EEG/MEG- and imaging-based diagnosis of Alzheimer’s disease
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Sarah Hulbert
Sarah Hulbert is a native of Michigan and a graduate of Western Michigan University and the Lee Honors College in Kalamazoo, MI. She received her BS in Physics in May 2013 and is currently a PhD student in the Biophysics Program at the Ohio State University. Her interests include the processing and analysis of brain signals and images with applications in disease diagnosis.
Hojjat Adeli is a Professor of Civil, Environmental, and Geodetic Engineering, Biomedical Engineering, Biomedical Informatics, Electrical and Computer Engineering, Neurological Surgery, and Neuroscience at the Ohio State University. He has authored/ coauthored 15 books. He is the co-author of
Automated EEG-Based Diagnosis of Neurological Disorders – Inventing the Future of Neurology (CRC Press, 2010). In 1998, he received the Ohio State University’s highest research honor, the Distinguished Scholar Award, ‘in recognition of extraordinary accomplishment in research and scholarship’. He is a fellow of AAAS and IEEE. He is the editor-in-chief of the international research journalsComputer-Aided Civil and Infrastructure Engineering , which he founded in 1986, andIntegrated Computer-Aided Engineering , which he founded in 1993. He is also the editor-in-chief of theInternational Journal of Neural Systems .
Abstract
In recent years, researchers have embarked on a search of computer-aided methods for diagnosis of the Alzheimer’s disease (AD) to help clinicians make the diagnosis earlier and more accurately such that treatment of the disease can begin sooner when there is a higher chance of success in slowing down the progression of this disease. This article presents a review of journal articles on brain signal- and image-based diagnosis of AD published in the past few years. The areas of signal processing, electroencephalogram and magnetoencephalogram are considered. In the area of image analysis, the following modalities are reviewed: magnetic resonance imaging (MRI), functional MRI, diffusion tensor MRI, and structural MRI. Computer-aided early diagnosis of the AD would be a major breakthrough with a very significant worldwide impact because medications would be able to slow down the progression of the disease. This review shows that this is a very active area in the frontier of brain research, with many multidisciplinary researchers exploring a variety of approaches using various types of brain signals and imaging technologies. The brain signal-based approaches will be able to point toward early onset diagnosis of the AD, but as the disease progresses, a multimodal approach can increase the accuracy of the diagnosis.
About the authors
Sarah Hulbert is a native of Michigan and a graduate of Western Michigan University and the Lee Honors College in Kalamazoo, MI. She received her BS in Physics in May 2013 and is currently a PhD student in the Biophysics Program at the Ohio State University. Her interests include the processing and analysis of brain signals and images with applications in disease diagnosis.
Hojjat Adeli is a Professor of Civil, Environmental, and Geodetic Engineering, Biomedical Engineering, Biomedical Informatics, Electrical and Computer Engineering, Neurological Surgery, and Neuroscience at the Ohio State University. He has authored/ coauthored 15 books. He is the co-author of Automated EEG-Based Diagnosis of Neurological Disorders – Inventing the Future of Neurology (CRC Press, 2010). In 1998, he received the Ohio State University’s highest research honor, the Distinguished Scholar Award, ‘in recognition of extraordinary accomplishment in research and scholarship’. He is a fellow of AAAS and IEEE. He is the editor-in-chief of the international research journals Computer-Aided Civil and Infrastructure Engineering, which he founded in 1986, and Integrated Computer-Aided Engineering, which he founded in 1993. He is also the editor-in-chief of the International Journal of Neural Systems.
References
Abásolo, D., Hornero, R., Espino, P., Poza, J., Sánchez, C.I., and de la Rosa, R. (2005). Analysis of regularity in the EEG background activity of Alzheimer’s disease patients with approximate entropy. Clin. Neurophysiol. 116, 1826–1834.10.1016/j.clinph.2005.04.001Suche in Google Scholar PubMed
Abásolo, D., Hornero, R., Espino, P., Alvarez, D., and Poza, J. (2006). Entropy analysis of the EEG background activity in Alzheimer’s disease patients. Physiol. Meas. 27, 241–253.10.1088/0967-3334/27/3/003Suche in Google Scholar PubMed
Acharya, U.R., Vinitha Sree, S., Alvin, A.P.C., and Suri, J.S. (2012). Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int. J. Neural Syst. 22, 1250002.10.1142/S0129065712500025Suche in Google Scholar PubMed
Adeli, H. and Ghosh-Dastidar, S. (2010). Automated EEG-Based Diagnosis of Neurological Disorders – Inventing the Future of Neurology (Boca Raton, FL: CRC Press, Taylor & Francis).10.1201/9781439815328Suche in Google Scholar
Adeli, H. and Hung, S.L. (1995). Machine Learning – Neural Networks, Genetic Algorithms, and Fuzzy Systems (New York: John Wiley and Sons).Suche in Google Scholar
Adeli, H., Ghosh-Dastidar, S, and Dadmehr, N. (2005a). Alzheimer’s disease and models of computation: imaging, classification, and neural models. J. Alzheimers Dis. 7, 187–199.10.3233/JAD-2005-7301Suche in Google Scholar
Adeli, H., Ghosh-Dastidar, S., and Dadmehr, N. (2005b). Alzheimer’s disease: models of computation and analysis of EEGs. Clin. EEG Neurosci. 36, 131–140.10.1177/155005940503600303Suche in Google Scholar PubMed
Adeli, H., Ghosh-Dastidar, S., and Dadmehr, N. (2007). A wavelet-chaos methodology for analysis of EEGs and EEG sub-bands to detect seizure and epilepsy. IEEE Trans. Biomed. Eng. 54, 205–211.10.1109/TBME.2006.886855Suche in Google Scholar PubMed
Adeli, H., Ghosh-Dastidar, S., and Dadmehr, N. (2008). A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer’s disease. Neurosci. Lett. 444, 190–194.10.1016/j.neulet.2008.08.008Suche in Google Scholar PubMed
Agosta, F., Pievani, M., Geroldi, C., Copetti, M., Frisoni, G.B., and Filippi, M. (2012). Resting state fMRI in Alzheimer’s disease: beyond the default mode network. Neurobiol. Aging 33, 1564–1578.10.1016/j.neurobiolaging.2011.06.007Suche in Google Scholar PubMed
Ahmadlou, M. and Adeli, H. (2010). Enhanced probabilistic neural network with local decision circles: a robust classifier. Integr. Comput. Aided Eng. 17, 197–210.10.3233/ICA-2010-0345Suche in Google Scholar
Ahmadlou, M. and Adeli, H. (2011). Fuzzy synchronization likelihood with application to attention-deficit/hyperactivity disorder. Clin. EEG Neurosci. 42, 6–13.10.1177/155005941104200105Suche in Google Scholar PubMed
Ahmadlou, M., Adeli, H., and Adeli, A. (2010). New diagnostic EEG markers of the Alzheimer’s disease using visibility graph. J. Neural Transm. 117, 1099–1109.10.1007/s00702-010-0450-3Suche in Google Scholar PubMed
Ahmadlou, A., Adeli, H., and Adeli, A. (2011). Fractality and a wavelet-chaos methodology for EEG-based diagnosis of Alzheimer’s disease. Alzheimer Dis. Assoc. Disord. 25, 85–92.10.1097/WAD.0b013e3181ed1160Suche in Google Scholar PubMed
Ahmadlou, M., Adeli, H., and Adeli, A. (2012a). Fractality analysis of frontal brain in major depressive disorder. Int. J. Psychophysiol. 85, 206–211.10.1016/j.ijpsycho.2012.05.001Suche in Google Scholar PubMed
Ahmadlou, M., Adeli, H., and Adeli, A. (2012b). Fuzzy synchronization likelihood-wavelet methodology for diagnosis of autism spectrum disorder. J. Neurosci. Methods 211, 203–209.10.1016/j.jneumeth.2012.08.020Suche in Google Scholar PubMed
Ahmadlou, M., Adeli, A., Bajo, R., and Adeli, H. (2014). Complexity of functional connectivity networks in mild cognitive impairment patients during a working memory task. Clin. Neurophysiol. (in press).Suche in Google Scholar
Akhand, M.A.H. and Murase, K. (2012). Ensembles of neural networks based on the alteration of input feature values. Int. J. Neural Syst. 22, 77.10.1142/S0129065712003079Suche in Google Scholar PubMed
Al-Naser, M. and Soderstrom, U. (2012). Reconstruction of occluded facial images using asymmetrical principal component analysis. Integr. Comput. Aided Eng. 19, 273–283.10.3233/ICA-2012-0406Suche in Google Scholar
Alonso, J.F., Poza, J., Mañanas, M.Á., Romero, S., Fernández, A., and Hornero, R. (2011). MEG connectivity analysis in patients with Alzheimer’s disease using cross mutual information and spectral coherence. Ann. Biomed. Eng. 39, 524–536.10.1007/s10439-010-0155-7Suche in Google Scholar PubMed
Babiloni, C., Ferri, R., Binetti, G., Vecchio, F., Frisoni, G.B., Lanuzza, B., Miniussi, C., Nobili, F., Rodriguez, G., Rundo, F., et al. (2009). Directionality of EEG synchronization in Alzheimer’s disease subjects. Neurobiol. Aging 30, 93–102.10.1016/j.neurobiolaging.2007.05.007Suche in Google Scholar PubMed
Barnes, J., Carmichael, O.T., Leung, K.K., Schwarz, C., Ridgway, G.R., Bartlett, J.W., Malone, I.B., Schott, J.M., Rosser, M.N., Biessels, G.J., et al. (2013). Vascular and Alzheimer’s disease markers independently predict brain atrophy rate in Alzheimer’s Disease Neuroimaging Initiative controls. Neurobiol. Aging 34, 1996–2002.10.1016/j.neurobiolaging.2013.02.003Suche in Google Scholar PubMed PubMed Central
Basser, P.J. and Pierpaoli, C. (1996). Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. B 111, 209–219.10.1006/jmrb.1996.0086Suche in Google Scholar PubMed
Bozoki, A.C., Korolev, I.O., Davis, N.C., Hoisington, L.A., and Berger, K.L. ( 2012). Disruption of limbic white matter pathways in mild cognitive impairment and Alzheimer’s disease: a DTI/FDG-PET study. Hum. Brain Mapp. 33, 1792–1802.10.1002/hbm.21320Suche in Google Scholar PubMed PubMed Central
Brier, M.R., Thomas, J.B., Snyder, A.Z., Benzinger, T.L., Zhang, D., Raichle, M.E., Holtzman, D.M., Morris, J.C., and Ances, B.M. (2012). Loss of intranetwork and internetwork resting state functional connections with Alzheimer’s disease progression. J. Neurosci. 32, 8890–8899.10.1523/JNEUROSCI.5698-11.2012Suche in Google Scholar PubMed PubMed Central
Bruña, R., Poza, J., Gómez, C., Garciá, M., Fernańdez, A., and Hornero, R. (2012). Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer’s disease using spectral entropies and statistical complexity measures. J. Neural Eng. 9, 036007.10.1088/1741-2560/9/3/036007Suche in Google Scholar PubMed
Buscema, M., Rossini, P., Babiloni, C., and Grossi, E. (2007). The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer’s disease patients with high degree of accuracy. Artif. Intell. Med. 40, 127–141.10.1016/j.artmed.2007.02.006Suche in Google Scholar PubMed
Canu, E., Agosta, F., Spinelli, E.G., Magnani, G., Marcone, A., Scola, E., Falautano, M., Giancarlo, C., Falini, A., and Filippi, M. (2013). White matter microstructural damage in Alzheimer’s disease at different ages of onset. Neurobiol. Aging 34, 2331–2340.10.1016/j.neurobiolaging.2013.03.026Suche in Google Scholar PubMed
Chan, H.L., Chu, J.H., Fung, H.C., Tsai, Y.T., Meng, L.F., Huang, C.C., Hsu, W.C., Chao, P.K., Wang, J.J., Lee, J.D., et al. (2013). Brain connectivity of patients with Alzheimer’s disease by coherence and cross mutual information of electroencephalograms during photic stimulation. Med. Eng. Phys. 35, 241–252.10.1016/j.medengphy.2011.10.005Suche in Google Scholar PubMed
Chételat, G., Desgranges, B., Landeau, B., Mézenge, F., Poline, J.B., de la Sayette, S.V., Viader, F., Eustache, F., and Baron, J.-C. (2008). Direct voxel-based comparison between grey matter hypometabolism and atrophy in Alzheimer’s disease. Brain 131, 60–71.10.1093/brain/awm288Suche in Google Scholar PubMed
Dai, H., Zhang, H., and Wang, W. (2012). Structural reliability assessment by local approximation of limit state functions using adaptive Markov chain simulation and support vector regression. Comput. Aided Civil Infrastruct. Eng. 27, 676–686.10.1111/j.1467-8667.2012.00767.xSuche in Google Scholar
Douaud, G., Menke, R.A., Gass, A., Monsch, A.U., Rao, A., Whitcher, B., Zamboni, G., Matthews, P.M., Sollberger, M., and Smith, S. (2013). Brain microstructure reveals early abnormalities more than two years prior to clinical progression from mild cognitive impairment to Alzheimer’s disease. J. Neurosci. 33, 2147–2155.10.1523/JNEUROSCI.4437-12.2013Suche in Google Scholar PubMed PubMed Central
Dukart, J., Kherif, F., Mueller, K., Adaszewski, S., Schroeter, M.L., Frackowiak, R.S.J., and Draganski, B. (2013). Generative FDG-PET and MRI model of aging and disease progression in Alzheimer’s disease. PLoS Comput. Biol. 9, e1002987.10.1371/journal.pcbi.1002987Suche in Google Scholar PubMed PubMed Central
Eskildsen, S.F., Coupe, P., Garcia-Lorenzo, D., Fonov, V., Pruessner, J.C., and Collins, D.L. (2013). Prediction of Alzheimer’s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. Neuroimage 65, 511–521.10.1016/j.neuroimage.2012.09.058Suche in Google Scholar PubMed PubMed Central
Ghorbanian, P., Devilbiss, D.M., Verma, A., Bernstein, A., Hess, T., Simon, A.J., and Ashrafiuon, H. (2013). Identification of resting and active state EEG features of Alzheimer’s disease using discrete wavelet transform. Ann. Biomed. Eng. 41, 1243–1257.10.1007/s10439-013-0795-5Suche in Google Scholar PubMed
Ghosh-Dastidar, S, Adeli, H., and Dadmehr, N. (2006). Voxel-based morphometry in Alzheimer’s patients. J. Alzheimers Dis. 10, 445–447.10.3233/JAD-2006-10414Suche in Google Scholar PubMed
Gómez, C., Hornero, R., Abásolo, D., Fernańdez, A., and Escudero, J. (2009). Analysis of MEG background activity in Alzheimer’s disease using nonlinear methods and ANFIS. Ann. Biomed. Eng. 37, 586–594.10.1007/s10439-008-9633-6Suche in Google Scholar PubMed
Ha, S.-Y., Youn, Y.C., Kim, S.Y., Hsiung, G.-Y.R., Ahn, S.-W., Shin, H.-W., Park, K.-Y., Park, T.H., Kim, S.-S., Kee, B.S., et al. (2012). A voxel-based morphometric study of cortical gray matter volume changes in Alzheimer’s disease with white matter hyperintensities. J. Clin. Neurosci. 19, 1506–1510.10.1016/j.jocn.2011.11.041Suche in Google Scholar PubMed
Hahn, K., Myers, N., Prigarin, S., Rodenacker, K., Kurz, A., Förstl, H., Zimmer, C., Wohlschlager, A.M., and Sorg, C. (2013). Selectively and progressively disrupted structural connectivity of functional brain networks in Alzheimer’s disease – revealed by a novel framework to analyze edge distributions of networks detecting disruptions with strong statistical evidence. Neuroimage 81, 96–109.10.1016/j.neuroimage.2013.05.011Suche in Google Scholar PubMed
Han, F., Wiercigroch, M., Fang, J.A., and Wang, Z. (2011). Excitement and synchronization of small-world neuronal networks with short-term synaptic plasticity. Int. J. Neural Syst. 21, 415–425.10.1142/S0129065711002924Suche in Google Scholar PubMed
Hsu, W.Y. (2012). Application of competitive Hopfield neural network clustering to brain-computer interface systems. Int. J. Neural Syst. 22, 51–62.10.1142/S0129065712002979Suche in Google Scholar PubMed
Hsu, W.Y. (2013). Single-trial motor imagery classification using asymmetry ratio, phase relation and wavelet-based fractal features, and their selected combination. Int. J. Neural Syst. 23, 1350007.10.1142/S012906571350007XSuche in Google Scholar PubMed
Hua, X., Hibar, D.P., Ching, C.R.K., Boyle, C.P., Rajagopalan, P., Gutman, B.A., Leow, A.D., Toga, A.W., Jack, C.R., Harvey, D., et al. (2013). Unbiased tensor-based morphometry: improved robustness and sample size estimates for Alzheimer’s disease clinical trials. Neuroimage 66, 648–661.10.1016/j.neuroimage.2012.10.086Suche in Google Scholar
Hung, S.L. and Adeli, H. (1993). Parallel backpropagation learning algorithms on Cray Y-MP8/864 supercomputer. Neurocomputing 5, 287–302.10.1016/0925-2312(93)90042-2Suche in Google Scholar
Jahng, G.H., Xu, S., Weiner, M.W., Meyerhoff, D.J., Park, S., and Schuff, N. (2011). DTI studies in patients with Alzheimer’s disease, mild cognitive impairment, or normal cognition with evaluation of the intrinsic background gradients. Neuroradiology 53, 749–762.10.1007/s00234-011-0845-3Suche in Google Scholar PubMed PubMed Central
Jiang, X. and Adeli, H. (2003). Fuzzy clustering approach for accurate embedding dimension identification in chaotic time series. Integr. Comput. Aided Eng. 10, 287–302.10.3233/ICA-2003-10305Suche in Google Scholar
Karageorgiou, E., Lewis, S.M., McCarten, J.R., Leuthold, A.C., Hemmy, L.S., McPherson, S.E., Rottunda, S.J., Rubins, D.M., and Georgopoulos, A.P. (2012). Canonical correlation analysis of synchronous neural interactions and cognitive deficits in Alzheimer’s dementia. J. Neural Eng. 9, 056003.10.1088/1741-2560/9/5/056003Suche in Google Scholar PubMed
Kim, J.H., Hwang, K.J., Kim, J.H., Lee, Y.H., Rhee, H.Y., and Park, K.C. (2011). Regional white matter hyperintensities in normal aging, single domain amnestic mild cognitive impairment, and mild Alzheimer’s disease. J. Clin. Neurosci. 18, 1101–1106.10.1016/j.jocn.2011.01.008Suche in Google Scholar PubMed
Knyazeva, M.G., Carmeli, C., Khadivi, A., Ghika, J., Meuli, R., and Frackowiak, R.S. (2013). Evolution of source EEG synchronization in early Alzheimer’s disease. Neurobiol. Aging 34, 694–705.10.1016/j.neurobiolaging.2012.07.012Suche in Google Scholar PubMed
Kodogiannis, V.S., Amina, M., and Petrounias, I. (2013). A clustering-based fuzzy-wavelet neural network model for short-term load forecasting. Int. J. Neural Syst. 23, 1350024.10.1142/S012906571350024XSuche in Google Scholar PubMed
Li, H., Huang, Y., Chen, W.L., Ma, M.L., Tao, D.W., and Ou, J.P. (2011a). Estimation and warning of fatigue damage of FRP stay cables based on acoustic emission technique and fractal theory. Comput. Aided Civil Infrastruct. Eng. 26, 500–512.10.1111/j.1467-8667.2010.00713.xSuche in Google Scholar
Li, H., Huang, Y., Ou, J., and Bao, Y. (2011b). Fractal dimension-based damage detection method for beams with a uniform cross-section. Comput. Aided Civil Infrastruct. Eng. 26, 190–206.10.1111/j.1467-8667.2010.00686.xSuche in Google Scholar
Li, D., Xu, L., Goodman, E., Xu, Y., and Wu, Y. (2013). Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking. Integr. Comput. Aided Eng. 20, 201–216.10.3233/ICA-130428Suche in Google Scholar
Lin, C.M., Ting, A.B., Hsu, C.F., and Chung, C.M. (2012). Adaptive control for MIMO uncertain nonlinear systems using recurrent wavelet neural network. Int. J. Neural Syst. 22, 37–50.10.1142/S0129065712002992Suche in Google Scholar PubMed
Liu, F. and Er M.J. (2012). A novel efficient learning algorithm for self-generating fuzzy neural network with applications. Int. J. Neural Syst. 22, 21–35.10.1142/S0129065712003067Suche in Google Scholar PubMed
Lopez-Rubio, E., Luque-Baena, R.M., and Dominguez, E. (2011). Foreground detection in video sequences with probabilistic self-organizing maps. Int. J. Neural Syst. 21, 225–246.10.1142/S012906571100281XSuche in Google Scholar PubMed
McBride, J., Zhao, X., Munro, N., Smith, C., Jicha, G., and Jiang, Y. (2013). Resting EEG discrimination of early stage Alzheimer’s disease from normal aging using inter-channel coherence network graphs. Ann. Biomed. Eng. 41, 1233–1242.10.1007/s10439-013-0788-4Suche in Google Scholar PubMed PubMed Central
Meraoumia, A., Chitroub, S., and Bouridane, A. (2013). 2D and 3D palmprint information, PCA and HMM for an improved person recognition performance. Integr. Comput. Aided Eng. 20, 303–319.10.3233/ICA-130431Suche in Google Scholar
Mizuno, T., Takahashi, T., Cho, R.Y., Kikuchi, M., Murata, T., Takahashi, K., and Wada, Y. (2010). Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy. Clin. Neurophysiol. 121, 1438–1446.10.1016/j.clinph.2010.03.025Suche in Google Scholar PubMed PubMed Central
Moretti, D.V., Frisoni, G.B., Fracassi, C., Pievani, M., Geroldi, C., Binetti, G., Rossini, P.M., and Zanetti, O. (2011). MCI patients’ EEGs show group differences between those who progress and those who do not progress to AD. Neurobiol. Aging 32, 563–571.10.1016/j.neurobiolaging.2009.04.003Suche in Google Scholar PubMed
Ortiz-Rosario, A. and Adeli, H. (2013). Brain-computer interface technologies: from signal to action. Rev. Neurosci. 24, 455–562.10.1515/revneuro-2013-0032Suche in Google Scholar PubMed
Polikar, R., Topalis, A., Green, D., Kounios, J., and Clark, C. (2007). Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classifiers approach for early diagnosis of Alzheimer’s disease. Comput. Biol. Med. 37, 542–558.10.1016/j.compbiomed.2006.08.012Suche in Google Scholar PubMed PubMed Central
Puscasu, G. and Codres, B. (2011). Nonlinear system identification and control based on modular neural networks. Int. J. Neural Syst. 21, 319–334.10.1142/S0129065711002869Suche in Google Scholar PubMed
Rangaprakash, D., Hu, X., and Deshpande, G. (2013). Phase synchronization in brain networks derived from correlation between probabilities of recurrences in functional MRI data. Int. J. Neural Syst. 23, 1350003.10.1142/S0129065713500032Suche in Google Scholar PubMed
Rose, S.E., Janke, P.D.A.L., and Chalk, J.B. (2008). Gray and white matter changes in Alzheimers disease: a diffusion tensor imaging study. J. Magn. Reson. Imaging 27, 20–26.10.1002/jmri.21231Suche in Google Scholar PubMed
Rossini, P.M., Buscema, M., Capriotti, M., Grossi, E., Rodriguez, G., Del, P.C., and Babiloni, C. (2008). Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy? Clin. Neurophysiol. 119, 1534–1545.10.1016/j.clinph.2008.03.026Suche in Google Scholar PubMed
Samant, A. and Adeli, H. (2000). Feature extraction for traffic incident detection using wavelet transform and linear discriminant analysis. Comput. Aided Civil Infrastruct. Eng. 13, 241–250.10.1111/0885-9507.00188Suche in Google Scholar
Sankari, Z. and Adeli, H. (2011). Probabilistic neural networks for EEG-based diagnosis of Alzheimer’s disease using conventional and wavelet coherence. J. Neurosci. Methods 197, 165–170.10.1016/j.jneumeth.2011.01.027Suche in Google Scholar PubMed
Sankari, Z., Adeli, H., and Adeli, A. (2011). Intrahemispheric, interhemispheric and distal EEG coherence in Alzheimer’s disease. Clin. Neurophysiol. 122, 897–906.10.1016/j.clinph.2010.09.008Suche in Google Scholar PubMed
Sankari, Z., Adeli, H., and Adeli, A. (2012). Wavelet coherence model for diagnosis of Alzheimer’s disease. Clin. EEG Neurosci. 43, 268–278.10.1177/1550059412444970Suche in Google Scholar PubMed
Serletis, D., Carlen, P.L., Valiante, T.A., and Bardakjian, B.L. (2013). Phase synchronization of neuronal noise in mouse hippocampal epileptiform dynamics. Int. J. Neural Syst. 23, 1250033.10.1142/S0129065712500335Suche in Google Scholar PubMed
Sherman, D., Zhang, N., Anderson, M., Garg, S., Hinich, M.J., Thakor, N.V., and Mirski, M.A. (2011). Detection of nonlinear interactions of EEG alpha waves in the brain by a new coherence measure and its application to epilepsy and anti-epileptic drug therapy. Int. J. Neural Syst. 21, 115–126.10.1142/S0129065711002754Suche in Google Scholar PubMed PubMed Central
Stam, C.J., de, H.W., Daffertshofer, A., Jones, B.F., Manshanden, I., van, C.W.A.M., Montez, T., Verbunt, J.P., de Munck, J.C., van Diijk, B.W., et al. (2009). Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain 132, 213–224.10.1093/brain/awn262Suche in Google Scholar PubMed
Subramanian, K. and Suresh, S. (2012). Human action recognition using meta-cognitive neuro-fuzzy inference system. Int. J. Neural Syst. 22, 1250028.10.1142/S0129065712500281Suche in Google Scholar PubMed
Tao, H., Zain, J.M., Ahmed, M.M., Abdalla, A.N., and Jing, W. (2012). A wavelet-based particle swarm optimization algorithm for digital image watermarking. Integr. Comput. Aided Eng. 19, 81–91.10.3233/ICA-2012-0392Suche in Google Scholar
Theodoridis, D., Boutalis, Y., and Christodoulou, M. (2012). Dynamical recurrent neuro-fuzzy identification schemes employing switching parameter hoping. Int. J. Neural Syst. 22, 1250004.10.1142/S0129065712500049Suche in Google Scholar PubMed
Tijms, B.M., Wink, A.M., de, H.W., van der Flier, F.W.M., Stam, C.J., Scheltens, P., and Barkhof, F. (2013). Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks. Neurobiol. Aging 34, 2023–2036.10.1016/j.neurobiolaging.2013.02.020Suche in Google Scholar PubMed
Tomasevic, N.M., Neskovic, A.M., and Neskovic, N.J. (2012). Artificial neural network-based approach to EEG signal simulation. Int. J. Neural Syst. 22, 1250008.10.1142/S0129065712500086Suche in Google Scholar PubMed
Trambaiolli, L.R., Lorena, A.C., Fraga, F.J., Kanda, P.A., Anghinah, R., and Nitrini, R. (2011). Improving Alzheimer’s disease diagnosis with machine learning techniques. Clin. EEG Neurosci. 42, 160–165.10.1177/155005941104200304Suche in Google Scholar PubMed
Yamanishi, T., Liu, J.Q., and Nishimura, H. (2012). Modeling fluctuations in default-mode brain network using a spiking neural network. Int. J. Neural Syst. 22, 1250016.10.1142/S0129065712500165Suche in Google Scholar PubMed
Zhou, Z. and Adeli, H. (2003). Time-frequency signal analysis of earthquake records using Mexican hat wavelets. Comput. Aided Civil Infrastruct. Eng. 18, 379–389.10.1111/1467-8667.t01-1-00315Suche in Google Scholar
Zhou, L.R., Ou, J.P., and Yan, G.R. (2013). Response surface method based on radial basis functions for modeling large-scale structures in model updating. Comput. Aided Civil Infrastruct. Eng. 28, 210–226.10.1111/j.1467-8667.2012.00803.xSuche in Google Scholar
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- Masthead
- EEG/MEG- and imaging-based diagnosis of Alzheimer’s disease
- Situationally appropriate behavior: translating situations into appetitive behavior modes
- New animal models of Alzheimer’s disease that display insulin desensitization in the brain
- Neural mechanisms underlying chronic fatigue
- 5-HT systems: emergent targets for memory formation and memory alterations
- Music education and its effect on intellectual abilities in children: a systematic review
