Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan.
College of Education, National Tsing Hua University, Hsinchu, 30013, Taiwan.
Neuroinformatics. 2024 Apr;22(2):119-134. doi: 10.1007/s12021-024-09653-x. Epub 2024 Feb 11.
The increasing lifespan and large individual differences in cognitive capability highlight the importance of comprehending the aging process of the brain. Contrary to visible signs of bodily ageing, like greying of hair and loss of muscle mass, the internal changes that occur within our brains remain less apparent until they impair function. Brain age, distinct from chronological age, reflects our brain's health status and may deviate from our actual chronological age. Notably, brain age has been associated with mortality and depression. The brain is plastic and can compensate even for severe structural damage by rewiring. Functional characterization offers insights that structural cannot provide. Contrary to the multitude of studies relying on structural magnetic resonance imaging (MRI), we utilize resting-state functional MRI (rsfMRI). We also address the issue of inclusion of subjects with abnormal brain ageing through outlier removal. In this study, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) to identify the 39 most predictive correlations derived from the rsfMRI data. The data is from a cohort of 176 healthy right-handed volunteers, aged 18-78 years (95/81 male/female, mean age 48, SD 17) collected at the Mind Research Imaging Center at the National Cheng Kung University. We establish a normal reference model by excluding 68 outliers, which achieves a leave-one-out mean absolute error of 2.48 years. By asking which additional features that are needed to predict the chronological age of the outliers with a smaller error, we identify correlations predictive of abnormal aging. These are associated with the Default Mode Network (DMN). Our normal reference model has the lowest prediction error among published models evaluated on adult subjects of almost all ages and is thus a candidate for screening for abnormal brain aging that has not yet manifested in cognitive decline. This study advances our ability to predict brain aging and provides insights into potential biomarkers for assessing brain age, suggesting that the role of DMN in brain aging should be studied further.
大脑的衰老过程。与身体衰老的明显迹象，如头发变白和肌肉减少不同，大脑内部的变化在功能受损之前不太明显。大脑年龄与实际年龄不同，反映了我们大脑的健康状况，可能与实际年龄不符。值得注意的是，大脑年龄与死亡率和抑郁症有关。大脑具有可塑性，即使是严重的结构损伤也可以通过重新布线来补偿。功能特征提供了结构无法提供的见解。与依赖结构磁共振成像 (MRI) 的众多研究相反，我们使用静息态功能 MRI (rsfMRI)。我们还通过异常值去除解决了包含异常大脑老化受试者的问题。在这项研究中，我们使用最小绝对收缩和选择算子 (LASSO) 来识别来自 rsfMRI 数据的 39 个最具预测性的相关性。该数据来自于国立成功大学思维研究成像中心的 176 名健康右撇子志愿者的队列，年龄在 18-78 岁之间（95/81 名男性/女性，平均年龄 48 岁，标准差 17 岁）。我们通过排除 68 个异常值来建立一个正常的参考模型，该模型的留一法平均绝对误差为 2.48 岁。通过询问需要哪些额外的特征来以较小的误差预测异常值的实际年龄，我们确定了与异常老化相关的预测相关性。这些与默认模式网络 (DMN) 有关。我们的正常参考模型在评估几乎所有年龄段的成年受试者的已发表模型中具有最低的预测误差，因此是筛选尚未表现出认知能力下降的异常大脑老化的候选模型。这项研究提高了我们预测大脑老化的能力，并提供了评估大脑年龄的潜在生物标志物的见解，表明 DMN 在大脑老化中的作用需要进一步研究。
