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Using OrPLS to identify asymptomatic women at risk for Alzheimer's disease
Author(s) -
Rayens William,
Liu Yushu,
Andersen Anders,
Smith Charles
Publication year - 2008
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1172
Subject(s) - disease , family history , alzheimer's disease , asymptomatic , psychology , medicine , artificial intelligence , audiology , computer science
Persons at risk for Alzheimer's disease (AD) demonstrate altered cortical activation measured by functional MRI (fMRI) years before symptoms of disease are expected. We used fMRI to study the differences in cortical activation between 13 women with a family history of AD and at least one apolipoprotein E4 allele, a risk factor for AD, and a control group of 11 women lacking both factors. Our primary goal was to assess how well the two groups are able to be statistically separated, a task which directly affects the performance of post hoc classification. The dimension of the dataset, however, precludes the use of ordinary classification methods. In this paper we show the superiority of using Oriented Partial Least Squares (OrPLS) to accomplish the classification in the presence of this dimensionality problem. We are able to reduce the misclassification rates on the standardized fMRI data from an average of about 48% for PCA, to an average of 27% for PLS, and then to perfect classification for OrPLS. Copyright © 2008 John Wiley & Sons, Ltd.