Algorithms for MR and OCT-based Architectonic and Laminar Segmentation

 描述(申请人提供)：人脑由一系列在功能和结构上定义的区域组成。定位这些区域对于阿尔茨海默病(AD)等一系列疾病的早期诊断以及旨在了解大脑功能和结构特性的神经科学研究以及耐药抑郁症的临床干预至关重要。不幸的是，作为皮质区域的定义特征之一的层流属性的直接可视化超出了当前的范围 成像技术，除非在专门病例中只占整个皮质的一小部分。在这个项目中，我们建议开发工具，使用超高分辨率的体外MRI和光学相干断层扫描(OCT)来分割皮质区域和板层边界，然后通过概率建模进行体内推断。这将允许我们开发和分发工具，这些工具应该允许自动区域和层状本地化，从而促进阵列 临床和神经科学研究。拟议的分析流有望自动得出新的层状和区域特定的形态测量指标，这是我们在治疗干预仍有可能的情况下在其病程早期检测神经退行性疾病的目标的关键进步，以及用于指导患有抑郁症或多发性硬化症等疾病的个别患者的疾病治疗和分期。

 DESCRIPTION (provided by applicant): The human brain is made up of an array of functionally and structurally defined regions. Localizing these regions is critical for early diagnosis of an array of diseases such as Alzheimer's disease (AD), as well as for neuroscientific research aimed at understanding the brain's functional and structural properties and clinical intervention in drug-resistant depression. Unfortunately, direct visualization of the laminar properties that are one of the defining characteristics of cortical areas is beyond current imaging technology, except in specialized cases that represent a small fraction of the entire cortex. In this project we propose to develop tools for segmenting cortical areas and laminar boundaries using ultra-high resolution ex vivo MRI and optical coherence tomography (OCT), then to make in vivo inferences via probabilistic modeling. This will allow us to develop and distribute tools that should permit automated areal and laminar localization, facilitating an array of clinical and neuroscientific research. The proposed analysis stream holds the promise of automatically deriving new laminar and area-specific morphometric measures, a critical advance in our goal of detecting neurodegenerative disorders early in their course, when therapeutic intervention is still possible, as well as for guiding disease treatment and staging in individual patients suffering from diseases such as depression or multiple sclerosis.