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: 9170211
• 负责人: MOO K CHUNG
• 金额: $ 37.61万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-27 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9170211
• 关键词: Address; Algorithms; BRAIN initiative; Base of the Brain; Brain; Brain Mapping; Brain imaging; Brain region; Bypass; Chromosome Mapping; Communities; Complex; Computer software; Data; Databases; Dizygotic Twins; Functional Magnetic Resonance Imaging; Future; Genetic; Genetic study; Goals; Graph; Heritability; Human; Image; Individual; Magnetic Resonance Imaging; Maps; Measures; Methods; Modeling; Phenotype; Population; Property; Psychopathology; Research Design; Resolution; Same-sex; Sampling; Software Tools; Structure; Techniques; Testing; Time; Twin Multiple Birth; Twin Studies; Weight; abstracting; base; cost; imaging genetics; imaging modality; interest; network models; neural circuit; novel strategies; open source; theories

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.

抽象的 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力 脑。可以使用同卵双胞胎 (MZ) 和同性双卵双胞胎 (DZ) 之间的变异性差异 在确定遗传力时。我们建议确定结构和功能的遗传力程度 使用 200 对双胞胎（400 个人）的 fMRI/DTI 和 MRI 构建体素水平的大脑网络。为了获得高 大脑网络的分辨率遗传力图，该项目需要采集超过 25,000 个体素 fMRI和MRI/DTI的120万体素作为网络节点，这是一个严峻的计算挑战。这 该项目提出了许多新算法来构建大规模大脑网络并随后进行绘图 网络的遗传力。这项研究将为大脑成像界提供大脑基线 网络遗传力图以及用于建模和可视化的多功能开源算法工具箱 三种不同成像方式的大规模大脑网络。