Affiliations: [a] College of Information Science and Technology, Beijing Normal University, Beijing, China | [b] Banner Alzheimer’s Institute and Banner Good Samaritan PET Center, Phoenix, Arizona, USA | [c] State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China | [d] Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Correspondence:
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Correspondence to: Xiaojuan Guo, PhD, College of Information Science and Technology, Beijing Normal University, No. 19, XinJieKouWai St., HaiDian District, Beijing, China. Tel.: +86 10 58800427; Fax: +86 10 58800056; gxj@bnu.edu.cn
Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Abstract: Alzheimer’s disease (AD) is one of the most serious progressive neurodegenerative diseases among the elderly, therefore the identification of conversion to AD at the earlier stage has become a crucial issue. In this study, we applied multimodal support vector machine to identify the conversion from normal elderly cognition to mild cognitive impairment (MCI) or AD based on magnetic resonance imaging and positron emission tomography data. The participants included two independent cohorts (Training set: 121 AD patients and 120 normal controls (NC); Testing set: 20 NC converters and 20 NC non-converters) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The multimodal results showed that the accuracy, sensitivity, and specificity of the classification between NC converters and NC non-converters were 67.5% , 73.33% , and 64% , respectively. Furthermore, the classification results with feature selection increased to 70% accuracy, 75% sensitivity, and 66.67% specificity. The classification results using multimodal data are markedly superior to that using a single modality when we identified the conversion from NC to MCI or AD. The model built in this study of identifying the risk of normal elderly converting to MCI or AD will be helpful in clinical diagnosis and pathological research.
Keywords: Alzheimer’s disease, classification, magnetic resonance imaging, normal elderly, positron emission tomography, support vector machine
