Department of Radiology, University of Chicago, Chicago, IL, USA.
Department of Radiology, University of Chicago, Chicago, IL, USA.
Neuroimage. 2021 Dec 1;244:118576. doi: 10.1016/j.neuroimage.2021.118576. Epub 2021 Sep 11.
Diffusion MRI tractography is the only noninvasive method to measure the structural connectome in humans. However, recent validation studies have revealed limitations of modern tractography approaches, which lead to significant mistracking caused in part by local uncertainties in fiber orientations that accumulate to produce larger errors for longer streamlines. Characterizing the role of this length bias in tractography is complicated by the true underlying contribution of spatial embedding to brain topology. In this work, we compare graphs constructed with ex vivo tractography data in mice and neural tracer data from the Allen Mouse Brain Connectivity Atlas to random geometric surrogate graphs which preserve the low-order distance effects from each modality in order to quantify the role of geometry in various network properties. We find that geometry plays a substantially larger role in determining the topology of graphs produced by tractography than graphs produced by tracers. Tractography underestimates weights at long distances compared to neural tracers, which leads tractography to place network hubs close to the geometric center of the brain, as do corresponding tractography-derived random geometric surrogates, while tracer graphs place hubs further into peripheral areas of the cortex. We also explore the role of spatial embedding in modular structure, network efficiency and other topological measures in both modalities. Throughout, we compare the use of two different tractography streamline node assignment strategies and find that the overall differences between tractography approaches are small relative to the differences between tractography- and tracer-derived graphs. These analyses help quantify geometric biases inherent to tractography and promote the use of geometric benchmarking in future tractography validation efforts.
弥散磁共振成像纤维束追踪是测量人类结构连接组的唯一非侵入性方法。然而，最近的验证研究揭示了现代纤维追踪方法的局限性，这些局限性导致了显著的误追踪，部分原因是纤维方向的局部不确定性会累积，从而导致更长的轨迹产生更大的误差。描述这种长度偏差在纤维追踪中的作用很复杂，因为空间嵌入对大脑拓扑结构的真正潜在贡献。在这项工作中，我们比较了在老鼠的离体纤维追踪数据和艾伦老鼠大脑连接图谱中的神经示踪剂数据构建的图，以及随机几何替代图，以保留每种模态的低阶距离效应，从而量化几何在各种网络特性中的作用。我们发现，在确定纤维追踪产生的图的拓扑结构方面，几何起着比示踪剂更大的作用。与神经示踪剂相比，纤维追踪在长距离上低估了权重，这导致纤维追踪将网络枢纽放置在大脑的几何中心附近，与相应的纤维追踪衍生的随机几何替代图一样，而示踪剂图将枢纽放置在皮质的外围区域。我们还探索了空间嵌入在两种模态的模块化结构、网络效率和其他拓扑度量中的作用。在整个过程中，我们比较了两种不同的纤维追踪流线节点分配策略的使用，并发现纤维追踪方法之间的总体差异相对于纤维追踪和示踪剂衍生图之间的差异较小。这些分析有助于量化纤维追踪中固有的几何偏差，并促进在未来的纤维追踪验证工作中使用几何基准测试。
