| 【概 要】 |
In this talk, I will briefly describe a) our "statistical region-of-interest (sROI)" technique to track fluorodeoxyglucose positron emission tomography (FDG PET) measurements of decline in the cerebral metabolic rate for glucose (CMRgl) with improved statistical power and freedom from the Type 1 error associated with multiple
regional comparisons, b) our "hypometabolic converSgence index (HCI)" to characterize in a single measurement the extent to which the magnitude and pattern of a person's regional CMRgl declines correspond to that in patients with AD, c) our "iterative principal component analysis (IPCA)" to automatically characterize the rate of whole brain shrinkage from sequential magnetic resonance images (MRIs), and d) our voxel-based "multi-modal partial least squares (MMPLS)" algorithm to haracterize the linkage between two or more complementary complex data sets from the same person. Next, I will suggest their potential roles in the detection and tracking of AD and the evaluation of AD-modifying treatments. Finally, I will summarize preliminary sample size estimates for the two presymptomatic AD treatment/surrogate marker evelopment trials in our proposed Alzheimer's Prevention Initiative (API).
The API is intended to evaluate amyloid-modifying treatments in people who, based on their age and genetic background, are at the highest imminent risk of symptomatic AD, determine the extent to which the treatments' biomarker effects predict a clinical outcome, and help provide both the means and accelerated regulatory approval pathway needed to find demonstrably effective treatments to postpone the onset, reduce the risk of, or completely prevent AD symptoms as soon as possible.
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