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Spatially guided functional correlation tensor: A new method to associate body mass index and white matter neuroimaging

机译:空间导向功能相关张量:一种使体重指数和白质的新方法和白质神经影像

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摘要

Obesity causes critical health problems including cardiovascular disease, diabetes, and stroke. Various neuroimaging methods including diffusion tensor imaging (DTI) are used to explore white matter (WM) alterations in obesity. The functional correlation tensor (FCT) is a method to simulate DTI in WM using resting-state functional magnetic resonance imaging (rs-fMRI). In this study, we enhanced the FCT with additional anatomical information from T1-weighted data in a regression framework. The goal was to 1) develop a spatially guided enhanced FCT (s-eFCT) and to 2) use it to identify imaging biomarkers for obesity. We computed fractional anisotropy (FA) and the mean diffusivity (MD) from the s-eFCT. The regional FA and MD values that can explain body mass index (BMI) well were chosen. The identified regional FA and MD values were used to predict BMI values. The correlation between real and predicted BMIs was 0.57. There was no significant correlation between real and predicted DTI using the MD. The BMI predicted using FA was used to classify participants into three obesity subgroups. The classification accuracy was 57.20%. In summary, we found potential imaging biomarkers of obesity based on the s-eFCT.
机译:肥胖导致包括心血管疾病,糖尿病和中风的关键健康问题。包括扩散张量成像(DTI)的各种神经影像方法用于探索肥胖症的白质(WM)改变。功能相关性张量(FCT)是使用静止状态功能磁共振成像(RS-FMRI)在WM中模拟DTI的方法。在这项研究中,我们通过从回归框架中的T1加权数据增强了FCT的FCT。目标为1)开发空间引导的增强FCT(S-EFCT)和2)使用它来识别肥胖的成像生物标志物。我们计算了来自S-EFCT的分数各向异性(FA)和平均扩散率(MD)。选择可以解释体重指数(BMI)井的区域FA和MD值。所识别的区域FA和MD值用于预测BMI值。真实和预测的BMI之间的相关性为0.57。使用MD的真实和预测的DTI之间没有显着相关性。使用FA预测的BMI用于将参与者分为三个肥胖子组。分类准确性为57.20%。总之,我们发现了基于S-EFCT的肥胖症的潜在成像生物标志物。

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