Anatomy-Driven Brain Connectivity Mapping

连接体是人类大脑中神经连接的综合地图，每个人都是独一无二的。即使是同卵双胞胎，神经连接的水平也不同。绘制人类连接体及其在个体之间的变异性对于深入了解大脑功能的未知认知方面至关重要，同时也有助于识别患病大脑的功能障碍特征。由于这些原因，了解人类大脑及其组织和最终功能是21世纪世纪的关键科学挑战之一。磁共振成像（MRI）通过独特地允许在活体人类中探测大脑解剖结构和功能，彻底改变了神经科学。即使MRI只允许恢复宏观特征（在相对较大的组织区域，而不是单个神经元细胞的水平上），其非侵入性和体内应用也为大脑研究开辟了巨大的可能性。弥散加权MRI（dMRI）是一种特殊的模式，它独特地允许映射纤维束，这是介导不同大脑区域之间信息流的潜在连接路径。连接映射通过经由被称为纤维束成像的计算算法处理dMRI图像来间接执行。 纤维束描记术已经为大脑解剖学提供了基本的新见解。大脑连接对我们理解大脑的重要性沿着，以及纤维束成像算法揭示的巨大潜力，导致大西洋两岸的大型倡议。这些利用dMRI收集健康成人和发育中大脑的最先进数据集，并通过纤维束成像绘制结构连接体。其中包括3000万美元的NIH人类连接组项目，1500万欧元的ERC开发人类连接组项目和3000万英镑的英国资助的生物银行成像。然而，如果没有最先进的分析方法和分析dMRI数据的新方法，研究人员将无法充分利用即将到来的大量数据。在这个项目中，我们提出了以神经解剖学为中心的纤维束成像方法的新框架。我们特别关注复杂几何形状（在大脑中非常常见）到dMRI测量的模糊映射所产生的问题。这些对现有方法的准确性构成了重大限制。我们建议通过计算和算法解决方案进行大规模的改变，这将允许以前所未有的细节在体内测量连接，以更精细的尺度研究整个大脑组织，并揭示现有技术不可见的解剖特征。这些进展将为神经解剖学研究开辟新的可能性，同时也为MRI处理和连接映射的新基础研究奠定了基础。我们将使用基础和临床神经科学的引人注目的演示应用程序来说明它们的潜力，包括评估使用新技术辅助神经外科规划的益处。

The connectome, the comprehensive map of neural connections in the human brain, is unique in every individual. Even identical twins differ at the level of neural connectivity. Mapping the human connectome and its variability across individuals is essential in getting insight into the unknown cognitive aspects of brain function, but also into identifying dysfunctional features of the diseased brain. For these reasons, understanding the human brain, its organisation and ultimately its function, is amongst the key scientific challenges of the 21st century. Magnetic resonance imaging (MRI) has revolutionised neuroscience by uniquely allowing both brain anatomy and function to be probed in living humans. Even if MRI allows only macroscopic features to be recovered (at the level of relatively large tissue regions, rather than individual neuronal cells), its non-invasive and in-vivo application has opened tremendous possibilities for brain research. Diffusion-weighted MRI (dMRI) is a particular modality that uniquely allows the mapping of fibre bundles, the underlying connection pathways that mediate information flow between different brain regions. The connection mapping is performed indirectly by processing dMRI images via computational algorithms referred to as tractography. Tractography has already provided fundamental new insights into brain anatomy. The importance of brain connectivity to our understanding of the brain along, with the great potential revealed by tractography algorithms have led to large initiatives from both sides of the Atlantic. These utilise dMRI to collect state-of-the-art datasets of the healthy adult and the developing brain and map the structural connectome through tractography. They include the $30M NIH Human Connectome Project, the 15M Euros ERC Developing Human Connectome Project and the £30M UK funded Biobank Imaging. However, without state-of-the-art analysis methods, and new ways of analysing dMRI data, researchers will fail to get the most out of this vast wealth of upcoming data. In this project, we propose new frameworks for tractography methods centred on neuroanatomy. We particularly focus on problems arising from ambiguous mapping of complex geometries (which are very common in the brain) to the dMRI measurements. These pose significant limits to the accuracy of existing approaches. We propose wholesale changes through computational and algorithmic solutions that will allow connections to be measured in-vivo with unprecedented detail, whole brain organization to be studied at a much finer scale and anatomical features -invisible to existing techniques- to be revealed. These advances will open new possibilities for neuroanatomical studies, but also set the foundations for new basic research in MRI processing and connectivity mapping. We will illustrate their potential using compelling demonstrator applications from basic and clinical neuroscience, including the assessment of benefits from using the new technology in assisting neurosurgical planning.