Kuo Chen-Yuan, Lee Pei-Lin, Hung Sheng-Che, Liu Li-Kuo, Lee Wei-Ju, Chung Chih-Ping, Yang Albert C, Tsai Shih-Jen, Wang Pei-Ning, Chen Liang-Kung, Chou Kun-Hsien, Lin Ching-Po
Department of Biomedical Imaging and Radiological Sciences, National Yang Ming University, Taipei 11221, Taiwan.
Institute of Neuroscience, National Yang Ming University, Taipei 11221, Taiwan.
Cereb Cortex. 2020 Oct 1;30(11):5844-5862. doi: 10.1093/cercor/bhaa161.
The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.
衰老过程伴随着大脑皮层在多个层面的变化。人们越来越有兴趣将这些复杂的大脑衰老特征总结为一个单一的定量指标，该指标既可以作为表征个体大脑健康的生物标志物，也可以用于识别神经退行性疾病和神经精神疾病。使用基于大规模结构协方差网络（SCN）的框架和机器学习算法，我们证明了该框架在大量中老年成年人样本中预测个体脑龄的能力，并从网络角度突出了其对几种疾病群体的临床特异性。一个提出的包含40个SCN的估计器可以预测个体脑龄，在模型复杂性和预测准确性之间取得平衡。值得注意的是，我们发现预测脑龄最重要的SCN包括尾状核、壳核、海马体、杏仁核和小脑区域。此外，我们的数据表明，精神分裂症和阿尔茨海默病患者的脑龄差异比健康对照者更大，而这一指标在重度抑郁症患者中没有显著差异。这些发现提供了支持从脑网络角度估计脑龄的实证证据，并证明了评估假设与加速脑衰老相关的神经系统疾病的临床可行性。
