Open AccessFeature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry
Author(s) -
Tsao Sinchai,
Gajawelli Niharika,
Zhou Jiayu,
Shi Jie,
Ye Jieping,
Wang Yalin,
Leporé Natasha
Publication year - 2017
Publication title -
brain and behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.915
H-Index - 41
ISSN - 2162-3279
DOI - 10.1002/brb3.733
Subject(s) - artificial intelligence , multivariate statistics , computer science , pattern recognition (psychology) , feature (linguistics) , cognition , jacobian matrix and determinant , hippocampus , statistical parametric mapping , principal component analysis , machine learning , neuroscience , mathematics , psychology , medicine , magnetic resonance imaging , philosophy , linguistics , radiology
Prediction of Alzheimer's disease ( AD ) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi‐task machine learning method ( cFSGL ) with a novel MR ‐based multivariate morphometric surface map of the hippocampus ( mTBM ) to predict future cognitive scores of patients. Methods Previous work has shown that a multi‐task learning framework that performs prediction of all future time points simultaneously ( cFSGL ) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer‐based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD . To this end, we applied a multivariate tensor‐based parametric surface analysis method ( mTBM ) to extract features from the hippocampal surfaces. Results We combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted. Conclusions By combining the power of the cFSGL multi‐task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.