Pub Date : 2024-11-13DOI: 10.1007/s11682-024-00923-5
Shanling Ji, Wei An, Jing Zhang, Cong Zhou, Chuanxin Liu, Hao Yu
In recent years, brain signal complexity has gained attention as an indicator of brain well-being and a predictor of disease and dysfunction. Brain entropy quantifies this complexity. Assessment of functional network centrality and connectivity reveals that information communication induces neural signal oscillations in certain brain regions. However, their relationship is uncertain. This work studied brain signal complexity, network centrality, and connectivity in both healthy and depressed individuals. The current work comprised a sample of 124 first-episode drug-naïve patients with major depressive disorder (MDD) and 105 healthy controls (HC). Six functional networks were created for each person using resting-state functional magnetic resonance imaging. For each network, entropy, centrality, and connectivity were computed. Using structural equation modeling, this study examined the associations between brain network entropy, centrality, and connectivity. The findings demonstrated substantial correlations of entropy with both centrality and connectivity in HC and these correlation patterns were disrupted in MDD. Compared to HC, MDD exhibited higher entropy in four networks and demonstrated changes in centralities across all networks. The structural equation modeling showed that network centralities, connectivity, and depression severity had impacts on brain entropy. Nevertheless, no impacts were observed in the opposite directions. This study indicated that the complexity of brain signals was influenced not only by the interactions among different areas of the brain but also by the severity level of depression. These findings enhanced our comprehension of the associations of brain entropy with its influential factors.
近年来，大脑信号的复杂性作为大脑健康的一个指标以及疾病和功能障碍的一个预测指标，越来越受到人们的关注。脑熵可以量化这种复杂性。对功能网络中心性和连通性的评估显示，信息交流会诱发某些脑区的神经信号振荡。然而，它们之间的关系还不确定。这项研究对健康人和抑郁症患者的大脑信号复杂性、网络中心性和连通性进行了研究。本次研究的样本包括124名首次发病、未接受过药物治疗的重度抑郁症（MDD）患者和105名健康对照组（HC）。利用静息态功能磁共振成像为每个人创建了六个功能网络。计算了每个网络的熵值、中心性和连通性。本研究采用结构方程建模法研究了大脑网络熵、中心性和连通性之间的关联。研究结果表明，HC 患者的熵与中心性和连通性都有很大的相关性，而 MDD 患者的这些相关模式被破坏。与 HC 相比，MDD 在四个网络中表现出更高的熵，并在所有网络中显示出中心性的变化。结构方程模型显示，网络中心性、连通性和抑郁严重程度对大脑熵有影响。然而，没有观察到相反方向的影响。这项研究表明，大脑信号的复杂性不仅受到大脑不同区域之间相互作用的影响，还受到抑郁症严重程度的影响。这些发现加深了我们对大脑熵与其影响因素之间关联的理解。
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The objective of this research was to examine the association between precuneus cortex thickness and mild behavioral impairment (MBI) in patients with mild stroke. Seventy-two patients were evaluated by high-resolution 3 T magnetic resonance and the mild behavioral impairment checklist (MBI-C). To determine the association between precuneus cortex thickness and MBI, we adjusted for demographics, vascular risk factors, and laboratory examination indicators in logistic regression analysis. In addition, we used mendelian randomization to further study the association through genetic databases. Of the 72 mild stroke patients in this study, 26 had MBI. We found a strong negative connection between precuneus cortex thickness and MBI after adjusting for any confounding variables. In patients with an initial mild stroke, the thinner the precuneus cortex, the higher the risk of MBI (OR: 0.02; 95% CI: 0.00-0.39; P < 0.05). Our study has uncovered a significant negative association between the thickness of the precuneus cortex and MBI. This finding provides a novel viewpoint for the radiological diagnosis of MBI, thereby augmenting the contribution of imaging to the diagnostic process of MBI and advancing the prediction of dementia. Specifically, in patients who have suffered mild stroke, a reduction in the cortical thickness of the precuneus has been pinpointed as crucial radiographic evidence of preclinical cognitive impairment. This insight could potentially facilitate earlier detection and intervention strategies for cognitive decline.
本研究旨在探讨轻度脑卒中患者楔前皮层厚度与轻度行为障碍（MBI）之间的关系。72名患者接受了高分辨率3 T磁共振和轻度行为障碍核对表（MBI-C）的评估。为了确定楔前皮层厚度与 MBI 之间的关系，我们在逻辑回归分析中对人口统计学、血管风险因素和实验室检查指标进行了调整。此外，我们还采用了 "泯灭随机法"，通过基因数据库进一步研究两者之间的关联。在本研究的 72 名轻度脑卒中患者中，26 人患有 MBI。在对混杂变量进行调整后，我们发现楔前皮层厚度与 MBI 之间存在很强的负相关。在最初发生轻度脑卒中的患者中，楔前皮层越薄，发生 MBI 的风险越高（OR：0.02；95% CI：0.00-0.39；P＜0.05）。
{"title":"The association between precuneus cortex thickness and mild behavioral impairment in patients with mild stroke.","authors":"Zhengxin Liu, Ziwei Xu, Aijuan Yan, Panpan Zhang, Wenshi Wei","doi":"10.1007/s11682-024-00955-x","DOIUrl":"https://doi.org/10.1007/s11682-024-00955-x","url":null,"abstract":"<p><p>The objective of this research was to examine the association between precuneus cortex thickness and mild behavioral impairment (MBI) in patients with mild stroke. Seventy-two patients were evaluated by high-resolution 3 T magnetic resonance and the mild behavioral impairment checklist (MBI-C). To determine the association between precuneus cortex thickness and MBI, we adjusted for demographics, vascular risk factors, and laboratory examination indicators in logistic regression analysis. In addition, we used mendelian randomization to further study the association through genetic databases. Of the 72 mild stroke patients in this study, 26 had MBI. We found a strong negative connection between precuneus cortex thickness and MBI after adjusting for any confounding variables. In patients with an initial mild stroke, the thinner the precuneus cortex, the higher the risk of MBI (OR: 0.02; 95% CI: 0.00-0.39; P < 0.05). Our study has uncovered a significant negative association between the thickness of the precuneus cortex and MBI. This finding provides a novel viewpoint for the radiological diagnosis of MBI, thereby augmenting the contribution of imaging to the diagnostic process of MBI and advancing the prediction of dementia. Specifically, in patients who have suffered mild stroke, a reduction in the cortical thickness of the precuneus has been pinpointed as crucial radiographic evidence of preclinical cognitive impairment. This insight could potentially facilitate earlier detection and intervention strategies for cognitive decline.</p>","PeriodicalId":9192,"journal":{"name":"Brain Imaging and Behavior","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142614536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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