Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.
NMR Biomed. 2019 Apr;32(4):e3752. doi: 10.1002/nbm.3752. Epub 2017 Jun 27.
Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments.
为什么扩散 MRI 已成为在体绘制连接组图谱的主要模态？不同的图像采集参数、纤维追踪算法和其他方法学选择如何影响连接组估计？决定连接组重建成败的主要因素有哪些？这些都是我们在本篇综述中旨在探讨的关键问题。我们提供了用于估计宏观连接组的节点和边的关键方法概述，并讨论了开放性问题和内在局限性。我们认为，基于扩散 MRI 的连接组图谱绘制方法仍处于起步阶段，并告诫不要盲目应用深度白质追踪技术，因为连接组重建存在固有挑战。我们回顾了一些研究，这些研究提供了在各种独立且具有生物学相关性的背景下提取有用的微观结构和网络特性的证据。最后，我们强调了当前宏观连接组图谱绘制方法学的一些关键缺陷，并为未来的发展提供了动力。
