Modeling Alzheimer's disease cognitive scores using multi-task sparse group lasso

Alzheimer's Disease Neuroimaging Initiative, Arindam Banerjee

Research output: Contribution to journalArticlepeer-review


Alzheimer's disease (AD) is a severe neurodegenerative disorder characterized by loss of memory and reduction in cognitive functions due to progressive degeneration of neurons and their connections, eventually leading to death. In this paper, we consider the problem of simultaneously predicting several different cognitive scores associated with categorizing subjects as normal, mild cognitive impairment (MCI), or Alzheimer's disease (AD) in a multi-task learning framework using features extracted from brain images obtained from ADNI (Alzheimer's Disease Neuroimaging Initiative). To solve the problem, we present a multi-task sparse group lasso (MT-SGL) framework, which estimates sparse features coupled across tasks, and can work with loss functions associated with any Generalized Linear Models. Through comparisons with a variety of baseline models using multiple evaluation metrics, we illustrate the promising predictive performance of MT-SGL on ADNI along with its ability to identify brain regions more likely to help the characterization Alzheimer's disease progression.

Original languageEnglish (US)
Pages (from-to)100-114
Number of pages15
JournalComputerized Medical Imaging and Graphics
StatePublished - Jun 2018
Externally publishedYes


  • Alzheimer's disease
  • Multi-task learning
  • Sparse group lasso

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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