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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
BRAIN Initiative:大脑复杂数据分析的理论、模型和方法
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

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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.
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Dynamic embedding time series models in functional brain imaging
  • 批准号:
    10711521
  • 项目类别:
  • 资助金额:
    $36.41万
  • 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
  • 批准号:
    10374109
  • 项目类别:
  • 资助金额:
    $31.24万
  • 财政年份:
    2020
  • 负责人:
    MOO K CHUNG
  • 依托单位:
BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain
  • 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:
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