Multimodal mapping of the neurocircuitry of the human prefrontal cortex

 描述(由申请人提供)：拟议研究的目标是用尖端的体外成像技术绘制人类白质(WM)的组织图。这项工作将产生几个远程WM投射的微观信息，以及更有针对性的服务于前额叶皮质(PFC)的电路图谱。这些回路对精神病学的应用特别感兴趣，但到目前为止，它们只在非人类灵长类动物中被广泛绘制。具体地说，最近在猕猴身上进行的示踪剂注射研究已经确定了起源于PFC不同区域的小纤维束是如何使用大型WM通路，如扣带束(CB)、穹隆体束(CC)和钩状束(UF)作为它们的管道到达目的地的。例如，UF由几个子束组成：一些子束跟随UF的整个轨迹，另一些仅在UF的部分轨迹加入UF，然后跳下并加入其他大型路径，如CB、CC等。在人脑中映射较大WM路径的这些不同组件在体内和体外都是具有挑战性的。侵入性注射研究不适用于人类，传统的2D组织学技术，如髓鞘染色，也不能用来推断轴突束的3D方向。扩散磁共振成像(DMRI)可以通过测量水分子在WM中的扩散来间接提供这些取向的估计。然而，在复杂的WM体系结构中，它很容易出错，并且需要由独立的测量来源进行验证。在这项工作中，我们将结合高分辨率、高信噪比的体外dMRI和尸检后的偏振敏感光学相干断层扫描(PS-OCT)来提取混淆常规dMRI的WM区域的微观信息，并对PFC的投影进行详细的标测。我们将利用MGH Connectom扫描仪，这是一种独特的仪器，可以实现比常规扫描仪强8倍的扩散编码梯度，是专门为高信噪比、高分辨率dMRI设计的。我们将开发一种专门的接收线圈阵列，用于在Connectom扫描仪上对体外人脑进行成像，这将使我们能够以前所未有的分辨率和信噪比收集全脑dMRI数据。这项工作产生的黄金标准dMRI和PS-OCT数据将用于构建一份新的WM解剖图集，该图集将在PI先前工作的基础上纳入自动化全球概率纤维束成像工具。该工具将允许从常规质量的活体dMRI数据中自动重建较小的WM束的新的详细分类以及大型WM路径的经典定义，这些数据可以在传统扫描仪上收集。这项拟议的工作有望推进我们对人类神经回路组织的理解；将人类dMRI研究从目前将WM通路作为单一束的观点转移到多个较小的束在通路上和通路沿其轨迹的不同部分合并的观点；并提供使用常规神经成像数据来研究这种详细的WM分类学的工具。

 DESCRIPTION (provided by applicant): The objective of the proposed research is to map the organization of human white matter (WM) with cutting-edge ex vivo imaging technologies. This work will produce microscopic-level information on several long-range WM projections, as well as a more targeted mapping of circuits that serve the prefrontal cortex (PFC). These circuits are of particular interest in psychiatric applications but they have been heretofore mapped extensively only in non-human primates. Specifically, recent work with tracer injection studies in macaque monkeys has established how small fiber bundles that originate in different areas of the PFC reach their destinations by using the large WM pathways, such as the cingulum bundle (CB), corpus callosum (CC) and uncinate fasciculus (UF), as their conduits. For example, the UF is composed of several sub-bundles: some that follow the entire trajectory of the UF and others that join the UF only for part of its trajectory to then jump off and join other large pathways, like the CB, CC, etc. Mapping these distinct components of larger WM pathways in the human brain is challenging both in vivo and ex vivo. Invasive injection studies are not applicable to humans and conventional 2D histological techniques like myelin staining cannot be used to infer the 3D orientation of axon bundles. Diffusion MRI (dMRI) can provide estimates of these orientations indirectly, by measuring the diffusion of water molecules through the WM. However, it is prone to errors in areas of complex WM architecture and requires validation by an independent source of measurements. In this work we will combine high-resolution, high-SNR ex vivo dMRI with polarization-sensitive optical coherence tomography (PS-OCT) in post mortem human brains to extract microscopic information on WM areas that confound conventional dMRI, and to perform a detailed mapping of the projections of the PFC. We will take advantage of the MGH Connectom scanner, a unique instrument that can achieve 8 times stronger diffusion-encoding gradients than routine scanners, and was designed specifically for high-SNR, high-resolution dMRI. We will develop a specialized receive coil array for imaging ex vivo human brains on the Connectom scanner, which will allow us to collect whole-brain dMRI data with unprecedented resolution and SNR. The gold-standard dMRI and PS-OCT data produced by this work will be used to construct a novel atlas of WM anatomy, which will be incorporated in a tool for automated global probabilistic tractography, building on prior work by the PI. This tool will allow both the new, detailed taxonomy of smaller WM bundles, as well as the classical definitions of large WM pathways, to be reconstructed automatically from routine-quality in vivo dMRI data that can be collected on conventional scanners. The proposed work promises to advance our understanding of the organization of human neurocircuitry; to move human dMRI studies from the current view of a WM pathway as a single bundle to one where multiple smaller bundles merge on and off a pathway at different parts along its trajectory; and to provide the tools for studying this detailed WM taxonomy using routine neuroimaging data.