The Informatics of Brain Mapping
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- Monday, February 24, 2014 24/02/2014 15:30:00 24/02/2014 16:30:00 6 The Informatics of Brain MappingThe complexity of neurodegenerative and psychiatric diseases often requires the collection of numerous data types from multiple modalities. These can be genetic, imaging, clinical and biosample data.University Park Campus
- 3:30 PM to 4:30 PM
- University Park Campus
- Kaprielian Hall
- (213) 740-4291
The complexity of neurodegenerative and psychiatric diseases often requires the collection of numerous data types from multiple modalities. These can be genetic, imaging, clinical and biosample data. In combination, they can provide biomarkers critical to chart the progression of the disease and to measure the efficacy of therapeutic intervention. The difficulties lie in how can these diverse data from different subjects, collected across multiple laboratories on a wide range of instruments using non-identical protocols be aggregated and mined to discover meaningful patterns. Mapping the human brain, and the brains of other species, has long been hampered by the fact that there is substantial variance in both the structure and function of this organ among individuals within a species. Previous brain atlases have relied on information from, at best, a few samples to draw conclusions. These limitations and the lack of quantification for the variance in brain structure and function have limited the pace and accuracy of research in the field of neuroscience. There are numerous probabilistic atlases that describe specific subpopulations, measure their variability and characterize the structural differences between them. Utilizing data from structural, functional, diffusion MRI, along with gwas studies and clinical measures we have built atlases with defined coordinate systems creating a framework for mapping and relating diverse data across studies. This talk describes the development and application of theoretical framework and computational tools for the construction of probabilistic atlases of large numbers of individuals in a population. These approaches are useful in understanding multidimensional data and their relationships over time. A specific and important example of mapping multimodal data is the study of Alzheimer's. The dynamic changes that occur in brain structure and function throughout life make the study of degenerative disorders of the aged difficult. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a large national consortia established to collect, longitudinally, distributed and well described cohorts of age matched normals, mci's and Alzheimer's patients. It results from the abnormal accumulation of misfolded amyloid and tau proteins in neurons and the extracellular space, ultimately leading to cell death and progressive cognitive decline. The consequences of this insult can be seen using a variety of imaging and other data analyzed from the ADNI database. Essential elements in performing this type of population based research are the informatics infrastructure to assemble, describe, disseminate and mine data collections along with computational resources necessary for large scale processing of big data such as whole genome sequence data and imaging data. This talk also describes the methods we have employed to address these challenges.
The Center for Applied Mathematical Sciences is an organized research unit based in the Department of Mathematics at USC. The purpose of CAMS is to foster research and graduate education in Mathematics in a broad sense and in an interdisciplinary mode. One goal of the center's participants is to facilitate and encourage the development of applicable mathematics and its utilization in problems in engineering and the sciences.