An MRI-Derived definition of MCI-To-AD conversion for Long-Term, automatic prognosis of MCI patients

Yaman Aksu, David J. Miller, George Kesidis, Don C. Bigler, Qing X. Yang

Research output: Contribution to journalArticle

28 Citations (Scopus)

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.

Original languageEnglish (US)
Article numbere25074
JournalPloS one
Volume6
Issue number10
DOIs
StatePublished - Oct 12 2011

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Alzheimer disease
Magnetic resonance imaging
prognosis
Alzheimer Disease
Brain
Disease control
Classifiers
Biomarkers
brain
biomarkers
Supervised learning
Cognitive Dysfunction
Consensus
disease control
learning
Learning
Research

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Cite this

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title = "An MRI-Derived definition of MCI-To-AD conversion for Long-Term, automatic prognosis of MCI patients",
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.",
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An MRI-Derived definition of MCI-To-AD conversion for Long-Term, automatic prognosis of MCI patients. / Aksu, Yaman; Miller, David J.; Kesidis, George; Bigler, Don C.; Yang, Qing X.

In: PloS one, Vol. 6, No. 10, e25074, 12.10.2011.

Research output: Contribution to journalArticle

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