Supervised Discriminative Group Sparse Representation for Mild Cognitive Impairment Diagnosis

Heung Il Suk, Chong Yaw Wee, Seong Whan Lee, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


Research on an early detection of Mild Cognitive Impairment (MCI), a prodromal stage of Alzheimer’s Disease (AD), with resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been of great interest for the last decade. Witnessed by recent studies, functional connectivity is a useful concept in extracting brain network features and finding biomarkers for brain disease diagnosis. However, it still remains challenging for the estimation of functional connectivity from rs-fMRI due to the inevitable high dimensional problem. In order to tackle this problem, we utilize a group sparse representation along with a structural equation model. Unlike the conventional group sparse representation method that does not explicitly consider class-label information, which can help enhance the diagnostic performance, in this paper, we propose a novel supervised discriminative group sparse representation method by penalizing a large within-class variance and a small between-class variance of connectivity coefficients. Thanks to the newly devised penalization terms, we can learn connectivity coefficients that are similar within the same class and distinct between classes, thus helping enhance the diagnostic accuracy. The proposed method also allows the learned common network structure to preserve the network specific and label-related characteristics. In our experiments on the rs-fMRI data of 37 subjects (12 MCI; 25 healthy normal control) with a cross-validation technique, we demonstrated the validity and effectiveness of the proposed method, showing the diagnostic accuracy of 89.19 % and the sensitivity of 0.9167.

Original languageEnglish
Pages (from-to)277-295
Number of pages19
Issue number3
Publication statusPublished - 2015 Dec 16

Bibliographical note

Publisher Copyright:
© 2014, Springer Science+Business Media New York.


  • Alzheimer’s Disease (AD)
  • Functional connectivity
  • Mild Cognitive Impairment (MCI)
  • Resting-state fMRI
  • Sparse regression learning

ASJC Scopus subject areas

  • Software
  • General Neuroscience
  • Information Systems


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