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。