Relative value of diverse brain MRI and blood-based biomarkers for predicting cognitive decline in the elderly

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

doi:10.1117/12.2216964

21 March 2016 Relative value of diverse brain MRI and blood-based biomarkers for predicting cognitive decline in the elderly

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

Author Affiliations +

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 978411 (2016) https://doi.org/10.1117/12.2216964
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

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Sarah K. Madsen, Greg Ver Steeg, Madelaine Daianu, Adam Mezher, Neda Jahanshad, Talia M. Nir, Xue Hua, Boris A. Gutman, Aram Galstyan, and Paul M. Thompson "Relative value of diverse brain MRI and blood-based biomarkers for predicting cognitive decline in the elderly", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978411 (21 March 2016); https://doi.org/10.1117/12.2216964

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