Modeling Alzheimer's disease progression with fused laplacian sparse group lasso

Xiaoli Liu, Peng Cao, André R. Gonçalves, Dazhe Zhao, Arindam Banerjee

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 thus an urgent need to infer trajectories of cognitive performance over time and identify biomarkers predictive of the progression. In this article, we propose 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 proposed 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.

Original languageEnglish (US)
Article number65
JournalACM Transactions on Knowledge Discovery from Data
Volume12
Issue number6
DOIs
StatePublished - Aug 2018

Bibliographical note

Funding Information:
The research was supported by the National Natural Science Foundation of China (No.61502091), the Fundamental Research Funds for the Central Universities (Nos. N161604001 and N150408001). The research was also supported by NSF grants IIS-1563950, IIS-1447566, IIS-1447574, IIS-1422557, CCF-1451986, and CNS-1314560. Authors’ addresses: X. Liu, P. Cao, and D. Zhao, College of Computer Science and Engineering, Northeastern University, Shenyang, China; emails: neuxiaoliliu@gmail.com, caopeng@cse.neu.edu.cn, zhaodz@neusoft.com; A. R. Gonçalves, Lawrence Livermore National Laboratory, CA; email: goncalves1@llnl.gov; A. Banerjee, Computing Science & Engineering, University of Minnesota, Twin Cities; email: banerjee@cs.umn.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2018 ACM 1556-4681/2018/08-ART65 $15.00 https://doi.org/10.1145/3230668

Keywords

  • ADMM
  • Alzheimer's disease
  • Disease progression
  • Graph laplacian
  • Multi-task learning

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