Brain age is a critical measure that reflects the biological ageing process
of the brain. The gap between brain age and chronological age, referred to as
brain PAD (Predicted Age Difference), has been utilized to investigate
neurodegenerative conditions. Brain age can be predicted using MRIs and machine
learning techniques. However, existing methods are often sensitive to
acquisition-related variabilities, such as differences in acquisition
protocols, scanners, MRI sequences, and resolutions, significantly limiting
their application in highly heterogeneous clinical settings. In this study, we
introduce Synthetic Brain Age (SynthBA), a robust deep-learning model designed
for predicting brain age. SynthBA utilizes an advanced domain randomization
technique, ensuring effective operation across a wide array of
acquisition-related variabilities. To assess the effectiveness and robustness
of SynthBA, we evaluate its predictive capabilities on internal and external
datasets, encompassing various MRI sequences and resolutions, and compare it
with state-of-the-art techniques. Additionally, we calculate the brain PAD in a
large cohort of subjects with Alzheimer's Disease (AD), demonstrating a
significant correlation with AD-related measures of cognitive dysfunction.
SynthBA holds the potential to facilitate the broader adoption of brain age
prediction in clinical settings, where re-training or fine-tuning is often
unfeasible. The SynthBA source code and pre-trained models are publicly
available at this https URL

通过使用合成大脑年龄（SynthBA）模型，我们可以预测大脑的年龄并且在临床环境中更加广泛地应用，而不受获取方式变化的限制。

SynthBA: 可靠的多个 MRI 序列和分辨率下的脑龄预测

SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and  Resolutions

Synthetic longitudinal brain MRI simulates brain aging and would enable more
efficient research on neurodevelopmental and neurodegenerative conditions.
Synthetically generated, age-adjusted brain images could serve as valuable
alternatives to costly longitudinal imaging acquisitions, serve as internal
controls for studies looking at the effects of environmental or therapeutic
modifiers on brain development, and allow data augmentation for diverse
populations. In this paper, we present a diffusion-based approach called
SynthBrainGrow for synthetic brain aging with a two-year step. To validate the
feasibility of using synthetically-generated data on downstream tasks, we
compared structural volumetrics of two-year-aged brains against
synthetically-aged brain MRI. Results show that SynthBrainGrow can accurately
capture substructure volumetrics and simulate structural changes such as
ventricle enlargement and cortical thinning. Our approach provides a novel way
to generate longitudinal brain datasets from cross-sectional data to enable
augmented training and benchmarking of computational tools for analyzing
lifespan trajectories. This work signifies an important advance in generative
modeling to synthesize realistic longitudinal data with limited lifelong MRI
scans. The code is available at XXX.

合成纵向脑 MRI 模拟脑老化，以更有效地研究神经发育和神经退行性疾病，提供代价较低的纵向脑成像替代品，使得环境和治疗改变对脑发育的影响可以得到更好的内部控制，并为不同人群的数据增强提供可能。

SynthBrainGrow: 青年人纵向 MRI 数据生成的合成扩散脑衰老

SynthBrainGrow: Synthetic Diffusion Brain Aging for Longitudinal MRI  Data Generation in Young People

This paper addresses the problem of improving POS tagging of transcripts of
speech from clinical populations. In contrast to prior work on parsing and POS
tagging of transcribed speech, we do not make use of an in domain treebank for
training. Instead, we train on an out of domain treebank of newswire using data
augmentation techniques to make these structures resemble natural, spontaneous
speech. We trained a parser with and without the augmented data and tested its
performance using manually validated POS tags in clinical speech produced by
patients with various types of neurodegenerative conditions.

该论文解决了改善语音转录文本的 POS 标注的问题。作者使用了数据增强技术来训练在领域外树库上的句法分析器，并在句法标注上测试了在不同类型的神经退行性疾病患者的临床语音中表现良好的效果。

使用数据增强改进临床语音的自发性词性标注

Improved POS tagging for spontaneous, clinical speech using data  augmentation

Functional magnetic resonance imaging (fMRI) is a commonly used technique to
measure neural activation. Its application has been particularly important in
identifying underlying neurodegenerative conditions such as Parkinson's,
Alzheimer's, and Autism. Recent analysis of fMRI data models the brain as a
graph and extracts features by graph neural networks (GNNs). However, the
unique characteristics of fMRI data require a special design of GNN. Tailoring
GNN to generate effective and domain-explainable features remains challenging.
In this paper, we propose a contrastive dual-attention block and a
differentiable graph pooling method called ContrastPool to better utilize GNN
for brain networks, meeting fMRI-specific requirements. We apply our method to
5 resting-state fMRI brain network datasets of 3 diseases and demonstrate its
superiority over state-of-the-art baselines. Our case study confirms that the
patterns extracted by our method match the domain knowledge in neuroscience
literature, and disclose direct and interesting insights. Our contributions
underscore the potential of ContrastPool for advancing the understanding of
brain networks and neurodegenerative conditions.

本文提出了一种对脑网络进行图神经网络分析的方法，名为 ContrastPool，以满足功能性磁共振成像数据的特殊需求，并在神经退行性疾病的研究中展示了其优势。