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Abnormal Dynamic Functional Networks in Subjective Cognitive Decline and Alzheimer's Disease.
Wang, Jue; Wang, Kexin; Liu, Tiantian; Wang, Li; Suo, Dingjie; Xie, Yunyan; Funahashi, Shintaro; Wu, Jinglong; Pei, Guangying.
  • Wang J; School of Life Science, Beijing Institute of Technology, Beijing, China.
  • Wang K; School of Life Science, Beijing Institute of Technology, Beijing, China.
  • Liu T; School of Life Science, Beijing Institute of Technology, Beijing, China.
  • Wang L; School of Life Science, Beijing Institute of Technology, Beijing, China.
  • Suo D; School of Life Science, Beijing Institute of Technology, Beijing, China.
  • Xie Y; Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
  • Funahashi S; Kokoro Research Center, Kyoto University, Kyoto, Japan.
  • Wu J; Laboratory of Cognitive Brain Science, Department of Cognitive and Behavioral Sciences, Graduate School of Human and Environmental Studies, Kyoto University, Kyoto, Japan.
  • Pei G; Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, China.
Front Comput Neurosci ; 16: 885126, 2022.
Article en En | MEDLINE | ID: mdl-35586480
ABSTRACT
Subjective cognitive decline (SCD) is considered to be the preclinical stage of Alzheimer's disease (AD) and has the potential for the early diagnosis and intervention of AD. It was implicated that CSF-tau, which increases very early in the disease process in AD, has a high sensitivity and specificity to differentiate AD from normal aging, and the highly connected brain regions behaved more tau burden in patients with AD. Thus, a highly connected state measured by dynamic functional connectivity may serve as the early changes of AD. In this study, forty-five normal controls (NC), thirty-six individuals with SCD, and thirty-five patients with AD were enrolled to obtain the resting-state functional magnetic resonance imaging scanning. Sliding windows, Pearson correlation, and clustering analysis were combined to investigate the different levels of information transformation states. Three states, namely, the low state, the middle state, and the high state, were characterized based on the strength of functional connectivity between each pair of brain regions. For the global dynamic functional connectivity analysis, statistically significant differences were found among groups in the three states, and the functional connectivity in the middle state was positively correlated with cognitive scales. Furthermore, the whole brain was parcellated into four networks, namely, default mode network (DMN), cognitive control network (CCN), sensorimotor network (SMN), and occipital-cerebellum network (OCN). For the local network analysis, statistically significant differences in CCN for low state and SMN for middle state and high state were found in normal controls and patients with AD. Meanwhile, the differences were also found in normal controls and individuals with SCD. In addition, the functional connectivity in SMN for high state was positively correlated with cognitive scales. Converging results showed the changes in dynamic functional states in individuals with SCD and patients with AD. In addition, the changes were mainly in the high strength of the functional connectivity state.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Screening_studies Idioma: En Año: 2022 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Screening_studies Idioma: En Año: 2022 Tipo del documento: Article