Applying sparse coding to surface multivariate tensor-based morphometry to predict future cognitive decline

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

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 hip-pocampal 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.

Original languageEnglish (US)
Title of host publication2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings
PublisherIEEE Computer Society
Pages646-650
Number of pages5
Volume2016-June
ISBN (Electronic)9781479923502
DOIs
StatePublished - Jun 15 2016
Event2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic
Duration: Apr 13 2016Apr 16 2016

Other

Other2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
CountryCzech Republic
CityPrague
Period4/13/164/16/16

Keywords

  • Alzheimer's disease
  • dictionary learning and sparse coding
  • multivariate tensor-based morphometry

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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    Zhang, J., Stonnington, C. M., 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. In 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings (Vol. 2016-June, pp. 646-650). [7493350] IEEE Computer Society. https://doi.org/10.1109/ISBI.2016.7493350