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Proceedings Paper

Relative value of diverse brain MRI and blood-based biomarkers for predicting cognitive decline in the elderly
Author(s): Sarah K. Madsen; Greg Ver Steeg; Madelaine Daianu; Adam Mezher; Neda Jahanshad; Talia M. Nir; Xue Hua; Boris A. Gutman; Aram Galstyan; Paul M. Thompson
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Paper Abstract

Cognitive decline accompanies many debilitating illnesses, including Alzheimer’s disease (AD). In old age, brain tissue loss also occurs along with cognitive decline. Although blood tests are easier to perform than brain MRI, few studies compare brain scans to standard blood tests to see which kinds of information best predict future decline. In 504 older adults from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we first used linear regression to assess the relative value of different types of data to predict cognitive decline, including 196 blood panel biomarkers, 249 MRI biomarkers obtained from the FreeSurfer software, demographics, and the AD-risk gene APOE. A subset of MRI biomarkers was the strongest predictor. There was no specific blood marker that increased predictive accuracy on its own, we found that a novel unsupervised learning method, CorEx, captured weak correlations among blood markers, and the resulting clusters offered unique predictive power.

Paper Details

Date Published: 21 March 2016
PDF: 6 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978411 (21 March 2016); doi: 10.1117/12.2216964
Show Author Affiliations
Sarah K. Madsen, The Univ. of Southern California (United States)
Greg Ver Steeg, The Univ. of Southern California (United States)
Madelaine Daianu, The Univ. of Southern California (United States)
UCLA School of Medicine (United States)
Adam Mezher, The Univ. of Southern California (United States)
Neda Jahanshad, The Univ. of Southern California (United States)
Talia M. Nir, The Univ. of Southern California (United States)
Xue Hua, The Univ. of Southern California (United States)
Boris A. Gutman, The Univ. of Southern California (United States)
Aram Galstyan, The Univ. of Southern California (United States)
Paul M. Thompson, The Univ. of Southern California (United States)
UCLA School of Medicine (United States)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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