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Alzheimer's disease (AD) and mild cognitive impairment (MCI), continue to bewidely studied. While there is no consensus on whether MCIs actually "convert"to AD, the more important question is not whether MCIs convert, but what is thebest such definition. We focus on automatic prognostication, nominally usingonly a baseline image brain scan, of whether an MCI individual will convert toAD within a multi-year period following the initial clinical visit. This is infact not a traditional supervised learning problem since, in ADNI, there are nodefinitive labeled examples of MCI conversion. Prior works have defined MCIsubclasses based on whether or not clinical/cognitive scores such as CDRsignificantly 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,even while physiological changes may be occurring. These works ignore richphenotypical information in an MCI patient's brain scan and labeled AD andControl examples, in defining conversion. We propose an innovative conversiondefinition, wherein an MCI patient is declared to be a converter if any of thepatient's brain scans (at follow-up visits) are classified "AD" by an(accurately-designed) Control-AD classifier. This novel definition bootstrapsthe design of a second classifier, specifically trained to predict whether ornot MCIs will convert. This second classifier thus predicts whether anAD-Control classifier will predict that a patient has AD. Our resultsdemonstrate this new definition leads not only to much higher prognosticaccuracy than by-CDR conversion, but also to subpopulations much moreconsistent with known AD brain region biomarkers. We also identify keyprognostic region biomarkers, essential for accurately discriminating theconverter and nonconverter groups.

An MRI-Derived Definition of MCI-to-AD Conversion for Long-Term, Automati c Prognosis of MCI Patients