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.
Project Narrative/Relevance We will develop computational tools for analyzing complex associations between images, genotype and clinical phenotype. The tools will be user-friendly and freely available, and will potentially facilitate accurate early diagnosis and prognosis of mental disorders such as Alzheimer's.
|Ge, Tian; Holmes, Avram J; Buckner, Randy L et al. (2017) Heritability analysis with repeat measurements and its application to resting-state functional connectivity. Proc Natl Acad Sci U S A 114:5521-5526|
|Sepulcre, Jorge; Sabuncu, Mert R; Li, Quanzheng et al. (2017) Tau and amyloid ? proteins distinctively associate to functional network changes in the aging brain. Alzheimers Dement 13:1261-1269|
|Nenning, Karl-Heinz; Liu, Hesheng; Ghosh, Satrajit S et al. (2017) Diffeomorphic functional brain surface alignment: Functional demons. Neuroimage 156:456-465|
|Aganj, Iman; Iglesias, Juan Eugenio; Reuter, Martin et al. (2017) Mid-space-independent deformable image registration. Neuroimage 152:158-170|
|Sabuncu, Mert R; Ge, Tian; Holmes, Avram J et al. (2016) Morphometricity as a measure of the neuroanatomical signature of a trait. Proc Natl Acad Sci U S A 113:E5749-56|
|Mormino, Elizabeth C; Sperling, Reisa A; Holmes, Avram J et al. (2016) Polygenic risk of Alzheimer disease is associated with early- and late-life processes. Neurology 87:481-8|
|Langs, Georg; Wang, Danhong; Golland, Polina et al. (2016) Identifying Shared Brain Networks in Individuals by Decoupling Functional and Anatomical Variability. Cereb Cortex 26:4004-14|
|Sepulcre, Jorge; Schultz, Aaron P; Sabuncu, Mert et al. (2016) In Vivo Tau, Amyloid, and Gray Matter Profiles in the Aging Brain. J Neurosci 36:7364-74|
|Zhang, Xiuming; Mormino, Elizabeth C; Sun, Nanbo et al. (2016) Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer's disease. Proc Natl Acad Sci U S A 113:E6535-E6544|
|Batmanghelich, Nematollah K; Dalca, Adrian; Quon, Gerald et al. (2016) Probabilistic Modeling of Imaging, Genetics and Diagnosis. IEEE Trans Med Imaging 35:1765-79|
Showing the most recent 10 out of 52 publications