Abstract #2353

Prediction of early stage of AD based on functional connectivity network characteristics: an fMRI-based study

Zhizheng Zhuo^1,2, Zhuqing Long¹, Bin Jing¹, Xiangyu Ma¹, Han Liu¹, Jianxin Dong¹, Xiao Mo¹, Qi Yan¹, and Haiyun Li¹

¹Bio-medical Engineering, Capital Medical University, Beijing, People's Republic of China, ²Clinical Science, Philips Healthcare, Beijing, People's Republic of China

MCI (Mild Cognitive Impairment) is a pre-stage of Alzheimer’s Disease (AD). And the early detection of MCI is important for early treatment of AD patients. In this work, prediction efficiency of early stage of AD based on the functional connectivity network characteristics was evaluated by using a couple of classifiers with AAL_90 and AAL_1024 templates. The results showed that brain functional characteristics were effective in the prediction of MCI with a SVM-based classifier. And a more fine template could improve prediction accuracy.

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