The Specific Index Model of Resting-State fMRI Functional Connectivity in the Application to the Evaluation of Cognitive Score of Healthy Elderly
Guo Zhitong1,2, Ge Manling1,2*, Zhang Fuyi1,2, Song Zibo1,2, Xie Chong1,2, Yang Zekun1,2
1(State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China) 2(Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China)
Abstract：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.
郭志彤, 葛曼玲, 张夫一, 宋子博, 谢冲, 杨泽坤. 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[J]. 中国生物医学工程学报, 2020, 39(6): 676-684.
Guo Zhitong, Ge Manling, Zhang Fuyi, Song Zibo, Xie Chong, Yang Zekun. The Specific Index Model of Resting-State fMRI Functional Connectivity in the Application to the Evaluation of Cognitive Score of Healthy Elderly. Chinese Journal of Biomedical Engineering, 2020, 39(6): 676-684.
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