Investigators will test a new computational method for analyzing MRI images of sub-regions of the brain’s hippocampus in Alzheimer’s disease, to determine whether this method can detect signs of the disease prior to cognitive symptoms and whether it also can serve as tool for predicting the likely rate of cognitive decline.
Cells in the brain’s hippocampus, a region vitally involved in memory and learning, progressively deteriorate and die in Alzheimer’s disease. While MRI imaging can detect atrophy (shrinkage) of the hippocampus that occurs when substantial numbers of cells die, some atrophy also occurs in normal aging that does not progress to Alzheimer’s disease. So, MRI imaging is insufficient for definitively diagnosing Alzheimer’s. While some PET imaging methods show promise in diagnosing the disease based on detecting accumulation of the protein “amyloid”—a hallmark of Alzheimer’s—in the hippocampus, current diagnosis is based on assessing progressive short-term memory and other cognitive decline. Moreover, once diagnosed, patients’ physicians and families have no reliable method for anticipating how fast cognitive functioning will decline.
Based on initial studies using a new computational MRI imaging technique to measure atrophy in different sub-regions of the hippocampus, however, the UC Davis investigators hypothesize that Alzheimer’s and mild cognitive impairment produce distinct spatial patterns of hippocampal atrophy compared to normal aging, and that this technique can provide a method for differentiating the two conditions and for predicting the rate of progression in Alzheimer’s disease.
They will now test this hypothesis and further refine the new computational MRI imaging method, called “Localized Components Analysis,” by analyzing MRI scans of 800 adults that are part of a large publicly available database called ADNI. The database contains MRI scans of the brains of Alzheimer’s patients, adults with mild cognitive impairment, and those with no cognitive symptoms. They will see whether the spatial patterns of hippocampal atrophy differ among these three groups. Then they will analyze MRI brain scans in more detail in 15 adults with mild cognitive impairment and 15 Alzheimer’s patients, to determine whether Local Components Analysis can distinguish between these two conditions. Thereafter, they will use this technique to analyze MRI scans from the ADNI database taken at baseline and at 6, 12, and 24 months and correlate the findings with participants’ cognitive tests to see if the method can predict rates of cognitive decline in Alzheimer’s.
