Altered Resting-State Brain Entropy in Cerebral Small Vessel Disease Patients with Cognitive Impairment.

IF 2.4 3区 医学 Q3 NEUROSCIENCES
Brain connectivity Pub Date : 2024-10-01 Epub Date: 2024-07-30 DOI:10.1089/brain.2024.0007
Ying Zhang, Minglu Hu, Siyu Fan, Shanshan Cao, Baogen Du, Shanshan Yin, Long Zhang, Yanghua Tian, Kai Wang, Qiang Wei
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引用次数: 0

Abstract

Objective: Cerebral small vessel disease (CSVD) is a primary vascular disease of cognitive impairment. Previous studies have predominantly focused on brain linear features. However, the nonlinear measure, brain entropy (BEN), has not been elaborated. Thus, this study aims to investigate if BEN abnormalities could manifest in CSVD patients with cognitive impairment. Methods: Thirty-four CSVD patients with cognitive impairment and 37 healthy controls (HCs) were recruited. Analysis of gray matter approximate entropy (ApEn) and sample entropy (SampEn) which are two indices of BEN was calculated. To explore whether BEN can provide unique information, we further performed brain linear methods, namely, amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), to observe their differences. The ratios of BEN/ALFF and BEN/ReHo which represent the coupling of nonlinear and linear features were introduced. Correlation analysis was conducted between imaging indices and cognition. Subsequently, the linear support vector machine (SVM) was used to assess their discriminative ability. Results: CSVD patients exhibited lower ApEn and SamEn values in sensorimotor areas, which were correlated with worse memory and executive function. In addition, the results of BEN showed little overlap with ALFF and ReHo in brain regions. Correlation analysis also revealed a relationship between the two ratios and cognition. SVM analysis using BEN and its ratios as features achieved an accuracy of 74.64% (sensitivity: 86.49%, specificity: 61.76%, and AUC: 0.82439). Conclusion: Our study reveals that the reduction of sensorimotor system complexity is correlated with cognition. BEN exhibits distinctive characteristics in brain activity. Combining BEN and the ratios can be new biomarkers to diagnose CSVD with cognitive impairment. Impact Statement Cerebral small vessel disease (CSVD) is regarded as the most important vascular disease of cognitive impairment. However, conventional brain imaging fails to adequately elucidate the pathogenesis of cognitive disorder related to CSVD. In this regard, exploring brain entropy (BEN) based on resting-state functional magnetic resonance imaging (rs-fMRI) represents a relatively novel and unexplored approach in the context of CSVD. This approach provides novel insights into the pathogenesis, diagnosis, and rehabilitation of cognitive disorder associated with CSVD.

伴有认知障碍的脑小血管疾病患者静息态脑熵的改变
目的:脑小血管病(CSVD)是导致认知障碍的原发性血管疾病。以往的研究主要关注脑线性特征。然而,非线性测量指标--脑熵 (BEN) 却未得到详细阐述。因此,本研究旨在探讨认知障碍的 CSVD 患者是否会出现 BEN 异常:方法:招募 34 名患有认知障碍的 CSVD 患者和 37 名健康对照组(HCs)。计算灰质近似熵(ApEn)和样本熵(SampEn)这两个 BEN 指标。为了探索 BEN 是否能提供独特的信息,我们进一步采用了脑线性方法,即低频波动幅度(ALFF)和区域同质性(ReHo),以观察它们之间的差异。我们引入了 BEN/ALFF 和 BEN/ReHo 的比率,它们代表了非线性特征和线性特征的耦合。在成像指数和认知之间进行了相关性分析。随后,使用线性支持向量机(SVM)评估了它们的鉴别能力:结果:CSVD 患者感觉运动区的 ApEn 和 SamEn 值较低,这与记忆和执行功能较差有关。此外,BEN的结果显示与ALFF和ReHo在大脑区域的重叠很少。相关性分析也显示了这两个比率与认知能力之间的关系。利用 BEN 及其比率作为特征的 SVM 分析的准确率达到了 74.64 %(灵敏度:86.49 %;特异性:61.76 %;AUC:0.82439):我们的研究揭示了感觉运动系统复杂性的降低与认知的相关性。BEN 在大脑活动中表现出独特的特征。结合 BEN 和比率可作为诊断 CSVD 认知功能障碍的新生物标记物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Brain connectivity
Brain connectivity Neuroscience-General Neuroscience
CiteScore
4.80
自引率
0.00%
发文量
80
期刊介绍: Brain Connectivity provides groundbreaking findings in the rapidly advancing field of connectivity research at the systems and network levels. The Journal disseminates information on brain mapping, modeling, novel research techniques, new imaging modalities, preclinical animal studies, and the translation of research discoveries from the laboratory to the clinic. This essential journal fosters the application of basic biological discoveries and contributes to the development of novel diagnostic and therapeutic interventions to recognize and treat a broad range of neurodegenerative and psychiatric disorders such as: Alzheimer’s disease, attention-deficit hyperactivity disorder, posttraumatic stress disorder, epilepsy, traumatic brain injury, stroke, dementia, and depression.
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