Research progress about different levels of cognitive recession using resting state functional connectivity network methods_Journal of Biomedical Engineering

Authors：

LIU Yanan , CHEN Yuanyuan , SHA Miao , QI Hongzhi , MING Dong ,  ZHOU Peng

Tianjin University, School of Precision Instrument and Opto-Electronics Engineering, Tianjin 300072, P.R.China;

Corresponding author：

ZHOU Peng, Email: zpzp@tju.edu.cn

Keywords：

aging brain; Alzheimer’s disease; mild cognitive impairment; functional connectivity

DOI：

10.7507/1001-5515.201610031

Video：

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Normal brain aging and a serious of neurodegenerative diseases may lead to decline in memory, attention and executive ability and poorer quality of life. The mechanism of the decline is not clear now and is still a hot issue in the fields of neuroscience and medicine. A large number of researches showed that resting state functional brain networks based functional magnetic resonance imaging (fMRI) are sensitive and susceptive to the change of cognitive function. In this paper, the researches of brain functional connectivity based on resting fMRI in recent years were compared, and the results of subjects with different levels of cognitive decline including normal brain aging, mild cognitive impairment (MCI) and Alzheimer’s disease (AD) were reviewed. And the changes of brain functional networks under three different levels of cognitive decline are introduced in this paper, which will provide the basis for the detection of normal brain aging and clinical diseases.

Citation： LIU Yanan, CHEN Yuanyuan, SHA Miao, QI Hongzhi, MING Dong, ZHOU Peng. Research progress about different levels of cognitive recession using resting state functional connectivity network methods. Journal of Biomedical Engineering, 2017, 34(4): 632-636. doi: 10.7507/1001-5515.201610031 Copy

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2.	Geerligs L, Maurits N M, Renken R J, et al. Reduced specificity of functional connectivity in the aging brain during task performance. Hum Brain Mapp, 2014, 35(1): 319-330.
3.	Mclaren D G, Sperling R A, Atri A. Flexible modulation of network connectivity related to cognition in Alzheimer’s disease. Neuroimage, 2014, 100: 544-557.
4.	Brier M R, Thomas J B, Fagan A M, et al. Functional connectivity and graph theory in preclinical Alzheimer’s disease. Neurobiol Aging, 2014, 35(4): 757-768.
5.	Liu Yong, Yu Chunshui, Zhang Xinqing, et al. Impaired long distance functional connectivity and weighted network architecture in Alzheimer’s disease. Cereb Cortex, 2014, 24(6): 1422-1435.
6.	陈玖, 束昊, 王赞, 等. 遗忘型轻度认知障碍人群内侧颞叶记忆系统大尺度功能连接研究//中华医学会第十七次全国神经病学学术会/中华医学会第十七次全国神经病学学术会议论文汇编(上), 厦门: 2014.
7.	Koch W, Teipel S, Mueller S, et al. Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer’s disease. Neurobiol Aging, 2012, 33(3): 466-478.
8.	La C, Mossahebi P, Nair V A, et al. Age-Related changes in Inter-Network connectivity by component analysis. Front Aging Neurosci, 2015, 7(2): 237.
9.	Bluhm R L, Osuch E A, Lanius R A, et al. Default mode network connectivity: effects of age sex, and analytic approach. Neuroreport, 2008, 19(8): 887-891.
10.	Wang Pan, Zhou Bo, Yao Hongxiang, et al. Aberrant intra-and inter-network connectivity architectures in Alzheimer’s disease and mild cognitive impairment, 2015. 5.
11.	吴晶涛. 老龄相关的静息状态脑功能网络变化的磁共振成像研究. 长春: 吉林大学, 2014.
12.	Tomasi D, Volkow N D. Aging and functional brain networks. Mol Psychiatry, 2012, 17(5): 471, 549-558.
13.	Lim H K, Nebes R, Snitz B, et al. Regional amyloid burden and intrinsic connectivity networks in cognitively normal elderly subjects. Brain, 2014, 137(Pt 12): 3327-3338.
14.	He Xiaoxi, Qin Wen, Liu Yong, et al. Abnormal salience network in normal aging and in amnestic mild cognitive impairment and Alzheimer’s disease. Hum Brain Mapp, 2014, 35(7): 3446-3464.
15.	Song Jie, Birn R M, Boly M, et al. Age-related reorganizational changes in modularity and functional connectivity of human brain networks. Brain Connect, 2014, 4(9): 662-676.
16.	Dipasquale O, Griffanti L, Clerici M, et al. High-Dimensional ICA analysis detects Within-Network functional connectivity damage of Default-Mode and Sensory-Motor networks in Alzheimer’s disease, Frontiers in Human Neuroscience, 2015, 9: 43.
17.	王盼, 周波, 姚洪祥, 等. 阿尔茨海默病和轻度认知损害患者异常网络内及网络间连接模式//全国痴呆与认知障碍学术研讨会及高级讲授班, 北京: 2015.
18.	彭国平, 冯湛, 张侃, 等. 遗忘型轻度认知障碍与阿尔茨海默病患者的脑功能连接网络研究//浙江省神经病学学术年会, 浙江: 2015.
19.	Binnewijzend M A, Schoonheim M M, Sanz-Arigita E, et al. Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging, 2012, 33(9): 2018-2028.
20.	Lee E S, Yoo K, Lee Y B, et al. Default mode network functional connectivity in early and late mild cognitive impairment: results from the Alzheimer’s disease neuroimaging initiative. Alzheimer Dis Assoc Disord, 2016, 30(4): 289-296.
21.	Damoiseaux J S, Prater K E, Miller B L, et al. Functional connectivity tracks clinical deterioration in Alzheimer’s disease. Neurobiol Aging, 2012, 33(4): 828. e19-828. e30.
22.	Zhou Bo, Yao Hongxiang, Wang Pan, et al. Aberrant functional connectivity architecture in Alzheimer’s disease and mild cognitive impairment: a Whole-Brain, Data-Driven analysis, Biomed Research International, 2015: 495375.
23.	Weiler M, FUKUDA Aya, Massabki L H, et al. Default mode, executive function, and language functional connectivity networks are compromised in mild Alzheimer’s disease. Curr Alzheimer Res, 2014, 11(3): 274-282.
24.	Zhan Yafeng, Ma Jianhua, Alexander-Bloch A F, et al. Longitudinal study of impaired intra- and Inter-Network brain connectivity in subjects at high risk for Alzheimer’s disease. J Alzheimers Dis, 2016, 52(3): 913-927.
25.	Balthazar M L, Pereira F R, Lopes T M, et al. Neuropsychiatric symptoms in Alzheimer’s disease are related to functional connectivity alterations in the salience network. Hum Brain Mapp, 2014, 35(4): 1237-1246.
26.	Zhang Zhaoyan, Zheng Hong, Liang Kun, et al. Functional degeneration in dorsal and ventral attention systems in amnestic mild cognitive impairment and Alzheimer’s disease: an fMRI study. Neurosci Lett, 2015, 585: 160-165.
27.	Qian Shaowen, Zhang Zhaoyan, Li Bo, et al. Functional-structural degeneration in dorsal and ventral attention systems for Alzheimer’s disease, amnestic mild cognitive impairment. Brain Imaging Behav, 2015, 9(4): 790-800.
28.	Balachandar R, John J P, Saini J, et al. A study of structural and functional connectivity in early Alzheimer’s disease using rest fMRI and diffusion tensor imaging. Int J Geriatr Psychiatry, 2015, 30(5): 497-504.
29.	Zhu Haoze, Zhou Peng, Alcauter S, et al. Changes of intranetwork and internetwork functional connectivity in Alzheimer’s disease and mild cognitive impairment. J Neural Eng, 2016, 13(4): 046008.
30.	王湘彬, 赵小虎, 江虹, 等. 轻度认知功能障碍患者大脑fMRI网络小世界特性. 中国医学影像技术, 2014(5): 790-793.
31.	Suckling J, Simas T, Chattopadhyay S, et al. A winding road: Alzheimer’s disease increases circuitous functional connectivity pathways. Front Comput Neurosci, 2015, 9(5): 140.

1. Geerligs L, Renken R J, Saliasi E, et al. A Brain-Wide study of Age-Related changes in functional connectivity. Cereb Cortex, 2015, 25(7): 1987-1999.
2. Geerligs L, Maurits N M, Renken R J, et al. Reduced specificity of functional connectivity in the aging brain during task performance. Hum Brain Mapp, 2014, 35(1): 319-330.
3. Mclaren D G, Sperling R A, Atri A. Flexible modulation of network connectivity related to cognition in Alzheimer’s disease. Neuroimage, 2014, 100: 544-557.
4. Brier M R, Thomas J B, Fagan A M, et al. Functional connectivity and graph theory in preclinical Alzheimer’s disease. Neurobiol Aging, 2014, 35(4): 757-768.
5. Liu Yong, Yu Chunshui, Zhang Xinqing, et al. Impaired long distance functional connectivity and weighted network architecture in Alzheimer’s disease. Cereb Cortex, 2014, 24(6): 1422-1435.
6. 陈玖, 束昊, 王赞, 等. 遗忘型轻度认知障碍人群内侧颞叶记忆系统大尺度功能连接研究//中华医学会第十七次全国神经病学学术会/中华医学会第十七次全国神经病学学术会议论文汇编(上), 厦门: 2014.
7. Koch W, Teipel S, Mueller S, et al. Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer’s disease. Neurobiol Aging, 2012, 33(3): 466-478.
8. La C, Mossahebi P, Nair V A, et al. Age-Related changes in Inter-Network connectivity by component analysis. Front Aging Neurosci, 2015, 7(2): 237.
9. Bluhm R L, Osuch E A, Lanius R A, et al. Default mode network connectivity: effects of age sex, and analytic approach. Neuroreport, 2008, 19(8): 887-891.
10. Wang Pan, Zhou Bo, Yao Hongxiang, et al. Aberrant intra-and inter-network connectivity architectures in Alzheimer’s disease and mild cognitive impairment, 2015. 5.
11. 吴晶涛. 老龄相关的静息状态脑功能网络变化的磁共振成像研究. 长春: 吉林大学, 2014.
12. Tomasi D, Volkow N D. Aging and functional brain networks. Mol Psychiatry, 2012, 17(5): 471, 549-558.
13. Lim H K, Nebes R, Snitz B, et al. Regional amyloid burden and intrinsic connectivity networks in cognitively normal elderly subjects. Brain, 2014, 137(Pt 12): 3327-3338.
14. He Xiaoxi, Qin Wen, Liu Yong, et al. Abnormal salience network in normal aging and in amnestic mild cognitive impairment and Alzheimer’s disease. Hum Brain Mapp, 2014, 35(7): 3446-3464.
15. Song Jie, Birn R M, Boly M, et al. Age-related reorganizational changes in modularity and functional connectivity of human brain networks. Brain Connect, 2014, 4(9): 662-676.
16. Dipasquale O, Griffanti L, Clerici M, et al. High-Dimensional ICA analysis detects Within-Network functional connectivity damage of Default-Mode and Sensory-Motor networks in Alzheimer’s disease, Frontiers in Human Neuroscience, 2015, 9: 43.
17. 王盼, 周波, 姚洪祥, 等. 阿尔茨海默病和轻度认知损害患者异常网络内及网络间连接模式//全国痴呆与认知障碍学术研讨会及高级讲授班, 北京: 2015.
18. 彭国平, 冯湛, 张侃, 等. 遗忘型轻度认知障碍与阿尔茨海默病患者的脑功能连接网络研究//浙江省神经病学学术年会, 浙江: 2015.
19. Binnewijzend M A, Schoonheim M M, Sanz-Arigita E, et al. Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging, 2012, 33(9): 2018-2028.
20. Lee E S, Yoo K, Lee Y B, et al. Default mode network functional connectivity in early and late mild cognitive impairment: results from the Alzheimer’s disease neuroimaging initiative. Alzheimer Dis Assoc Disord, 2016, 30(4): 289-296.
21. Damoiseaux J S, Prater K E, Miller B L, et al. Functional connectivity tracks clinical deterioration in Alzheimer’s disease. Neurobiol Aging, 2012, 33(4): 828. e19-828. e30.
22. Zhou Bo, Yao Hongxiang, Wang Pan, et al. Aberrant functional connectivity architecture in Alzheimer’s disease and mild cognitive impairment: a Whole-Brain, Data-Driven analysis, Biomed Research International, 2015: 495375.
23. Weiler M, FUKUDA Aya, Massabki L H, et al. Default mode, executive function, and language functional connectivity networks are compromised in mild Alzheimer’s disease. Curr Alzheimer Res, 2014, 11(3): 274-282.
24. Zhan Yafeng, Ma Jianhua, Alexander-Bloch A F, et al. Longitudinal study of impaired intra- and Inter-Network brain connectivity in subjects at high risk for Alzheimer’s disease. J Alzheimers Dis, 2016, 52(3): 913-927.
25. Balthazar M L, Pereira F R, Lopes T M, et al. Neuropsychiatric symptoms in Alzheimer’s disease are related to functional connectivity alterations in the salience network. Hum Brain Mapp, 2014, 35(4): 1237-1246.
26. Zhang Zhaoyan, Zheng Hong, Liang Kun, et al. Functional degeneration in dorsal and ventral attention systems in amnestic mild cognitive impairment and Alzheimer’s disease: an fMRI study. Neurosci Lett, 2015, 585: 160-165.
27. Qian Shaowen, Zhang Zhaoyan, Li Bo, et al. Functional-structural degeneration in dorsal and ventral attention systems for Alzheimer’s disease, amnestic mild cognitive impairment. Brain Imaging Behav, 2015, 9(4): 790-800.
28. Balachandar R, John J P, Saini J, et al. A study of structural and functional connectivity in early Alzheimer’s disease using rest fMRI and diffusion tensor imaging. Int J Geriatr Psychiatry, 2015, 30(5): 497-504.
29. Zhu Haoze, Zhou Peng, Alcauter S, et al. Changes of intranetwork and internetwork functional connectivity in Alzheimer’s disease and mild cognitive impairment. J Neural Eng, 2016, 13(4): 046008.
30. 王湘彬, 赵小虎, 江虹, 等. 轻度认知功能障碍患者大脑fMRI网络小世界特性. 中国医学影像技术, 2014(5): 790-793.
31. Suckling J, Simas T, Chattopadhyay S, et al. A winding road: Alzheimer’s disease increases circuitous functional connectivity pathways. Front Comput Neurosci, 2015, 9(5): 140.

Journal of Biomedical Engineering

Research progress about different levels of cognitive recession using resting state functional connectivity network methods

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