标题： 基于扩散MRI的纤维追踪优化与验证，以神经追踪数据为参考 显示英文标题

标题： Optimization and Validation of Diffusion MRI-based Fiber Tracking with Neural Tracer Data as a Reference

摘要： 扩散加权磁共振成像（dMRI）允许对全脑连接进行非侵入性研究，这可能有助于揭示大脑的全局网络结构以及神经和精神障碍中的异常情况。 然而，基于dMRI的纤维追踪得出的连接推断的可靠性仍存在争议，这是由于灵敏度低、假阳性占主导地位，以及长距离连接的不准确和不完整重建。 此外，追踪算法的参数通常是通过启发式方式进行调整的，这为有意操纵结果留下了空间。 在此，我们提出了一种数据驱动的框架，使用神经示踪数据作为参考，来优化和验证基于dMRI的纤维追踪算法的参数。 日本的Brain/MINDS项目提供了包含来自同一批灵长类动物的dMRI和神经示踪数据的宝贵数据集。 我们考虑了四个纤维追踪质量标准：距离加权覆盖率、真/假阳性比率、投影一致性以及连合通过性，并使用多目标优化算法进行应用。 我们实现了一种非支配排序遗传算法II（NSGA-II）的变体，以在多个脑样本中并行优化全局纤维追踪算法的五个主要参数。 与默认参数相比，使用优化后的参数，基于dMRI的纤维追踪性能显著提高，同时最小化了假阳性和不可能的半球间连接。 针对10个示踪剂注射位点优化的参数对于其他脑样本表现出良好的泛化能力。 这些结果证明了数据驱动调整纤维追踪算法的重要性，并支持了基于dMRI的轨迹追踪的有效性，只要采用了适当的调整。 显示英文摘要

摘要： Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can potentially help to reveal the brain's global network architecture and abnormalities involved in neurological and mental disorders. However, the reliability of connection inferences from dMRI-based fiber tracking is still debated, due to low sensitivity, dominance of false positives, and inaccurate and incomplete reconstruction of long-range connections. Furthermore, parameters of tracking algorithms are typically tuned in a heuristic way, which leaves room for manipulation of an intended result. Here we propose a data-driven framework to optimize and validate parameters of dMRI-based fiber-tracking algorithms using neural tracer data as a reference. Japan's Brain/MINDS Project provides invaluable datasets containing both dMRI and neural tracer data from the same primates. We considered four criteria for goodness of fiber tracking: distance-weighted coverage, true/false positive ratio, projection coincidence, and commissural passage, applied using a multi-objective optimization algorithm. We implemented a variant of non-dominated sorting genetic algorithm II (NSGA-II) to optimize five major parameters of a global fiber-tracking algorithm over multiple brain samples in parallel. Using optimized parameters compared to the default parameters, dMRI-based fiber tracking performance was significantly improved, while minimizing false positives and impossible cross-hemisphere connections. Parameters optimized for 10 tracer injection sites showed good generalization capability for other brain samples. These results demonstrate the importance of data-driven adjustment of fiber-tracking algorithms and support the validity of dMRI-based tractography, if appropriate adjustments are employed.