Study of brain network in patients with stress hyperglycemia after acute cerebral infarction based on graph theory

doi:10.3969/j.issn.1004-583X.2025.09.002

Abstract

Abstract:

Objective To explore the neurophysiological mechanisms of stress-induced hyperglycemia (SIH) after acute cerebral infarction (ACI) at the level of structural brain networks based on diffusion tensor imaging (DTI) and graph theory. Methods A total of 32 SIH patients after ACI and 35 healthy controls (HC) matched for sex and age were prospectively selected. SIH patients were identified based on the stress hyperglycemia ratio (SHR). All subjects underwent magnetic resonance imaging (MRI), and structural brain networks were constructed using deterministic tractography. Graph theory analysis was applied to calculate the global attribute indicators and the Rich-Club attribute parameters of the structural brain network. Additionally, this study analyzed connection strength based on the edges of the network, and the potential correlation between the abnormal network attribute indicators and SHR in post-ACI patients. Results In the global attribute indicators, the clustering coefficient (Cp), global efficiency (Eglob), and local efficiency (Eloc) of the SIH group were significantly lower compared to the HC group, while the characteristic path length (Lp) was significantly higher compared to the HC group ( $P$ <0.01). Both the SIH group and the HC group exhibited small-world organization, and the values of small-worldness (σ), normalized characteristic path length (λ), and normalized clustering coefficient (γ) in the SIH group were significantly larger than those in the HC group ( $P$ <0.01). Compared to the HC group, the connection strength of rich connections (connections between Rich-Club nodes), local connections (connections between non-Rich-Club nodes), and feeder connections (connections between Rich-Club nodes and non-Rich-Club nodes) were significantly lower in the SIH group ( $P$ <0.01). Network-based edge analysis showed that after network-based statistics (NBS) correction, a subnetwork with reduced connection strength was found to exist in the SIH group ( $P$ =0.0002), namely the left frontal-insula-limbic system subnetwork, consisting mainly of 6 nodes and 5 connected edges. The results of the correlation analysis showed no significant association between the abnormal brain network parameter indicators and SHR ( $P$ >0.05). Conclusion Structural brain network is significantly impaired in SIH patients after ACI, and the network has a tendency to transform into a regular network. At the same time, Rich-Club properties are severely impaired in SIH patients after ACI, and the strength of multiple connections is reduced. The reduced connectivity of the left frontal-islet-limbic system sub-network involved damage to the core nodes of the brain, suggesting that this sub-network may be an indication of a pathogenic core region, as well as future multiple dysfunctions and potential disease risks.

Key words: brain infarction, stress-induced hyperglycemia, diffusion tensor imaging, rich-club, brain network topological properties

CLC Number:

R743.33

Liu Liying, Cui Kaige, Yu Jiaqi, Jia Juan, Sun Liqiang, Yang Jiping. Study of brain network in patients with stress hyperglycemia after acute cerebral infarction based on graph theory[J]. Clinical Focus, 2025, 40(9): 781-789.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.lchc.cn/EN/10.3969/j.issn.1004-583X.2025.09.002

https://www.lchc.cn/EN/Y2025/V40/I9/781

Figures/Tables 10

Fig.1 Structural network matrix construction for the SIH group (Fig.1a) and the HC group (Fig.1b)

Tab.1 General clinical data between groups

组别	例数	性别(男/女)	年龄(岁)	SIH比率	NIHSS评分(分)	利手(右/左)
SIH组	32	21/11	57.0±7.3	1.12±0.24	2.78±2.47	32/0
HC组	35	16/19	54.4±7.6	-	-	35/0
χ²/ $t$ 值		2.680	1.447	-	-	-
$P$ 值		0.102	0.153	-	-	-

Tab.1 General clinical data between groups

组别	例数	性别(男/女)	年龄(岁)	SIH比率	NIHSS评分(分)	利手(右/左)
SIH组	32	21/11	57.0±7.3	1.12±0.24	2.78±2.47	32/0
HC组	35	16/19	54.4±7.6	-	-	35/0
χ²/ $t$ 值		2.680	1.447	-	-	-
$P$ 值		0.102	0.153	-	-	-

Tab.2 Parameters analysis of brain network topology properties

组别	例数	Cp	Lp	γ	λ	σ	Eglob	Eloc
SIH组	32	0.440±0.039	2.838±0.138	5.886±0.433	1.190±0.024	4.944±0.338	0.353±0.017	0.596±0.058
对照组	35	0.474±0.031	2.578±0.177	5.063±0.665	1.166±0.026	4.333±0.499	0.389±0.026	0.664±0.047
$t$ 值		-4.019	6.65	6.047	3.872	5.912	-6.893	-5.319
$P$ 值		<0.01	<0.01	<0.01	<0.01	<0.01	<0.01	<0.01

Tab.2 Parameters analysis of brain network topology properties

组别	例数	Cp	Lp	γ	λ	σ	Eglob	Eloc
SIH组	32	0.440±0.039	2.838±0.138	5.886±0.433	1.190±0.024	4.944±0.338	0.353±0.017	0.596±0.058
对照组	35	0.474±0.031	2.578±0.177	5.063±0.665	1.166±0.026	4.333±0.499	0.389±0.026	0.664±0.047
$t$ 值		-4.019	6.65	6.047	3.872	5.912	-6.893	-5.319
$P$ 值		<0.01	<0.01	<0.01	<0.01	<0.01	<0.01	<0.01

Fig.2 Difference in global properties of structural networks between the SIH group and the HC group

Tab.3 Connection strength analysis for different connection types

组别	例数	rich连接	feeder连接	local连接
SIH组	32	6.224±0.936	46.954±4.935	70.782±7.465
HC组	35	8.052±1.631	59.526±10.341	90.909±17.023
$t$ 值		-5.554	-6.254	-6.165
$P$ 值		<0.01	<0.01	<0.01

Tab.3 Connection strength analysis for different connection types

组别	例数	rich连接	feeder连接	local连接
SIH组	32	6.224±0.936	46.954±4.935	70.782±7.465
HC组	35	8.052±1.631	59.526±10.341	90.909±17.023
$t$ 值		-5.554	-6.254	-6.165
$P$ 值		<0.01	<0.01	<0.01

Fig.3 Distribution of Rich-Club nodes and schematic diagram of the structural brain network in the two groups of subjects Note: FFG.R: Fusiform_R; FFG.L: Fusiform_L; PCUN.L: Precuneus_L; PreCG.L: Precentral_L; PreCG.R: Precentral_R; PCUN.R: Precuneus_R; DCG.L: Cingulum_Mid_L; MOG.L: Occipital_Mid_L; PUT.L: Putamen_L; PUT.R: Putamen_R; INS.L: Insula_L; DCG.R: Cingulum_Mid_R; HIP.L: Hippocampus_L

Fig.4 Schematic diagram of the three types of Rich-Club connections

Fig.5 Differences in the strength of three types of Rich-Club connections

Fig.6 Connection of reduced structural networks in the SIH group compared to the HC group Note: IFGoperc.L: Frontal_Inf_Oper_L; IFGtriang.L: Frontal_Inf_Tri_L; ORBinf.L: Frontal_Inf_Orb_L; INS.L: Insula_L; HIP.L: Hippocampus_L; AMYG.L: Amygdala_L

Fig.7 Correlation analysis of abnormal brain network parameter indicators with SHR in the SIH group

References 37

[1]	Feske SK. Ischemic stroke[J]. Am J Med, 2021, 134(12): 1457-1464. doi:10.1016/j.amjmed.2021.07.027. pmid: 34454905
[2]	Putaala J. Ischemic stroke in young adults[J]. Continuum (Minneap Minn), 2020, 26(2): 386-414. doi:10.1212/CON.0000000000000833. pmid: 32224758
[3]	Karakasis P, Stalikas N, Patoulias D, et al. Prognostic value of stress hyperglycemia ratio in patients with acute myocardial infarction: A systematic review with Bayesian and frequentist meta-analysis[J]. Trends Cardiovasc Med, 2024, 34(7): 453-465. doi:10.1016/j.tcm.2023.11.006.
[4]	Li J, Quan K, Wang Y, et al. Effect of stress hyperglycemia on neurological deficit and mortality in the acute ischemic stroke people with and without diabetes[J]. Front Neurol, 2020, 11: 576895. doi:10.3389/fneur.2020.576895.
[5]	孙海燕, 王云成, 田晶. 急性脑梗死早期血糖升高对病情及预后的影响[J]. 中国老年学杂志, 2020, 40(22): 4734-4736. doi:10.3969/j.issn.1005-9202.2020.22.010.
[6]	Lei J, Peng Y, Li W, et al. Stress hyperglycemia is associated with early neurologic deterioration in patients with acute ischemic stroke after intravenous thrombolysis without hemorrhagic transformation[J]. Diabetol Metab Syndr, 2024, 16(1): 285. doi:10.1186/s13098-024-01537-z.
[7]	Wilmskoetter J, He X, Caciagli L, et al. Language recovery after brain injury: A structural network control theory study[J]. J Neurosci, 2022, 42(4): 657-669. doi:10.1523/JNEUROSCI.1096-21.2021.
[8]	Zhang Y, Mu N, Qi S, et al. Improved brain functional network in major depressive disorder with suicidal ideation after individual target-transcranial magnetic stimulation treatment: A graph-theory analysis[J]. Front Psychiatry, 2025, 16: 1486835. doi:10.3389/fpsyt.2025.1486835.
[9]	Liu T, Yan Y, Ai J, et al. Disrupted rich-club organization of brain structural networks in Parkinson's disease[J]. Brain Struct Funct, 2021, 226(7): 2205-2217. doi:10.1007/s00429-021-02319-3. pmid: 34173868
[10]	邓杰, 邱丽华. 磁共振扩散张量成像在青少年抑郁症中的研究进展[J]. 中国医学影像学杂志, 2024, 32(8): 850-854. doi:10.3969/j.issn.1005-5185.
[11]	Peng L, Feng J, Ma D, et al. Rich-Club organization disturbances of the individual morphological network in subjective cognitive decline[J]. Front Aging Neurosci, 2022, 14: 834145. doi:10.3389/fnagi.2022.834145.
[12]	van den Heuvel MP, Sporns O. Rich-club organization of the human connectome[J]. J Neurosci, 2011, 31(44): 15775-15786. doi:10.1523/JNEUROSCI.3539-11.2011.
[13]	Shu N, Duan Y, Huang J, et al. Progressive brain rich-club network disruption from clinically isolated syndrome towards multiple sclerosis[J]. Neuroimage Clin, 2018, 19: 232-239. doi:10.1016/j.nicl.2018.03.034.
[14]	中华医学会神经病学分会, 中华医学会神经病学分会脑血管病学组. 中国急性缺血性卒中诊治指南2023[J]. 中华神经科杂志, 2024, 57(6): 523-559. doi:10.3760/cma.j.cn113694-20240410-00221.
[15]	张东, 李治璋, 马瑞楠, 等. 应激性高血糖比值与急性缺血性卒中患者临床预后的相关性研究[J]. 中国卒中杂志, 2022, 17(5): 483-490. doi:10.3969/j.issn.1673-5765.2022.05.008.
[16]	Yan T, Wang W, Yang L, et al. Rich club disturbances of the human connectome from subjective cognitive decline to Alzheimer's disease[J]. Theranostics, 2018, 8(12): 3237-3255. doi:10.7150/thno.23772. pmid: 29930726
[17]	Van den Heuvel MP, Sporns O, Collin G, et al. Abnormal rich club organization and functional brain dynamics in schizophrenia[J]. JAMA Psychiatry, 2013, 70(8): 783-792. doi:10.1001/jamapsychiatry.2013.1328. pmid: 23739835
[18]	Zhao X, Tian L, Yan J, et al. Abnormal Rich-Club organization associated with compromised cognitive function in patients with schizophrenia and their unaffected parents[J]. Neurosci Bull, 2017, 33(4): 445-454.doi:10.1007/s12264-017-0151-0. pmid: 28646350
[19]	Shi M, Liu S, Chen H, et al. Disrupted brain functional network topology in unilateral acute brainstem ischemic stroke[J]. Brain Imaging Behav, 2021, 15(1): 444-452. doi:10.1007/s11682-020-00353-z.
[20]	卢鹏超. 急性脑梗死后应激性血糖升高的脑网络改变及功能连接分析[D]. 石家庄: 河北医科大学, 2023.
[21]	Zhang J, Liu Z, Li Z, et al. Disrupted white matter network and cognitive decline in type 2 diabetes patients[J]. J Alzheimers Dis, 2016, 53(1): 185-195. doi:10.3233/JAD-160111. pmid: 27163818
[22]	Qin C, 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. pmid: 31275222
[23]	Reijmer YD, Leemans A, Brundel M, et al. Disruption of the cerebral white matter network is related to slowing of information processing speed in patients with type 2 diabetes[J]. Diabetes, 2013, 62(6): 2112-2115. doi:10.2337/db12-1644. pmid: 23349494
[24]	王晓艳, 王鹏程, 侯立霞, 等. 2型糖尿病脑病患者大脑功能磁共振网络小世界特性[J]. 中国医学物理学杂志, 2016, 33(5): 501-504. doi:10.3969/j.issn.1005-202X.2016.05.014.
[25]	Goodkind M, Eickhoff SB, Oathes DJ, et al. Identification of a common neurobiological substrate for mental illness[J]. JAMA Psychiatry, 2015, 72(4): 305-315. doi:10.1001/jamapsychiatry.2014.2206. pmid: 25651064
[26]	Zhou Y, Jing J, Zhang Z, et al. Disrupted pattern of rich-club organization in structural brain network from prediabetes to diabetes: A population-based study[J]. Hum Brain Mapp, 2024, 45(2): e26598. doi:10.1002/hbm.26598.
[27]	Cui Y, Tang TY, Lu CQ, et al. Insulin resistance and cognitive impairment: Evidence from neuroimaging[J]. J Magn Reson Imaging, 2022, 56(6): 1621-1649. doi:10.1002/jmri.28358. pmid: 35852470
[28]	Yaribeygi H, Sathyapalan T, Atkin SL, et al. Molecular mechanisms linking oxidative stress and diabetes mellitus[J]. Oxid Med Cell Longev, 2020, 2020(4): 8609213. doi:10.1155/2020/8609213.
[29]	Daianu M, Jahanshad N, Nir TM, et al. Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network[J]. Hum Brain Mapp, 2015, 36(8): 3087-3103. doi:10.1002/hbm.22830. pmid: 26037224
[30]	Crossley NA, Mechelli A, Scott J, et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders[J]. Brain, 2014, 137(8): 2382-2395. doi:10.1093/brain/awu132.
[31]	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.
[32]	Craig AD. How do you feel--now? The anterior insula and human awareness[J]. Nat Rev Neurosci, 2009, 10(1): 59-70. doi:10.1038/nrn2555. pmid: 19096369
[33]	Perani D, Farsad M, Ballarini T, et al. The impact of bilingualism on brain reserve and metabolic connectivity in Alzheimer's dementia[J]. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114(7): 1690-1695. doi:10.1073/pnas.1610909114. pmid: 28137833
[34]	Chowdhury S, Kamatkar NG, Wang WX, et al. Brainstem neuropeptidergic neurons link a neurohumoral axis to satiation[J]. Cell, 2025, 188(6): 1563-1579. doi:10.1016/j.cell.2025.01.018.
[35]	蔡颖源, 陆小伟. 老年急性岛叶梗死与应激性高血糖发生风险的相关性[J]. 实用老年医学, 2021, 35(11): 1126-1130. doi:10.3969/j.issn.1003-9198.2021.11.006.
[36]	Uno H, Itokazu T, Yamashita T. Inhibition of repulsive guidance molecule A ameliorates diabetes-induced cognitive decline and hippocampal neurogenesis impairment in mice[J]. Commun Biol, 2025, 8(1): 263. doi:10.1038/s42003-025-07696-7.
[37]	McEwen BS. Neurobiological and systemic effects of chronic stress[J]. Chronic Stress (Thousand Oaks), 2017, 1: 2470547017692328. doi:10.1177/2470547017692328.