BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain
BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain
批准号:
9360100
负责人:
MOO K CHUNG
金额:
$37.61万
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-27 至 2019-06-30
关键词:
AddressAlgorithmsBRAIN initiativeBase of the BrainBrainBrain imagingBrain regionBypassCommunitiesComplexComputer softwareDataData AnalysesDatabasesDizygotic TwinsFunctional Magnetic Resonance ImagingFutureGeneticGenetic studyGoalsGraphHeritabilityHumanImageIndividualMagnetic Resonance ImagingMapsMeasuresMethodologyMethodsModelingPhenotypePopulationPropertyPsychopathologyResearch DesignResolutionSame-sexSamplingSoftware ToolsStructureTechniquesTestingTimeTwin Multiple BirthTwin Studiesbasecostimaging modalityimaging studyinterestmultimodalitynetwork modelsneural circuitnovel strategiesopen sourcetheories
中文摘要
摘要
脑成像中的双胞胎研究设计提供了一种非常有效的方法来确定人类的遗传性
个脑袋单卵双胞胎(MZ)和同性双卵双胞胎(DZ)之间的变异性差异可用于
来决定遗传性。我们建议确定结构和功能的遗传程度,
使用200对双胞胎(400个人)的fMRI/DTI和MRI在体素水平上的脑网络。为了获得高-
分辨率遗传地图的大脑网络,该项目需要采取超过25000体素,
fMRI和MRI/DTI的120万体素作为网络节点,这是一个严重的计算挑战。的
一个项目提出了许多新的算法来构建大规模的大脑网络,并随后映射
网络的可遗传性。这项研究将为大脑成像界提供基线大脑
网络遗传性图以及用于建模和可视化的多功能开源算法工具箱
三种不同成像模式的大规模大脑网络。
英文摘要
Abstract
The twin study design in brain imaging offers a very effective way of determining heritability of the human
brain. The difference in variability between monozygotic (MZ) and same-sex dizygotic (DZ) twins can be used
in determining heritability. We propose to determine the extent of heritability of both structural and functional
brain networks at the voxel-level using 200 pairs of twin (400 individuals) of fMRI/DTI and MRI. To obtain high-
resolution heritability map of the brain networks, the project requires taking more than 25 thousands voxels for
fMRI and 1.2million voxels for MRI/DTI as network nodes, which is a serious computational challenge. The
project proposes many new algorithms for constructing large-scale brain networks and subsequently mapping
the heritability of the networks. This study will provide the brain imaging community with the baseline brain
network heritability maps as well as a versatile open-source toolbox of algorithms for modeling and visualizing
large-scale brain networks of three different imaging modalities.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Dynamic embedding time series models in functional brain imaging
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批准号:10711521
-
项目类别:
-
资助金额:$36.41万
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财政年份:2023
-
负责人:MOO K CHUNG
-
依托单位:
Dynamic manifold-valued time series model in functional brain imaging
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批准号:10374109
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项目类别:
-
资助金额:$31.24万
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财政年份:2020
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负责人:MOO K CHUNG
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依托单位:
BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain
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批准号:9170211
-
项目类别:
-
资助金额:$37.61万
-
财政年份:2016
-
负责人:MOO K CHUNG
-
依托单位:
海外基金