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Original Article
Analysis of local efficiency and node local efficiency changes in patients with type 2 diabetes based on graph theory
DU Wei  LIU Yangyingqiu  JIANG Jian  LI Wanyao  JIANG Yuhan  MIAO Yanwei  WANG Weiwei 

Cite this article as: Du W, Liu YYQ, Jiang J, et al. Analysis of local efficiency and node local efficiency changes in patients with type 2 diabetes based on graph theory[J]. Chin J Magn Reson Imaging, 2022, 13(5): 70-76. DOI:10.12015/issn.1674-8034.2022.05.013.

[Abstract] Objective To investigate the small-world properties change of functional networks in patients with type 2 diabetes mellitus (T2DM) based on resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory analysis.Materials and Methods Blood oxygenation level dependent rs-fMRI was performed on 29 patients with clinically confirmed T2DM (T2DM group) and 20 age, gender, years of education matched healthy controls (HC) group. The parameters of the small-world networks, including σ, λ, γ, the path length (Lp), clustering coefficient (Cp), global efficiency, local efficiency (Eloc) and nodal local efficiency were obtained from all subjects. The differences of all small-world networks parameters, clinical data, cognitive scale scores were compared between the two groups. The correlation between small-world networks parameters, clinical data, cognitive scale scores were also performed.Results In the sparsity range of 0.05~0.50, both two groups showed economic small world network. Compared with the HC group, the Eloc AUC value of T2DM group was significantly lower than HC group in the sparsity range of 0.05 to 0.50. The T2DM group exhibited decreased Eloc value at 0.30, 0.34, 0.36, 0.40 sparsity threshold (P<0.05). And T2DM group presented significant decreases of integrated nodal efficiency in the right opercular part of inferior frontal gyrus, olfactory cortex, supramarginal gyrus, and left middle temporal gyrus, and increases in the left orbital part of superior and middle frontal gyrus, the right medial orbital of superior frontal gyrus, and the left cuneus (Bonferroni corrected,P<0.05). In addition, Lp AUC values in T2DM group were positively correlated with SDMT scores (r=0.38, P=0.04); σ AUC values (r=-0.45, P=0.02), γ AUC values (r=-0.40, P=0.03) was negatively correlated with SDMT score; λ AUC value was positively correlated with SDMT score (r=0.45, P=0.01), and positively correlated with MoCA score (r=0.45, P=0.02). In addition, Cp AUC values were positively correlated with homocysteicacid (r=0.39, P=0.04) and positively correlated with hemoglobin (r=0.46, P=0.01).Conclusions The brain networks of both the T2DM group and the HC group showed economic small-world network property. The local information transmission efficiency of brain networks in T2DM patients is reduced and correlated with cognitive function, homocysteine and hemoglobin. In addition, the local efficiency of multiple brain regions in patients with T2DM is abnormal, indicating abnormal cognitive and emotional function activities, which provides a new perspective for the study of diabetic encephalopathy, and provides clues for further exploration of the mechanism of brain network changes in T2DM.
[Keywords] type 2 diabetes mellitus;functional magnetic resonance imaging;small world brain network;graph theory analysis;network efficiency

DU Wei   LIU Yangyingqiu   JIANG Jian   LI Wanyao   JIANG Yuhan   MIAO Yanwei   WANG Weiwei*  

Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

Wang WW, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81801657).
Received  2021-12-21
Accepted  2022-04-07
DOI: 10.12015/issn.1674-8034.2022.05.013
Cite this article as: Du W, Liu YYQ, Jiang J, et al. Analysis of local efficiency and node local efficiency changes in patients with type 2 diabetes based on graph theory[J]. Chin J Magn Reson Imaging, 2022, 13(5): 70-76. DOI:10.12015/issn.1674-8034.2022.05.013.

Saeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9 th edition[J]. Diabetes Res Clin Pract, 2019, 157: 107843. DOI: 10.1016/j.diabres.2019.107843.
Degen C, Toro P, Schönknecht P, et al. Diabetes mellitus Type Ⅱ and cognitive capacity in healthy aging, mild cognitive impairment and Alzheimer's disease[J]. Psychiatry Res, 2016, 240: 42-46. DOI: 10.1016/j.psychres.2016.04.009.
Stam CJ. Modern network science of neurological disorders[J]. Nat Rev Neurosci, 2014, 15(10): 683-695. DOI: 10.1038/nrn3801.
Chen GQ, Zhang X, Xing Y, et al. Resting-state functional magnetic resonance imaging shows altered brain network topology in Type 2 diabetic patients without cognitive impairment[J]. Oncotarget, 2017, 8(61): 104560-104570. DOI: 10.18632/oncotarget.21282.
Xu J, Chen FQ, Liu TY, et al. Brain functional networks in type 2 diabetes mellitus patients: a resting-state functional MRI study[J]. Front Neurosci, 2019, 13: 239. DOI: 10.3389/fnins.2019.00239.
Zhang DS, Huang Y, Gao J, et al. Altered functional topological organization in type-2 diabetes mellitus with and without microvascular complications[J]. Front Neurosci, 2021, 15: 726350. DOI: 10.3389/fnins.2021.726350.
Association AD. Diagnosis and classification of diabetes mellitus[J]. Diabetes Care, 2014, 37(Suppl 1): S81-S90. DOI: 10.2337/dc14-S081.
Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks[J]. Nature, 1998, 393(6684): 440-442. DOI: 10.1038/30918.
Zhang WT, Zhao WN, Wang JL, et al. Imaging diagnosis of central nervous system damage in patients with T2DM[J]. Neurosci Lett, 2020, 733: 135092. DOI: 10.1016/j.neulet.2020.135092.
Zhang Y, Cao YJ, Xie YJ, et al. Altered brain structural topological properties in type 2 diabetes mellitus patients without complications[J]. J Diabetes, 2019, 11(2): 129-138. DOI: 10.1111/1753-0407.12826.
Qin CH, Liang Y, Tan X, et al. Altered whole-brain functional topological organization and cognitive function in type 2 diabetes mellitus patients[J]. Front Neurol, 2019, 10: 599. DOI: 10.3389/fneur.2019.00599.
Xiong Y, Chen XD, Zhao X, et al. Altered regional homogeneity and functional brain networks in Type 2 diabetes with and without mild cognitive impairment[J]. Sci Rep, 2020, 10(1): 21254. DOI: 10.1038/s41598-020-76495-3.
Achard S, Bullmore E. Efficiency and cost of economical brain functional networks[J]. PLoS Comput Biol, 2007, 3(2): e17. DOI: 10.1371/journal.pcbi.0030017.
Damanik J, Mayza A, Rachman A, et al. Association between serum homocysteine level and cognitive function in middle-aged type 2 diabetes mellitus patients[J]. PLoS One, 2019, 14(11): e0224611. DOI: 10.1371/journal.pone.0224611.
Sainson C, Barat M, Aguert M. Communication disorders and executive function impairment after severe traumatic brain injury: an exploratory study using the GALI (a grid for linguistic analysis of free conversational interchange)[J]. Ann Phys Rehabil Med, 2014, 57(9-10): 664-683. DOI: 10.1016/
Li C, Li CM, Yang QF, et al. Cortical thickness contributes to cognitive heterogeneity in patients with type 2 diabetes mellitus[J]. Medicine (Baltimore), 2018, 97(21): e10858. DOI: 10.1097/MD.0000000000010858.
Zhang D, Shi L, Song XB, et al. Neuroimaging endophenotypes of type 2 diabetes mellitus: a discordant sibling pair study[J]. Quant Imaging Med Surg, 2019, 9(6): 1000-1013. DOI: 10.21037/qims.2019.05.18.
Swan GE, Carmelli D. Impaired olfaction predicts cognitive decline in nondemented older adults[J]. Neuroepidemiology, 2002, 21(2): 58-67. DOI: 10.1159/000048618.
Vassilaki M, Christianson TJ, Mielke MM, et al. Neuroimaging biomarkers and impaired olfaction in cognitively normal individuals[J]. Ann Neurol, 2017, 81(6): 871-882. DOI: 10.1002/ana.24960.
Zhang Z, Zhang B, Wang X, et al. Olfactory dysfunction mediates adiposity in cognitive impairment of type 2 diabetes: insights from clinical and functional neuroimaging studies[J]. Diabetes Care, 2019, 42(7): 1274-1283. DOI: 10.2337/dc18-2584.
Doty RL. Olfactory dysfunction in neurodegenerative diseases: is there a common pathological substrate?[J]. Lancet Neurol, 2017, 16(6): 478-488. DOI: 10.1016/S1474-4422(17)30123-0.
Li W, Huang E. An update on type 2 diabetes mellitus as a risk factor for dementia[J]. J Alzheimers Dis, 2016, 53(2): 393-402. DOI: 10.3233/JAD-160114.
Zhou C, Li J, Dong M, et al. Altered white matter microstructures in type 2 diabetes mellitus: a coordinate-based meta-analysis of diffusion tensor imaging studies[J]. Front Endocrinol (Lausanne), 2021, 12: 658198. DOI: 10.3389/fendo.2021.658198.
Kohn N, Toygar T, Weidenfeld C, et al. In a sweet mood? Effects of experimental modulation of blood glucose levels on mood-induction during fMRI[J]. Neuroimage, 2015, 113: 246-256. DOI: 10.1016/j.neuroimage.2015.03.024.
Liu DH, Duan SS, Zhang JQ, et al. Aberrant brain regional homogeneity and functional connectivity in middle-aged T2DM patients: a resting-state functional MRI study[J]. Front Hum Neurosci, 2016, 10: 490. DOI: 10.3389/fnhum.2016.00490.
Zhuo YY, Fang F, Lu LB, et al. White matter impairment in type 2 diabetes mellitus with and without microvascular disease[J]. Neuroimage Clin, 2019, 24: 101945. DOI: 10.1016/j.nicl.2019.101945.
Wang JK, Fan YL, Dong Y, et al. Combining gray matter volume in the cuneus and the cuneus-prefrontal connectivity may predict early relapse in abstinent alcohol-dependent patients[J]. PLoS One, 2018, 13(5): e0196860. DOI: 10.1371/journal.pone.0196860.
Cooke S, Pennington K, Jones A, et al. Effects of exercise, cognitive, and dual-task interventions on cognition in type 2 diabetes mellitus: a systematic review and meta-analysis[J]. PLoS One, 2020, 15(5): e0232958. DOI: 10.1371/journal.pone.0232958.
Yu KKK, Cheing GLY, Cheung C, et al. Gray matter abnormalities in type 1 and type 2 diabetes: a dual disorder ALE quantification[J]. Front Neurosci, 2021, 15: 638861. DOI: 10.3389/fnins.2021.638861.
Qi N, Cui Y, Liu JC, et al. Follow-up of resting-state brain function with magnetic resonance imaging in patients with type 2 diabetes mellitus[J]. Natl Med J China, 2017, 97(39): 3057-3061. DOI: 10.3760/cma.j.issn.0376-2491.2017.39.004.
Zhang DS, Gao J, Yan XJ, et al. Altered functional connectivity of brain regions based on a meta-analysis in patients with T2DM: a resting-state fMRI study[J]. Brain Behav, 2020, 10(8): e01725. DOI: 10.1002/brb3.1725.

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