Functional connectivity between white matter and gray matter based on fMRI for Alzheimer's disease classification

Brain and Behavior

Jie Zhao¹  Xuetong Ding¹  Xuehu Wang¹  Yuhang Du¹  Guozun Men² 

[1] College of Electronic and Information Engineering Hebei University Baoding China;School of Economics Hebei University Baoding China;

关键词: Alzheimer's disease;    dynamic functional connectivity;    resting state fMRI;    support vector machine;    white matter;   

DOI  :  10.1002/brb3.1407

来源: DOAJ

【 摘 要 】

Abstract Introduction Alzheimer's disease (AD) is a chronic neurodegenerative disease that generally starts slowly and leads to deterioration over time. Finding biomarkers more effective to predict AD transition is important for clinical medicine. And current research indicated that the lesion regions occur in both gray matter (GM) and white matter (WM). Methods This paper extracted BOLD time series from WM and GM, combined WM and GM together for analysis, constructed functional connectivity (FC) of static (sWGFC) and dynamic (dWGFC) between WM and GM, as well as static (sGFC) and dynamic (dGFC) FC within GM in order to evaluate the methods and areas most useful as feature sets for distinguishing NC from AD. These features will be evaluated using support vector machine (SVM) classifiers. Results The FC constructed by WM BOLD time series based on fMRI showed widely differences between the AD group and NC group. In terms of the results of the classification, the performance of feature subsets selected from sWGFC was better than sGFC, and the performance of feature subsets selected from dWGFC was better than dGFC. Overall, the feature subsets selected from dWGFC was the best. Conclusion These results indicated that there is a wide range of disconnection between WM and GM in AD, and association between WM and GM based on fMRI only is an effective strategy, and the FC between WM and GM could be a potential biomarker in the process of cognitive impairment and AD.

【 授权许可】

Unknown