January 6-9, 2013 Asilomar, California
The ability to statistically and visually compare and contrast brain image data from multiple subjects is essential to understanding normal variability and differentiating normal from diseased populations. Most neuroimaging studies now include a diverse array of data types such as cognitive measures, biosample data and genetics. Taken together, these data present numerous challenges to creating usable databases, coupled analytics and efficient dissemination. Furthermore, these data pose considerable demands on storage, network and computational infrastructure. This talk describes the concerns that must be dealt with to maximize the value of data contained in databases. The problem is particularly acute when the ultimate goal is the synthesis of atlases, the creation of models and statistical comparisons across different cohorts. Data coming from multiple sources, subjects, protocols and devices must either be treated or described sufficiently to make it comparable. Legacy data presents unique problems as an accurate history may be unavailable. Newly acquired data affords the opportunity to create detailed metadata but must be extendable, as needed, to accommodate pre-processing and other data manipulations. The ever increasing size of emerging databases also poses challenges for automation and direct linkages between data archives and processing procedures. Examples of these and related issues will be illustrated in applications with several consortia and projects.
A good example of these and related issues is the Alzheimer’s Disease Neuroimaging Initiative (ADNI). This is a large national consortia established to collect, longitudinally, distributed and well described cohorts of age matched normals, mci's and alzheimer patients. The dynamic changes that occur in brain structure and function throughout life make the study of degenerative disorders of the aged difficult. Alzheimer’s disease (AD) is the most common neurodegenerative disease in the elderly. 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 approaches. Perhaps one of the most sought after goals in the study of this degenerative disease is an imaging biomarker that is sensitive and precise to these neuropathological changes. Further, the objective is to enable reliable detection of these changes as early in its progression as possible, hopefully prior to behavioral and cognitive manifestations.
Arthur W. Toga is a Distinguished Professor of Neurology and University Professor at the University of California at Los Angeles (UCLA). His research is focused on neuroimaging, informatics, mapping brain structure and function, and brain atlasing. He also studies cerebral metabolism and neurovascular coupling. He was trained in neuroscience and computer science and has written more than 700 papers, chapters and abstracts, including eight books. Recruited to UCLA in 1987, he formed and directs the Laboratory of Neuro Imaging. This 120-member laboratory includes graduate students from computer science, biostatistics and neuroscience. It is funded with grants from the National Institutes of Health grants as well as industry partners. He has received numerous awards and honors in computer science, graphics and neuroscience. He is Associate-Director of the UCLA Brain Mapping Division within the Neuropsychiatric Institute, Associate Dean, Geffen School of Medicine at UCLA, Associate Vice Provost for Informatics and the founding Editor-in-Chief of the journal NeuroImage and holds the chairmanship of numerous committees within UCLA, NIH and a variety of international task forces.