? The overall goal of this project is to develop a powerful computational framework to map disease and genetic effects on the brain. Blending neuroimaging and genetics techniques, we will go beyond the mapping and visualization of disease-specific patterns in the brain to develop a framework to map how genes affect brain structure in health and disease. In pilot projects, we will develop the mathematics and software necessary to link large-scale brain imaging and genetic studies of the brain. These algorithms will detect, map, and help understand patterns of abnormality in subjects at genetic risk for disease. They will also empower the identification and investigation of quantitative trait loci (QTLs) that confer vulnerability for disease. First, we will encode how brain structure varies in large populations. Novel algorithms will chart how the brain changes dynamically with age, gender, in childhood, and in health and disease. Specialized methods will track average, group-specific anatomical patterns in the cerebral cortex. These patterns will be stored in a computational/statistical brain atlas, and linked with cognitive, clinical and therapeutic parameters. To detect and map how genes affect brain development and disease, we will test new tools in genetically informed designs. These include large neuroimaging data components from (1) the Finnish twin registry, (2) normally developing young twins (ages 6-20) scanned longitudinally, (3) identical and fraternal twins discordant for schizophrenia, and (4) subjects with known risk genotypes (including a schizophrenia risk allele on chromosome 1q). Following up on our recent findings, our algorithms will be tested for uncovering deficits and gene effects in schizophrenia, but they will be designed to be applicable to any brain disease (Alzheimer's, autism, bipolar disorder, drug addiction alcoholism, and other neurodevelopmental/psychiatric disorders). We will mathematically combine algorithms from computational anatomy, partial differential equations, pattern theory, random field theory, and harmonic maps, to detect gene effects on the brain with maximal power. We will also map the heritability of brain structure. Novel tools for automated segmentation and labeling of brain structures will also accelerate large scale studies with these techniques. Validated on unique datasets, these tools will greatly empower biomedical studies that bridge imaging and genetics. They will help investigate the genetic transmission, triggers, and dynamics of disease in whole human populations. ? ?
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