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Constrained Sparse Functional Connectivity Networks for MCI Classification

  • Chong-Yaw Wee
  • Pew-Thian Yap
  • Daoqiang Zhang
  • Lihong Wang
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7511)

Abstract

Mild cognitive impairment (MCI) is difficult to diagnose due to its subtlety. Recent emergence of advanced network analysis techniques utilizing resting-state functional Magnetic Resonance Imaging (rs-fMRI) has made the understanding of neurological disorders more comprehensively at a whole-brain connectivity level. However, inferring effective brain connectivity from fMRI data is a challenging task, particularly when the ultimate goal is to obtain good control-patient classification performance. Incorporating sparsity into connectivity modeling can potentially produce results that are biologically more meaningful since most biologically networks are formed by a relatively few number of connections. However, this constraint, when applied at an individual level, will degrade classification performance due to inter-subject variability. To address this problem, we consider a constrained sparse linear regression model associated with the least absolute shrinkage and selection operator (LASSO). Specifically, we introduced sparsity into brain connectivity via l 1-norm penalization, and ensured consistent non-zero connections across subjects via l 2-norm penalization. Our results demonstrate that the constrained sparse network gives better classification performance than the conventional correlation-based network, indicating its greater sensitivity to early stage brain pathologies.

Keywords

Mild Cognitive Impairment Blood Oxygen Level Dependent Mild Cognitive Impairment Patient Blood Oxygen Level Dependent Signal Sparse Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chong-Yaw Wee
    • 1
  • Pew-Thian Yap
    • 1
  • Daoqiang Zhang
    • 1
  • Lihong Wang
    • 2
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA
  2. 2.Brain Imaging and Analysis CenterDuke University Medical CenterDurhamUSA

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