Abstract
Nowadays, there is a population ageing which leads to an increasing of geriatric and non-communicable diseases. One of the major socio-sanitary challenges our society is facing is dementia, with Alzheimer’s disease (AD) as the most prevalent one. AD is a progressive neurodegenerative disorder over years, with several stages. One of them is the prodromal one, also called Mild Cognitive Impairment (MCI). Despite the recent advances in diagnostic criteria for AD, its definitive diagnosis is just possible post-mortem because there is nonspecific AD biomarker. Therefore, an early and differential diagnosis of AD is still an issue of high concern. Extensive research looking for appropriate methods of diagnosis has been done.
In this paper, we will present an innovative smart computing solution based on a hybrid and ontogenetic neural architecture, to deal with these challenges. It is an intelligent clinical decision-making system which has a non-neural pre-processing module and a neural processing one. This latter is a Modular Hybrid Growing Neural Gas (MyGNG), developed in this work. MyGNG consists of an input layer a Growing Neural Gas and a labelling layer based on the Perceptron algorithm. These modules are hierarchically organized and have different neurodynamic, connection topologies and learning laws.
Using just neuropsychological tests of 495 patients (150 AD, 345 MCI) from ADNI repository, our proposal has provided very promising results in the early detection of AD versus MCI, reaching values of AUC of 0.95; Sensitivity of 0.89 and Accuracy of 0.81. It is an appropriate diagnosis system for any clinical setting.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
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References
Alzheimer’s Disease International: World Alzheimer Report 2010. Alzheimer’s disease International, London (2010)
Romo-Galindo, D.A., Padilla-Moya, E.: Utilidad de los test cognoscitivos breves para detectar la demencia en población mexicana. Archivos de Neurociencias 23(4), 26–34 (2018)
World Health Organization: Risk reduction of cognitive decline and dementia: WHO guidelines. World Health Organization (2019)
Medina, M., de Arriba-Enríquez, J., Frontera, A., Flores, A., Valero, S.: Informe Anual CIBERNED 2016. Ministerio de Economía, Industria y Competitividad, Instituto de Salud Carlos III, CIBERNED (2017)
Grupo Estatal de Demencias: Plan Integral de Alzheimer y otras Demencias (2019–2023). Ministerio de Sanidad, Consumo y Bienestar Social (2019)
Grupo de Neurología Cognitiva de la sociedad Valenciana de Neurología COGVAL. Guía de manejo práctico de la enfermedad de Alzheimer, Sociedad valenciana de Neurología (2017)
Mitchell, A., Shiri-Feshki, M.: Rate of progression of mild cognitive impairment to dementia–meta-analysis of 41 robust inception cohort studies. Acta Psychiatr. Scand. 119(4), 252–265 (2009)
Erkinjuntti, T., Ostbye, T., Steenhuis, R., Hachinski, V.: The effect of different diagnostic criteria on the prevalence of dementia. N. Engl. J. Med. 337(23), 1667–1674 (1997)
Hecht-Nielsen, R.: Neurocomputing. Addison-Wesley, Boston (1990)
Ebrahimi-Ghahnavieh, A., Luo, S., Chiong, R.: Transfer learning for Alzheimer's disease detection on MRI images. In: 2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), pp. 133–138. IEEE (2019). https://doi.org/10.1109/ICIAICT.2019.8784845
Khvostikov, A., Aderghal, K., Benois-Pineau, J., Krylov, A.S., Catheline, G.: 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies (2018)
Basaiaa, S., et al.: Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage Clin. 21, 101645 (2019). https://doi.org/10.1016/j.nicl.2018.101645
Asl, E.H., Gimel’farb, G., El-Baz, A.: Alzheimer’s disease diagnostics by a deeply supervised adaptable 3D convolutional network. Front. Bioscience-Landmark 23(3), 584–596 (2016). https://doi.org/10.2741/4606
Ramzan, F., et al.: A Deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks. J. Med. Syst. 44(2), 1–16 (2019). https://doi.org/10.1007/s10916-019-1475-2
Kruthika, K.R., Maheshappa, H.D.: Multistage classifier-based approach for Alzheimer’s disease prediction and retrieval. Inform. Med. Unlocked 14, 34–42 (2019). https://doi.org/10.1016/j.imu.2018.12.003
Cabrera-León, Y., Garcia, P., Ruiz-Alzola, J., Suárez-Araujo, C.P.: Classification of mild cognitive impairment stages using machine learning methods. In: 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES), pp. 000067–000072. IEEE (2018). doi: https://doi.org/10.1109/INES.2018.8523858
Cui, R., Liu, M., Li, G.: Longitudinal analysis for Alzheimer's disease diagnosis using RNN. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1398–1401. IEEE (2018). doi: https://doi.org/10.1109/ISBI.2018.8363833
Tabarestani, S., et al.: A distributed multitask multimodal approach for the prediction of Alzheimer’s disease in a longitudinal study. Neuroimage 206, 116317 (2019). https://doi.org/10.1016/j.neuroimage.2019.116317
Manzak, D., Çetinel, G., Manzak, A.: Automated Classification of Alzheimer’s disease using deep neural network (DNN) by random forest feature elimination. In: 2019 14th International Conference on Computer Science and Education (ICCSE), pp. 1050–1053. IEEE (2019). https://doi.org/10.1109/ICCSE.2019.8845325
Jiang, J., Kang, L., Huang, J., Zhang, T.: Deep learning based mild cognitive impairment diagnosis using structure MR images. Neurosci. Lett. 730, 134971 (2020). https://doi.org/10.1016/j.neulet.2020.134971
Suárez-Araujo, C.P., García, P., Cabrera-León, Y., Prochazka, A., Rodríguez, N., Fernandez, C.: A real-time clinical decision support system, for mild cognitive impairment detection, based on a hybrid neural architecture. Computational and Mathematical Methods in Medicine (2021)
Zhu, H., Adeli, E., Shi, F., Shen, D.: FCN based label correction for multi-atlas guided organ segmentation. Neuroinformatics 18(2), 319–331 (2020). https://doi.org/10.1007/s12021-019-09448-5
Pellegrini, E., et al.: Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: a systematic review. Alzheimer’s Dement. Diagn. Assess. Dis. Monit. 10, 519–535 (2018). https://doi.org/10.1016/j.dadm.2018.07.004
Yao, X., Yan, J.A.G.: Mapping longitudinal scientific progress, collaboration and impact of the Alzheimer’s disease neuroimaging initiative. PLoS ONE 12(11), 1–19 (2017). https://doi.org/10.1371/journal.pone.0186095
Rojas-Gualdrón, D.F., Segura, A., Cardona, D., Segura, A., Garzón, M.O.: Análisis Rasch del Mini Mental State Examination (MMSE) en adultos mayores de Antioquia Colombia. Rev. CES Psico 10(2), 17–27 (2017). https://doi.org/10.21615/cesp.10.2.2
Ito, K., Hutmacher, M.M., Corrigan, B.W.: Modeling of functional assessment questionnaire (FAQ) as continuous bounded data from the ADNI database. Pharmacokinet Pharmacodyn. 39, 601–618 (2012). https://doi.org/10.1007/s10928-012-9271-3
Rosen, W.G., Mohs, R.C., Davis, k.L.: A new rating scale for Alzheimer’s disease. Am. J. Psychiatry 144, 1356–1363 (1984). https://doi.org/10.1176/ajp.141.11.1356
Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18(1), 50–60 (1947). https://doi.org/10.1214/aoms/1177730491
Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the 20th international conference on machine learning (ICML 2003) pp. 856–863 (2003)
Peña, D.: Componentes Principales. Análisis de datos multivariante. McGraw Hill, Madrid (2003)
Reddy, C.K., Aggarwal C.C.: Data Clustering. Chapman and Hall, Boca Raton (2013)
García Báez, P., Suárez Araujo, C.P., Fernández Viadero, C., Regidor García, J.: Automatic prognostic determination and evolution of cognitive decline using artificial neural networks. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 898–907. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77226-2_90
Sell, S., Widen, G., Prough, D., Hellmich, H.: Principal component analysis of blood microRNA datasets facilitates diagnosis of diverse diseases. PLoS ONE 15(6), 1–26 (2020)
Fritzke, B.: A Growing Neural Gas Network Learns Topologies (1994)
Ryotaro, K.: Information enhancement for interpreting competitive learning. Int. J. Gen. Syst. 39(7), 705–728 (2010). https://doi.org/10.1080/03081071003601421
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006). https://doi.org/10.1016/j.patrec.2005.10.010
Acknowledgments
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12–2-0012). ADNI is funded by the National Institute on Ageing, the National Institute of Biomedical Imageing and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
We would like to thank the anonymous reviewers for their valuable comments, which allowed improving the quality of the paper.
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Sosa-Marrero, A. et al. (2021). Detection of Alzheimer’s Disease Versus Mild Cognitive Impairment Using a New Modular Hybrid Neural Network. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12862. Springer, Cham. https://doi.org/10.1007/978-3-030-85099-9_18
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