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Longitudinal score prediction for Alzheimer's disease based on ensemble correntropy and spatial-temporal constraint.

Neuroimaging data has been widely used to predict clinical scores for automatic diagnosis of Alzheimer's disease (AD). For accurate clinical score prediction, one of the major challenges is high feature dimension of the imaging data. To address this issue, this paper presents an effective framework...

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Published in:Brain Imaging & Behavior Vol. 13; no. 1; pp. 126 - 138
Main Authors: Lei, Baiying, Hou, Wen, Zou, Wenbin, Li, Xia, Zhang, Cishen, Wang, Tianfu
Format: Journal Article
Published: Springer Nature Feb2019
Online Access:View this record in EBSCOhost
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s11682-018-9834-z
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        atl: Longitudinal score prediction for Alzheimer's disease based on ensemble correntropy and spatial-temporal constraint.
      aug:
        au:
          Lei, Baiying
          Hou, Wen
          Zou, Wenbin
          Li, Xia
          Zhang, Cishen
          Wang, Tianfu
        affil: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, 518060, Shenzhen, China
      sug:
        subj:
          Alzheimer's Disease
          Brain
          Image Interpretation, Computer Assisted Methods
          Magnetic Resonance Imaging Methods
          Neuroradiography Methods
          Prospective Studies
          Statistics Methods
          Information Science Methods
          Scales
      ab: Neuroimaging data has been widely used to predict clinical scores for automatic diagnosis of Alzheimer's disease (AD). For accurate clinical score prediction, one of the major challenges is high feature dimension of the imaging data. To address this issue, this paper presents an effective framework using a novel feature selection model via sparse learning. In contrast to previous approaches focusing on a single time point, this framework uses information at multiple time points. Specifically, a regularized correntropy with the spatial-temporal constraint is used to reduce the adverse effect of noise and outliers, and promote consistent and robust selection of features by exploring data characteristics. Furthermore, ensemble learning of support vector regression (SVR) is exploited to accurately predict AD scores based on the selected features. The proposed approach is extensively evaluated on the Alzheimer's disease neuroimaging initiative (ADNI) dataset. Our experiments demonstrate that the proposed approach not only achieves promising regression accuracy, but also successfully recognizes disease-related biomarkers.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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