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


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
Issue number6
StatePublished - Aug 2018
Externally publishedYes


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

ASJC Scopus subject areas

  • General Computer Science


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