单位:[1]Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China,[2]Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China,深圳市康宁医院深圳医学信息中心[3]State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China,[4]Department of Neurology, China-Japan Friendship Hospital, Beijing, China
People with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are both at high risk for Alzheimer's disease (AD). Behaviorally, both SCD and aMCI have subjective reports of cognitive decline, but the latter suffers a more severe objective cognitive impairment than the former. However, it remains unclear how the brain develops from SCD to aMCI. In the current study, we aimed to investigate the topological characteristics of the white matter (WM) network that can successfully identify individuals with SCD or aMCI from healthy control (HC) and to describe the relationship of pathological changes between these two stages. To this end, three groups were recruited, including 22 SCD, 22 aMCI, and 22 healthy control (HC) subjects. We constructed WM network for each subject and compared large-scale topological organization between groups at both network and nodal levels. At the network level, the combined network indexes had the best performance in discriminating aMCI from HC. However, no indexes at the network level can significantly identify SCD from HC. These results suggested that aMCI but not SCD was associated with anatomical impairments at the network level. At the nodal level, we found that the short-path length can best differentiate between aMCI and HC subjects, whereas the global efficiency has the best performance in differentiating between SCD and HC subjects, suggesting that both SCD and aMCI had significant functional integration alteration compared to HC subjects. These results converged on the idea that the neural degeneration from SCD to aMCI follows a gradual process, from abnormalities at the nodal level to those at both nodal and network levels.
基金:
Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [32071100, 32000793]; Science and Technology Planning Project of Guangdong Province, China [2019A050510048]; Natural Science Foundation of Guangdong Province, ChinaNational Natural Science Foundation of Guangdong Province [2020A1515011394]; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions [2021SHIBS0003]; Natural Science Foundation of Shenzhen, China [JCYJ20190808121415365]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2020M682846]; Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning [CNLYB2005]
第一作者单位:[1]Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China,[2]Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China,
通讯作者:
通讯机构:[1]Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China,[2]Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China,
推荐引用方式(GB/T 7714):
Wuhai Tao,Hehui Li,Xin Li,et al.Progressive Brain Degeneration From Subjective Cognitive Decline to Amnestic Mild Cognitive Impairment: Evidence From Large-Scale Anatomical Connection Classification Analysis[J].FRONTIERS in AGING NEUROSCIENCE.2021,13:doi:10.3389/fnagi.2021.687530.
APA:
Wuhai Tao,Hehui Li,Xin Li,Rong Huang,Wen Shao...&Zhanjun Zhang.(2021).Progressive Brain Degeneration From Subjective Cognitive Decline to Amnestic Mild Cognitive Impairment: Evidence From Large-Scale Anatomical Connection Classification Analysis.FRONTIERS in AGING NEUROSCIENCE,13,
MLA:
Wuhai Tao,et al."Progressive Brain Degeneration From Subjective Cognitive Decline to Amnestic Mild Cognitive Impairment: Evidence From Large-Scale Anatomical Connection Classification Analysis".FRONTIERS in AGING NEUROSCIENCE 13.(2021)