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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Mentored Quantitative Research Career Development Award (K25)
Project #
5K25EB013649-03
Application #
8535152
Study Section
Special Emphasis Panel (ZEB1-OSR-D (M1))
Program Officer
Erim, Zeynep
Project Start
2011-09-01
Project End
2016-08-31
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
3
Fiscal Year
2013
Total Cost
$175,392
Indirect Cost
$12,992
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199
Aganj, Iman; Reuter, Martin; Sabuncu, Mert R et al. (2015) Avoiding symmetry-breaking spatial non-uniformity in deformable image registration via a quasi-volume-preserving constraint. Neuroimage 106:238-51
Sabuncu, Mert R; Konukoglu, Ender; Alzheimer’s Disease Neuroimaging Initiative (2015) Clinical prediction from structural brain MRI scans: a large-scale empirical study. Neuroinformatics 13:31-46
Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Aganj, Iman et al. (2015) An algorithm for optimal fusion of atlases with different labeling protocols. Neuroimage 106:451-63
Sabuncu, Mert R; Bernal-Rusiel, Jorge L; Reuter, Martin et al. (2014) Event time analysis of longitudinal neuroimage data. Neuroimage 97:9-18
Dalca, Adrian Vasile; Sridharan, Ramesh; Cloonan, Lisa et al. (2014) Segmentation of cerebrovascular pathologies in stroke patients with spatial and shape priors. Med Image Comput Comput Assist Interv 17:773-80
Biffi, Alessandro; Sabuncu, Mert R; Desikan, Rahul S et al. (2014) Genetic variation of oxidative phosphorylation genes in stroke and Alzheimer's disease. Neurobiol Aging 35:1956.e1-8
Sabuncu, Mert R (2014) A universal and efficient method to compute maps from image-based prediction models. Med Image Comput Comput Assist Interv 17:353-60
Van Leemput, Koen; Sabuncu, Mert R (2014) A cautionary analysis of STAPLE using direct inference of segmentation truth. Med Image Comput Comput Assist Interv 17:398-406
Dalca, Adrian Vasile; Sridharan, Ramesh; Cloonan, Lisa et al. (2014) Segmentation of cerebrovascular pathologies in stroke patients with spatial and shape priors. Med Image Comput Comput Assist Interv 17:773-80
Greve, Douglas N; Van der Haegen, Lise; Cai, Qing et al. (2013) A surface-based analysis of language lateralization and cortical asymmetry. J Cogn Neurosci 25:1477-92

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