静息态功能磁共振成像功能连接特异性指数模型在评估健康老年人认知分数中的应用

与传统量表法和任务态功能磁共振成像(fMRI)相比,静息态功能磁共振(rfMRI)在认知功能检测上有很大优势(特别是针对老年人),但脑老化功能影像学标记尚在探索中。提出功能连接特异性指数模型,试图与健康壮年人对照,分析健康老年人脑功能特异性,寻求区分认知分数的功能影像学指标,探索其分类认知分数优差的可能性,为替代提供研究基础。实验数据来自98名健康老年人和90名健康壮年人,前者来源于葡萄牙健康老年人认知功能的队列研究,在rfMRI扫描前,在认知量表测试分数最优和最差中,分别选出55名和43名作为实验组;后者数据来自哈佛医学院GSP影像组学,年龄在18～35岁之间,在rfMRI扫描前的认知功能评价分数居中,作为对照组。首先,对rfMRI预处理后,计算每人全脑功能连接,构建以脑区为单位的功能连接特异性指数模型,分析老年人脑功能连接与壮年人的偏移程度,统计、对比获得对优、差分数敏感的标志性脑区;然后,以其特异性指数值形成特征向量;最后,应用概率神经网络(PNN)模型对优、差分数组进行分类和N折交叉验证以检验所建指数模型的分类能力。健康老年人脑功能连接特异性指数模型可定位于健康老年人认知分数敏感的标志性脑区,分别处于额叶、颞叶、顶叶中的5个脑区;以这些脑区的指数为特征向量,可有效地区分优、差认知分数,准确度可达81.7%。通过对评价指数的建模并联合机器学习方法,可为rfMRI评估健康老年人认知分数提供有效的评价指标和新方法。

The Specific Index Model of Resting-State fMRI Functional Connectivity in the Application to the Evaluation of Cognitive Score of Healthy Elderly

The resting-state functional magnetic resonance imaging (rfMRI) has more advantages than the traditional scale test and task-state fMRI, especially in the cognitive detection on the elderly. However, functional bio-markers of healthy brain aging remain not totally clear. Herein, a specific index model derived from functional connectivity was proposed, aiming to study the possibility to identify the excellent or poor cognitive scores of the healthy elderly by the index model, and to seek the potential functional bio-markers to evaluate the cognitive scores by rfMRI instead of the traditional scale testing. A total of 98 healthy old people and a total of 90 healthy young people were volunteers. The former came from a cohort study of cognitive function of healthy elderly people in Portugal. According to the cognitive scores estimated by the scale tests before the rfMRI scan, 55 subjects with the excellent scores and 43 subjects with the poor scores were involved in the experiment group; the latter data came from GSP opened by the Harvard Hospital, the subjects aged between 18-35 years old with a medium level of cognition evaluation tested before rfMRI, were involved in the control group. After pre-processing the rfMRI data, the functional connectivity (FC) was computed on the whole brain one by one, then a FC-based specific index model was built up to estimate the FC deviation degree of old people relative to that of youth at a single brain area. Furthermore, the specific index values of the marked brain areas sensitive to the excellent scores and the poor scores could be estimated by statistics in a comparison study, by which the eigenvectors matrix were formed and input the machine learning model thereafter. Finally, the model of probability neural network (PNN) was utilized to classify the scores in the experimental groups and then the sorting rate was defined by N-fold validation. The specific index model could localize the functional bio-markers brain regions sensitive to the cognitive scores of healthy aging. There were 5 brain regions in the frontal lobe, temporal lobe and parietal lobe. By considering the indexes of 5 brain regions as inputs to the machine learning, the cognitive scores of healthy aging could be effectively classified with a sorting rate of 81.7%. This work was expected to provide an effective index and a new method for rfMRI to test the cognitive scores of the healthy elderly by combining the specific index modeling with a machine learning model.