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.

摘要