[1]曾辉,刘莹,努尔比亚·克然木,等.阿尔兹海默病患者脑网络连接参数与患者认知控制能力的关系[J].卒中与神经疾病杂志,2025,32(02):128-135.[doi:10.3969/j.issn.1007-0478.2025.02.005]
　Zeng Hui,Liu Ying,Nubian Keranmu,et al.The relationship between brain network connectivity parameters and cognitive control ability in Alzheimer's disease patients[J].Stroke and Nervous Diseases,2025,32(02):128-135.[doi:10.3969/j.issn.1007-0478.2025.02.005]
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阿尔兹海默病患者脑网络连接参数与患者认知控制能力的关系()
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《卒中与神经疾病》杂志[ISSN:1007-0478/CN:42-1402/R]

卷:
第32卷
期数:
2025年02期
页码:
128-135
栏目:
认知功能障碍
出版日期:
2025-04-20

文章信息/Info

Title:
The relationship between brain network connectivity parameters and cognitive control ability in Alzheimer's disease patients
文章编号:
1007-0478(2025)02-0128-08
作者:
曾辉; 刘莹; 努尔比亚·克然木; 纪璇; 辛娟; 王云玲
844099 新疆喀什地区第一人民医院新疆人工智能影像辅助诊断重点实验室、新疆医科大学第二附属医院医学影像科[曾辉 刘莹努尔比亚·克然木 纪璇 辛娟 王云玲(通信作者)]
Author(s):
Zeng Hui;  Liu Ying;  Nubian Keranmu;  et al.
Xinjiang Key Laboratory of Artificial Intelligence Assisted Imaging Diagnosis, Department of Medical Imaging, the Second Affiliated Hospital of Xinjiang Medical University,Kashi Xinjiang 844099
关键词:
阿尔兹海默病 脑网络 功能连接 认知控制能力
Keywords:
Alzheimer's disease Brain network Functional connection Cognitive control ability
分类号:
R742
DOI:
10.3969/j.issn.1007-0478.2025.02.005
文献标志码:
A
摘要:
目的 探讨阿尔兹海默病(Alzheimer's disease,AD)患者脑网络连接参数与患者认知控制能力的关系。方法 回顾性选择2023年8月-2024年8月就诊于新疆医科大学第二附属医院AD患者300例作为研究对象,采用简易精神状态检查量表(Mini mental state examination,MMSE)评估AD患者认知功能障碍严重程度,分为轻度障碍组(n=150)和中重度障碍组(n=150); 所有研究对象均进行静息态功能磁共振成像(Resting-state functional magnetic resonance imaging,rs-MRI)和脑电图(Electroencephalogram,EEG)并收集数据; 根据信号之间的相位同步性,使用相位锁定值(Phase locking value,PLV)方法分别在θ和α频段下计算脑功能连接; 基于图论方法再分别计算加权网络的强度、平均特征路径长度和平均聚类系数; 利用多重阈值和网络参数的关系提取各个网络参数的曲线下面积(Area under curve,AUC)作为新特征; 使用支持向量机(Support vector machine,SVM)将2组受试者的网络参数和网络参数的AUC作为特征进行分类,评估其预测价值。结果 轻度障碍组和中重度障碍组患者的一般资料比较均无显著差异(P>0.05); 通过脑网络改变比较可知,2组均具有小世界属性(α均>1)。2组的“小世界”属性参数值统计可知,中重度障碍组的λ值较轻度障碍组变长(P<0.05); α频段连接显著强于θ频段,且轻度障碍组相较于中重度障碍组展现出更高的连接强度; 在θ频段轻度障碍组的大脑连接紧密度较为均衡; 在α频段中重度障碍在大脑右侧的连接强度低于轻度障碍组; 轻度障碍组α频段参数均高于θ频段,且较中重度组更高,差异在强度、路径长度、平均聚类系数上均有显著性差异(P<0.05); 以平均聚类系数为例,构建多重阈值与网络参数关系图; 每阈值对应一网络参数值,计算其AUC作为优化分类特征; SVM分类结合留一交叉验证,优化参数为特征后整体精度提升,α频段分类精度优于θ频段。结论 AD患者脑网络连接参数与患者认知控制能力有关,认知控制减弱致AD患者多脑区间功能连接降低,相关脑区涉及多个脑网络,这可能是其脑功能受损的神经基础; 基于图论量化脑功能网络并对网络参数特征优化能够对AD患者认知控制能力的计算机辅助诊断提供一定的帮助和理论支撑。
Abstract:
Objective Exploring the relationship between brain network connectivity parameters and cognitive control ability in patients with Alzheimer's disease(AD).Methods A retrospective study was conducted on 300 AD patients who visited the Second Affiliated Hospital of Xinjiang Medical University from August 2023 to August 2024. The Mini Mental State Examination(MMSE)was used to assess the severity of cognitive impairment in AD patients, and they were divided into a mild impairmentgroup(n=150)and a moderate to severe impairment group(n=150). All study subjects underwent Resting-state functional magnetic resonance imaging(rs-MRI)and Electroencephalography(EEG), and data was collected. Based on the phase synchronization between signals, the Phase Locked Value(PLV)method wass used to calculate brain functional connectivity in the θ and α frequency bands respectively. Based on graph theory methods, the strength, average feature path length, and average clustering coefficient of the weighted network was calculated separately. The Area under the curve(AUC)of each network parameter was extracted as a new feature with the help of the relationship between multiple thresholds and network parameters. With Support vector machine(SVM), the network parameters and AUC of two groups of subjects were classified as features and their predictive value was evaluated.Results There was no significant difference in general information between patients with mild and moderate to severe disabilities(P>0.05). By comparing the changes in brain networks, it can be seen that both groups have small world properties(α>1). The statistical results of the “small world” attribute parameter values of the two groups show that the λ value of the moderate to severe impairment group is longer than that of the mild impairment group(P<0.05). The connection strength of the alpha frequency band is significantly higher than that of the theta frequency band, and the mild impairment group shows greater connection strength than the moderate to severe impairment group. In the θ frequency band, the brain connectivity of the mild impairment group is relatively balanced. In the alpha frequency band, the connectivity strength on the right side of the brain is lower in the moderate to severe impairment group than in the mild impairment group. The parameter values of the alpha frequency band in the mild impairment group are higher than those in the theta frequency band, and the parameter values of the mild impairment group are higher than those of the moderate to severe impairment group. The differences in intensity, feature path length, and average clustering coefficient between the two groups are statistically significant(P<0.05). Taking the average clustering coefficient as an example, the relationship between multiple thresholds and network parameter values was constructed. Each threshold value corresponds to a network parameter value and the AUC of network parameters was obtained as the optimized classification feature. SVM algorithm was used for classification and compare with leave one cross validation method. By using optimized parameters as classification features, the accuracy has been improved, and the classification accuracy in the alpha frequency band is higher than that in the theta frequency band. Conclusion The brain network connectivity parameters of AD patients are closely related to their cognitive control ability. With cognitive control ability decreases, the functional connections between multiple brain regions in patients weaken, and the relevant brain regions involve multiple brain networks, which may be the neural basis for their impaired brain function. Quantifying brain functional networks based on graph theory and optimizing network parameter features can provide certain assistance and theoretical support for computer-aided diagnosis of cognitive control ability in AD patients.

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备注/Memo

备注/Memo:
基金项目:新疆人工智能影像辅助诊断重点实验室立项开放课题(编号为XJRGZN2024022)
更新日期/Last Update: 2025-04-20