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Machine Learning Amplifies the Effect of Parental Family History of Alzheimer's Disease on List Learning Strategy

Published online by Cambridge University Press:  10 February 2012

Timothy S. Chang ,
Michael H. Coen ,
Asenath La Rue ,
Erin Jonaitis ,
Rebecca L. Koscik ,
Bruce Hermann  and
Mark A. Sager
Timothy S. Chang*
Affiliation:
Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
Michael H. Coen
Affiliation:
Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin Department of Computer Science, University of Wisconsin-Madison, Madison, Wisconsin
Asenath La Rue
Affiliation:
Wisconsin Alzheimer's Institute, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
Erin Jonaitis
Affiliation:
Wisconsin Alzheimer's Institute, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
Rebecca L. Koscik
Affiliation:
Wisconsin Alzheimer's Institute, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
Bruce Hermann
Affiliation:
Wisconsin Alzheimer's Institute, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin Department of Neurology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
Mark A. Sager
Affiliation:
Wisconsin Alzheimer's Institute, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin Section of Geriatrics and Gerontology, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
*
Correspondence and reprint requests to: Timothy S. Chang, Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, 6795 Medical Sciences Center, 1300 University Avenue, Madison, WI 53706. E-mail: tschang3@wisc.edu
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Abstract

Identification of preclinical Alzheimer's disease (AD) is an essential first step in developing interventions to prevent or delay disease onset. In this study, we examine the hypothesis that deeper analyses of traditional cognitive tests may be useful in identifying subtle but potentially important learning and memory differences in asymptomatic populations that differ in risk for developing Alzheimer's disease. Subjects included 879 asymptomatic higher-risk persons (middle-aged children of parents with AD) and 355 asymptotic lower-risk persons (middle-aged children of parents without AD). All were administered the Rey Auditory Verbal Learning Test at baseline. Using machine learning approaches, we constructed a new measure that exploited finer differences in memory strategy than previous work focused on serial position and subjective organization. The new measure, based on stochastic gradient descent, provides a greater degree of statistical separation (p = 1.44 × 10−5) than previously observed for asymptomatic family history and non-family history groups, while controlling for apolipoprotein epsilon 4, age, gender, and education level. The results of our machine learning approach support analyzing memory strategy in detail to probe potential disease onset. Such distinct differences may be exploited in asymptomatic middle-aged persons as a potential risk factor for AD. (JINS, 2012, 18, 428–439)

Keywords

Cohort studyMemoryNeuropsychological testsPre-symptomatic diseaseStatistical modelsMedical informatics
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 18 , Issue 3 , May 2012 , pp. 428 - 439
Copyright
Copyright © The International Neuropsychological Society 2012

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