Novel MRI-based Hippocampal Markers for Alzheimer’s Disease
Owen Carmichael, Ph.D.
University of California, Davis, Davis, CA, Department of Neurology
David Mahoney Neuroimaging Program
December 2008, for 3 years
New MRI Analytic Method Might Advance Diagnostic and Prognostic Abilities in Alzheimer’s
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.
Novel MRI-based Hippocampal Markers for Alzheimer's Disease
This project develops and verifies a novel neuroimaging-based biomarker for Alzheimer’s disease (AD), an insidious, progressive dementia syndrome whose prevalence is expected to grow from 2 million to 10 million cases between 2000 and 2050. The innovation of the biomarker is that it uses a novel computational method to compute quantitative measures that capture the spatial pattern of atrophy to the hippocampus (HP), which is known to occur early in the AD course and is known to be largely distinct from the spatial pattern of HP atrophy associated with healty aging.
The computational method will be optimized for automated application to a large-scale set of elderly HP, and the viability of using the HP measures to discriminate AD-associated HP atrophy from aging-associated HP atrophy will be assessed by comparing the atrophy measures between 15 mildly-impaired subjects who show evidence of AD pathology on PET scans with the Pittsburgh Compound B (PIB) ligand, which binds selectively to AD pathology; and 15 mildly-impaired subjects who show no evidence of AD pathology from a PET-PIB scan. Then, the method will be applied to the approximately 3000 HP that are automatically delineated from routine MR scans by the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and longitudinal statistical models will determine whether changes in the HP atrophy measures can be useful early predictors of future decline in cognitive function.
Thus, the project has the potential to provide clinicians, patients, and caregivers with a novel tool for predicting AD course, targeting subjects for clinical trials, and detecting treatment effects on the brain before those effects are manifested in cognitive changes. All ADNI data are already being collected by a large-scale NIH-funded study, making the current project a highly cost-effective analysis of pre-existing data.
Owen Carmichael, Ph.D.
Owen Carmichael, Ph.D., is an assistant professor in the neurology department at the University of California, Davis. His laboratory develops novel methods for processing neuroimaging data, and uses the methods to clarify the biological course of late-life cognitive decline. Currently, his efforts are focused on developing techniques for delineating and quantifying regions of interest on brain MRI, and using the methods to assess the effects of white matter disease and Alzheimer’s disease in large elderly MRI data sets including the Alzheimer’s Disease Neuroimaging Initiative and Cardiovascular Health Study Cognition Study.
Dr. Carmichael received a B.S. in electrical engineering and computer science from the University of California, Berkeley, in 1997, and a Ph.D. in robotics from Carnegie Mellon University in 2003. He then completed a two-year postdoctoral fellowship in radiology at the University of Pittsburgh and joined the UC Davis neurology department in 2003. He maintains a courtesy appointment in the UC Davis computer science department, as well as appointments in the UC Davis graduate groups of computer science, applied mathematics, and biomedical engineering. He has been the recipient of a National Science Foundation Graduate Student Fellowship, an NIH T32 Postdoctoral Fellowship, and an NIH K01 Mentored Career Development award.