Zhizheng Zhuo1, Xiao Mo1, Xiangyu Ma1, Ying Han2, Haiyun Li3. 1. Lab of Computer Simulation and Medical Imaging Processing, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China. 2. Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, China. Electronic address: hanying@xwh.ccmu.edu.cn. 3. Lab of Computer Simulation and Medical Imaging Processing, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China. Electronic address: haiyunli@ccmu.edu.cn.
Abstract
PURPOSE: To investigate the subtle functional connectivity alterations of aMCI based on AAL atlas with 1024 regions (AAL_1024 atlas). MATERIALS AND METHODS: Functional MRI images of 32 aMCI patients (Male/Female: 15/17, Ages: 66.8 ± 8.36 y) and 35 normal controls (Male/Female:13/22, Ages: 62.4 ± 8.14 y) were obtained in this study. Firstly, functional connectivity networks were constructed by Pearson's Correlation based on the subtle AAL_1024 atlas. Then, local and global network parameters were calculated from the thresholding functional connectivity matrices. Finally, multiple-comparison analysis was performed on these parameters to find the functional network alterations of aMCI. And furtherly, a couple of classifiers were adopted to identify the aMCI by using the network parameters. RESULTS: More subtle local brain functional alterations were detected by using AAL_1024 atlas. And the predominate nodes including hippocampus, inferior temporal gyrus, inferior parietal gyrus were identified which was not detected by AAL_90 atlas. The identification of aMCI from normal controls were significantly improved with the highest accuracy (98.51%), sensitivity (100%) and specificity (97.14%) compared to those (88.06%, 84.38% and 91.43% for the highest accuracy, sensitivity and specificity respectively) obtained by using AAL_90 atlas. CONCLUSION: More subtle functional connectivity alterations of aMCI could be found based on AAL_1024 atlas than those based on AAL_90 atlas. Besides, the identification of aMCI could also be improved.
PURPOSE: To investigate the subtle functional connectivity alterations of aMCI based on AAL atlas with 1024 regions (AAL_1024 atlas). MATERIALS AND METHODS: Functional MRI images of 32 aMCIpatients (Male/Female: 15/17, Ages: 66.8 ± 8.36 y) and 35 normal controls (Male/Female:13/22, Ages: 62.4 ± 8.14 y) were obtained in this study. Firstly, functional connectivity networks were constructed by Pearson's Correlation based on the subtle AAL_1024 atlas. Then, local and global network parameters were calculated from the thresholding functional connectivity matrices. Finally, multiple-comparison analysis was performed on these parameters to find the functional network alterations of aMCI. And furtherly, a couple of classifiers were adopted to identify the aMCI by using the network parameters. RESULTS: More subtle local brain functional alterations were detected by using AAL_1024 atlas. And the predominate nodes including hippocampus, inferior temporal gyrus, inferior parietal gyrus were identified which was not detected by AAL_90 atlas. The identification of aMCI from normal controls were significantly improved with the highest accuracy (98.51%), sensitivity (100%) and specificity (97.14%) compared to those (88.06%, 84.38% and 91.43% for the highest accuracy, sensitivity and specificity respectively) obtained by using AAL_90 atlas. CONCLUSION: More subtle functional connectivity alterations of aMCI could be found based on AAL_1024 atlas than those based on AAL_90 atlas. Besides, the identification of aMCI could also be improved.