School of Computing Technologies, RMIT University, Melbourne, Victoria, Australia.
Department of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia.
NMR Biomed. 2021 Dec;34(12):e4605. doi: 10.1002/nbm.4605. Epub 2021 Sep 13.
Diffusion MRI tractography is the most widely used macroscale method for mapping connectomes in vivo. However, tractography is prone to various errors and biases, and thus tractography-derived connectomes require careful validation. Here, we critically review studies that have developed or utilized phantoms and tracer maps to validate tractography-derived connectomes, either quantitatively or qualitatively. We identify key factors impacting connectome reconstruction accuracy, including streamline seeding, propagation and filtering methods, and consider the strengths and limitations of state-of-the-art connectome phantoms and associated validation studies. These studies demonstrate the inherent limitations of current fiber orientation models and tractography algorithms and their impact on connectome reconstruction accuracy. Reconstructing connectomes with both high sensitivity and high specificity is challenging, given that some tractography methods can generate an abundance of spurious connections, while others can overlook genuine fiber bundles. We argue that streamline filtering can minimize spurious connections and potentially improve the biological plausibility of connectomes derived from tractography. We find that algorithmic choices such as the tractography seeding methodology, angular threshold, and streamline propagation method can substantially impact connectome reconstruction accuracy. Hence, careful application of tractography is necessary to reconstruct accurate connectomes. Improvements in diffusion MRI acquisition techniques will not necessarily overcome current tractography limitations without accompanying modeling and algorithmic advances.
弥散磁共振纤维束追踪是目前在体绘制连接组图谱最广泛使用的宏观方法。然而,纤维追踪容易出现各种误差和偏差,因此需要对纤维追踪得到的连接组图谱进行仔细验证。本文批判性地回顾了利用或开发体模和示踪图来验证纤维追踪得到的连接组图谱的研究,这些研究从定性和定量两个方面进行了验证。我们确定了影响连接组重建准确性的关键因素,包括流线种子、传播和过滤方法,并考虑了最先进的连接组体模及其相关验证研究的优缺点。这些研究表明,当前的纤维方向模型和纤维追踪算法存在固有局限性,会对连接组重建准确性产生影响。由于一些纤维追踪方法会产生大量虚假连接,而另一些方法则可能忽略真实的纤维束,因此,用高灵敏度和高特异性来重建连接组是具有挑战性的。我们认为,流线过滤可以最小化虚假连接,并可能提高纤维追踪得到的连接组的生物学合理性。我们发现,纤维追踪的种子生成方法、角度阈值和流线传播方法等算法选择会极大地影响连接组重建准确性。因此,有必要仔细应用纤维追踪来重建准确的连接组。如果没有建模和算法的改进,仅仅改进扩散磁共振成像采集技术不一定能克服当前纤维追踪的局限性。