ADNI will compare neuroimaging, biological and clinical information from study participants seeking correlations among the data that will track the progress of memory loss from its earliest stages. Neuroimaging research has suggested that PET or MRI may serve as a more sensitive and consistent measure of disease progression than the neuropsychological and cognitive assessments now typically used in research. Ultimately, standardizing the methodology for neuroimaging could provide a better way to compare results from different trials and studies, a major goal of the Initiative.
What is learned from the study will be used to help researchers and clinicians develop new treatments and monitor their effectiveness, increasing safety and efficiency of drug development by decreasing the time and cost of clinical trials. The project is the most comprehensive effort to date to find neuroimaging and other biomarkers for the cognitive changes associated with MCI and AD.
Research Cores:
The Administrative Core, is directed by Dr Michael Weiner, PI of the entire ADNI application, based at UCSF. This Core is responsible for overseeing all aspects of the entire ADNI including the monitoring all financial and budetary aspects of the project.
The Infomatics Core (IC), based at the Laboratory of Neuroimaging (LONI) at UCLA is directed by Dr Arthur Toga. The IC is responsible for receiving, archiving, and displaying all MRI and PET data including all raw and processed data. All of this data will be available to ADNI investigators, and to the general public within weeks-months after receipt. The IC also describes a number of infomatics and image processing and image display tools which are used by the ADNI.
The Biostatistics Core (BC) is directed by Dr Laurel Beckett at UC Davis, and consists of statisticans at UCSD and UCSF as well. The BC implements mechanisms for data monitoring as data is acquired, to identify outliers and errrors. In addition, the BC describes a statistical approach to analyze data acquired by the ADNI, in order to test the apriori hypotheses described in the proposal, and presents a power analysis to justify the proposed sample size.
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