The Implicit Function as Squashing Time Model: A Novel Parallel Nonlinear EEG Analysis Technique Distinguishing Mild Cognitive Impairment and Alzheimer's Disease Subjects with High Degree of Accuracy

Buscema, Massimo; Capriotti, Massimiliano; Bergami, Francesca; Babiloni, Claudio; Rossini, Paolo; Grossi, Enzo

doi:https://doi.org/10.1155/2007/35021

Computational Intelligence and Neuroscience

On this page

Abstract References Copyright Related Articles

Special Issue

EEG/MEG Signal Processing

View this Special Issue

Research Article | Open Access

Volume 2007 | Article ID 035021 | https://doi.org/10.1155/2007/35021

The Implicit Function as Squashing Time Model: A Novel Parallel Nonlinear EEG Analysis Technique Distinguishing Mild Cognitive Impairment and Alzheimer's Disease Subjects with High Degree of Accuracy

Massimo Buscema,¹Massimiliano Capriotti,¹Francesca Bergami,¹Claudio Babiloni,^2,3,4Paolo Rossini,^3,5,6and Enzo Grossi⁷

Academic Editor: Saied Sanei

Received19 Dec 2006

Revised07 Jun 2007

Accepted01 Aug 2007

Published25 Nov 2007

Abstract

Objective. This paper presents the results obtained using a protocol based on special types of artificial neural networks (ANNs) assembled in a novel methodology able to compress the temporal sequence of electroencephalographic (EEG) data into spatial invariants for the automatic classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects. With reference to the procedure reported in our previous study (2007), this protocol includes a new type of artificial organism, named TWIST. The working hypothesis was that compared to the results presented by the workgroup (2007); the new artificial organism TWIST could produce a better classification between AD and MCI. Material and methods. Resting eyes-closed EEG data were recorded in 180 AD patients and in 115 MCI subjects. The data inputs for the classification, instead of being the EEG data, were the weights of the connections within a nonlinear autoassociative ANN trained to generate the recorded data. The most relevant features were selected and coincidently the datasets were split in the two halves for the final binary classification (training and testing) performed by a supervised ANN. Results. The best results distinguishing between AD and MCI were equal to 94.10% and they are considerable better than the ones reported in our previous study (∼92%) (2007). Conclusion. The results confirm the working hypothesis that a correct automatic classification of MCI and AD subjects can be obtained by extracting spatial information content of the resting EEG voltage by ANNs and represent the basis for research aimed at integrating spatial and temporal information content of the EEG.

References

C. Babiloni, G. Binetti, E. Cassetta et al., “Mapping distributed sources of cortical rhythms in mild Alzheimer's disease. A multicentric EEG study,” NeuroImage, vol. 22, no. 1, pp. 57–67, 2004.
View at: Publisher Site | Google Scholar
C. Babiloni, G. Frisoni, M. Steriade et al., “Frontal white matter volume and delta EEG sources negatively correlate in awake subjects with mild cognitive impairment and Alzheimer's disease,” Clinical Neurophysiology, vol. 117, no. 5, pp. 1113–1129, 2006.
View at: Publisher Site | Google Scholar
C. Babiloni, L. Benussi, G. Binetti et al., “Apolipoprotein E and alpha brain rhythms in mild cognitive impairment: a multicentric electroencephalogram study,” Annals of Neurology, vol. 59, no. 2, pp. 323–334, 2006.
View at: Publisher Site | Google Scholar
N. Tsuno, M. Shigeta, K. Hyokid, P. L. Faber, and D. Lehmann, “Fluctuations of source locations of EEG activity during transition from alertness to sleep in Alzheimer's disease and vascular dementia,” Neuropsychobiology, vol. 50, no. 3, pp. 267–272, 2004.
View at: Publisher Site | Google Scholar
C. Huang, L.-O. Wahlund, T. Dierks, P. Julin, B. Winblad, and V. Jelic, “Discrimination of Alzheimer's disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional and longitudinal study,” Clinical Neurophysiology, vol. 111, no. 11, pp. 1961–1967, 2000.
View at: Publisher Site | Google Scholar
T. Dierks, R. Ihl, L. Frölich, and K. Maurer, “Dementia of the Alzheimer type: effects on the spontaneous EEG described by dipole sources,” Psychiatry Research, vol. 50, no. 3, pp. 151–162, 1993.
View at: Publisher Site | Google Scholar
T. Dierks, V. Jelic, R. D. Pascual-Marqui et al., “Spatial pattern of cerebral glucose metabolism (PET) correlates with localization of intracerebral EEG-generators in Alzheimer's disease,” Clinical Neurophysiology, vol. 111, no. 10, pp. 1817–1824, 2000.
View at: Publisher Site | Google Scholar
T. Dierks, L. Frölich, R. Ihl, and K. Maurer, “Correlation between cognitive brain function and electrical brain activity in dementia of Alzheimer type,” Journal of Neural Transmission, vol. 99, no. 1–3, pp. 55–62, 1995.
View at: Publisher Site | Google Scholar
J. Hara, W. R. Shankle, and T. Musha, “Cortical atrophy in Alzheimer's disease unmasks electrically silent sulci and lowers EEG dipolarity,” IEEE Transactions on Biomedical Engineering, vol. 46, no. 8, pp. 905–910, 1999.
View at: Publisher Site | Google Scholar
C. Huang, L.-O. Wahlund, T. Dierks, P. Julin, B. Winblad, and V. Jelic, “Discrimination of Alzheimer's disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional and longitudinal study,” Clinical Neurophysiology, vol. 111, no. 11, pp. 1961–1967, 2000.
View at: Publisher Site | Google Scholar
K. Bennys, G. Rondouin, C. Vergnes, and J. Touchon, “Diagnostic value of quantitative EEG in Alzheimer's disease,” Neurophysiologie Clinique, vol. 31, no. 3, pp. 153–160, 2001.
View at: Publisher Site | Google Scholar
M. Nuwer, “Assessment of digital EEG, quantitative EEG, and EEG brain mapping: report of the American Academy of Neurology and the American Clinical Neurophysiology Society,” Neurology, vol. 49, no. 1, pp. 277–292, 1997.
View at: Google Scholar
G. Adler, S. Brassen, and A. Jajcevic, “EEG coherence in Alzheimer's dementia,” Journal of Neural Transmission, vol. 110, no. 9, pp. 1051–1058, 2003.
View at: Publisher Site | Google Scholar
T. Musha, T. Asada, F. Yamashita et al., “A new EEG method for estimating cortical neuronal impairment that is sensitive to early stage Alzheimer's disease,” Clinical Neurophysiology, vol. 113, no. 7, pp. 1052–1058, 2002.
View at: Publisher Site | Google Scholar
C. Melissant, A. Ypma, E. E. E. Frietman, and C. J. Stam, “A method for detection of Alzheimer's disease using ICA-enhanced EEG measurements,” Artificial Intelligence in Medicine, vol. 33, no. 3, pp. 209–222, 2005.
View at: Publisher Site | Google Scholar
A. A. Petrosian, D. V. Prokhorov, W. Lajara-Nanson, and R. B. Schiffer, “Recurrent neural network-based approach for early recognition of Alzheimer's disease in EEG,” Clinical Neurophysiology, vol. 112, no. 8, pp. 1378–1387, 2001.
View at: Publisher Site | Google Scholar
F. Vialatte, A. Cichocki, G. Dreyfus, T. Musha, S. L. Shishkin, and R. Gervais, “Early detection of Alzheimer's disease by blind source separation, time frequency representation, and bump modeling of EEG signals,” in Proceedings of the 15th International Conference on Artificial Neural Networks: Biological Inspirations (ICANN '05), vol. 3696 of Lecture Notes in Computer Science, pp. 683–692, Springer, Warsaw, Poland, September 2005.
View at: Publisher Site | Google Scholar
J. Jeong, “EEG dynamics in patients with Alzheimer's disease,” Clinical Neurophysiology, vol. 115, no. 7, pp. 1490–1505, 2004.
View at: Publisher Site | Google Scholar
A. Cichocki, S. L. Shishkin, T. Musha, Z. Leonowicz, T. Asada, and T. Kurachi, “EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease,” Clinical Neurophysiology, vol. 116, no. 3, pp. 729–737, 2005.
View at: Publisher Site | Google Scholar
A. Cichocki, “Blind signal processing methods for analyzing multichannel brain signals,” International Journal of Bioelectromagtism, vol. 6, no. 1, 2004.
View at: Google Scholar
A. Cichocki and S.-I. Amari, Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications, Wiley, New York, NY, USA, 2003.
M. Buscema, P. Rossini, C. Babiloni, and E. Grossi, “The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer's disease patients with high degree of accuracy,” Arificial Intelligence in Medicine, vol. 40, no. 2, pp. 127–141, 2007.
View at: Publisher Site | Google Scholar
D. E. Rumelhart, P. Smolensky, J. L. McClelland, and G. E. Hinton, “Schemata and sequential thought processes in PDP models,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, J. L. McClelland and D. E. Rumelhart, Eds., vol. 2, pp. 7–57, The MIT Press, Cambridge, Mass, USA, 1986.
View at: Google Scholar
M. Buscema, “Constraint satisfaction neural networks,” Substance Use & Misuse, vol. 33, no. 2, pp. 389–408, 1998, special issue on artificial neural networks and complex social systems.
View at: Google Scholar
M. Buscema, “Recirculation neural networks,” Substance Use & Misuse, vol. 33, no. 2, pp. 383–388, 1998, special issue on artificial neural networks and complex social systems.
View at: Google Scholar
G. E. Hinton and J. L. McClelland, “Learning representation by recirculation,” in Proceedings of IEEE Conference on Neural Information Processing Systems, Denver, Colo, USA, November 1988.
View at: Google Scholar
Y. Chauvin and D. E. Rumelhart, Eds., Backpropagation: Theory, Architectures, and Applications, Lawrence Erlbaum Associates, Hillsdale, NJ, USA, 1995.
J. L. Elman, “Finding structure in time,” Cognitive Science, vol. 14, no. 2, pp. 179–211, 1990.
View at: Publisher Site | Google Scholar
M. Buscema, “I FAST Software, Semeion Software #32,” Rome, Italy, 2005.
View at: Google Scholar
M. Buscema, E. Grossi, M. Intraligi, N. Garbagna, A. Andriulli, and M. Breda, “An optimized experimental protocol based on neuro-evolutionary algorithms: application to the classification of dyspeptic patients and to the prediction of the effectiveness of their treatment,” Artificial Intelligence in Medicine, vol. 34, no. 3, pp. 279–305, 2005.
View at: Publisher Site | Google Scholar
M. Buscema, “TWIST Software, Semeion Software #32,” Rome, Italy, 2005.
View at: Google Scholar
M. Buscema, “Genetic doping algorithm (GenD): theory and applications ,” Expert Systems, vol. 21, no. 2, pp. 63–79, 2004.
View at: Publisher Site | Google Scholar
L. Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, NY, USA, 1991.
S. Harp, T. Samed, and A. Guha, “Designing application-specific neural networks using the genetic algorithm,” in Advances in Neural Information Processing Systems, D. Touretzky, Ed., vol. 2, pp. 447–454, Morgan Kaufman, San Mateo, Calif, USA, 1990.
View at: Google Scholar
M. Mitchell, An Introduction to Genetic Algorithms, The MIT Press, Cambridge, Mass, USA, 1996.
D. Quagliarella, J. Periaux, C. Polani, and G. Winter, Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, John Wiley & Sons, Chichester, UK, 1998.
G. Rawling, Foundations of Genetic Algorithms, Morgan Kaufman, San Mateo, Calif, USA, 1991.
T. G. Dietterich, “Approximate statistical tests for comparing supervised classification learning algorithms,” Neural Computation, vol. 10, no. 7, pp. 1895–1923, 1998.
View at: Publisher Site | Google Scholar
E. H. Rubin, J. C. Morris, F. A. Grant, and T. Vendegna, “Very mild senile dementia of the Alzheimer type I. Clinical assessment,” Archives of Neurology, vol. 46, no. 4, pp. 379–382, 1989.
View at: Google Scholar
M. Albert, L. A. Smith, P. A. Scherr, J. O. Taylor, D. A. Evans, and H. H. Funkenstein, “Use of brief cognitive tests to identify individuals in the community with clinically diagnosed Alzheimer’s disease,” International Journal of Neuroscience, vol. 57, no. 3-4, pp. 167–178, 1991.
View at: Google Scholar
C. Flicker, S. H. Ferris, and B. Reisberg, “Mild cognitive impairment in the elderly,” Neurology, vol. 41, no. 7, pp. 1006–1009, 1991.
View at: Google Scholar
M. Zaudig, “A new systematic method of measurement and diagnosis of “mild cognitive impairment” and dementia according to ICD-10 and DSM-III-R criteria,” International Psychogeriatrics, vol. 4, 2, pp. 203–219, 1992.
View at: Publisher Site | Google Scholar
D. P. Devanand, M. Folz, M. Gorlyn, J. R. Moeller, and Y. Stern, “Questionable dementia: clinical course and predictors of outcome,” Journal of the American Geriatrics Society, vol. 45, no. 3, pp. 321–328, 1997.
View at: Google Scholar
R. C. Petersen, G. E. Smith, R. J. Ivnik et al., “Apolipoprotein E status as a predictor of the development of Alzheimer’s disease in memory-impaired individuals,” Journal of the American Medical Association, vol. 273, no. 16, pp. 1274–1278, 1995.
View at: Publisher Site | Google Scholar
R. C. Petersen, G. E. Smith, S. C. Waring, R. J. Ivnik, E. Kokmen, and E. G. Tangelos, “Aging, memory, and mild cognitive impairment,” International Psychogeriatrics, vol. 9, no. 1, pp. 65–69, 1997.
View at: Publisher Site | Google Scholar
R. C. Petersen, R. Doody, A. Kurz et al., “Current concepts in mild cognitive impairment,” Archives of Neurology, vol. 58, no. 12, pp. 1985–1992, 2001.
View at: Publisher Site | Google Scholar
G. McKhann, D. Drachman, M. Folstein, R. Katzman, D. Price, and E. M. Stadlan, “Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA work group under the auspices of department of health and human services task force on Alzheimer’s disease,” Neurology, vol. 34, pp. 939–944, 1984.
View at: Google Scholar
M. F. Folstein, S. E. Folstein, and P. R. McHugh, “Mini mental state: a practical method for grading the cognitive state of patients for the clinician,” Journal of Psychiatric Research, vol. 12, no. 3, pp. 189–198, 1975.
View at: Publisher Site | Google Scholar
C. P. Hughes, L. Berg, W. L. Danziger, L. A. Coben, and R. L. Martin, “A new clinical scale for the staging of dementia,” The British Journal of Psychiatry , vol. 140, pp. 566–572, 1982.
View at: Google Scholar
J. A. Yesavage, T. L. Brink, T. L. Rose et al., “Development and validation of a geriatric depression screening scale: a preliminary report,” Journal of Psychiatric Research, vol. 17, no. 1, pp. 37–49, 1983.
View at: Publisher Site | Google Scholar
W. G. Rosen, R. D. Terry, P. A. Fuld, R. Katzman, and A. Peck, “Pathological verification of ischemic score in differentiation of dementias,” Annals of Neurology, vol. 7, no. 5, pp. 486–488, 1980.
View at: Publisher Site | Google Scholar
M. P. Lawton and E. M. Brody, “Assessment of older people: self maintaining ad instrumental activities of daily living,” Gerontologist, vol. 9, no. 3, pp. 179–186, 1969.
View at: Google Scholar
A. Brun, B. Englund, L. Gustafson et al., “Consensus on clinical and neuropathological criteria for fronto-temporal dementia,” Journal of Neurology, Neurosurgery and Psychiatry, vol. 57, pp. 416–418, 1994.
View at: Google Scholar
G. C. Roman, T. K. Tatemichi, T. Erkinjuntti et al., “Vascular dementia: diagnostic criteria for research studies: report of the NINDS-AIREN international workshop,” Neurology, vol. 43, no. 2, pp. 250–260, 1993.
View at: Google Scholar
G. B. Frisoni, A. Beltramello, G. Binetti et al., “Computed tomography in the detection of the vascular component in dementia,” Gerontology, vol. 41, no. 2, pp. 121–128, 1995.
View at: Google Scholar
S. Galluzzi, C. F. Sheu, O. Zanetti, and G. B. Frisoni, “Distinctive clinical features of mild cognitive impairment with subcortical cerebrovascular disease,” Dementia and Geriatric Cognitive Disorders, vol. 19, no. 4, pp. 196–203, 2005.
View at: Publisher Site | Google Scholar
I. G. McKeith, D. Galasko, K. Kosaka et al., “Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): report of the consortium on DLB international workshop,” Neurology, vol. 47, no. 5, pp. 1113–1124, 1996.
View at: Google Scholar
R. J. Buchan, K. Nagata, E. Yokoyama et al., “Regional correlations between the EEG and oxygen metabolism in dementia of Alzheimer’s type,” Electroencephalography and Clinical Neurophysiology, vol. 103, no. 3, pp. 409–417, 1997.
View at: Publisher Site | Google Scholar
E. Pucci, N. Belardinelli, G. Cacchiò, M. Signorino, and F. Angeleri, “EEG power spectrum differences in early and late onset forms of Alzheimer’s disease,” Clinical Neurophysiology, vol. 110, no. 4, pp. 621–631, 1999.
View at: Publisher Site | Google Scholar
B. Szelies, R. Mielke, J. Kessler, and W.-D. Heiss, “EEG power changes are related to regional cerebral glucose metabolism in vascular dementia,” Clinical Neurophysiology, vol. 110, no. 4, pp. 615–620, 1999.
View at: Publisher Site | Google Scholar
G. Rodriguez, P. Vitali, C. De Leo, F. De Carli, N. Girtler, and F. Nobili, “Quantitative EEG changes in Alzheimer patients during long-term donepezil therapy,” Neuropsychobiology, vol. 46, no. 1, pp. 49–56, 2002.
View at: Publisher Site | Google Scholar
C. Babiloni, R. Ferri, D. V. Moretti et al., “Abnormal fronto-parietal coupling of brain rhythms in mild Alzheimer’s disease: a multicentric EEG study,” European Journal of Neuroscience, vol. 19, no. 9, pp. 2583–2590, 2004.
View at: Publisher Site | Google Scholar
D. V. Moretti, F. Babiloni, F. Carducci et al., “Computerized processing of EEG-EOG-EMG artifacts for multi-centric studies in EEG oscillations and event-related potentials,” International Journal of Psychophysiology, vol. 47, no. 3, pp. 199–216, 2003.
View at: Publisher Site | Google Scholar
Y. Stern, “Cognitive reserve and Alzheimer disease,” Alzheimer Disease and Associated Disorders, vol. 20, no. 2, pp. 112–117, 2006.
View at: Publisher Site | Google Scholar
S. G. Gauthier, “Alzheimer’s disease: the benefits of early treatment,” European Journal of Neurology, vol. 12, no. 3, pp. 11–16, 2005.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2007 Massimo Buscema et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation Order printed copies

Views

362

Downloads

1050

Citations