Huang Weijie, Li Xuanyu, Li Xin, Kang Guixia, Han Ying, Shu Ni
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.
Front Aging Neurosci. 2021 Jul 30;13:687927. doi: 10.3389/fnagi.2021.687927. eCollection 2021.
Individuals with subjective cognitive decline (SCD) or amnestic mild cognitive impairment (aMCI) represent important targets for the early detection and intervention of Alzheimer's disease (AD). In this study, we employed a multi-kernel support vector machine (SVM) to examine whether white matter (WM) structural networks can be used for screening SCD and aMCI.
A total of 138 right-handed participants [51 normal controls (NC), 36 SCD, 51 aMCI] underwent MRI brain scans. For each participant, three types of WM networks with different edge weights were constructed with diffusion MRI data: fiber number-weighted networks, mean fractional anisotropy-weighted networks, and mean diffusivity (MD)-weighted networks. By employing a multiple-kernel SVM, we seek to integrate information from three weighted networks to improve classification performance. The accuracy of classification between each pair of groups was evaluated via leave-one-out cross-validation.
For the discrimination between SCD and NC, an area under the curve (AUC) value of 0.89 was obtained, with an accuracy of 83.9%. Further analysis revealed that the methods using three types of WM networks outperformed other methods using single WM network. Moreover, we found that most of discriminative features were from MD-weighted networks, which distributed among frontal lobes. Similar classification performance was also reported in the differentiation between subjects with aMCI and NCs (accuracy = 83.3%). Between SCD and aMCI, an AUC value of 0.72 was obtained, with an accuracy of 72.4%, sensitivity of 74.5% and specificity of 69.4%. The highest accuracy was achieved with features only selected from MD-weighted networks.
White matter structural network features help machine learning algorithms accurately identify individuals with SCD and aMCI from NCs. Our findings have significant implications for the development of potential brain imaging markers for the early detection of AD.
主观认知下降（SCD）或遗忘型轻度认知障碍（aMCI）个体是阿尔茨海默病（AD）早期检测和干预的重要目标。在本研究中，我们采用多核支持向量机（SVM）来检验白质（WM）结构网络是否可用于筛查SCD和aMCI。
共有138名右利手参与者[51名正常对照（NC）、36名SCD、51名aMCI]接受了脑部MRI扫描。对于每位参与者，利用扩散MRI数据构建了三种具有不同边权重的WM网络：纤维数量加权网络、平均分数各向异性加权网络和平均扩散率（MD）加权网络。通过采用多核支持向量机，我们试图整合来自三个加权网络的信息以提高分类性能。通过留一法交叉验证评估每组之间的分类准确性。
在SCD和NC的区分中，曲线下面积（AUC）值为0.89，准确率为83.9%。进一步分析表明，使用三种类型WM网络的方法优于使用单个WM网络的其他方法。此外，我们发现大多数判别特征来自MD加权网络，其分布在额叶。在aMCI与NCs的区分中也报告了类似的分类性能（准确率 = 83.3%）。在SCD和aMCI之间，AUC值为0.72，准确率为72.4%，敏感性为74.5%，特异性为69.4%。仅从MD加权网络中选择特征时获得了最高准确率。
白质结构网络特征有助于机器学习算法从NCs中准确识别出SCD和aMCI个体。我们的研究结果对开发用于AD早期检测的潜在脑成像标志物具有重要意义。
