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Title: Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group Lasso

Abstract

Alzheimer’s disease (AD), the most common type of dementia, not only imposes a huge financial burden on the health care system, but also a psychological and emotional burden on patients and their families. There is furthermore an urgent need to infer trajectories of cognitive performance over time and identify biomarkers predictive of the progression. Here, we introduce the multi-task learning with fused Laplacian sparse group lasso model, which can identify biomarkers closely related to cognitive measures due to its sparsity-inducing property, and model the disease progression with a general weighted (undirected) dependency graphs among the tasks. An efficient alternative directions method of multipliers based optimization algorithm is derived to solve the proposed non-smooth objective formulation. The effectiveness of the presented model is demonstrated by its superior prediction performance over multiple state-of-the-art methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are consistent with prior medical studies.

Authors:
 [1];  [2];  [3];  [2];  [4]
  1. Northeastern Univ., Shenyang (China); Univ. of Minnesota, Minneapolis, MN (United States)
  2. Northeastern Univ., Shenyang (China)
  3. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  4. Univ. of Minnesota, Minneapolis, MN (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); National Natural Science Foundation of China (NSF)
OSTI Identifier:
1557047
Report Number(s):
LLNL-JRNL-753218
Journal ID: ISSN 1556-4681; 939429
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Accepted Manuscript
Journal Name:
ACM Transactions on Knowledge Discovery from Data
Additional Journal Information:
Journal Volume: 12; Journal Issue: 6; Journal ID: ISSN 1556-4681
Publisher:
Association for Computing Machinery
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Alzheimer’s Disease; Disease Progression; Multi-task Learning; Graph Laplacian; ADMM

Citation Formats

Liu, Xiaoli, Cao, Peng, Gonçalves, André R., Zhao, Dazhe, and Banerjee, Arindam. Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group Lasso. United States: N. p., 2018. Web. doi:10.1145/3230668.
Liu, Xiaoli, Cao, Peng, Gonçalves, André R., Zhao, Dazhe, & Banerjee, Arindam. Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group Lasso. United States. doi:10.1145/3230668.
Liu, Xiaoli, Cao, Peng, Gonçalves, André R., Zhao, Dazhe, and Banerjee, Arindam. Wed . "Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group Lasso". United States. doi:10.1145/3230668. https://www.osti.gov/servlets/purl/1557047.
@article{osti_1557047,
title = {Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group Lasso},
author = {Liu, Xiaoli and Cao, Peng and Gonçalves, André R. and Zhao, Dazhe and Banerjee, Arindam},
abstractNote = {Alzheimer’s disease (AD), the most common type of dementia, not only imposes a huge financial burden on the health care system, but also a psychological and emotional burden on patients and their families. There is furthermore an urgent need to infer trajectories of cognitive performance over time and identify biomarkers predictive of the progression. Here, we introduce the multi-task learning with fused Laplacian sparse group lasso model, which can identify biomarkers closely related to cognitive measures due to its sparsity-inducing property, and model the disease progression with a general weighted (undirected) dependency graphs among the tasks. An efficient alternative directions method of multipliers based optimization algorithm is derived to solve the proposed non-smooth objective formulation. The effectiveness of the presented model is demonstrated by its superior prediction performance over multiple state-of-the-art methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are consistent with prior medical studies.},
doi = {10.1145/3230668},
journal = {ACM Transactions on Knowledge Discovery from Data},
number = 6,
volume = 12,
place = {United States},
year = {2018},
month = {10}
}

Journal Article:
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Cited by: 2 works
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Figures / Tables:

Fig. 1 Fig. 1: The change patterns of several patients’ cognitive scores over the 6 time points: (a) ADAS, (b) MMSE, and (c) RAVLT.TOATL. The different colors indicate different patients from our dataset.

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