Research progress of disrupted brain connectivity in mild cognitive impairment: findings from graph theoretical studies of whole brain networks_Journal of Biomedical Engineering

Authors：

WEI Long ^1,2 , YANG Cheng ¹ , WANG Lijia ¹ , WANG Yuanjun ¹ [综述] ,  NIE Shengdong ¹ [审校]

1. Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China;
2. Laiwu Vocational and Technical College, Laiwu, Shandong 271100, P.R.China;

Corresponding author：

NIE Shengdong, Email: nsd4647@163.com

Keywords：

mild cognitive impairment; graph theory; human brain structural network; human brain functional network; small-world property

DOI：

10.7507/1001-5515.201603074

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Mild cognitive impairment (MCI) is a clinical transition state between age-related cognitive decline and dementia. Researchers can use neuroimaging and neurophysiological techniques to obtain structural and functional information about the human brain. Using this information researchers can construct the brain network based on complex network theory. The literature on graph theory shows that the large-scale brain network of MCI patient exhibits small-world property, which ranges intermediately between Alzheimer's disease and that in the normal control group. But brain connectivity of MCI patients presents topologically structural disorder. The disorder is significantly correlated to the cognitive functions. This article reviews the recent findings on brain connectivity of MCI patients from the perspective of multimodal data. Specifically, the article focuses on the graph theory evidences of the whole brain structural and functional and the joint covariance network disorders. At last, the article shows the limitations and future research directions in this field.

Citation： WEILong, YANGCheng, WANGLijia. Research progress of disrupted brain connectivity in mild cognitive impairment: findings from graph theoretical studies of whole brain networks. Journal of Biomedical Engineering, 2017, 34(1): 140-144. doi: 10.7507/1001-5515.201603074 Copy

1.	Prince M, Wimo A, Guerchet M, et al. World Alzheimer Report 2015: The Global Impact of Dementia. London: Alzheimer's Disease International, 2015.
2.	Lopez O L. Mild cognitive impairment. Continuum (Minneap Minn), 2013, 19(2): 411-424.
3.	He Yong, Chen Zhang, Evans A. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. J Neurosci, 2008, 28(18): 4756-4766.
4.	Fornito A, Zalesky A, Breakspear M. Graph analysis of the human connectome: promise, progress, and pitfalls. Neuroimage, 2013, 80: 426-444.
5.	Sun F T, Miller L M, D'Esposito M. Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. Neuroimage, 2004, 21(2): 647-658.
6.	Lerch J P, Worsley K, Shaw W P, et al. Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage, 2006, 31(3): 993-1003.
7.	Gong Gaolang, He Yong, Concha L, et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex, 2009, 19(3): 524-536.
8.	Humphries M D, Gurney K. Network 'small-world-ness': a quantitative method for determining canonical network equivalence. PLoS One, 2008, 3(4): e0002051.
9.	Irimia A, Van Horn J D. The structural, connectomic and network covariance of the human brain. Neuroimage, 2013, 66(10): 489-499.
10.	Yao Zhijun, Zhang Yuanchao, Lin Lei, et al. Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease. PLoS Comput Biol, 2010, 6(11): e1001006.
11.	Phillips D J, Mcglaughlin A, Ruth D, et al. Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods. Neuroimage Clin, 2015, 7: 377-390.
12.	Li Qiongling, Li Xinwei, Wang Xuetong, et al. Topological properties of large-scale cortical networks based on multiple morphological features in amnestic mild cognitive impairment. Neural Plast, 2016, 2016: 3462309.
13.	Bai Feng, Shu Ni, Yuan Yonggui, et al. Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. J Neurosci, 2012, 32(12): 4307-4318.
14.	Daianu M, Jahanshad N, Nir T M, et al. Breakdown of brain connectivity between normal aging and Alzheimer's disease: a structural k-core network analysis. Brain Connect, 2013, 3(4): 407-422.
15.	Daianu M, Jahanshad N, Nir T M, et al. Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network. Hum Brain Mapp, 2015, 36(8): 3087-3103.
16.	Zhang Bing, Zhang Xin, Zhang Fang, et al. Characterizing topological patterns in amnestic mild cognitive impairment by quantitative water diffusivity. J Alzheimers Dis, 2015, 43(2): 687-697.
17.	Vecchio F, Miraglia F, Quaranta D, et al. Cortical connectivity and memory performance in cognitive decline: A study via graph theory from EEG data. Neuroscience, 2016, 316: 143-150.
18.	Lu C F, Wang Y J, Shin T, et al. Altered network organization in mild Alzheimer's disease compared to mild cognitive impairment using resting-state EEG. International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, 2014, 8(11): 780-786.
19.	Lu C F, Wang Y J, Wu Y T, et al. Relations of progression in cognitive decline with initial EEG resting-state functional network in mild cognitive impairment. International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, 2014, 8(11): 787-793.
20.	Miraglia F, Vecchio F, Bramanti P, et al. EEG characteristics in "eyes-open" versus "eyes-closed" conditions: Small-world network architecture in healthy aging and age-related brain degeneration. Clin Neurophysiol, 2016, 127(2): 1261-1268.
21.	Tóth B, File B, Boha R, et al. EEG network connectivity changes in mild cognitive impairment--preliminary results. Int J Psychophysiol, 2014, 92(1): 1-7.
22.	Gardini S, Venneri A, Sambataro F, et al. Increased functional connectivity in the default mode network in mild cognitive impairment: a maladaptive compensatory mechanism associated with poor semantic memory performance. J Alzheimers Dis, 2015, 45(2): 457-470.
23.	Wang Jinhui, Zuo Xinian, Dai Zhengjia, et al. Disrupted functional brain connectome in individuals at risk for Alzheimer's disease. Biol Psychiatry, 2013, 73(5): 472-481.
24.	Bai Lijun, Zhang Ming, Chen Shangjie, et al. Characterizing acupuncture de qi in mild cognitive impairment: relations with small-world efficiency of functional brain networks. Evid Based Complement Alternat Med, 2013: 304804.
25.	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.
26.	Sun Yu, Yin Qihua, Fang Rong, et al. Disrupted functional brain connectivity and its association to structural connectivity in amnestic mild cognitive impairment and Alzheimer's disease. PLoS One, 2014, 9(5): e96505.
27.	Tijms B M, Wink A M, De Haan W, et al. Alzheimer's disease: connecting findings from graph theoretical studies of brain networks. Neurobiol Aging, 2013, 34(8): 2023-2036.
28.	Zhu Dajiang, Li Kaiming, Terry D P, et al. Connectome-scale assessments of structural and functional connectivity in MCI. Hum Brain Mapp, 2014, 35(7): 2911-2923.
29.	Wang Zhiqun, Wang Jianli, Zhang Han, et al. Interhemispheric functional and structural disconnection in Alzheimer's disease: a combined resting-state fMRI and DTI study. PLoS One, 2015, 10(5): e0126310.
30.	Vecchio F, Miraglia F, Curcio G, et al. Cortical brain connectivity evaluated by graph theory in dementia: a correlation study between functional and structural data. J Alzheimers Dis, 2015, 45(3): 745-756.
31.	Zhong Suyu, He Yong, Gong Gaolang. Convergence and divergence across construction methods for human brain white matter networks: an assessment based on individual differences. Hum Brain Mapp, 2015, 36(5): 1995-2013.

1. Prince M, Wimo A, Guerchet M, et al. World Alzheimer Report 2015: The Global Impact of Dementia. London: Alzheimer's Disease International, 2015.
2. Lopez O L. Mild cognitive impairment. Continuum (Minneap Minn), 2013, 19(2): 411-424.
3. He Yong, Chen Zhang, Evans A. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. J Neurosci, 2008, 28(18): 4756-4766.
4. Fornito A, Zalesky A, Breakspear M. Graph analysis of the human connectome: promise, progress, and pitfalls. Neuroimage, 2013, 80: 426-444.
5. Sun F T, Miller L M, D'Esposito M. Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. Neuroimage, 2004, 21(2): 647-658.
6. Lerch J P, Worsley K, Shaw W P, et al. Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage, 2006, 31(3): 993-1003.
7. Gong Gaolang, He Yong, Concha L, et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex, 2009, 19(3): 524-536.
8. Humphries M D, Gurney K. Network 'small-world-ness': a quantitative method for determining canonical network equivalence. PLoS One, 2008, 3(4): e0002051.
9. Irimia A, Van Horn J D. The structural, connectomic and network covariance of the human brain. Neuroimage, 2013, 66(10): 489-499.
10. Yao Zhijun, Zhang Yuanchao, Lin Lei, et al. Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease. PLoS Comput Biol, 2010, 6(11): e1001006.
11. Phillips D J, Mcglaughlin A, Ruth D, et al. Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods. Neuroimage Clin, 2015, 7: 377-390.
12. Li Qiongling, Li Xinwei, Wang Xuetong, et al. Topological properties of large-scale cortical networks based on multiple morphological features in amnestic mild cognitive impairment. Neural Plast, 2016, 2016: 3462309.
13. Bai Feng, Shu Ni, Yuan Yonggui, et al. Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. J Neurosci, 2012, 32(12): 4307-4318.
14. Daianu M, Jahanshad N, Nir T M, et al. Breakdown of brain connectivity between normal aging and Alzheimer's disease: a structural k-core network analysis. Brain Connect, 2013, 3(4): 407-422.
15. Daianu M, Jahanshad N, Nir T M, et al. Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network. Hum Brain Mapp, 2015, 36(8): 3087-3103.
16. Zhang Bing, Zhang Xin, Zhang Fang, et al. Characterizing topological patterns in amnestic mild cognitive impairment by quantitative water diffusivity. J Alzheimers Dis, 2015, 43(2): 687-697.
17. Vecchio F, Miraglia F, Quaranta D, et al. Cortical connectivity and memory performance in cognitive decline: A study via graph theory from EEG data. Neuroscience, 2016, 316: 143-150.
18. Lu C F, Wang Y J, Shin T, et al. Altered network organization in mild Alzheimer's disease compared to mild cognitive impairment using resting-state EEG. International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, 2014, 8(11): 780-786.
19. Lu C F, Wang Y J, Wu Y T, et al. Relations of progression in cognitive decline with initial EEG resting-state functional network in mild cognitive impairment. International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, 2014, 8(11): 787-793.
20. Miraglia F, Vecchio F, Bramanti P, et al. EEG characteristics in "eyes-open" versus "eyes-closed" conditions: Small-world network architecture in healthy aging and age-related brain degeneration. Clin Neurophysiol, 2016, 127(2): 1261-1268.
21. Tóth B, File B, Boha R, et al. EEG network connectivity changes in mild cognitive impairment--preliminary results. Int J Psychophysiol, 2014, 92(1): 1-7.
22. Gardini S, Venneri A, Sambataro F, et al. Increased functional connectivity in the default mode network in mild cognitive impairment: a maladaptive compensatory mechanism associated with poor semantic memory performance. J Alzheimers Dis, 2015, 45(2): 457-470.
23. Wang Jinhui, Zuo Xinian, Dai Zhengjia, et al. Disrupted functional brain connectome in individuals at risk for Alzheimer's disease. Biol Psychiatry, 2013, 73(5): 472-481.
24. Bai Lijun, Zhang Ming, Chen Shangjie, et al. Characterizing acupuncture de qi in mild cognitive impairment: relations with small-world efficiency of functional brain networks. Evid Based Complement Alternat Med, 2013: 304804.
25. 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.
26. Sun Yu, Yin Qihua, Fang Rong, et al. Disrupted functional brain connectivity and its association to structural connectivity in amnestic mild cognitive impairment and Alzheimer's disease. PLoS One, 2014, 9(5): e96505.
27. Tijms B M, Wink A M, De Haan W, et al. Alzheimer's disease: connecting findings from graph theoretical studies of brain networks. Neurobiol Aging, 2013, 34(8): 2023-2036.
28. Zhu Dajiang, Li Kaiming, Terry D P, et al. Connectome-scale assessments of structural and functional connectivity in MCI. Hum Brain Mapp, 2014, 35(7): 2911-2923.
29. Wang Zhiqun, Wang Jianli, Zhang Han, et al. Interhemispheric functional and structural disconnection in Alzheimer's disease: a combined resting-state fMRI and DTI study. PLoS One, 2015, 10(5): e0126310.
30. Vecchio F, Miraglia F, Curcio G, et al. Cortical brain connectivity evaluated by graph theory in dementia: a correlation study between functional and structural data. J Alzheimers Dis, 2015, 45(3): 745-756.
31. Zhong Suyu, He Yong, Gong Gaolang. Convergence and divergence across construction methods for human brain white matter networks: an assessment based on individual differences. Hum Brain Mapp, 2015, 36(5): 1995-2013.

Previous Article
Recognition study of double strand DNA with hairpin oligopolyamide
Next Article
A review of progress of real-time tumor tracking radiotherapy technology based on dynamic multi-leaf collimator

Journal of Biomedical Engineering

Research progress of disrupted brain connectivity in mild cognitive impairment: findings from graph theoretical studies of whole brain networks

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content