COMPUTATIONAL NEUROANATOMY OF AGING USING SHAPE ANALYSIS
使用形状分析进行衰老的计算神经解剖学
基本信息
- 批准号:6169001
- 负责人:
- 金额:$ 12.65万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:1998
- 资助国家:美国
- 起止时间:1998-08-01 至 2003-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
DESCRIPTION (Adapted from Applicant's Abstract): The long term goal of this
project is to develop the mathematical framework and the computer
implementation for quantitatively analyzing brain morphology and
characterizing the way it is affected by normal and diseased aging. The
basis of this methodological framework is a shape transformation that adapts
the morphology of one brain, which is treated as a template, to the
morphology of the brain under analysis. This transformation quantifies
global and local morphological characteristics of the brain under analysis,
with respect to the template which serves as a measurement unit.
Inter-subject and inter-population comparisons are performed by comparing
the corresponding shape transformations. The first specific aim of this
project is to develop and validate a geometry-based shape transformation
methodology, utilizing anatomical features extracted from MR images.
Special emphasis is given to the geometric analysis of the cortical sulci
often demarcating the boundaries between different functional cortical
regions, and to structural irregularities, such as ventricular expansion and
brain atrophy, occurring with aging and brain diseases. The second specific
aim is to develop and validate a framework for characterizing shape
properties of brain structures, such as local tissue loss and shape
abnormalities, utilizing the shape transformation above. Finally, the third
specific aim of this project is to test the utility of these methodologies
in brain imaging studies, by applying them to a longitudinal study of aging.
The goal here is to localize subtle morphological changes occurring in the
brain with aging, and to associate such morphological changes with
concurrent or subsequent functional and cognitive changes, which might be
predictors of Alzheimer's disease.
描述(改编自申请人摘要):该项目的长期目标
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Christos Davatzikos其他文献
Christos Davatzikos的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Christos Davatzikos', 18)}}的其他基金
Disentangling the anatomical, functional and clinical heterogeneity of major depression, using machine learning methods
使用机器学习方法解开重度抑郁症的解剖学、功能和临床异质性
- 批准号:
10714834 - 财政年份:2023
- 资助金额:
$ 12.65万 - 项目类别:
Generalizable quantitative imaging and machine learning signatures in glioblastoma, for precision diagnostics and personalized treatment: the ReSPOND consortium
胶质母细胞瘤的通用定量成像和机器学习特征,用于精确诊断和个性化治疗:ReSPOND 联盟
- 批准号:
10625442 - 财政年份:2022
- 资助金额:
$ 12.65万 - 项目类别:
Generalizable quantitative imaging and machine learning signatures in glioblastoma, for precision diagnostics and personalized treatment: the ReSPOND consortium
胶质母细胞瘤的通用定量成像和机器学习特征,用于精确诊断和个性化治疗:ReSPOND 联盟
- 批准号:
10421222 - 财政年份:2022
- 资助金额:
$ 12.65万 - 项目类别:
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
超大规模机器学习助力阿尔茨海默病生物库的发现
- 批准号:
10696100 - 财政年份:2020
- 资助金额:
$ 12.65万 - 项目类别:
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
超大规模机器学习助力阿尔茨海默病生物库的发现
- 批准号:
10263220 - 财政年份:2020
- 资助金额:
$ 12.65万 - 项目类别:
Benchmarking and Comparing AD-Related AI Methods Across Sites on a Standardized Dataset
在标准化数据集上跨站点对 AD 相关 AI 方法进行基准测试和比较
- 批准号:
10825403 - 财政年份:2020
- 资助金额:
$ 12.65万 - 项目类别:
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
超大规模机器学习助力阿尔茨海默病生物库的发现
- 批准号:
10475286 - 财政年份:2020
- 资助金额:
$ 12.65万 - 项目类别:
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
超大规模机器学习助力阿尔茨海默病生物库的发现
- 批准号:
10028746 - 财政年份:2020
- 资助金额:
$ 12.65万 - 项目类别:
Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortium
机器学习和大规模成像分析,用于衰老和临床前阿尔茨海默氏病大脑轨迹的维度表示:大脑衰老图表和 iSTAGING 联盟
- 批准号:
10839623 - 财政年份:2017
- 资助金额:
$ 12.65万 - 项目类别: