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Multiple Sclerosis International
Volume 2012 (2012), Article ID 312503, 17 pages
Clinical Study

Overall Memory Impairment Identification with Mathematical Modeling of the CVLT-II Learning Curve in Multiple Sclerosis

1Department of Neuropharmacology, Institute for Experimental Medicine, The Russian Academy of Medical Sciences, Acad. Pavlov Street 12, 197376 St. Petersburg, Russia
2Department of Psychology, Oklahoma State University, 116 N. Murray, Stillwater, OK 74078, USA
3The Jacobs Neurological Institute, Buffalo General Hospital, 100 High Street, Buffalo, NY 14203, USA
4Department of Neurology, State University of New York (SUNY) at Buffalo, Buffalo General Hospital, Suite D6, 100 High Street, Buffalo, NY 14203, USA

Received 1 November 2011; Revised 4 March 2012; Accepted 13 April 2012

Academic Editor: Peter Arnett

Copyright © 2012 Igor I. Stepanov 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.


The CVLT-II provides standardized scores for each of the List A five learning trials, so that the clinician can compare the patient's raw trials 1–5 scores with standardized ones. However, frequently, a patient's raw scores fluctuate making a proper interpretation difficult. The CVLT-II does not offer any other methods for classifying a patient's learning and memory status on the background of the learning curve. The main objective of this research is to illustrate that discriminant analysis provides an accurate assessment of the learning curve, if suitable predictor variables are selected. Normal controls were ninety-eight healthy volunteers (78 females and 20 males). A group of MS patients included 365 patients (266 females and 99 males) with clinically defined multiple sclerosis. We show that the best predictor variables are coefficients 𝐵 3 and 𝐵 4 of our mathematical model 𝐵 3 ∗ e x p ( − 𝐵 2 ∗ ( 𝑋 − 1 ) ) + 𝐵 4 ∗ ( 1 − e x p ( − 𝐵 2 ∗ ( 𝑋 − 1 ) ) ) because discriminant functions, calculated separately for 𝐵 3 and 𝐵 4 , allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both.