While many neuroscience questions aim to understand the human brain, much current knowledge has been gained using animal models, which replicate genetic, structural, and connectivity aspects of the human brain. While voxel-based analysis (VBA) of preclinical magnetic resonance images is widely-used, a thorough examination of the statistical robustness, stability, and error rates is hindered by high computational demands of processing large arrays, and the many parameters involved therein. Thus, workflows are often based on intuition or experience, while preclinical validation studies remain scarce. To increase throughput and reproducibility of quantitative small animal brain studies, we have developed a publicly shared, high throughput VBA pipeline in a high-performance computing environment, called SAMBA. The increased computational efficiency allowed large multidimensional arrays to be processed in 1–3 days—a task that previously took ~1 month. To quantify the variability and reliability of preclinical VBA in rodent models, we propose a validation framework consisting of morphological phantoms, and four metrics. This addresses several sources that impact VBA results, including registration and template construction strategies. We have used this framework to inform the VBA workflow parameters in a VBA study for a mouse model of epilepsy. We also present initial efforts towards standardizing small animal neuroimaging data in a similar fashion with human neuroimaging. We conclude that verifying the accuracy of VBA merits attention, and should be the focus of a broader effort within the community. The proposed framework promotes consistent quality assurance of VBA in preclinical neuroimaging, thus facilitating the creation and communication of robust results.

中文翻译：

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小动物多元大脑分析（SAMBA）-具有验证框架的高通量管道。

尽管许多神经科学问题旨在理解人脑，但使用动物模型已经获得了许多最新知识，该模型复制了人脑的遗传，结构和连通性方面。虽然临床前磁共振图像的基于体素的分析（VBA）被广泛使用，但是处理大型阵列的高计算要求以及其中涉及的许多参数阻碍了对统计鲁棒性，稳定性和错误率的彻底检查。因此，工作流通常基于直觉或经验，而临床前验证研究仍然很少。为了提高定量小动物脑研究的通量和可重复性，我们在称为SAMBA的高性能计算环境中开发了公共共享的高通量VBA管道。计算效率的提高使得大型多维数组可以在1-3天之内进行处理，而这项工作以前需要大约1个月的时间。为了量化啮齿动物模型中临床前VBA的变异性和可靠性，我们提出了一个由形态模型和四个指标组成的验证框架。这解决了影响VBA结果的多种来源，包括注册和模板构建策略。我们已使用此框架在VBA研究中为癫痫小鼠模型提供VBA工作流程参数。我们还提出了以与人类神经影像相似的方式标准化小动物神经影像数据的初步努力。我们得出的结论是，验证VBA的准确性值得关注，并且应该成为社区内广泛工作的重点。