Ms. Congling Wu Profile

Congling Wu

at South China Univ of Technology

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Proceedings Article | 8 February 2017 Paper

Discriminant analysis of multiple cortical changes in mild cognitive impairment

Congling Wu, Shengwen Guo, Chunren Lai, Yupeng Wu, Di Zhao, Xingjun Jiang

Proceedings Volume 10225, 1022525 (2017) https://doi.org/10.1117/12.2266229

KEYWORDS: Brain, Magnetic resonance imaging, Dementia, Alzheimer's disease, Neuroimaging, Feature extraction, Dubnium, Biological research, Magnetism, Image classification

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To reveal the differences in brain structures and morphological changes between the mild cognitive impairment (MCI) and the normal control (NC), analyze and predict the risk of MCI conversion. First, the baseline and 2-year longitudinal follow-up magnetic resonance (MR) images of 73 NC, 46 patients with stable MCI (sMCI) and 40 patients with converted MCI (cMCI) were selected. Second, the FreeSurfer was used to extract the cortical features, including the cortical thickness, surface area, gray matter volume and mean curvature. Third, the support vector machine-recursive feature elimination method (SVM-RFE) were adopted to determine salient features for effective discrimination. Finally, the distribution and importance of essential brain regions were described. The experimental results showed that the cortical thickness and gray matter volume exhibited prominent capability in discrimination, and surface area and mean curvature behaved relatively weak. Furthermore, the combination of different morphological features, especially the baseline combined with the longitudinal changes, can be used to evidently improve the performance of classification. In addition, brain regions with high weights predominately located in the temporal lobe and the frontal lobe, which were relative to emotional control and memory functions. It suggests that there were significant different patterns in the brain structure and changes between the compared group, which could not only be effectively applied for classification, but also be used to evaluate and predict the conversion of the patients with MCI.

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