Anatomy-Driven Brain Connectivity Mapping

连接体是人类大脑中神经连接的综合图谱，每个人都是独一无二的。即使是同卵双胞胎，在神经连接的水平上也不同。绘制人类连接体及其在不同个体之间的变异性图对于洞察大脑功能的未知认知方面至关重要，也对于识别疾病大脑的功能障碍特征至关重要。出于这些原因，了解人脑及其组织和最终的功能是21世纪的关键科学挑战之一。核磁共振成像(MRI)使神经科学发生了革命性的变化，它独特地允许在活人身上探索大脑解剖和功能。即使MRI只允许恢复宏观特征(在相对较大的组织区域水平上，而不是单个神经细胞)，其非侵入性和体内应用也为脑研究打开了巨大的可能性。弥散加权磁共振成像(DMRI)是一种特殊的方式，它唯一地允许对纤维束进行映射，纤维束是在不同大脑区域之间调节信息流的潜在连接路径。连接映射是通过经由称为轨迹图的计算算法处理dMRI图像来间接执行的。脑束造影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，以及跟踪成像算法所揭示的巨大潜力，导致了大西洋两岸的重大倡议。他们利用dMRI来收集健康成年人和发育中的大脑的最先进的数据集，并通过纤维束成像绘制结构连接体的地图。其中包括耗资3000万美元的NIH人类连接体项目，1500万欧元的ERC开发人类连接体项目，以及英国资助的3000万GB的生物库成像。然而，如果没有最先进的分析方法和分析dMRI数据的新方法，研究人员将无法最大限度地利用即将到来的海量数据。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。我们特别关注从复杂几何图形(这在大脑中非常常见)到dMRI测量的模糊映射所产生的问题。这对现有方法的准确性构成了很大的限制。我们建议通过计算和算法解决方案进行大规模改变，允许以前所未有的细节在体内测量连接，以更精细的规模研究整个大脑组织，并揭示现有技术看不见的解剖特征。这些进展将为神经解剖学研究开辟新的可能性，也为磁共振成像处理和连接映射的新基础研究奠定基础。我们将使用基础和临床神经科学中引人注目的演示应用来说明它们的潜力，包括评估使用新技术辅助神经外科计划的好处。

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.