Classification of Alzheimer’s Disease, Mild Cognitive Impairment and Normal Control Subjects Using Resting-State fMRI Based Network Connectivity Analysis
| العنوان: | Classification of Alzheimer’s Disease, Mild Cognitive Impairment and Normal Control Subjects Using Resting-State fMRI Based Network Connectivity Analysis |
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| المؤلفون: | David C. Zhu, Yu Zheng, Tongtong Li, Zhe Wang, Andrea Bozoki |
| المصدر: | IEEE Journal of Translational Engineering in Health and Medicine |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | medicine.diagnostic_test, Resting state fMRI, Computer science, business.industry, Feature vector, fMRI, Biomedical Engineering, Pattern recognition, General Medicine, Electroencephalography, Linear discriminant analysis, Article, 030218 nuclear medicine & medical imaging, Correlation, 03 medical and health sciences, 0302 clinical medicine, Sample size determination, medicine, brain connectivity analysis, Artificial intelligence, Functional magnetic resonance imaging, business, Alzheimer’s disease, 030217 neurology & neurosurgery, Default mode network |
| الوصف: | This paper proposes a robust method for the Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal control subject classification under size limited fMRI data samples by exploiting the brain network connectivity pattern analysis. First, we select the regions of interest (ROIs) within the default mode network and calculate the correlation coefficients between all possible ROI pairs to form a feature vector for each subject. Second, we propose a regularized linear discriminant analysis (LDA) approach to reduce the noise effect due to the limited sample size. The feature vectors are then projected onto a one-dimensional axis using the proposed regularized LDA. Finally, an AdaBoost classifier is applied to carry out the classification task. The numerical analysis demonstrates that the purposed approach can increase the classification accuracy significantly. Our analysis confirms the previous findings that the hippocampus and the isthmus of the cingulate cortex are closely involved in the development of AD and MCI. This paper reports more accurate classification of Alzheimer's disease and mild cognitive impairment using resting-state fMRI based network connectivity pattern analysis. A clinical trial showed an average accuracy of 75.8% in the classification of Alzheimer's disease, mild cognitive impairment and normal control subjects from resting-state fMRI signals. |
| تدمد: | 2168-2372 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18e3a16b46cea10a31458c8b01138e69 https://doi.org/10.1109/jtehm.2018.2874887 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....18e3a16b46cea10a31458c8b01138e69 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21682372 |
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