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Abstract #3532

Prediction of the cognitive performance among Type 2 diabetes mellitus patients: a multivariable pattern analysis of Diffusion Tensor Imaging data

Zhenchao Tang1, Zhenyu Liu2, Xinwei Cui3, Enqing Dong1, and Jie Tian2

1School of Mechanical, Electrical & Information Engineering, Shandong University (Weihai), Weihai, People's Republic of China, 2Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China, 3Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, People's Republic of China

In the current study, we employed multivariate pattern analysis method together with Diffusion Tensor Imaging measures to make prediction on the cognitive performance of Type 2 diabetes mellitus (T2DM) patients, and explore the white matter tracts associated with cognitive changes in T2DM. The prediction model obtained relatively satisfying performance in the Montreal Cognitive Assessment (MoCA) scores estimation among T2DM patients, suggesting the effectiveness of the multivariable analysis method. The white matter identified in the current study mainly concerned the tracts closely related with cognitive function and memory performance, which were consistent with the finding of previous T2DM cognitive studies.

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