Published date:Nov 01 2018

Journal Title: KnE Life Sciences

Issue title: The Fifth International Luria Memorial Congress «Lurian Approach in International Psychological Science»

Pages:763–772

DOI: 10.18502/kls.v4i8.3334

N RiveraNieves.Rivera@uab.cat

M Cabrera-Bean

G Sánchez-Benavides

C Gallego-González

J A Lupiáñez-Pretel

J Peña-Casanova

Objective: To develop and implement an online Artificial Neural Network (ANN) that provides the probability of a subject having mild cognitive impairment (MCI) or Alzheimer’s disease (AD).

Method: Different ANNs were trained using a sample of 350 controls (CONT), 75 MCI and 93 AD subjects. The ANN structure chosen was the following: (1) an input layer of 33 cognitive variables from the Neuronorma battery plus two sociodemographic variables, age and education. This layer was reduced to a 15 features input vector using Multiple Discriminant Analysis method, (2) one hidden layer with 8 neurons, and (3) three output neurons corresponding to the 3 expected cognitive states. This ANN was defined in a previous study [28]. The ANN was implemented on the web site www.test-barcelona.com (Test Barcelona Workstation) [9].

Results: When comparing CONT, MCI and AD participants, the best ANN correctly classifies up to 94,87% of the study participants.

Conclusions: The online implemented ANN, delivers the probabilities (%) of belonging to the CONT, MCI and AD groups of a subject assessed using the 35 characteristics (variables) of the Neuronorma profile. This tool is a good complement for the interpretation of cognitive profiles. This technology improves clinical decision making.

Keywords: Artificial Neural Network, Probability, Alzheimer disease, Test Barcelona Workstation.

[1] Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013). Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine, 11, 47-58.


[2] Benton, A. L., Sivan, A. B., Hamsher, K. S., Varney, N. R., & Spreen, O. (1994). Contributions to neuropsychological assessment. New York, NY: Oxford University Press.


[3] Buschke, H. (1984). Cuedrecallin amnesia. Journal of Clinical Neuropsychology, 6, 433-440.


[4] Buscema, M. P., Grossi, E., Snowdon, D., Antuono, P., Intraligi, M., Maurelli, G., et al. (2004). Artificial Neural Networks and Artificial Organisms Can Predict Alzheimer Pathology in Individual Patients Only on The Basis of Cognitive and Functional Status. Neuroinformatics, 2, 399-416.


[5] Culbertson, W. C., & Zillmer, E. A. (2001). Tower of London. Drexel University. TOLDX. North Tonawanda, NY: Multi-Health Systems.


[6] De Renzi, E., & Faglioni, P. (1978). Development of a shortened version of the Token Test. Cortex, 14, 41-49.


[7] Di Luca, M., Grossi, E., Borroni, B., Zimmermann, M., Marcello, E., Colciaghi, F., et al. (2005). Artificial neural networks allow the use of simultaneous measurements of Alzheimer Disease markers for early detection of the disease. Journal of Translational Medicine, 3, 1-7.


[8] Golden, C. J. (1978). Stroop Color and Word Test. Chicago, IL: Stoeling.


[9] Gumbau, G. (2018). Programació del sistema de comunicacions entre dos servidors per aplicacions biomèdiques. Barcelona: Universitat Politèctica de Catalunya.


[10] Kaplan, E., Fein, D., Morris, R., & Delis, D. (1991). WAIS-R as a neuropsychological instrument. San Antonio, TX: The Psychological Corporation.


[11] Kaplan, E., Goodglass, H., & Weintraub, S. (2001). The Boston Naming Test (2nd ed.). Philadelphia, PA: Lippincott Williams & Wilkins.


[12] Lezak, M. D., Howieson, D. B., Bigler, E. D., & Tranel, D. (2012). Neuropsychological Assessment (Fifth ed.). New York: Oxford University Press, Inc.


[13] Osterrieth, P. A. (1944). Le test de copie d’une figure complexe: Contribution è l’étude de la perception et la mémoire. Archives de Psychologie, 30, 286-356.


[14] Partington, J., & Leiter, R. (1949). Partington’s pathways test. The Psychological Service Center Bulletin, 1, 9-20.


[15] Peña-Casanova, J. (2005). Programa Integrado de Exploración Neuropsicológica. Test Barcelona-Revisado. Barcelona: Masson. [Integrated program of neuropsychological assessment- Revised Barcelona Test].


[16] Peña-Casanova, J., Blesa, R., Aguilar, M., Gramunt-Fombuena, N., Gómez-Ansón, B., Oliva, R., et al. (2009). Spanish Multicenter Normative Studies (NEURONORMA Project): methods and sample characteristics. Archives of Clinical Neuropsychology, 24, 307-319.


[17] Peña-Casanova, J., Quiñones-Úbeda, S., Quintana-Aparicio, M., Aguilar, M., Badenes, D., Molinuevo, J.L., et al. (2009). Spanish Multicenter Normative Studies (NEURONORMA Project): norms for verbal span, visuospatial span, letter and number sequencing, trail making test, and symbol digit modalities test. Archives of Clinical Neuropsychology, 24, 321-341.


[18] Peña-Casanova, J., Quiñones-Úbeda, S., Gramunt-Fombuena, N., Aguilar, M., Casas, L., Molinuevo, J.L., et al. (2009). Spanish Multicenter Normative Studies (NEURONORMA Project): norms for Boston naming test and token test. Archives of Clinical Neuropsychology, 24, 343-354.


[19] Peña-Casanova, J., Quiñones-Úbeda, S., Gramunt-Fombuena, N., Quintana-Aparicio, M., Aguilar, M., Badenes, D., et al. (2009). Spanish Multicenter Normative Studies (NEURONORMA Project): norms for verbal fluency tests. Archives of Clinical Neuropsychology, 24, 395-411.


[20] Peña-Casanova, J., Gramunt-Fombuena, N., Quiñones-Úbeda, S., SánchezBenavides, G., Aguilar, M., Badenes, D., et al. (2009). Spanish Multicenter Normative Studies (NEURONORMA Project): norms for the Rey-Osterrieth complex figure (copy and memory), and free and cued selective reminding test. Archives of Clinical Neuropsychology, 24, 371-393.


[21] Peña-Casanova, J., Quiñones-Úbeda, S., Gramunt-Fombuena, N., Quintana-Aparicio, M., Aguilar, M., Molinuevo, J.L., et al. (2009). Spanish Multicenter Normative Studies (NEURONORMA Project): norms for the Stroop color-word interference test and the Tower of London-Drexel. Archives of Clinical Neuropsychology, 24, 413-429.


[22] Peña-Casanova, J., Quintana-Aparicio, M., Quiñones-Úbeda, S., Aguilar, M., Molinuevo, J.L., Serradell, M., et al. (2009). Spanish Multicenter Normative Studies (NEURONORMA Project): norms for the visual object and space perception batteryabbreviated, and judgment of line orientation. Archives of Clinical Neuropsychology, 24, 355-370.


[23] Peña-Casanova, J., Sánchez-Benavides, G., de Sola, S., Manero-Borrás, R. M., & Casals-Coll, M. (2012). Neuropsychology of Alzheimer’s Disease. Archives of Medical Research, 43, 686-693.


[24] Petersen, R. C., Smith, G. E., Waring, S. C., Invik, R. J., Tangalos, E. G., & Kokmen, E. (1999). Mild cognitive impairment: Clinical characterization and outcome. Archives of Neurology, 56, 303-308.


[25] Quintana, M., Guàrdia, J., Sánchez-Benavides, G., Aguilar, M., Molinuevo, J. L., Robles, A., et al. (2012). Using artificial neural networks in clinical neuropsychology: High performance in mild cognitive impairment and Alzheimer’s disease. Journal of Clinical and Experimental Neuropsychology, 34, 195-208.


[26] Reitan, R. M., & Wolfson, D. (1993). The Halstead-Reitan neuropsychological test battery. Theory and clinical interpretation (2nd ed.). Tucson, AZ: Neuropsychology Press.


[27] Rey, A. (1941). L’examen psychologique dans les cas d’encéphalopathie traumatique. Archives de Psychologie, 28, 286-340.


[28] Rivera, N. (2016). Predicció de la demència tipus Alzheimer mitjançant xarxes neuronals a partir de dades cognitives. Barcelona: Universitat Politèctica de Catalunya.


[29] Smith, A. (1973). Symbol Digit Modalities Test Manual. Los Angeles: Western Psychological Services.


[30] Warrington, E. K., & James, M. (1991). Visual Object and Space Perception Battery. Suffolk: Thames Valley Test Co.


[31] Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L.O., et al. (2004). Mild cognitive impairment–beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine, 256, 240-246.