Share this content in WeChat
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.

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

Tian JZ, Xie HG, Wang LN, et al. Chinese guideline for the diagnosis and treatment of Alzheimer's disease dementia(2020)[J]. Chin J Geriatr, 2021(3): 269-283.
Liss JL, Seleri Assunção S, Cummings J, et al. Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer's disease (MCI and dementia) in primary care: a review and synthesis[J]. J Intern Med, 2021, 290(2): 310-334. DOI: 10.1111/joim.13244.
Gomersall T, Smith SK, Blewett C, et al. 'It's definitely not Alzheimer's': perceived benefits and drawbacks of a mild cognitive impairment diagnosis[J]. Br J Health Psychol, 2017, 22(4): 786-804. DOI: 10.1111/bjhp.12255.
Soman SM, Raghavan S, Rajesh PG, et al. Does resting state functional connectivity differ between mild cognitive impairment and early Alzheimer's dementia?[J/OL]. J Neurol Sci, 2020, 418 [2022-4-26]. DOI: 10.1016/j.jns.2020.117093.
Berron D, Vogel JW, Insel PS, et al. Early stages of tau pathology and its associations with functional connectivity, atrophy and memory[J]. Brain, 2021, 144(9): 2771-2783. DOI: 10.1093/brain/awab114.
Liu MY, Duan G. Changes of connectivity of dorsal stream in patients with mild cognitive impairment by using resting-state functional magnetic resonance imaging[J]. Chin J Med Imaging, 2018, 26(2): 94-98. DOI: 10.3969/j.issn.1005-5185.2018.02.004.
Chen H, Chen W, Liu K, et al. Feature analysis of brain functional connectivity degree regarding disease progression in patients with mild cognitive impairment[J]. J Clin Radiol, 2019, 38(7): 1169-1174. DOI: 10.13437/j.cnki.jcr.2019.07.001.
Dickerson BC, Salat DH, Bates JF, et al. Medial temporal lobe function and structure in mild cognitive impairment[J]. Ann Neurol, 2004, 56(1): 27-35. DOI: 10.1002/ana.20163.
Hu RH, Fan CX, Bi XY. Progress in neuroimaging of mild cognitive impairment[J]. Chin J Stroke, 2019, 14(3): 297-300.
Zhou Z, Zhong Y. Dynamic functional connectivity analysis and its application in neuropsychiatric disorders[J]. Chin J Magn Reson Imaging, 2021, 12(1): 73-76. DOI: 10.12015/issn.1674-8034.2021.01.016.
Yuan YM, Zhang L, Zhang ZG. A review of methods and clinical applications for dynamic functional connectivity analysis based on resting-state functional magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2018, 9(8): 579-588. DOI: 10.12015/issn.1674-8034.2018.08.005.
Quevenco FC, Preti MG, van Bergen JMG, et al. Memory performance-related dynamic brain connectivity indicates pathological burden and genetic risk for Alzheimer's disease[J]. Alzheimers Res Ther, 2017, 9(1): 24. DOI: 10.1186/s13195-017-0249-7.
Schumacher J, Peraza LR, Firbank M, et al. Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer's disease[J/OL]. Neuroimage Clin, 2019, 22 [2022-4-26]. DOI: 10.1016/j.nicl.2019.101812.
Núñez P, Poza J, Gómez C, et al. Characterizing the fluctuations of dynamic resting-state electrophysiological functional connectivity: reduced neuronal coupling variability in mild cognitive impairment and dementia due to Alzheimer's disease[J]. J Neural Eng, 2019, 16(5): 056030. DOI: 10.1088/1741-2552/ab234b.
Jarrahi B. An ICA investigation into the effect of physiological noise correction on dynamic functional network connectivity and meta-state metrics[A]. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society [C]. Mexico. IEEE, 2021: 3137-3140. DOI: 10.1109/EMBC46164.2021.9630968.
Damoiseaux JS, Rombouts SARB, Barkhof F, et al. Consistent resting-state networks across healthy subjects[J]. Proc Natl Acad Sci USA, 2006, 103(37): 13848-13853. DOI: 10.1073/pnas.0601417103.
Saha DK, Damaraju E, Rashid B, et al. A classification-based approach to estimate the number of resting functional magnetic resonance imaging dynamic functional connectivity states[J]. Brain Connect, 2021, 11(2): 132-145. DOI: 10.1089/brain.2020.0794.
Steenland K, Zhao LP, John SE, et al. A 'Framingham-like' algorithm for predicting 4-year risk of progression to amnestic mild cognitive impairment or Alzheimer's disease using multidomain information[J]. J Alzheimers Dis, 2018, 63(4): 1383-1393. DOI: 10.3233/JAD-170769.
Dong GZ, Zeng XT, Yang L, et al. Early prediction of Alzheimer disease based on dynamic graph theory[J]. Beijing Biomed Eng, 2019, 38(6): 560-567. DOI: 10.3969/j.issn.1002-3208.2019.06.002.
Supekar K, Menon V, Rubin D, et al. Network analysis of intrinsic functional brain connectivity in Alzheimer's disease[J/OL]. PLoS Comput Biol, 2008, 4(6) [2022-4-26]. DOI: 10.1371/journal.pcbi.1000100.
Wang L, Zang YF, He Y, et al. Changes in hippocampal connectivity in the early stages of Alzheimer's disease: evidence from resting state fMRI[J]. Neuroimage, 2006, 31(2): 496-504. DOI: 10.1016/j.neuroimage.2005.12.033.
Delbeuck X, van der Linden M, Collette F. Alzheimer's disease as a disconnection syndrome?[J]. Neuropsychol Rev, 2003, 13(2): 79-92. DOI: 10.1023/a:1023832305702.
Berron D, van Westen D, Ossenkoppele R, et al. Medial temporal lobe connectivity and its associations with cognition in early Alzheimer's disease[J]. Brain, 2020, 143(4): 1233-1248. DOI: 10.1093/brain/awaa068.
Lu GQ, Zhang SZ, Li R. The functional connectivity of default mode network and hippocampus in Alzheimer's disease: a Meta-analysis based on SDM[J]. Chin J Magn Reson Imaging, 2022, 13(3): 54-60. DOI: 10.12015/issn.1674-8034.2022.03.011.
Binnewijzend MAA, Schoonheim MM, Sanz-Arigita E, et al. Resting-state fMRI changes in Alzheimer's disease and mild cognitive impairment[J]. Neurobiol Aging, 2012, 33(9): 2018-2028. DOI: 10.1016/j.neurobiolaging.2011.07.003.
Zhao Y. Individual metabolic network construction and MCI progression prediction based on longitudinal FDG-PET[D]. Lanzhou: Lanzhou University, 2018.
Li YX, Wang XN, Zhou J, et al. Resting-state functional MRI study at the baseline in mild cognitive impairment converting to Alzheimer's disease[J]. Chin J Radiol, 2017(10): 744-749.
Cha J, Hwang JM, Jo HJ, et al. Assessment of functional characteristics of amnestic mild cognitive impairment and Alzheimer's disease using various methods of resting-state FMRI analysis[J/OL]. Biomed Res Int, 2015 [2022-4-26]. DOI: 10.1155/2015/907464.
Yetkin FZ, Rosenberg RN, Weiner MF, et al. FMRI of working memory in patients with mild cognitive impairment and probable Alzheimer's disease[J]. Eur Radiol, 2006, 16(1): 193-206. DOI: 10.1007/s00330-005-2794-x.
Collin SH, Milivojevic B, Doeller CF. Memory hierarchies map onto the hippocampal long axis in humans[J]. Nat Neurosci, 2015, 18(11): 1562-1564. DOI: 10.1038/nn.4138.
Córdova-Palomera A, Kaufmann T, Persson K, et al. Disrupted global metastability and static and dynamic brain connectivity across individuals in the Alzheimer's disease continuum[J/OL]. Sci Rep, 2017, 7 [2022-4-26]. DOI: 10.1038/srep40268.
Hellyer PJ, Scott G, Shanahan M, et al. Cognitive flexibility through metastable neural dynamics is disrupted by damage to the structural connectome[J]. J Neurosci, 2015, 35(24): 9050-9063. DOI: 10.1523/jneurosci.4648-14.2015.

PREV Application progress of MRI-T2 mapping in tumor
NEXT Construction of nomogram model for predicting prognosis of low-grade gliomas based on diffusion kurtosis imaging histogram

Tel & Fax: +8610-67113815    E-mail: