Abstract：
UNASSIGNED: We previously demonstrated the validity of a regression model that included ethnicity as a novel predictor for predicting normative brain volumes in old age. The model was optimized using brain volumes measured with a standard tool FreeSurfer.
UNASSIGNED: Here we further verified the prediction model using newly estimated brain volumes from Neuro I, a quantitative brain analysis system developed for Korean populations.
UNASSIGNED: Lobar and subcortical volumes were estimated from MRI images of 1,629 normal Korean and 786 Caucasian subjects (age range 59-89) and were predicted in linear regression from ethnicity, age, sex, intracranial volume, magnetic field strength, and scanner manufacturers.
UNASSIGNED: In the regression model predicting the new volumes, ethnicity was again a substantial predictor in most regions. Additionally, the model-based z-scores of regions were calculated for 428 AD patients and the matched controls, and then employed for diagnostic classification. When the AD classifier adopted the z-scores adjusted for ethnicity, the diagnostic accuracy has noticeably improved (AUC = 0.85, ΔAUC = + 0.04, D = 4.10, p < 0.001).
UNASSIGNED: Our results suggest that the prediction model remains robust across different measurement tool, and ethnicity significantly contributes to the establishment of norms for brain volumes and the development of a diagnostic system for neurodegenerative diseases.
UNASSIGNED: Here we further verified the prediction model using newly estimated brain volumes from Neuro I, a quantitative brain analysis system developed for Korean populations.
UNASSIGNED: Lobar and subcortical volumes were estimated from MRI images of 1,629 normal Korean and 786 Caucasian subjects (age range 59-89) and were predicted in linear regression from ethnicity, age, sex, intracranial volume, magnetic field strength, and scanner manufacturers.
UNASSIGNED: In the regression model predicting the new volumes, ethnicity was again a substantial predictor in most regions. Additionally, the model-based z-scores of regions were calculated for 428 AD patients and the matched controls, and then employed for diagnostic classification. When the AD classifier adopted the z-scores adjusted for ethnicity, the diagnostic accuracy has noticeably improved (AUC = 0.85, ΔAUC = + 0.04, D = 4.10, p < 0.001).
UNASSIGNED: Our results suggest that the prediction model remains robust across different measurement tool, and ethnicity significantly contributes to the establishment of norms for brain volumes and the development of a diagnostic system for neurodegenerative diseases.
摘要：
■我们先前证明了回归模型的有效性，该模型包括种族作为预测老年规范脑容量的新预测因子。使用用标准工具FreeSurfer测量的脑体积优化模型。
■在这里，我们使用来自NeuroI的新估计的脑体积进一步验证了预测模型，为韩国人口开发的定量大脑分析系统。
■从1,629名正常韩国人和786名高加索人（年龄范围59-89）的MRI图像中估算了大叶和皮质下体积，并根据种族进行了线性回归预测，年龄,性别,颅内容积,磁场强度,扫描仪制造商。
■在预测新卷的回归模型中，在大多数地区，种族再次成为重要的预测指标。此外,对428例AD患者和匹配的对照进行基于模型的区域z评分计算，然后用于诊断分类。当AD分类器采用根据种族调整的z分数时，诊断准确性明显提高(AUC=0.85,ΔAUC=+0.04,D=4.10,p<0.001)。
■我们的结果表明，预测模型在不同的测量工具中保持稳健，和种族极大地有助于建立脑容量规范和神经退行性疾病诊断系统的发展。
■在这里，我们使用来自NeuroI的新估计的脑体积进一步验证了预测模型，为韩国人口开发的定量大脑分析系统。
■从1,629名正常韩国人和786名高加索人（年龄范围59-89）的MRI图像中估算了大叶和皮质下体积，并根据种族进行了线性回归预测，年龄,性别,颅内容积,磁场强度,扫描仪制造商。
■在预测新卷的回归模型中，在大多数地区，种族再次成为重要的预测指标。此外,对428例AD患者和匹配的对照进行基于模型的区域z评分计算，然后用于诊断分类。当AD分类器采用根据种族调整的z分数时，诊断准确性明显提高(AUC=0.85,ΔAUC=+0.04,D=4.10,p<0.001)。
■我们的结果表明，预测模型在不同的测量工具中保持稳健，和种族极大地有助于建立脑容量规范和神经退行性疾病诊断系统的发展。
