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Evaluating the Predictive Power of Multivariate Tensor-based Morphometry in Alzheimers Disease Progression via Convex Fused Sparse Group Lasso
BibTeX
@MISC{Tsaoa_evaluatingthe,
author = {Sinchai Tsaoa and Niharika Gajawellib and Jiayu Zhouc and Jie Shic and Jieping Yec and Yalin Wangc and Natasha Leporeb},
title = {Evaluating the Predictive Power of Multivariate Tensor-based Morphometry in Alzheimers Disease Progression via Convex Fused Sparse Group Lasso},
year = {}
}
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Abstract
Prediction of Alzheimers 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 predic-tive multi-task machine learning method1 with novel MR-based multivariate morphometric surface map of the hippocampus2 to predict future cognitive scores of patients. Previous work by Zhou et al.1 has shown that a multi-task learning framework that performs prediction of all future time points (or tasks) simultaneously can be used to encode both sparsity as well as temporal smoothness. They showed that this can be used in predicting cognitive outcomes of Alzheimers Disease Neuroimaging Initiative (ADNI) subjects based on 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 Shi et al.2s recently developed multivariate tensor-based (mTBM) parametric surface analysis method to extract features from the hippocampal surface. We show that by combining the power of the multi-task framework with the sensitivity of mTBM features of the hippocampus surface, we are able to improve significantly improve predictive performance of ADAS cognitive
Keyphrases
sparse group lasso predictive power alzheimers disease progression multivariate tensor-based morphometry predic-tive multi-task machine hippocampus specific information baseline measure future time point predictive performance parametric surface analysis method apoe status alzheimers disease neuroimaging initiative predictive modeling previous work hippocampal surface brain status cognitive outcome freesurfer-based baseline mri feature temporal smoothness hippocampus surface future cognitive score demographic information treatment strategy generalized information ada cognitive whilst volumetric information alzheimers disease disease progression mtbm feature multi-task learning framework multi-task framework
