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

Abstract

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 B3 and B4 of our mathematical model B3 ∗ exp(−B2 ∗ (X − 1)) + B4 ∗ (1 − exp(−B2 ∗ (X − 1))) because discriminant functions, calculated separately for B3 and B4, allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both.

The Importance of Mathematical Models to Explore the Effects of Marijuana and Other Plant Based Products on Learning and Memory

“…The multiple sclerosis group included 365 patients (266 females and 99 males) with clinically defined multiple sclerosis and was tested with the CVLT-II [14]. The raw learning data (the number of correctly recalled words from List A) were modeled and the sets of B3 and B4 values were used for further analysis [3]. Female and male sets of B3 values, as well as B4 values, were analyzed separately because it was shown that females scored significantly higher than males on the CVLT [15].…”

“…Second, we advocate the use of the first order system transfer function. We have used this model in a wide variety of studies ranging from the assessment of memory in drug addicts and alcoholics [1] to the Open Access Natural Science detection of memory impairment in individuals suffering from either Type-2 diabetes mellitus [2] or multiple sclerosis [3]. The model has also been applied to both the child and adult versions of the California Verbal Learning Test [4,5].…”

Learning in Plants: Lessons from Mimosa pudica

“…These models can be applied to plant data and comparisons can made on the basis of, for example, differences in exponents ( Stepanov and Abramson, 2008 ). Learning models have successfully been applied in a wide variety of situations including the effects of pesticides on learning in bees ( De Stefano et al, 2014 ) and the assessment of recall in multiple sclerosis patients ( Stepanov et al, 2012 ).…”