Neuroimaging and Informatics in Alzheimer

Arthur W. Toga, Ph.D.

November 18(Fri) - November 18(Fri), 2022

12PM

N센터 86120호

CNIR Seminar

Title: Neuroimaging and Informatics in Alzheimer's disease Research

Abstract: 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.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. 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. 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. The consequences of this disease can be seen using a variety of imaging (including structural, diffusion and amyloid) 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.