基于多种皮质特征结合海马亚区体积测量从健康衰老中识别轻度认知障碍亚型。
Identifying subtypes of mild cognitive impairment from healthy aging based on multiple cortical features combined with volumetric measurements of the hippocampal subfields.
作者信息
Guo Shengwen, Xiao Benheng, Wu Congling
机构信息
Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.
出版信息
Quant Imaging Med Surg. 2020 Jul;10(7):1477-1489. doi: 10.21037/qims-19-872.
BACKGROUND
Mild cognitive impairment (MCI) is subtle cognitive decline with an estimated 10-15% yearly conversion rate toward Alzheimer's disease (AD). It remains unexplored in brain cortical association areas in different lobes and its changes with progression and conversion of MCI.
METHODS
Brain structural magnetic resonance (MR) images were collected from 102 stable MCI (sMCI) patients. One hundred eleven were converted MCI (cMCI) patients, and 109 were normal control (NC). The cortical surface features and volumes of subcortical hippocampal subfields were calculated using the FreeSurfer software, followed by an analysis of variance (ANOVA) model, to reveal the differences between the NC-sMCI, NC-cMCI, and sMCI-cMCI groups. Afterward, the support vector machine-recursive feature elimination (SVM-RFE) method was applied to determine the differences between the groups.
RESULTS
The experimental results showed that there were progressive degradations in either range or degree of the brain structure from NC to sMCI, and then to cMCI. The SVM classifier obtained accuracies with 64.62%, 78.96%, and 70.33% in the sMCI-NC, cMCI-NC, and cMCI-sMCI groups, respectively, using the volumes of hippocampal subfields independently. The combination of the volumes from the hippocampal subfields and cortical measurements could significantly increase the performance to 71.86%, 84.64%, and 76.86% for the sMCI-NC, cMCI-NC, and cMCI-sMCI classifications, respectively. Also, the brain regions corresponding to the dominant features with strong discriminative power were widely located in the temporal, frontal, parietal, olfactory cortexes, and most of the hippocampal subfields, which were associated with cognitive decline, memory impairment, spatial navigation, and attention control.
CONCLUSIONS
The combination of cortical features with the volumes of hippocampal subfields could supply critical information for MCI detection and its conversion.
背景
轻度认知障碍（MCI）是一种轻微的认知衰退，估计每年有10%-15%的转化率会发展为阿尔茨海默病（AD）。目前在不同脑叶的大脑皮质联合区域及其随MCI进展和转化的变化情况仍未得到充分研究。
方法
收集了102例稳定型MCI（sMCI）患者、111例转化型MCI（cMCI）患者和109例正常对照（NC）的脑结构磁共振（MR）图像。使用FreeSurfer软件计算皮质表面特征和海马亚区的皮质下体积，然后采用方差分析（ANOVA）模型，以揭示NC-sMCI、NC-cMCI和sMCI-cMCI组之间的差异。之后，应用支持向量机递归特征消除（SVM-RFE）方法来确定各组之间的差异。
结果
实验结果表明，从NC到sMCI，再到cMCI，大脑结构的范围或程度都存在渐进性退化。使用海马亚区体积，SVM分类器在sMCI-NC、cMCI-NC和cMCI-sMCI组中的准确率分别为64.62%、78.96%和70.33%。海马亚区体积和皮质测量值的组合可将sMCI-NC、cMCI-NC和cMCI-sMCI分类的性能分别显著提高到71.86%、84.64%和76.86%。此外，具有强判别力的主要特征对应的脑区广泛分布在颞叶、额叶、顶叶、嗅觉皮质以及大部分海马亚区，这些区域与认知衰退、记忆障碍、空间导航和注意力控制有关。
结论
皮质特征与海马亚区体积的组合可为MCI检测及其转化提供关键信息。
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