轻度认知障碍的BOLD信号MEMD降噪处理

早期诊断轻度认知障碍是干预阿尔茨海默症的有效途径.目前常使用静息态功能磁共振成像和机器学习方法进行轻度认知障碍的辅助诊断,其关键是使用血氧水平依赖(blood oxygenation level dependent, BOLD)信号构建大脑的功能性连接.针对大脑静息态BOLD信号中存在各种外界噪音干扰的问题,提出结合多元经验模态分解与皮尔逊相关的重构方法与极正极负重构准则,将大脑默认模式网络的中心节点后扣带回皮层作为模板,重构BOLD信号以降低外界噪音干扰.实验结果表明,基于极正极负重构准则降噪后的BOLD信号构建功能性连接,相较降噪前的数据,在分类性能方面可以提高数据的差异性,在特征选择性能方面可以对数据集降维的同时进一步提升分类性能.此外,以上性能均优于传统重构准则.最后,对降噪后的最优特征子集进行统计性分析,发现脑岛可能是默认模式网络的相关脑区,小脑蚓体与后扣带回皮层可能构成一种认知功能补偿网络,这是以往研究中少有提出的结论.

Blood oxygenation level dependent signals noise reduction for mild cognitive impairment using multivariate empirical mode decomposition

Early diagnosis of mild cognitive impairment is an effective way to intervene Alzheimer''s disease. At present, rest-state functional magnetic resonance imaging and machine learning methods are often used in the auxiliary diagnosis of mild cognitive impairment. The key is to use blood oxygen level dependent (BOLD) signals to construct the functional connection of the brain. Aiming at the problem of various external noise interference in brain resting BOLD signals, a reconstruction method combined with multiple empirical mode decomposition and Pearson correlation and a polar positive negative reconstruction criterion are proposed. The posterior cingulate cortex, the central node of the brain default mode network, is used as a template to reconstruct BOLD signals to reduce external noise interference. The experimental results show that the functional connection based on pole positive negative reconstruction criterion can improve the difference of data in classification performance compared with the data before noise reduction, and further improve the classification performance while reducing the dimension of the data set in feature selection performance. In addition, the above performance is better than the traditional reconstruction criterion. Finally, the optimal feature subset after noise reduction is statistically analyzed. It is found that the insula may be the relevant brain area of the default mode network, and the cerebellar vermis and posterior cingulate cortex may constitute a cognitive function compensation network, which is a rare conclusion in previous studies.