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Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies.从高密度脑电图数据中检测静息态功能连接性:头部建模策略的影响。
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头部皮下脂肪组织与体重指数的相关性:对功能神经影像学的启示。

Correlation between subcutaneous adipose tissue of the head and body mass index: Implications for functional neuroimaging.

机构信息

Department of Health and Human Performance, University of Houston, Houston, TX 77204, United States of America.

Department of Health and Human Performance, University of Houston, Houston, TX 77204, United States of America.

出版信息

Hum Mov Sci. 2022 Oct;85:102997. doi: 10.1016/j.humov.2022.102997. Epub 2022 Aug 27.

DOI:10.1016/j.humov.2022.102997
PMID:36041254
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11908070/
Abstract

High body mass index (BMI) is generally assumed to represent overall amounts of body adipose tissue (fat). Increased adipose tissue amounts in persons with increased BMI has been cited as a barrier to assessment of body tissues such as muscle. Significant increases in the amount of adipose tissue between the dermal layer and the skull may result in high electrical impedance and/or increased light diffusion causing a lower signal to noise ratio during use of neuroimaging tools such as electroencepholography (EEG), transcranial direct current stimulation (tDCS), and functional near infrared spectroscopy (fNIRS). Investigating how subcutaneous adipose tissue in the head region increases with respect to total body fat percentage and BMI is an important step in developing mathematical corrections in neuroimaging measurements as BMI increases, as recommended in other measurement modalities such as electromyography (EMG). We hypothesized that percentage of subcutaneous adipose tissue in the head region would increase with respect to both total body fat percentage and BMI. A statistically significant increase in subcutaneous head fat percentage occurred with increased BMI and total body fat percentage. The data investigated in this study indicate that participant age, sex, and BMI are important features to consider in model corrections during data signal processing and analyses for subcutaneous head fat in neuroimaging approaches. The data in this project serve to provide physiological justification for this practice along with regression analyses to be considered for physiologically-based signal to noise correction algorithms.

摘要

高身体质量指数(BMI)通常被认为代表身体脂肪组织的总体含量。增加 BMI 人群的脂肪组织量被认为是评估身体组织(如肌肉)的障碍。真皮层和颅骨之间的脂肪组织量显著增加,可能导致高电阻和/或增加光扩散,从而在使用神经影像学工具(如脑电图(EEG)、经颅直流电刺激(tDCS)和功能性近红外光谱(fNIRS))时降低信噪比。研究头部区域的皮下脂肪组织如何随着全身脂肪百分比和 BMI 的增加而增加,是在 BMI 增加时开发神经影像学测量数学校正的重要步骤,正如在其他测量模式(如肌电图(EMG))中所建议的那样。我们假设头部区域的皮下脂肪组织百分比将随着全身脂肪百分比和 BMI 的增加而增加。随着 BMI 和全身脂肪百分比的增加,皮下头部脂肪百分比呈统计学显著增加。本研究中调查的数据表明,参与者的年龄、性别和 BMI 是神经影像学方法中进行数据信号处理和皮下头部脂肪分析时模型校正中需要考虑的重要特征。该项目中的数据为这种做法提供了生理学依据,并提供了回归分析,以供基于生理学的信噪比校正算法考虑。