A multi-modal framework for characterizing myelin microstructure

磁共振成像（MRI）显示了很大的希望，在发育，衰老，疾病和治疗过程中的大脑微观结构的特征。然而，以目前的分辨率（毫米级），MRI仍然是一种钝工具，只能提供构成大脑中大部分白色物质的复杂纤维网络（微米级）的鸟瞰图。在过去的十年中，我们在定量磁共振成像（qMRI）领域取得了巨大的进步，使我们能够在比原生MRI分辨率小几个数量级的尺度上收集微观结构信息。 能够这样做将增加我们对大脑发育的理解，并成为诊断和治疗神经退行性疾病的宝贵工具。 白色物质中的纤维由轴突组成，其中大部分被包裹在髓鞘中，使得信息能够快速传导。 我最近提出了一种新的生物物理模型的白色物质的微观结构特征，如相对髓鞘厚度（g比），宏观量，如髓鞘体积分数（MVF）和轴突体积分数（AVF）。其他研究人员已经独立证实了该模型的理论合理性，因此下一步是使用该模型来增加MRI在表征髓鞘微结构方面的特异性。 我建议结合定量MRI生物标志物，使之有可能解耦的贡献轴突髓鞘的MR信号。在多模态框架中描述单个MR生物标志物将大大增加MRI对髓鞘微观结构特征的特异性，使得首次非侵入性地测量髓鞘厚度（g比）成为可能。 该方法将在小鼠脱髓鞘模型中进行测试，然后将其转化为多发性硬化症的临床研究。 测量多发性硬化症的髓鞘厚度将为神经科医生提供一种实时工具，用于跟踪脱髓鞘和髓鞘再生过程中病变的进展，这对于开发和评估促进髓鞘再生的新治疗药物非常有价值。 沿着这条路，许多小的发现将导致更好的和更相关的定量MR协议。这项资助中提出的多模态框架将有助于扩大qMRI工具箱，为科学家提供新的工具来解决与大脑微观结构相关的一系列基础科学和临床问题。

Magnetic resonance imaging (MRI) shows great promise for characterizing brain microstructure during development, aging, disease, and treatment. However, with its current resolution (on the order of millimeters), MRI remains a blunt tool that provides only a birds-eye view of the complex network of fibers (on the order of micrometers) that make up the bulk of white matter in the brain. The last ten years have seen tremendous advances in the field of quantitative magnetic resonance imaging (qMRI), enabling us to glean microstructural information on a scale that is orders of magnitude smaller than the native MRI resolution. Being able to do so will increase our understanding of brain development and become an invaluable tool for diagnosis and treatment of neurodegenerative disorders. The fibers in white matter consist of axons, most of which are wrapped in a myelin sheath that enables fast conduction of information. I have recently proposed a novel biophysical model of white matter relating the microstructural features, such as the relative myelin thickness (g-ratio), to macroscopic quantities such as the myelin volume fraction (MVF) and the axon volume fraction (AVF). Other researchers have independently confirmed the theoretical soundness of the model, so the next step is to use this model to increase the specificity of MRI in characterizing myelin microstructure. I propose combining quantitative MRI biomarkers to make it possible to decouple the contribution of axons from the contribution of myelin to the MR signal. Incorporating the individual MR biomarkers in a multi-modal framework will greatly increase the specificity of MRI to the microstructural features of myelin, making it possible to measure for the first time the myelin thickness (g-ratio) non-invasively. The methodology will be tested in mouse demyelination models, before translating it to clinical studies in multiple sclerosis. Measuring myelin thickness in multiple sclerosis will give neurologists a real-time tool for tracking the progression of a lesion during demyelination and remyelination, which would be extremely valuable in the development and evaluation of new therapeutic agents that promote remyelination. Along the way, many small discoveries will be made that lead toward better and more relevant quantitative MR protocols. The multi-modal framework proposed in this grant will contribute to expanding the qMRI toolbox, giving scientists new tools to attack a range of basic science and clinical questions related to brain microstructure.