Inferring in Vivo Cytoarchitectural Borders in the Medial Temporal Lobe

体内颞叶内侧细胞结构边界的推断

基本信息

批准号：
7828127
负责人：
Bruce Fischl
金额：
$ 46.55万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-08-13 至 2012-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7828127
关键词：
Accounting Affect Alzheimer&apos s Disease Amygdaloid structure Area Back Brain Brain imaging Brain region Brodmann&apos s area Cell Density Cerebrospinal Fluid Characteristics Classification Clinical Complex Data Data Set Detection Development Diagnostic Imaging Dimensions Early Diagnosis Evaluation Evolution Face Generations Glean Goals Gold Hippocampus (Brain)Histology Image Imaging technology Individual Intervention Label Link Location Luxol Fast Blue MBS Magnetic Resonance Imaging Maps Mechanics Medial Memory Methods Microanatomy Microscopy Modeling Myelin Neuroanatomy Neurosciences Noise Pattern Phase Population Study Probability Procedures Process Property Protocols documentation Relative (related person)Research Personnel Resolution Sampling Scanning Signal Transduction Staining method Stains Statistical Models Structure Surface System Techniques Technology Temporal Lobe Testing Thalamic structure Time Tissue Sample Tissues Validation area striata base clinically relevant cost data acquisition gray matter healthy aging image registration improved in vivo myelination nervous system disorder novel programs relating to nervous system tool ultra high resolution white matter

项目摘要

DESCRIPTION (provided by applicant): Standard structural brain imaging protocols result in images that cannot resolve structures smaller than 1- 2mm in size. Achieving significantly higher resolution would be of fundamental clinical and neuroscientific value, as it would allow the in-vivo detection and analysis of cytoarchitectural features of the cortex, as well as substructures of brain regions such as the hippocampus, thalamus and amygdala. Unfortunately, such resolution is extremely difficult to obtain in-vivo, as the signal-to-noise ratio goes down with the third power of the linear dimension of each voxel. While some recent studies have pushed this limit to under 1A mm, this is at the cost of extremely long scan sessions and specialized imaging hardware, and even this is still a coarse resolution relative to what is required to visualize correlates of the cytoarchitecture with MRI. Here we take a different approach, and propose to image ex-vivo tissue samples, both blocks of tissue and whole hemispheres, in which exceedingly high-resolution is obtainable, on the order of lOOujns. In these images, many MR signatures of cytoarchitectural features are apparent, and hence they can be used for the construction of models including these cytoarchitectonically defined boundaries. For those features that are not distinguishable from the MR, we propose to perform histological analysis of the tissue, and use cross modal registration techniques to transfer the information from the histology back to the models. High dimensional mapping procedures are then proposed to map these models, obtained from ultra high-resolution imaging and histology, back to the more standard resolution in-vivo data to predict the probability of a given cytoarchitectural boundary occurring at each location in the in-vivo data. We focus on cortical areas in the medial temporal lobe as they are of great clinical relevance, as they are thought to be one of the earliest loci of Alzheimer's disease, and are critical to normal memory function. The ability to more accurately localize these cortical regions would be a critical step in the early diagnosis of AD, and in the assessment of the efficacy of potential clinical interventions.

描述（由申请人提供）：标准结构脑成像协议导致无法解析大小小于1-2mm的结构的图像。实现明显更高的分辨率将具有基本的临床和神经科学价值，因为它可以允许对皮质的细胞构造特征进行体内检测和分析，以及海马，丘脑，丘脑和杏仁核等大脑区域的子结构。不幸的是，这种分辨率极难获得体内，因为信噪比随着每个体素的线性维度的第三强度下降。尽管最近的一些研究将这一限制推到了1A毫米以下，但这是以极其长时间的扫描和专业成像硬件为代价的，即使这仍然是与MRI的细胞划分相关性所需的相对于可视化相关性所需的粗糙分辨率。在这里，我们采用不同的方法，并提议在looujns的顺序上成像组织和整个半球的块，即组织和整个半球的块。在这些图像中，许多细胞结构特征的MR签名显而易见，因此可以用于构建包括这些细胞结构定义的边界在内的模型。对于那些与MR无法区分的特征，我们建议对组织进行组织学分析，并使用交叉模态登记技术将信息从组织学转移回模型。然后提出了高维映射程序来映射这些模型，该模型是从超高分辨率成像和组织学获得的，回到了更标准的Vivo内分辨率数据，以预测在维多维沃数据中每个位置发生的给定细胞构造边界的概率。我们专注于内侧颞叶中的皮质区域，因为它们具有很大的临床相关性，因为它们被认为是阿尔茨海默氏病的最早基因座之一，并且对正常记忆功能至关重要。更准确地定位这些皮质区域的能力将是AD早期诊断的关键步骤，以及评估潜在临床干预措施的功效。