Multi-modal discriminative dictionary learning for Alzheimer's disease and mild cognitive impairment. (October 2017)

• Record Type: Journal Article
• Authors: Li, Qing; Wu, Xia; Xu, Lele; Chen, Kewei; Yao, Li; Li, Rui
• Is Part Of: Computer methods and programs in biomedicine. Volume 150(2017)
• Page Start: 1
• Page End: 8
• Publication Date: 2017-10
• Subjects: Mild cognitive impairment (MCI) -- Alzheimer's disease (AD) -- Multimodal neuroimaging data -- Discriminative dictionary -- Brain disorders; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.07.003
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A multi-modal discriminant DL algorithm for AD/MCI classification is proposed. The mSCDDL could enhance the recognition rate compared with some other algorithms. A weighted combination framework for multi-feature fusion is adopted into the DL scheme. Abstract: Background and objective: The differentiation of mild cognitive impairment (MCI), which is the prodromal stage of Alzheimer's disease (AD), from normal control (NC) is important as the recent research emphasis on early pre-clinical stage for possible disease abnormality identification, intervention and even possible prevention. Methods: The current study puts forward a multi-modal supervised within-class-similarity discriminative dictionary learning algorithm (SCDDL) we introduced previously for distinguishing MCI from NC. The proposed new algorithm was based on weighted combination and named as multi-modality SCDDL (mSCDDL). Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET) and florbetapir PET data of 113 AD patients, 110 MCI patients and 117 NC subjects from the Alzheimer's disease Neuroimaging Initiative database were adopted for classification between MCI and NC, as well as between AD and NC. Results: Adopting mSCDDL, the classification accuracy achieved 98.5% for AD vs. NC and 82.8% for MCI vs. NC, which were superior to or comparable with the results of some other state-of-the-art approaches as reported in recent multi-modality publications. Conclusions: The mSCDDL procedure was a promising tool in assisting early diseases diagnosis using neuroimaging data.