Abstract
We presented and evaluated three sparsity learning based regression models with application to the automated prediction of the Mini-Mental State Examination (MMSE) scores in Alzheimer’s disease(AD) using T1-weight magnetic resonance images (MRIs) from 678 subjects, including 190 healthy control (HC) subjects, 331 mild cognitive impairment (MCI) subjects, and 157 AD subjects. The raw features were obtained from a validated multi-granularity whole-brain analysis pipeline, providing multi-level whole-brain segmentation volumes. We employed the ridge, lasso, and elastic-net as our regression algorithms, with the whole-brain volumes at each level being the independent variables and the MMSE score being the dependent variable. We used 10-fold cross-validation to evaluate the prediction performance and another 10-fold inner loop to estimate the optimal parameters in each model. According to our results, the combination of elastic-net and the second level of whole-brain segmentation volumes (a total of 137 volumes) worked the best compared to all other possible combinations. The work presented in this paper provides a potentially powerful and novel non-invasive biomarker for AD.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (81501546) and the SYSU-CMU Shunde International Joint Research Institute Start-up Grant (20150306). We would like to thank Yuanyuan Wei, Jingyuan Li, and Huilin Yang for valuable discussions.
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Zhang, J., Luo, Y., Jiang, Z., Tang, X. (2017). Regression Analysis and Prediction of Mini-Mental State Examination Score in Alzheimer’s Disease Using Multi-granularity Whole-Brain Segmentations. In: Chen, H., Zeng, D., Karahanna, E., Bardhan, I. (eds) Smart Health. ICSH 2017. Lecture Notes in Computer Science(), vol 10347. Springer, Cham. https://doi.org/10.1007/978-3-319-67964-8_20
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