Gómez-Ramírez Jaime, Fernández-Blázquez Miguel A, González-Rosa Javier J
Institute of Biomedical Research Cadiz (INiBICA), Universidad de Cádiz, 11003 Cádiz, Spain.
Department of Biological and Health Psychology, Universidad Autónoma de Madrid, 28049 Madrid, Spain.
Brain Sci. 2022 Apr 29;12(5):579. doi: 10.3390/brainsci12050579.
Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitative understanding of age-related brain changes can shed light on successful aging. To investigate the effect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonance imaging scans were analyzed using automated brain segmentation and parcellation methods in elderly healthy individuals (69-88 years of age). The machine learning algorithm extreme gradient boosting (XGBoost) achieved a mean absolute error of 2 years in predicting the age of new subjects. Feature importance analysis showed that the brain-to-intracranial-volume ratio is the most important feature in predicting age, followed by the hippocampi volumes. The cortical thickness in temporal and parietal lobes showed a superior predictive value than frontal and occipital lobes. Insights from this approach that integrate model prediction and interpretation may help to shorten the current explanatory gap between chronological age and biological brain age.
正常衰老与脑萎缩的体积指标变化有关。对与年龄相关的脑部变化进行定量理解有助于揭示成功衰老的奥秘。为了研究年龄对全脑和区域脑容量以及皮质厚度的影响，研究人员使用自动脑分割和分区方法对3514例老年健康个体（69 - 88岁）的磁共振成像扫描进行了分析。机器学习算法极端梯度提升（XGBoost）在预测新受试者年龄时的平均绝对误差为2岁。特征重要性分析表明，脑与颅内体积比是预测年龄的最重要特征，其次是海马体体积。颞叶和顶叶的皮质厚度比额叶和枕叶具有更高的预测价值。这种整合模型预测和解释的方法所获得的见解可能有助于缩短目前实际年龄与生物脑年龄之间的解释差距。
