Ying Zhang, Minglu Hu, Siyu Fan, Shanshan Cao, Baogen Du, Shanshan Yin, Long Zhang, Yanghua Tian, Kai Wang, Qiang Wei
{"title":"Altered Resting-State Brain Entropy in Cerebral Small Vessel Disease Patients with Cognitive Impairment.","authors":"Ying Zhang, Minglu Hu, Siyu Fan, Shanshan Cao, Baogen Du, Shanshan Yin, Long Zhang, Yanghua Tian, Kai Wang, Qiang Wei","doi":"10.1089/brain.2024.0007","DOIUrl":null,"url":null,"abstract":"<p><p><b><i>Objective:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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). <b><i>Conclusion:</i></b> 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.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":"418-429"},"PeriodicalIF":2.4000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain connectivity","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1089/brain.2024.0007","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/30 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
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.
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.