| Literature DB >> 34891164 |
Yu Zhou1, Xiaopeng Si2,3,4, Yuanyuan Chen2,3, Yiping Chao5,6, Ching-Po Lin7, Sicheng Li2,3, Xingjian Zhang2,3, Dong Ming2,3, Qiang Li1.
Abstract
Early diagnosis of mild cognitive impairment (MCI) fascinates screening high-risk Alzheimer's disease (AD). White matter is found to degenerate earlier than gray matter and functional connectivity during MCI. Although studies reveal white matter degenerates in the limbic system for MCI, how other white matter degenerates during MCI remains unclear. In our method, regions of interest with a high level of resting-state functional connectivity with hippocampus were selected as seeds to track fibers based on diffusion tensor imaging (DTI). In this way, hippocampus-temporal and thalamus-related fibers were selected, and each fiber's DTI parameters were extracted. Then, statistical analysis, machine learning classification, and Pearson's correlations with behavior scores were performed between MCI and normal control (NC) groups. Results show that: 1) the mean diffusivity of hippocampus-temporal and thalamus-related fibers are significantly higher in MCI and could be used to classify 2 groups effectively. 2) Compared with normal fibers, the degenerated fibers detected by the DTI indexes, especially for hippocampus-temporal fibers, have shown significantly higher correlations with cognitive scores. 3) Compared with the hippocampus-temporal fibers, thalamus-related fibers have shown significantly higher correlations with depression scores within MCI. Our results provide novel biomarkers for the early diagnoses of AD.Entities:
Keywords: Alzheimer’s disease; cognition; depression; diffusion tensor imaging; mean diffusivity
Mesh:
Year: 2022 PMID: 34891164 DOI: 10.1093/cercor/bhab407
Source DB: PubMed Journal: Cereb Cortex ISSN: 1047-3211 Impact factor: 4.861