Abstract #4878

Characterizing MRI Biomarkers for Conversion Prediction of Preclinical Mild Cognitive Impairment

Yongsheng Pan^1,2, Mingxia Liu*², Chunfeng Lian², Ling Yue³, Shifu Xiao³, Yong Xia*¹, and Dinggang Shen*²

¹School of Computer Science, Northwestern Polytechnical University, Xi'an, China, ²Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ³School of Medicine, Shanghai Mental Health Center, Shanghai Jiao Tong University, Shanghai, China

Identifying subjects at the stage of preclinical mild cognitive impairment (pre-MCI) is fundamental for early intervention of pathologic cognitive decline. This study aims to investigate the progression from cognitive normal (CN) and subjective cognitive decline (SCD) to MCI, by characterizing imaging biomarkers in brain MRI data via a deep-learning framework. This deep-learning framework is designed to first evaluate the discriminative capability of regions-of-interest (ROIs) in brain MR images, and then to predict the progression of CN/SCD subjects to MCI within 36 months. The results suggest that brain structure changes at the pre-MCI stage can be objectively detected in MR images by our method.

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