Common mental disorders such as Alzheimer's disease and schizophrenia are largely heritable with complex genetic underpinnings. Large-scale genome-wide association studies that contrast DNA sequence data from patients and controls have recently identified novel genetic risk variants for these disorders. Nevertheless, the processes through which genotype increases risk are yet to be fully characterized. Neuroimaging offers a richer picture of the underlying disease processes than a clinical diagnosis. Thus the joint analysis of neuroimaging and genetics data promises to advance our understanding of these processes. Today, neuroimaging genetics studies however face important challenges that obstruct progress: small sample sizes, modest effect sizes, and the extreme dimensionality of the data limit statistical power and thus our ability to explore the complex and subtle associations between genes, neuroanatomy and clinical decline. Currently, the prevalent approach in neuroimaging genetics is to concentrate the analysis on a small number of anatomic regions of interest and/or candidate genes and often ignore a large portion of the data. The core goal of the proposed project is to develop computational tools that will take full advantage of the richness in the datasets and facilitate the exploration of the multifaceted associations between genotype, neuroimaging measurements and clinical phenotype. The proposed project will use advanced multivariate pattern analysis methods such as support vector machines to compute image-based and genetic scores that reflect pathology. We will validate the tools based on their association with classical biomarkers of disease. Finally, we will develop a model that uses both imaging and genotype data to predict future clinical outcome. We expect these tools will enable progress along three directions relevant to complex mental disorders, e.g. late-onset Alzheimer's disease (AD): (1) confirming and characterizing risk genes, (2) identifying disease-specific anatomical alterations in healthy individuals, and (3) early diagnosis and prognosis. The project will (1) use three already-collected large-scale datasets to apply the developed tools to AD, (2) build on cutting-edge image processing algorithms that we have been developing, and (3) allow the candidate to receive further training in neuroanatomy, mental disorders and genetics, forming the foundation for his future career as an independent researcher.
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