Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess
the brain white matter. One of the most common computations in dMRI involves
cross-subject tract-specific analysis, whereby dMRI-derived biomarkers are
compared between cohorts of subjects. The accuracy and reliability of these
studies hinges on the ability to compare precisely the same white matter tracts
across subjects. This is an intricate and error-prone computation. Existing
computational methods such as Tract-Based Spatial Statistics (TBSS) suffer from
a host of shortcomings and limitations that can seriously undermine the
validity of the results. We present a new computational framework that
overcomes the limitations of existing methods via (i) accurate segmentation of
the tracts, and (ii) precise registration of data from different
subjects/scans. The registration is based on fiber orientation distributions.
To further improve the alignment of cross-subject data, we create detailed
atlases of white matter tracts. These atlases serve as an unbiased reference
space where the data from all subjects is registered for comparison. Extensive
evaluations show that, compared with TBSS, our proposed framework offers
significantly higher reproducibility and robustness to data perturbations. Our
method promises a drastic improvement in accuracy and reproducibility of
cross-subject dMRI studies that are routinely used in neuroscience and medical
research.

提出了一种新的计算框架，通过准确地分割束与不同主体 / 扫描的数据的精确注册，基于纤维定向分布，创建白质束详细解剖图，并为所有主体的数据创建一个基准空间进行比较，从而改善白质束间跨主体数据的比较的准确性和重复性。

TBSS++: 一种新的基于轨迹的空间统计计算方法

TBSS++: A novel computational method for Tract-Based Spatial Statistics

In recent years, multitudes of researches have applied deep learning to
automatic sleep stage classification. Whereas actually, these works have paid
less attention to the issue of cross-subject in sleep staging. At the same
time, emerging neuroscience theories on inter-subject correlations can provide
new insights for cross-subject analysis. This paper presents the MViTime model
that have been used in sleep staging study. And we implement the inter-subject
correlation theory through contrastive learning, providing a feasible solution
to address the cross-subject problem in sleep stage classification. Finally,
experimental results and conclusions are presented, demonstrating that the
developed method has achieved state-of-the-art performance on sleep staging.
The results of the ablation experiment also demonstrate the effectiveness of
the cross-subject approach based on contrastive learning.

该研究应用深度学习于自动睡眠阶段分类，并通过提出的 MViTime 模型实现了跨主体睡眠阶段分类问题中的交叉主体关联理论，并通过对比学习提供了解决方案。研究结果表明，基于对比学习的跨主体方法在睡眠分期方面取得了最先进的性能。