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Abstract #1577

Application of a fussed lasso logistic regression classifier to the study of corpus callosum thickness in early Alzheimer's disease

Babak A Ardekani 1 , Sang Han A Lee 1 , Donghyun Yu 2 , Johan Lim 2 , and Alvin H Bachman 1

1 The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, United States, 2 Statistics, Seoul National University, Seoul, Korea

We describe a multi-atlas-based method for corpus callosum segmentation and a fused Lasso logistic regression (FLLR) classifier that is able to differentiate patients with very mild/mild AD from normal controls (NC) using their CC thickness profile. We evaluated this technique using data from 196 individuals (98 AD and 98 NC) in the OASIS database. The FLLR classification accuracy was estimated to be 84% using cross-validation. Furthermore, the FLLR method highlights regions of the CC that are significantly thinner in AD relative to NC. The FLLR model presented can be extended to include other imaging or chemical biomarkers of AD.

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