The brain-age paradigm is proving increasingly useful for exploring aging-related disease and can predict important future health outcomes. Most brain-age research uses structural neuroimaging to index brain volume. However, aging affects multiple aspects of brain structure and function, which can be examined using multimodality neuroimaging. Using UK Biobank, brain-age was modeled in n = 2205 healthy people with T1-weighted MRI, T2-FLAIR, T2∗, diffusion-MRI, task fMRI, and resting-state fMRI. In a held-out healthy validation set (n = 520), chronological age was accurately predicted (r = 0.78, mean absolute error = 3.55 years) using LASSO regression, higher than using any modality separately. Thirty-four neuroimaging phenotypes were deemed informative by the regression (after bootstrapping); predominantly gray-matter volume and white-matter microstructure measures. When applied to new individuals from UK Biobank (n = 14,701), significant associations with multimodality brain-predicted age difference (brain-PAD) were found for stroke history, diabetes diagnosis, smoking, alcohol intake and some, but not all, cognitive measures (corrected p < 0.05). Multimodality neuroimaging can improve brain-age prediction, and derived brain-PAD values are sensitive to biomedical and lifestyle factors that negatively impact brain and cognitive health.

中文翻译：

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英国生物银行的多模态神经影像脑年龄：与生物医学、生活方式和认知因素的关系

事实证明，大脑年龄范式对于探索与衰老相关的疾病越来越有用，并且可以预测重要的未来健康结果。大多数脑年龄研究使用结构神经成像来索引脑容量。然而，衰老会影响大脑结构和功能的多个方面，这可以使用多模态神经成像进行检查。使用 UK Biobank，对 n = 2205 名健康人的脑年龄进行建模，这些人具有 T1 加权 MRI、T2-FLAIR、T2*、扩散 MRI、任务 fMRI 和静息状态 fMRI。在保留的健康验证集（n = 520）中，使用 LASSO 回归准确预测实足年龄（r = 0.78，平均绝对误差 = 3.55 岁），高于单独使用任何模式。回归（引导后）认为 34 种神经成像表型具有信息量；主要是灰质体积和白质微观结构测量。当应用于来自英国生物银行的新个体 (n = 14,701) 时，发现中风史、糖尿病诊断、吸烟、饮酒和一些（但不是全部）认知测量与多模态脑预测年龄差异 (brain-PAD) 显着相关（校正后的 p < 0.05）。多模态神经影像学可以改善大脑年龄预测，并且衍生的大脑 PAD 值对生物医学和生活方式因素敏感，这些因素对大脑和认知健康产生负面影响。0.05）。多模态神经影像学可以改善大脑年龄预测，并且衍生的大脑 PAD 值对生物医学和生活方式因素敏感，这些因素对大脑和认知健康产生负面影响。0.05）。多模态神经影像学可以改善大脑年龄预测，并且衍生的大脑 PAD 值对生物医学和生活方式因素敏感，这些因素对大脑和认知健康产生负面影响。