Ivo Dinov
UCLA Statistics, Neurology, LONI

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Automated Brain Tissue Assessment in the Elderly and Demented Population: Construction and Validation of a Sub-Volume Probabilistic Brain Atlas.
Michael S. Mega, Ivo D. Dinov, Paul Thompson, Mario Manese, Chris Lindshield, Jacob Moussai, Nah Tran, Kirsten Olsen, Jenaro Felix, Chris I. Zoumalan, Roger P. Woods, Arthur W. Toga, John C. Mazziotta.

Abstract

Neuroimaging in aging and dementia is now at a critical turning point. The accumulation of findings since the first functional and structural studies of dementia has produced sufficient observational data to bring the field to the threshold of a new challengethe identification of incipient Alzheimer's disease in the individual. Results from past observational studies in patients, and elderly normal subjects, enable us to test the prospective sensitivity and specificity of a few discrete regional abnormalities in correctly identifying incipient AD.

Unfortunately, no single institution can easily amass enough longitudinal population data to power the analysis of an individuals' likelihood of having incipient AD. The urgency in meeting the challenge of identifying the individual, who may not even have cognitive complaints, prior to developing dementia symptoms is now apparent given our society's changing demographics and the emergence of disease modifying treatments. A major impediment to meeting this challenge is the development of an imaging strategy that can be universally applied and possess sufficient power to identify an individual's disease risk compared to an unaffected population. Four difficulties face the development of this imaging strategy:

1) The imaging strategy must control for anatomic variability and registration errors produced when comparing datasets in a common co-ordinate system; 2) The imaging strategy must allow for regionally testable hypotheses; 3) To ensure inter-center application the imaging strategy must be automated, freely available, and not require extensive computer resources; and 4) The imaging analysis should accommodate growth in its population data. This study demonstrates a candidate imaging strategy that addresses the above difficulties.

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