Wang Huiquan, Feng Tianzi, Zhao Zhe, Bai Xue, Han Guang, Wang Jinhai, Dai Zongrui, Wang Rong, Zhao Weibiao, Ren Fuxin, Gao Fei
School of Life Sciences, Tiangong University, Tianjin, China.
School of Electrical and Information Engineering, Tiangong University, Tianjin, China.
Front Aging Neurosci. 2022 Jul 12;14:927217. doi: 10.3389/fnagi.2022.927217. eCollection 2022.
To improve the diagnosis and classification of Alzheimer's disease (AD), a modeling method is proposed based on the combining magnetic resonance images (MRI) brain structural data with metabolite levels of the frontal and parietal regions. First, multi-atlas brain segmentation technology based on T1-weighted images and edited magnetic resonance spectroscopy (MRS) were used to extract data of 279 brain regions and levels of 12 metabolites from regions of interest (ROIs) in the frontal and parietal regions. The -test combined with false discovery rate (FDR) correction was used to reduce the dimensionality in the data, and MRI structural data of 54 brain regions and levels of 4 metabolites that obviously correlated with AD were screened out. Lastly, the stacked auto-encoder neural network (SAE) was used to classify AD and healthy controls (HCs), which judged the effect of classification method by fivefold cross validation. The results indicated that the mean accuracy of the five experimental model increased from 96 to 100%, the AUC value increased from 0.97 to 1, specificity increased from 90 to 100%, and F1 value increased from 0.97 to 1. Comparing the effect of each metabolite on model performance revealed that the gamma-aminobutyric acid (GABA) + levels in the parietal region resulted in the most significant improvement in model performance, with the accuracy rate increasing from 96 to 98%, the AUC value increased from 0.97 to 0.99 and the specificity increasing from 90 to 95%. Moreover, the GABA + levels in the parietal region was significantly correlated with Mini Mental State Examination (MMSE) scores of patients with AD ( = 0.627), and the F statistics were largest ( = 25.538), which supports the hypothesis that dysfunctional GABAergic system play an important role in the pathogenesis of AD. Overall, our findings support that a comprehensive method that combines MRI structural and metabolic data of brain regions can improve model classification efficiency of AD.
为了改进阿尔茨海默病（AD）的诊断和分类，提出了一种基于将磁共振成像（MRI）脑结构数据与额叶和顶叶区域代谢物水平相结合的建模方法。首先，基于T1加权图像的多图谱脑分割技术和编辑后的磁共振波谱（MRS）被用于从额叶和顶叶区域的感兴趣区域（ROI）中提取279个脑区的数据和12种代谢物的水平。采用t检验结合错误发现率（FDR）校正来降低数据维度，筛选出54个脑区的MRI结构数据和4种与AD明显相关的代谢物水平。最后，使用堆叠自动编码器神经网络（SAE）对AD患者和健康对照（HC）进行分类，并通过五折交叉验证来判断分类方法的效果。结果表明，五个实验模型的平均准确率从96%提高到100%，AUC值从0.97提高到1，特异性从90%提高到100%，F1值从0.97提高到1。比较每种代谢物对模型性能的影响发现，顶叶区域的γ-氨基丁酸（GABA）+水平使模型性能得到最显著改善，准确率从96%提高到98%，AUC值从0.97提高到0.99，特异性从90%提高到95%。此外，顶叶区域的GABA +水平与AD患者的简易精神状态检查表（MMSE）评分显著相关（r = 0.627），F统计量最大（F = 25.538），这支持了GABA能系统功能障碍在AD发病机制中起重要作用的假设。总体而言，我们的研究结果支持将脑区的MRI结构和代谢数据相结合的综合方法可以提高AD的模型分类效率。
