The brain's biological age has been considered as a promising candidate for a
neurologically significant biomarker. However, recent results based on
longitudinal magnetic resonance imaging data have raised questions on its
interpretation. A central question is whether an increased biological age of
the brain is indicative of brain pathology and if changes in brain age
correlate with diagnosed pathology (state hypothesis). Alternatively, could the
discrepancy in brain age be a stable characteristic unique to each individual
(trait hypothesis)? To address this question, we present a comprehensive study
on brain aging based on clinical EEG, which is complementary to previous
MRI-based investigations. We apply a state-of-the-art Temporal Convolutional
Network (TCN) to the task of age regression. We train on recordings of the
Temple University Hospital EEG Corpus (TUEG) explicitly labeled as
non-pathological and evaluate on recordings of subjects with non-pathological
as well as pathological recordings, both with examinations at a single point in
time and repeated examinations over time. Therefore, we created four novel
subsets of TUEG that include subjects with multiple recordings: I) all labeled
non-pathological; II) all labeled pathological; III) at least one recording
labeled non-pathological followed by at least one recording labeled
pathological; IV) similar to III) but with opposing transition (first
pathological then non-pathological). The results show that our TCN reaches
state-of-the-art performance in age decoding with a mean absolute error of 6.6
years. Our extensive analyses demonstrate that the model significantly
underestimates the age of non-pathological and pathological subjects (-1 and -5
years, paired t-test, p <= 0.18 and p <= 0.0066). Furthermore, the brain age
gap biomarker is not indicative of pathological EEG.

通过对临床脑电图进行全面研究，利用时序卷积网络 (TCN) 进行年龄回归任务，结果表明模型对非病理和病理样本的年龄均有明显低估，而大脑生物年龄差生物标记不表示脑电图病理。

脑龄再探：用深度学习研究脑电图衍生的脑龄动态的状态对个体假设与特质对个体假设

Brain Age Revisited: Investigating the State vs. Trait Hypotheses of  EEG-derived Brain-Age Dynamics with Deep Learning

In recent years, there are various methods of estimating Biological Age (BA)
have been developed. Especially with the development of machine learning (ML),
there are more and more types of BA predictions, and the accuracy has been
greatly improved. The models for the estimation of BA play an important role in
monitoring healthy aging, and could provide new tools to detect health status
in the general population and give warnings to sub-healthy people. We will
mainly review three age prediction methods by using ML. They are based on blood
biomarkers, facial images, and structural neuroimaging features. For now, the
model using blood biomarkers is the simplest, most direct, and most accurate
method. The face image method is affected by various aspects such as race,
environment, etc., the prediction accuracy is not very good, which cannot make
a great contribution to the medical field. In summary, we are here to track the
way forward in the era of big data for us and other potential general
populations and show ways to leverage the vast amounts of data available today.

本研究主要评估基于血液生物标志物、面部图像和神经影像结构特征的三种生物年龄预测方法，并发现血液生物标志物法最为简便、直接且准确。面部图像法在受种族、环境等多方面影响下，预测准确度有所欠缺，不能为医疗领域做出太大的贡献。总的来说，我们在大数据时代寻找前进的道路，并展示了利用当今可用的大量数据的方法。