source (somatosensory cortex (SC) in parietal lobe) and AD sources (right amygdala (RA) and left amygdala (LA) in medial temporal lobe). Then, we compare the AD sourced EEGs to the SC sourced EEG for studying the nature of potential changes due to sources and 5% to 20% brain tissue distortions. We find an average of 0.15 magnification errors produced by AD sourced EEGs. Different brain tissue distortion models also generate the maximum 0.07 magnification. EEGs obtained from AD sources and different brain tissue distortion levels vary scalp potentials from normal source, and the electrodes residing in parietal and temporal lobes are more sensitive than other electrodes for AD sourced EEG.
[1] S. Kloppel, C. M. Stonnington, C. Chu , B. Draganski, R. I. Scahill, J.
D. Rohrer, N. C. Fox, C. R. Jack Jr, J. Ashburner and R. S. J.
Frackowiak, "Automatic classification of MR scans in Alzheimer-s
disease," Brain, vol. 131, pp. 681-689, Jan. 2008.
[2] L. Mosconi, S. Sorbi, M. J. de Leon, Y. Li , B. Nacmias, and P. S.
Myong, "Hypometabolism exceeds atrophy in presymptomic early-onset
familial Alzheimer's disease," Journal of Neuclear Medicine, vol. 47, pp.
1778-1786, Nov. 2006.
[3] J. C. Baron, G. Chetelat, B. Desgranges, G. Perchey, B. Landeau, V. de
la Sayette, and F. Eustache, "In vivo mapping of gray matter loss with
voxel-based morphometry in mild Alzheimer's disease," NeuroImage,
vol. 14, pp. 298-309, Aug. 2001.
[4] A. D. Smith, and K. A. Jobst, "Use of structural imaging to study the
progression of Alzheimer's diseases," British Medical Bulletin, vol. 52,
no. 3, pp. 575-586, 1996.
[5] M. Chupi, A. R. Mukuna-Bantumbakulu, D. Hasboun, E. Bardinet, S.
Baillet, S. Kinkingnehun, L. Lemieux, B. Dubois, and L. Garnero, ,
"Anatomically constrained region deformation for the automated
segmentation of the hippocampus and amygdala: Method and validation
on controls and patients with Alzheimer disease," NeuroImage, vol. 34,
pp. 996-1019, Feb. 2007.
[6] T. Patel, R. Polikar, C. Davatzikos, and C. M. Clark, "EEG and MRI
Data Fusion for Early Diagnosis of Alzheimer-s Diseas," in proc. 30th
Annual International IEEE EMBS Conference, Canada, 2008, pp. 1757-
1760.
[7] R. Polikar, T. D. Green, J. Kounios, and C. M. Clark, "Comparative
multiresolution wavelet analysis of ERP spectral bands using an
ensamble of classifier approach for early diagnosis for Alzheimer-s
disease," Computers in. Biology and Medicine, vol. 37, no. 4, pp. 542-
558, Apr. 2007.
[8] G. Chetelat, B. Desgranges, B. Landeau, F. Mezenge, J. B. Poline, V. de
la Sayette, F. Viader, F. Eustache, and J. C. Baron, "Direct voxel-based
comparison between grey matter hypometabolism and atrophy in
Alzheimer-s disease," Brain, vol. 131, pp. 60-71, Jan. 2008.
[9] (Online source) D. W. Shattuck. (2005). BrainSuite 2 Tutorial.
Available: http://brainsuite.usc.edu
[10] D. W. Shattuck, S. R. Sandor-Leahy, K. A. Schaper, D. A. Rottenberg,
and R. M. Leahy,"Magnetic Resonance Image Tissue Classification
Using a Partial Volume Model," NeuroImage, vol. 13, no. 5, pp. 856-
876, 2001.
[11] D. W. Shattuck, and R. M. Leahy, "BrainSuite: An Automated Cortical
Surface Identification Tool," Medical Image Analysis, vol. 8, no. 2, pp.
129-142, June 2002.
[12] B. Dogdas, D. W. Shattuck, and R. M. Leahy, "Segmentation of Skull
and Scalp in 3-D Human MRI using Mathematical Morphology,"
Human Brain Mapping, vol. 26, pp. 273-285, June 2005.
[13] M. R. Bashar, Y. Li, and P. Wen, "EEG analysis on skull conductivity
perturbations using realistic head model," Lecture Notes on Computer
Science, vol. 5589, pp. 208-215, July 2009.
[14] R. N. Klepfer, C. R. Johnson, and S. M. Robert, "The effects of
Inhomogeneities and Anisotropies on Electrocardiographic Fields: A 3-
D Finite -element Study," IEEE Transactions on Biomedical
Engineering, vol. 44, no. 8, pp. 706-719, Aug. 1997.
[15] C. H. Wolters, "Influence of Tissue Conductivity Inhomogeneity and
Anisotropy on EEG/MEG based Source Localization in the Human
Brain," PhD thesis. University of Leipzig. France; July 2003.
[16] M. R. Bashar, Y. Li, and P. Wen, "Influence of white matter
inhomogeneous anisotropy on EEG forward computing," Australasian
Physical & Engineering Sciences in Medicine, vol. 31, no. 2, pp. 122-
130, 2008.
[17] P. Wen P, and Y. Li, "EEG human head modelling based on
heterogeneous tissue conductivity," Australasian Physical &
Engineering Sciences in Medicine, vol. 29, no. 3, pp. 235-240, 2006.
[18] D. Gullmar, J. Haueisen, M. Wiselt, F. Giebler, L. Flemming, A.
Anwander, T. R. Knosche, C. H. Wolters, M. Dumpelmann, D. S. Tuch
and J. R. Reichenbach., "Influence of Anisotropic Conductivity on EEG
source Reconstruction: Investigations in a Rabbit Model," IEEE
Transactions on Biomedical Engineering, vol. 53, no. 9, pp. 1841-1850,
Sep. 2006.
[19] J. Hauseisen, C. Ramon, M. Eiselt, H. Brauer, and H. Nowak,"Influence
of Tissue Resistivities on Neuromagnetic Fields and Electric Potentials
studied with a Finite Element Model of the Head," IEEE Transactions
on Biomedical Engineering, vol. 44, no. 8, pp. 727-735, Aug. 1997.
[20] K. A. Awada, S. B. Baumann, and D. R. Jackson," Effect of conductivity
uncertainties on EEG source localization using a 2D finite element
model," in Proc. of 19th International Conference - IEEE/EMBS, USA,
1997, pp. 2124-2127.
[21] (Online Source) H. Si. (2004). TetGen. Available:
http://tetgen.berlios.de
[22] S. Baillet, J. C. Mosher, and R. M. Leahy, "Electromagnetic Brain
Imaging using Brainstorm," in Proc. IEEE Int. Symposium on Biomed.
Eng.: Macro to Nano 2004, pp. 652-655.
[23] G. B. Frisoni, M. Pievani, C. Testa, S. F. Francesca, L. Bresciani , M.
Bonetti, A. Beltramello, K. M. Hayashi, A. W. Toga, and P. M.
Thompson, "The topography of grey matter involvement in early and
late onset Alzheimer-s disease," Brain, vol. 130, pp. 720-730, Feb. 2007.
[24] J. W. Meijis, O. W. Weier, M. J. Peters, and A. van Oosterom, "On the
numerical accuracy of the boundary element method," IEEE
Transactions on Biomedical Engineering, vol. 36, no. 10, pp. 1038-
1049, Oct. 1989.
[25] (Online Source) ANT Software, The Netherland. (2007). Advanced
Source Analysis. Available: www.ant-neuro.com
[1] S. Kloppel, C. M. Stonnington, C. Chu , B. Draganski, R. I. Scahill, J.
D. Rohrer, N. C. Fox, C. R. Jack Jr, J. Ashburner and R. S. J.
Frackowiak, "Automatic classification of MR scans in Alzheimer-s
disease," Brain, vol. 131, pp. 681-689, Jan. 2008.
[2] L. Mosconi, S. Sorbi, M. J. de Leon, Y. Li , B. Nacmias, and P. S.
Myong, "Hypometabolism exceeds atrophy in presymptomic early-onset
familial Alzheimer's disease," Journal of Neuclear Medicine, vol. 47, pp.
1778-1786, Nov. 2006.
[3] J. C. Baron, G. Chetelat, B. Desgranges, G. Perchey, B. Landeau, V. de
la Sayette, and F. Eustache, "In vivo mapping of gray matter loss with
voxel-based morphometry in mild Alzheimer's disease," NeuroImage,
vol. 14, pp. 298-309, Aug. 2001.
[4] A. D. Smith, and K. A. Jobst, "Use of structural imaging to study the
progression of Alzheimer's diseases," British Medical Bulletin, vol. 52,
no. 3, pp. 575-586, 1996.
[5] M. Chupi, A. R. Mukuna-Bantumbakulu, D. Hasboun, E. Bardinet, S.
Baillet, S. Kinkingnehun, L. Lemieux, B. Dubois, and L. Garnero, ,
"Anatomically constrained region deformation for the automated
segmentation of the hippocampus and amygdala: Method and validation
on controls and patients with Alzheimer disease," NeuroImage, vol. 34,
pp. 996-1019, Feb. 2007.
[6] T. Patel, R. Polikar, C. Davatzikos, and C. M. Clark, "EEG and MRI
Data Fusion for Early Diagnosis of Alzheimer-s Diseas," in proc. 30th
Annual International IEEE EMBS Conference, Canada, 2008, pp. 1757-
1760.
[7] R. Polikar, T. D. Green, J. Kounios, and C. M. Clark, "Comparative
multiresolution wavelet analysis of ERP spectral bands using an
ensamble of classifier approach for early diagnosis for Alzheimer-s
disease," Computers in. Biology and Medicine, vol. 37, no. 4, pp. 542-
558, Apr. 2007.
[8] G. Chetelat, B. Desgranges, B. Landeau, F. Mezenge, J. B. Poline, V. de
la Sayette, F. Viader, F. Eustache, and J. C. Baron, "Direct voxel-based
comparison between grey matter hypometabolism and atrophy in
Alzheimer-s disease," Brain, vol. 131, pp. 60-71, Jan. 2008.
[9] (Online source) D. W. Shattuck. (2005). BrainSuite 2 Tutorial.
Available: http://brainsuite.usc.edu
[10] D. W. Shattuck, S. R. Sandor-Leahy, K. A. Schaper, D. A. Rottenberg,
and R. M. Leahy,"Magnetic Resonance Image Tissue Classification
Using a Partial Volume Model," NeuroImage, vol. 13, no. 5, pp. 856-
876, 2001.
[11] D. W. Shattuck, and R. M. Leahy, "BrainSuite: An Automated Cortical
Surface Identification Tool," Medical Image Analysis, vol. 8, no. 2, pp.
129-142, June 2002.
[12] B. Dogdas, D. W. Shattuck, and R. M. Leahy, "Segmentation of Skull
and Scalp in 3-D Human MRI using Mathematical Morphology,"
Human Brain Mapping, vol. 26, pp. 273-285, June 2005.
[13] M. R. Bashar, Y. Li, and P. Wen, "EEG analysis on skull conductivity
perturbations using realistic head model," Lecture Notes on Computer
Science, vol. 5589, pp. 208-215, July 2009.
[14] R. N. Klepfer, C. R. Johnson, and S. M. Robert, "The effects of
Inhomogeneities and Anisotropies on Electrocardiographic Fields: A 3-
D Finite -element Study," IEEE Transactions on Biomedical
Engineering, vol. 44, no. 8, pp. 706-719, Aug. 1997.
[15] C. H. Wolters, "Influence of Tissue Conductivity Inhomogeneity and
Anisotropy on EEG/MEG based Source Localization in the Human
Brain," PhD thesis. University of Leipzig. France; July 2003.
[16] M. R. Bashar, Y. Li, and P. Wen, "Influence of white matter
inhomogeneous anisotropy on EEG forward computing," Australasian
Physical & Engineering Sciences in Medicine, vol. 31, no. 2, pp. 122-
130, 2008.
[17] P. Wen P, and Y. Li, "EEG human head modelling based on
heterogeneous tissue conductivity," Australasian Physical &
Engineering Sciences in Medicine, vol. 29, no. 3, pp. 235-240, 2006.
[18] D. Gullmar, J. Haueisen, M. Wiselt, F. Giebler, L. Flemming, A.
Anwander, T. R. Knosche, C. H. Wolters, M. Dumpelmann, D. S. Tuch
and J. R. Reichenbach., "Influence of Anisotropic Conductivity on EEG
source Reconstruction: Investigations in a Rabbit Model," IEEE
Transactions on Biomedical Engineering, vol. 53, no. 9, pp. 1841-1850,
Sep. 2006.
[19] J. Hauseisen, C. Ramon, M. Eiselt, H. Brauer, and H. Nowak,"Influence
of Tissue Resistivities on Neuromagnetic Fields and Electric Potentials
studied with a Finite Element Model of the Head," IEEE Transactions
on Biomedical Engineering, vol. 44, no. 8, pp. 727-735, Aug. 1997.
[20] K. A. Awada, S. B. Baumann, and D. R. Jackson," Effect of conductivity
uncertainties on EEG source localization using a 2D finite element
model," in Proc. of 19th International Conference - IEEE/EMBS, USA,
1997, pp. 2124-2127.
[21] (Online Source) H. Si. (2004). TetGen. Available:
http://tetgen.berlios.de
[22] S. Baillet, J. C. Mosher, and R. M. Leahy, "Electromagnetic Brain
Imaging using Brainstorm," in Proc. IEEE Int. Symposium on Biomed.
Eng.: Macro to Nano 2004, pp. 652-655.
[23] G. B. Frisoni, M. Pievani, C. Testa, S. F. Francesca, L. Bresciani , M.
Bonetti, A. Beltramello, K. M. Hayashi, A. W. Toga, and P. M.
Thompson, "The topography of grey matter involvement in early and
late onset Alzheimer-s disease," Brain, vol. 130, pp. 720-730, Feb. 2007.
[24] J. W. Meijis, O. W. Weier, M. J. Peters, and A. van Oosterom, "On the
numerical accuracy of the boundary element method," IEEE
Transactions on Biomedical Engineering, vol. 36, no. 10, pp. 1038-
1049, Oct. 1989.
[25] (Online Source) ANT Software, The Netherland. (2007). Advanced
Source Analysis. Available: www.ant-neuro.com
@article{"International Journal of Business, Human and Social Sciences:51371", author = "Md R. Bashar and Yan Li and Peng Wen", title = "Study of EEGs from Somatosensory Cortex and Alzheimer's Disease Sources", abstract = "This study is to investigate the electroencephalogram (EEG) differences generated from a normal and Alzheimer-s disease (AD) sources. We also investigate the effects of brain tissue distortions due to AD on EEG. We develop a realistic head model from T1 weighted magnetic resonance imaging (MRI) using finite element method (FEM) for normal source (somatosensory cortex (SC) in parietal lobe) and AD sources (right amygdala (RA) and left amygdala (LA) in medial temporal lobe). Then, we compare the AD sourced EEGs to the SC sourced EEG for studying the nature of potential changes due to sources and 5% to 20% brain tissue distortions. We find an average of 0.15 magnification errors produced by AD sourced EEGs. Different brain tissue distortion models also generate the maximum 0.07 magnification. EEGs obtained from AD sources and different brain tissue distortion levels vary scalp potentials from normal source, and the electrodes residing in parietal and temporal lobes are more sensitive than other electrodes for AD sourced EEG.
", keywords = "Alzheimer's disease (AD), brain tissue distortion, electroencephalogram, finite element method.", volume = "5", number = "9", pages = "1140-5", }
