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A novel conversion prediction method of MCI to AD based on longitudinal dynamic morphological features using ADNI structural MRIs

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Abstract

Mild cognitive impairment (MCI) is a pre-existing state of Alzheimer's disease (AD). An accurate prediction on the conversion from MCI to AD is of vital clinical significance for potential prevention and treatment of AD. Longitudinal studies received widespread attention for investigating the disease progression, though most studies did not sufficiently utilize the evolution information. In this paper, we proposed a cerebral similarity network with more progression information to predict the conversion from MCI to AD efficiently. First, we defined the new dynamic morphological feature to mine longitudinal information sufficiently. Second, based on the multiple dynamic morphological features the cerebral similarity network was constructed by sparse regression algorithm with optimized parameters to obtain better prediction performance. Then, leave-one-out cross-validation and support vector machine (SVM) were employed for the training and evaluation of the classifiers. The proposed methodology obtained a high accuracy of 92.31% (Sensitivity = 100%, Specificity = 82.86%) in a three-year ahead prediction of MCI to AD conversion. Experiment results suggest the effectiveness of the dynamic morphological feature, serving as a more sensitive biomarker in the prediction of MCI conversion.

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Acknowledgements

This work was supported by the National Basic Research Program of China (973 Program) (No.2014CB744600), the National Natural Science Foundation of China (Grant No.61632014, No.61210010), the Program of Bei**g Municipal Science & Technology Commission (No.Z171100000117005), the Program of International S & T Cooperation of MOST (No.2013DFA11140), Fundamental Research Funds for the Central Universities (lzujbky-2018-it64), and the Postdoctoral Funding of Zhejiang Province, China (514000-X81901).

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  1. College of Information Science and Engineering, Lanzhou University, Lanzhou, China

    Man Guo, Yongchao Li, ** Hu

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  1. Man Guo
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  2. Yongchao Li
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  3. Weihao Zheng
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  4. Keman Huang
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  • Zhijun Yao
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    Correspondence to **** Hu, Zhijun Yao or Bin Hu.

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    Data involved in the study came from the publicly open Alzheimer's disease neuroimaging initiative (ADNI) database. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the Helsinki Declaration of 1975.

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    Guo, M., Li, Y., Zheng, W. et al. A novel conversion prediction method of MCI to AD based on longitudinal dynamic morphological features using ADNI structural MRIs. J Neurol 267, 2983–2997 (2020). https://doi.org/10.1007/s00415-020-09890-5

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