Qing Li1, Xia Wu2, Lele Xu3, Kewei Chen4, Li Yao5, Rui Li6. 1. College of Information Science and Technology, Beijing Normal University, Beijing 100875, China. Electronic address: liqing_lq@mail.bnu.edu.cn. 2. College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China. Electronic address: wuxia@bnu.edu.cn. 3. College of Information Science and Technology, Beijing Normal University, Beijing 100875, China. Electronic address: xulelebnu@gmail.com. 4. Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ 850006, USA. Electronic address: Kewei.Chen@bannerhealth.com. 5. College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China. Electronic address: yaoli@bnu.edu.cn. 6. Center on Aging Psychology, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China. Electronic address: lir@psych.ac.cn.
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.
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 ADpatients, 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.
Authors: Da Ma; Evangeline Yee; Jane K Stocks; Lisanne M Jenkins; Karteek Popuri; Guillaume Chausse; Lei Wang; Stephan Probst; Mirza Faisal Beg Journal: J Alzheimers Dis Date: 2021 Impact factor: 4.472