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

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...

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Bibliographic Details
Published in:Proceedings (International Symposium on Biomedical Imaging) Vol. 2016; pp. 646 - 650
Main Authors: Jie Zhang, Stonnington, Cynthia, Qingyang Li, Jie Shi, Bauer, Robert J., Gutman, Boris A., Kewei Chen, Reiman, Eric M., Thompson, Paul M., Jieping Ye, Yalin Wang
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.04.2016
Subjects:
Alzheimer's disease
Brain
Classification
Coding
Design analysis
Dictionaries
dictionary learning and sparse coding
Encoding
Feature extraction
Imaging
Medical imaging
multivariate tensor-based morphometry
Risk
Surface morphology
Alzheimer’s disease
multivariate tensor-based morphometry
dictionary learning and sparse coding
ISSN:1945-7928, 1945-8452
Online Access:Get full text
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Summary: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.
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The research was supported in part by NIH (R21AG043760, R21AG049216, R01AG031581, P30AG19610), NSF (DMS-1413417, IIS-1421165) and Arizona Alzheimer’s Disease Consortium (ADHS14-052688). Funded in part by NIH ENIGMA Center grant U54EB020403, supported by the Big Data to Knowledge (BD2K) Centers of Excellence program.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2016.7493350