Cortical surface biomarkers for predicting cognitive outcomes using group l2,1 norm

Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

25 Scopus citations


Regression models have been widely studied to investigate the prediction power of neuroimaging measures as biomarkers for inferring cognitive outcomes in the Alzheimer's disease study. Most of these models ignore the interrelated structures either within neuroimaging measures or between cognitive outcomes, and thus may have limited power to yield optimal solutions. To address this issue, we propose to use a new sparse multitask learning model called Group-Sparse Multi-task Regression and Feature Selection (G-SMuRFS) and demonstrate its effectiveness by examining the predictive power of detailed cortical thickness measures toward 3 types of cognitive scores in a large cohort. G-SMuRFS proposes a group-level l2,1-norm strategy to group relevant features together in an anatomically meaningful manner and use this prior knowledge to guide the learning process. This approach also takes into account the correlation among cognitive outcomes for building a more appropriate predictive model. Compared with traditional methods, G-SMuRFS not only demonstrates a superior performance but also identifies a small set of surface markers that are biologically meaningful.

Original languageEnglish (US)
Pages (from-to)S185-S193
JournalNeurobiology of Aging
Issue numberS1
StatePublished - Jan 1 2015
Externally publishedYes


  • Alzheimer's disease neuroimaging initiative (ADNI)
  • Cognitive function prediction
  • Cortical thickness
  • Magnetic resonance imaging (MRI)
  • Sparse learning

ASJC Scopus subject areas

  • Clinical Neurology
  • Neuroscience(all)
  • Aging
  • Developmental Biology
  • Geriatrics and Gerontology

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