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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Differentiated Regional Homogeneity in Progressive Mild Cognitive Impairment: A Study With Post Hoc Label
Suping Cai1, Yubo Wang1, Yafei Kang1
11 School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.
Purpose:
Mild cognitive impairment (MCI) is considered to be the clinical transition stage between patients with cognitively intact geriatrics and Alzheimer's disease (AD). When observed longitudinally, however, a certain proportion of patients with MCI are expected to revert to a cognitively intact state (MCI_R) while others either remain in the MCI state (MCI_S) or deteriorate into AD (MCI_P). It is worthwhile to investigate the divergence in the brain activities of these MCI groups with different post hoc labels.
Methods:
In this study, we employed the regional homogeneity (ReHo) measure to explore the characteristics of local brain activity in these MCI groups.
Results:
Compared to age-matched normal controls, our results exhibited that (1) in MCI_R group, ReHo index showed an increase in the left insula and a decrease in the left superior temporal gyrus; (2) in MCI_S group, ReHo index increased in the left orbital part of the inferior frontal gyrus (IFG_orb) and decreased in the left inferior parietal lobe; and (3) in MCI_P group, ReHo index elevated in the left IFG_orb and decreased in the left putamen.
Conclusions:
The distinct ReHo changes in the individual MCI groups indicated a potential evidence for differentially active interventions for a specific patient with MCI.
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