Abstract
Mild Cognitive Impairment (MCI) is a preclinical stage of Alzheimer's Disease (AD) and is clinical heterogeneity. The classification of MCI is crucial for the early diagnosis and treatment of AD. In this study, we investigated the potential of using both labeled and unlabeled samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to classify MCI through the multimodal co-training method. We utilized both structural magnetic resonance imaging (sMRI) data and genotype data of 364 MCI samples including 228 labeled and 136 unlabeled MCI samples from the ADNI-1 cohort. First, the selected quantitative trait (QT) features from sMRI data and SNP features from genotype data were used to build two initial classifiers on 228 labeled MCI samples. Then, the co-training method was implemented to obtain new labeled samples from 136 unlabeled MCI samples. Finally, the random forest algorithm was used to obtain a combined classifier to classify MCI patients in the independent ADNI-2 dataset. The experimental results showed that our proposed framework obtains an accuracy of 85.50 percent and an AUC of 0.825 for MCI classification, respectively, which showed that the combined utilization of sMRI and SNP data through the co-training method could significantly improve the performances of MCI classification.
Highlights
Alzheimer’s Disease (AD) is a progressive and irreversible complex neurodegenerative disease with responsible for about half a million deaths worldwide per year [1]
228 labeled Mild Cognitive Impairment (MCI) samples from the Alzheimer’s Disease Neuroimaging Initiative (ADNI)-1 cohort were categorized into 115 sMCI and 113 pMCI according to their diagnostic information at 36-months’ follow-up, while other 136 MCI samples were considered as unlabeled MCI due to lack of follow-up diagnostic information or diagnosis fluctuate. 83 samples from the ADNI-2 cohort were grouped to 62 sMCI and 21 pMCI according to the previous criterion
The results indicated that the pMCI group showed marked cognitive dysfunctions compared to the sMCI group at baseline in the ADNI-1 cohort
Summary
Alzheimer’s Disease (AD) is a progressive and irreversible complex neurodegenerative disease with responsible for about half a million deaths worldwide per year [1]. Mild Cognitive Impairment (MCI) is considered as a preclinical stage of AD. MCI has clinical heterogeneity [2]. Some MCI patients will stay stable (stable MCI, sMCI) after 10-years’ follow-up or even return to normal cognitive status by timely interventions [3], [4]. Other patients will progress to AD (progressive MCI, pMCI) after a period of time [5] and will die after more than three years [6].
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More From: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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