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• Clinical Article •
A study of functional brain networks in patients with mild cognitive impairment based on graph theory
HE Wenjuan  XIE Qi  WANG Yajie  TAN Zhilin  LIAO Yanhui 

Cite this article as: He WJ, Xie Q, Wang YJ, et al. A study of functional brain networks in patients with mild cognitive impairment based on graph theory[J]. Chin J Magn Reson Imaging, 2022, 13(5): 1-5. DOI:10.12015/issn.1674-8034.2022.05.001.


[Abstract] Objective To investigate the changes and application values of functional brain network topological properties in patients with mild cognitive impairment (MCI).Materials and Methods A sample of 28 MCI patients and 15 normal controls (NC) was included. After the resting-state functional magnetic resonance imaging (rs-fMRI) data of all subjects were collected, using the Graph Theoretical Network Analysis (GRETNA) to do data preprocessing and construct the functional network, and then graph theory was utilized to calculate the network topological properties. Then, differences between the two groups were compared based on independent-samples t-test, with Bonrerroni corrections for multiple comparisons.Results Compared with the NC group, the clustering coefficient (Cp) and local efficiency (Eloc) of the MCI group decreased, and the visual and auditory functional areas suffered the biggest drop, and the differences were statistically significant (P＜0.05); the shortest path length (Lp) of the MCI group was shortened (P＞0.05), and the global efficiency (Eglob) increased (P＞0.05), but the differences were not statistically significant.Conclusions Compared with the NC group, the Cp and Eloc decreased significantly in the MCI patients, and the visual and auditory functional areas suffered the biggest drop, which may become potential biomarkers for accurate diagnosis of MCI.
[Keywords] mild cognitive impairment；resting-state functional magnetic resonance imaging；global efficiency；clustering coefficient；the shortest path length；local efficiency

HE Wenjuan   XIE Qi*   WANG Yajie   TAN Zhilin   LIAO Yanhui  

Medical Imaging Department of Nansha, Guangzhou First People's Hospital (School of Medicine, South China University of Technology), Guangzhou 510180, China

Xie Q, E-mail: eyqixie@scut.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS Guangzhou Science and Technology Plan Project (No. 201907010020).
Received  2021-11-03
Accepted  2022-04-29
DOI: 10.12015/issn.1674-8034.2022.05.001
Cite this article as: He WJ, Xie Q, Wang YJ, et al. A study of functional brain networks in patients with mild cognitive impairment based on graph theory[J]. Chin J Magn Reson Imaging, 2022, 13(5): 1-5. DOI:10.12015/issn.1674-8034.2022.05.001.

[1]
Anderson ND. State of the science on mild cognitive impairment (MCI)[J]. CNS Spectr, 2019, 24(1): 78-87. DOI: 10.1017/S1092852918001347.
[2]
Hojjati SH, Ebrahimzadeh A, Khazaee A, et al. Alzheimer's Disease Neuroimaging I. Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI[J]. Comput Biol Med, 2018, 102(9): 30-39. DOI: 10.1016/j.compbiomed.2018.09.004.
[3]
Jie B, Liu M, Zhang D, et al. Sub-network kernels for measuring similarity of brain connectivity networks in disease diagnosis[J]. IEEE Trans Image Process, 2018, 27(5): 2340-2353. DOI: 10.1109/tip.2018.2799706.
[4]
Liss JL, Assuncao SS, 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.
[5]
Wang Q, Xia JG, Tian WZ. Advances in research on resting brain network functional magnetic resonance imaging for PD with cognitive impairmente[J]. Chin J Magn Reson Imaging, 2020, 11(11): 1044-1047. DOI: 10.12015/issn.1674-8034.2020.11.020.
[6]
Nelson CJ, Bonner S. Neuronal graphs: A graph theory primer for microscopic, functional networks of neurons recorded by calcium imaging[J]. Front Neural Circuits, 2021, 15: 662882. DOI: 10.3389/fncir.2021.662882.
[7]
Xin Z, Chen X, Zhang Q, et al. Alteration in topological properties of brain functional network after 2-year high altitude exposure: A panel study[J]. Brain Behav, 2020, 10(10): e01656. DOI: 10.1002/brb3.1656.
[8]
Liu H, Hu H, Wang H, et al. A brain network constructed on an l1-norm regression model is more sensitive in detecting small world network changes in early AD[J]. Neural Plast, 2020, 2020: 9436406. DOI: 10.1155/2020/9436406.
[9]
Bondi MW, Edmonds EC, Jak AJ, et al. Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates[J]. J Alzheimers Dis, 2014, 42(1): 275-289. DOI: 10.3233/JAD-140276.
[10]
Guan X, Guo T, Zeng Q, et al. Oscillation-specific nodal alterations in early to middle stages parkinson's disease[J]. Transl Neurodegener, 2019, 8(1): 36. DOI: 10.1186/s40035-019-0177-5.
[11]
Medaglia JD. Graph theoretic analysis of resting state functional mr imaging[J]. Neuroimaging Clin N Am, 2017, 27(4): 593-607. DOI: 10.1016/j.nic.2017.06.008.
[12]
Mihaescu AS, Kim J, Masellis M, et al. Graph theory analysis of the dopamine d2 receptor network in parkinson's disease patients with cognitive decline[J]. J Neurosci Res, 2021, 99(3): 947-965. DOI: 10.1002/jnr.24760.
[13]
Zhang T, Liao Q, Zhang D, et al. Predicting mci to ad conversation using integrated sMRI and rs-fMRI: Machine learning and graph theory approach[J]. Front Aging Neurosci, 2021, 13: 688926. DOI: 10.3389/fnagi.2021.688926.
[14]
Wang XY, Zhang HX, Wang B, et al. Abnormalities of efficiency in resting state functional brain network in first-episode paranoid schizophrenia[J]. Chin J Behav Med Brain Sci, 2021, 30(3): 219-225. DOI: 10.3760/cma.j.cn371468-20201104-01838.
[15]
Zhao XY, Zhang W, Zhong WJ, et al. Predictive value of alterations of brain structural network topology in early-stage Parkinson's disease with mild cognitive impairment[J]. Chin J Magn Reson Imaging, 2022, 13(3): 12-17. DOI: 10.12015/issn.1674-8034.2022.03.003.
[16]
Yao Z, Zhang Y, Lin L, et al. Abnormal cortical networks in mild cognitive impairment and alzheimer's disease[J]. PLoS Comput Biol, 2010, 6(11): e1001006. DOI: 10.1371/journal.pcbi.1001006.
[17]
Son SJ, Kim J, Seo J, et al. Connectivity analysis of normal and mild cognitive impairment patients based on FDG and PiB-PET images[J]. Neurosci Res, 2015, 98(8): 50-58. DOI: 10.1016/j.neures.2015.04.002.
[18]
Zhang L, Ni H, Yu Z, et al. Investigation on the alteration of brain functional network and its role in the identification of mild cognitive impairment[J]. Front Neurosci, 2020, 14: 558434. DOI: 10.3389/fnins.2020.558434.
[19]
Xu X, Li W, Mei J, et al. Feature selection and combination of information in the functional brain connectome for discrimination of mild cognitive impairment and analyses of altered brain patterns[J]. Front Aging Neurosci, 2020, 12: 28. DOI: 10.3389/fnagi.2020.00028.
[20]
Xue C, Sun H, Hu G, et al. Disrupted patterns of rich-club and diverse-club organizations in subjective cognitive decline and amnestic mild cognitive impairment[J]. Front Neurosci, 2020, 14: 575652. DOI: 10.3389/fnins.2020.575652.
[21]
Iordan AD, Cooke KA, Moored KD, et al. Aging and network properties: Stability over time and links with learning during working memory training[J]. Front Aging Neurosci, 2017, 9: 419. DOI: 10.3389/fnagi.2017.00419.
[22]
Kahl M, Wagner G, Cruz FDL, et al. Resilience and cortical thickness: A mri study[J]. Eur Arch Psychiatry Clin Neurosci, 2020, 270(5): 533-539. DOI: 10.1007/s00406-018-0963-6.
[23]
Cooray GK, Sundgren M, Brismar T. Mechanism of visual network dysfunction in relapsing-remitting multiple sclerosis and its relation to cognition[J]. Clin Neurophysiol, 2020, 131(2): 361-367. DOI: 10.1016/j.clinph.2019.10.029.

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