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Zhang J, Stonnington C, Li Q, et al. APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE. Proc IEEE Int Symp Biomed Imaging. 2016;2016:646-650doi: 10.1109/ISBI.2016.7493350.
Zhang, J., Stonnington, C., Li, Q., Shi, J., Bauer, R. J., Gutman, B. A., Chen, K., Reiman, E. M., Thompson, P. M., Ye, J., & Wang, Y. (2016). APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE. Proceedings. IEEE International Symposium on Biomedical Imaging, 2016646-650. https://doi.org/10.1109/ISBI.2016.7493350
Zhang, Jie, et al. "APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2016 (2016): 646-650. doi: https://doi.org/10.1109/ISBI.2016.7493350
Zhang J, Stonnington C, Li Q, Shi J, Bauer RJ, Gutman BA, Chen K, Reiman EM, Thompson PM, Ye J, Wang Y. APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE. Proc IEEE Int Symp Biomed Imaging. 2016 Apr;2016:646-650. doi: 10.1109/ISBI.2016.7493350. PMID: 27499829; PMCID: PMC4974012.
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Proc IEEE Int Symp Biomed Imaging. 2016 Apr;2016:646-650. doi: 10.1109/ISBI.2016.7493350.

APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE.

Proceedings. IEEE International Symposium on Biomedical Imaging

Jie Zhang, Cynthia Stonnington, Qingyang Li, Jie Shi, Robert J Bauer, Boris A Gutman, Kewei Chen, Eric M Reiman, Paul M Thompson, Jieping Ye, Yalin Wang

Affiliations

  1. School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ.
  2. Dept. of Psychiatry and Psychology, Mayo Clinic Arizona, Scottsdale, AZ.
  3. Banner Alzheimer's Institute, Phoenix, AZ.
  4. Imaging Genetics Center, Institute for Neuroimaging and Informatics, Univ.of Southern California, Marina del Rey, CA.
  5. Dept. of Computational Medicine and Bioinformatics, Univ. of Michigan, Ann Arbor, MI.

PMID: 27499829 PMCID: PMC4974012 DOI: 10.1109/ISBI.2016.7493350

Abstract

Alzheimer's disease (AD) is a progressive brain disease. Accurate diagnosis of AD and its prodromal stage, mild cognitive impairment, is crucial for clinical trial design. There is also growing interests in identifying brain imaging biomarkers that help evaluate AD risk presymptomatically. Here, we applied a recently developed multivariate tensor-based morphometry (mTBM) method to extract features from hippocampal surfaces, derived from anatomical brain MRI. For such surface-based features, the feature dimension is usually much larger than the number of subjects. We used dictionary learning and sparse coding to effectively reduce the feature dimensions. With the new features, an Adaboost classifier was employed for binary group classification. In tests on publicly available data from the Alzheimers Disease Neuroimaging Initiative, the new framework outperformed several standard imaging measures in classifying different stages of AD. The new approach combines the efficiency of sparse coding with the sensitivity of surface mTBM, and boosts classification performance.

Keywords: Alzheimer’s disease; dictionary learning and sparse coding; multivariate tensor-based morphometry

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