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Clinical Article
Dynamic functional connectivity analysis of stable and progressive mild cognitive impairment
QIAO Zhen  YUAN Leilei  ZHAO Xiaobin  WANG Kai  ZHANG Shu  LI Xiaotong  CHEN Qian  AI Lin 

Cite this article as: Qiao Z, Yuan LL, Zhao XB, et al. Dynamic functional connectivity analysis of stable and progressive mild cognitive impairment[J]. Chin J Magn Reson Imaging, 2022, 13(8): 1-6. DOI:10.12015/issn.1674-8034.2022.08.001.

[Abstract] Objective In this study, resting-state functional magnetic resonance imaging (rs-fMRI) in patients with mild cognitive impairment (MCI) was analyzed using the dynamic functional connection (dFC) to evaluate the characteristics and differences of functional connectivity changes in patients with progressive and stable MCI.Materials and Methods The data in this study were derived from the Alzheimer's disease neuroimaging initiative (ADNI) database. Patients with MCI were retrieved and patients with progressive MCI were screened according to the follow-up results, and patients with stable MCI with matched gender and age were selected as the control group. Based on independent component analysis (ICA), rs-fMRI was processed and independent components (IC) of interest were extracted. The dFC analysis was performed by sliding time window method, and k-means clustering and elbow method were used to divide dFC matrix into several representative dFC states. The changes of dFC states were compared between progressive and stable MCI groups, and dFC feature parameters (fraction time and dwell time for each state, and the times of transition between states) were compared between the two groups.Results Twenty-three patients with progressive MCI and twenty-six patients with stable MCI were included in this study. A series of real-time dFC matrix were divided into four kinds of dFC states: sparse connection state-a, strong local connection state, sparse connection state-b and strong positive-connection state. Compared with the stable MCI group, fraction time and dwell time in strong local connection state decreased significantly in patients with progressive MCI (P=0.049, P=0.049), fraction time and dwell time in sparse connection state-b increased significantly in patients with progressive MCI (P=0.045, P=0.033).Conclusions Compared with the stable MCI, patients with progressive MCI showed a characteristic of increasing strong local connection state and decreasing sparse connection state. The rs-fMRI-based dFC analysis can objectively reflect the changes of brain function in patients with progressive and stable MCI, and may be helpful in differential diagnosis of progressive MCI from stable MCI.
[Keywords] progressive mild cognitive impairment;stable mild cognitive impairment;resting-state functional magnetic resonance imaging;dynamic functional connection;independent component analysis

QIAO Zhen   YUAN Leilei   ZHAO Xiaobin   WANG Kai   ZHANG Shu   LI Xiaotong   CHEN Qian   AI Lin*  

Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China

Ai L, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Beijing Natural Science Found (No.7192054).
Received  2022-04-06
Accepted  2022-08-04
DOI: 10.12015/issn.1674-8034.2022.08.001
Cite this article as: Qiao Z, Yuan LL, Zhao XB, et al. Dynamic functional connectivity analysis of stable and progressive mild cognitive impairment[J]. Chin J Magn Reson Imaging, 2022, 13(8): 1-6. DOI:10.12015/issn.1674-8034.2022.08.001.

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