Key to this and other discoveries from ADNI has been its Biostatistics Core, one of eight ADNI cores, led by Laurel Beckett, chief of the Division of Biostatistics in the UC Davis Department of Public Health Sciences. Beckett and her colleagues detail their work in “The Alzheimer’s Disease Neuroimaging Initiative phase 2: Increasing the length, breadth, and depth of our understanding,” published online today in a special issue of Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association which coincides with the Alzheimer’s Association International Conference 2015 in Washington, D.C., July 18-23.
“ADNI highlights the complexities and the richness of the data that inform our current knowledge of the progression of cognitive impairment to Alzheimer’s disease,” Beckett said.
ADNI aggregates sensitive and specific markers of very early Alzheimer’s progression, including magnetic resonance imaging (MRI), positron emission tomography (PET), and clinical and neuropsychological assessments, for impaired and control participants from age 50 to 90 at 57 sites across the United States and Canada, including UC Davis.
The Biostatistics Core has developed and applied new methods to characterize the entire spectrum of disease progression, illustrating both the classic progression of Alzheimer’s through the buildup of amyloid plaques and alternative pathways to cognitive decline.
“New imaging measures clearly show that amyloid pathology in the brain is an ominous prodromal sign for progression, whether defined as conversion to mild cognitive impairment (MCI), or deteriorating cognitive and functional measurements,” the study states. Other data show that certain other participants have “many characteristics consistent with vascular pathology, rather than amyloid-based abnormalities.”
“ADNI provides a rich dataset of imaging and fluid markers as well as clinical information for individuals across the full spectrum of cognitive abilities,” the study concludes. The work of the Biostatistics Core proves “a comprehensive picture of what can be learned by ADNI” and “supports the study of Alzheimer’s disease progression by assisting non-ADNI investigators in understanding the complexities of the ADNI data.”
Other study authors include Danielle J. Harvey and Naomi Saito of UC Davis and Michael C. Donohue and Paul Aisen of UC San Diego.
Funding for the research was provided through several grants including the Alzheimer’s Disease Neuroimaging Initiative National Institutes of Health Grant U01 AG024904 and the United States Department of Defense W81XWH-12-2-0012.