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

Age progression and regression refers to aesthetically render-ing a given
face image to present effects of face aging and rejuvenation, respectively.
Although numerous studies have been conducted in this topic, there are two
major problems: 1) multiple models are usually trained to simulate different
age mappings, and 2) the photo-realism of generated face images is heavily
influenced by the variation of training images in terms of pose, illumination,
and background. To address these issues, in this paper, we propose a framework
based on conditional Generative Adversarial Networks (cGANs) to achieve age
progression and regression simultaneously. Particularly, since face aging and
rejuvenation are largely different in terms of image translation patterns, we
model these two processes using two separate generators, each dedicated to one
age changing process. In addition, we exploit spatial attention mechanisms to
limit image modifications to regions closely related to age changes, so that
images with high visual fidelity could be synthesized for in-the-wild cases.
Experiments on multiple datasets demonstrate the ability of our model in
synthesizing lifelike face images at desired ages with personalized features
well preserved, and keeping age-irrelevant regions unchanged.

本文提出一种基于条件生成对抗网络的框架，用于同时实现面部年龄进展和退步，结合空间注意机制和两种分开的生成器，以解决多个模型的训练和图像逼真度问题。实验结果表明，该模型能够在多个数据集上合成高度逼真的面部图像，年龄无关的区域保持不变。

具有空间注意模块的年龄变化推断与回归

Age Progression and Regression with Spatial Attention Modules

"If I provide you a face image of mine (without telling you the actual age
when I took the picture) and a large amount of face images that I crawled
(containing labeled faces of different ages but not necessarily paired), can
you show me what I would look like when I am 80 or what I was like when I was
5?" The answer is probably a "No." Most existing face aging works attempt to
learn the transformation between age groups and thus would require the paired
samples as well as the labeled query image. In this paper, we look at the
problem from a generative modeling perspective such that no paired samples is
required. In addition, given an unlabeled image, the generative model can
directly produce the image with desired age attribute. We propose a conditional
adversarial autoencoder (CAAE) that learns a face manifold, traversing on which
smooth age progression and regression can be realized simultaneously. In CAAE,
the face is first mapped to a latent vector through a convolutional encoder,
and then the vector is projected to the face manifold conditional on age
through a deconvolutional generator. The latent vector preserves personalized
face features (i.e., personality) and the age condition controls progression
vs. regression. Two adversarial networks are imposed on the encoder and
generator, respectively, forcing to generate more photo-realistic faces.
Experimental results demonstrate the appealing performance and flexibility of
the proposed framework by comparing with the state-of-the-art and ground truth.

本文从生成式建模的角度出发，提出了一种基于条件对抗自编码器的人脸年龄变化模型，该模型可以无需配对数据样本，直接对未标注的图片进行年龄属性变化，从而实现同时平滑的人脸年龄变化和回归。实验结果表明，本文提出的方法在性能和灵活性方面表现出了优秀的表现。