CORPUS CALLOSUM CHANGES IN MILD ALZHEIMER'S DISEASE

轻度阿尔茨海默病的胼胝体变化

• 批准号: 8363461
• 负责人: GAETANO THOMAS CALTAGIRONE
• 金额: $ 2.03万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8363461
• 关键词: Alzheimer' s Disease; Corpus Callosum; Funding; Grant; Image; Laboratories; Modeling; Morphology; National Center for Research Resources; Patients; Principal Investigator; Protocols documentation; Research; Research Infrastructure; Resolution; Resources; Source; Surface; Techniques; Thick; United States National Institutes of Health; base; computational anatomy; cost; mild neurocognitive impairment

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Corpus Callosum Changes in Mild Alzheimer Disease and Amnesic Mild Cognitive Impairment Corpus callosum morphology will be assessed in healthy controls and patients with Alzheimer's disease and Mild Cognitive impairment. The corpus callosum region will be delineated manually following a defined tracing protocol. Computational surfaced-based techniques to encode callosal thickness at high resolution will then be applied to compare groups. We wish to analyze callosal thickness utilizing a computational surface-based approach that was developed at the Laboratory of Neuro Imaging (LONI).

该副本是利用资源的众多研究子项目之一 由NIH/NCRR资助的中心赠款提供。对该子弹的主要支持 而且，副投影的主要研究员可能是其他来源提供的 包括其他NIH来源。 列出的总费用可能 代表subproject使用的中心基础架构的估计量， NCRR赠款不直接向子弹或副本人员提供的直接资金。 轻度阿尔茨海默氏病和失忆性轻度认知障碍的call体发生变化 call体形态将在健康对照和阿尔茨海默氏病和轻度认知障碍的患者中进行评估。 call体区域将按照定义的跟踪协议手动划定。然后将基于计算表面的技术在高分辨率上编码Callosal厚度，然后将其应用于比较组。我们希望利用在Neuro成像实验室（LONI）开发的基于计算的表面方法来分析callosal厚度。