Gorniak Stacey L, Meng Hao, Yazdekhasti Saba, Pollonini Luca
Department of Health and Human Performance, University of Houston, Houston, TX, United States.
Department of Engineering Technology, University of Houston, Houston, TX, United States.
Exp Biol Med (Maywood). 2024 Feb 8;249:10030. doi: 10.3389/ebm.2024.10030. eCollection 2024.
High body mass index (BMI) is presumed to signify high amounts of fat (subcutaneous adipose tissue) distributed across the body. High amounts of fat co-occurring with increased BMI has been cited as a potential neuroimaging barrier. Presence of increased fat may result in high electrical impedance and increased light diffusion-resulting in low signal to noise ratios during electroencepholography (EEG), functional near-infrared spectroscopy (fNIRS), and transcranial direct current stimulation (tDCS) measurements. Examining if subcutaneous fat in the head increases with respect to total body fat percentage and BMI in school-aged children and adolescents is an essential next step in developing possible mathematical corrections for neuroimaging modalities. We hypothesized that percentage of subcutaneous adipose tissue in the head region would increase with respect to both total body fat percentage and BMI. Increased subcutaneous head fat percentage was associated with a positive linear relationship with BMI and a quadratic relationship with total body fat. The data indicate that participant age, sex, and adiposity should be considered in the development of model corrections for neuroimaging signal processing in school-aged children and adolescents. Strength of regression coefficients in our models differed from those in adults, indicating that age-specific models should be utilized.
高身体质量指数(BMI)被认为表示身体各处分布的大量脂肪(皮下脂肪组织)。与 BMI 增加相关的大量脂肪已被认为是潜在的神经影像学障碍。脂肪的存在可能导致高电阻和光扩散增加-导致脑电图(EEG)、功能近红外光谱(fNIRS)和经颅直流电刺激(tDCS)测量中的信噪比降低。检查学龄儿童和青少年头部的皮下脂肪相对于总体脂肪百分比和 BMI 是否增加,是为神经影像学模式开发可能的数学校正的重要下一步。我们假设头部区域的皮下脂肪百分比将随着总体体脂肪百分比和 BMI 的增加而增加。头部皮下脂肪百分比的增加与 BMI 呈正线性关系,与总体体脂肪呈二次关系。数据表明,在为学龄儿童和青少年的神经影像学信号处理开发模型校正时,应考虑参与者的年龄、性别和肥胖程度。我们模型中的回归系数强度与成年人不同,表明应使用特定年龄的模型。