An MRI-Derived Definition of MCI-to-AD Conversion for Long-Term, Automatic Prognosis of MCI Patients
Year: 2011
Type: article
Abstract: Alzheimer's disease (AD) and mild cognitive impairment (MCI) are of great current research interest. While there is no consensus on whether MCIs actually "convert" to AD, this concept is widely applied. Thus, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline brain image, of whether an MCI will convert within a multi-year period following the initial clinical visit. This is not a traditional supervised learning problem since, in ADNI, there are no definitive labeled conversion examples. It is not unsupervised, either, since there are (labeled) ADs and Controls, as well as cognitive scores for MCIs. Prior works have defined MCI subclasses based on whether or not clinical scores significantly change from baseline. There are concerns with these definitions, however, since, e.g., most MCIs (and ADs) do not change from a baseline CDR = 0.5 at any subsequent visit in ADNI, even while physiological changes may be occurring. These works ignore rich phenotypical information in an MCI patient's brain scan and labeled AD and Control examples, in defining conversion. We propose an innovative definition, wherein an MCI is a converter if any of the patient's brain scans are classified "AD" by a Control-AD classifier. This definition bootstraps design of a second classifier, specifically trained to predict whether or not MCIs will convert. We thus predict whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate that this definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations more consistent with known AD biomarkers (including CSF markers). We also identify key prognostic brain region biomarkers.
Source: PLoS ONE
Institution: Pennsylvania State University
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,382
Funders: Foundation for the National Institutes of Health, Alzheimer's Association, Dana Foundation, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, AstraZeneca, GlaxoSmithKline, Novartis, Pennsylvania Department of Health, F. Hoffmann-La Roche, Medpace, Synarc, Alzheimer's Disease Neuroimaging Initiative, Northern California Institute for Research and Education, Elan, Eisai, National Institutes of Health, U.S. Food and Drug Administration, Genentech, Johnson and Johnson, University of California, Los Angeles, University of California, San Diego, National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering
Awards: G104586243, G1103752518, UC Davis Alzheimer's Disease Core Center, G2152643862, G2283900753, G2409989180, G2835601961, G29951154, G3131374986, G3146424788, G3182212420, G3218891203, G3553156159, G3705772492, G3710456697, G3870774570, G3966393351, "MR Morphometrics and Cognitive Decline Rate in Large-Scale Aging Studies", G43180419, G4401988282, G4660785924, G4684857399, G5207404921, G5209990037, G5366509656, G5389965057, G5520316217, Alzheimer's Disease Neuroimaging Initiative, G6069485058, G6259281481, G6412110892, G6899163935, G6948409098, G7414321945, G7799614282, G781725486, Alzheimer's Disease Cooperative Study, G8461690939, G8636843673, G9000039018, G9000074849, G9000108235, G9000122335, G920654007, G991187644
OpenAlex ID: W2156311295