Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging

Kwangsik Nho, Li Shen, Sungeun Kim, Shannon L. Risacher, John D. West, Tatiana Foroud, Clifford R. Jack, Michael W. Weiner, Andrew Saykin

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Mild Cognitive Impairment (MCI) is thought to be a precursor to the development of early Alzheimer's disease (AD). For early diagnosis of AD, the development of a model that is able to predict the conversion of amnestic MCI to AD is challenging. Using automatic whole-brain MRI analysis techniques and pattern classification methods, we developed a model to differentiate AD from healthy controls (HC), and then applied it to the prediction of MCI conversion to AD. Classification was performed using support vector machines (SVMs) together with a SVM-based feature selection method, which selected a set of most discriminating predictors for optimizing prediction accuracy. We obtained 90.5% cross-validation accuracy for classifying AD and HC, and 72.3% accuracy for predicting MCI conversion to AD. These analyses suggest that a classifier trained to separate HC vs. AD has substantial potential for predicting MCI conversion to AD.

Original languageEnglish (US)
Pages (from-to)542-546
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2010
StatePublished - 2010

Fingerprint

Alzheimer Disease
Magnetic Resonance Imaging
Cognitive Dysfunction
Early Diagnosis
Brain

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging. / Nho, Kwangsik; Shen, Li; Kim, Sungeun; Risacher, Shannon L.; West, John D.; Foroud, Tatiana; Jack, Clifford R.; Weiner, Michael W.; Saykin, Andrew.

In: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, Vol. 2010, 2010, p. 542-546.

Research output: Contribution to journalArticle

@article{5ff7bfe551f245e9ad4f6eab770b7208,
title = "Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging",
abstract = "Mild Cognitive Impairment (MCI) is thought to be a precursor to the development of early Alzheimer's disease (AD). For early diagnosis of AD, the development of a model that is able to predict the conversion of amnestic MCI to AD is challenging. Using automatic whole-brain MRI analysis techniques and pattern classification methods, we developed a model to differentiate AD from healthy controls (HC), and then applied it to the prediction of MCI conversion to AD. Classification was performed using support vector machines (SVMs) together with a SVM-based feature selection method, which selected a set of most discriminating predictors for optimizing prediction accuracy. We obtained 90.5{\%} cross-validation accuracy for classifying AD and HC, and 72.3{\%} accuracy for predicting MCI conversion to AD. These analyses suggest that a classifier trained to separate HC vs. AD has substantial potential for predicting MCI conversion to AD.",
author = "Kwangsik Nho and Li Shen and Sungeun Kim and Risacher, {Shannon L.} and West, {John D.} and Tatiana Foroud and Jack, {Clifford R.} and Weiner, {Michael W.} and Andrew Saykin",
year = "2010",
language = "English (US)",
volume = "2010",
pages = "542--546",
journal = "AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium",
issn = "1559-4076",
publisher = "American Medical Informatics Association",

}

TY - JOUR

T1 - Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging

AU - Nho, Kwangsik

AU - Shen, Li

AU - Kim, Sungeun

AU - Risacher, Shannon L.

AU - West, John D.

AU - Foroud, Tatiana

AU - Jack, Clifford R.

AU - Weiner, Michael W.

AU - Saykin, Andrew

PY - 2010

Y1 - 2010

N2 - Mild Cognitive Impairment (MCI) is thought to be a precursor to the development of early Alzheimer's disease (AD). For early diagnosis of AD, the development of a model that is able to predict the conversion of amnestic MCI to AD is challenging. Using automatic whole-brain MRI analysis techniques and pattern classification methods, we developed a model to differentiate AD from healthy controls (HC), and then applied it to the prediction of MCI conversion to AD. Classification was performed using support vector machines (SVMs) together with a SVM-based feature selection method, which selected a set of most discriminating predictors for optimizing prediction accuracy. We obtained 90.5% cross-validation accuracy for classifying AD and HC, and 72.3% accuracy for predicting MCI conversion to AD. These analyses suggest that a classifier trained to separate HC vs. AD has substantial potential for predicting MCI conversion to AD.

AB - Mild Cognitive Impairment (MCI) is thought to be a precursor to the development of early Alzheimer's disease (AD). For early diagnosis of AD, the development of a model that is able to predict the conversion of amnestic MCI to AD is challenging. Using automatic whole-brain MRI analysis techniques and pattern classification methods, we developed a model to differentiate AD from healthy controls (HC), and then applied it to the prediction of MCI conversion to AD. Classification was performed using support vector machines (SVMs) together with a SVM-based feature selection method, which selected a set of most discriminating predictors for optimizing prediction accuracy. We obtained 90.5% cross-validation accuracy for classifying AD and HC, and 72.3% accuracy for predicting MCI conversion to AD. These analyses suggest that a classifier trained to separate HC vs. AD has substantial potential for predicting MCI conversion to AD.

UR - http://www.scopus.com/inward/record.url?scp=84964937651&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84964937651&partnerID=8YFLogxK

M3 - Article

VL - 2010

SP - 542

EP - 546

JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

SN - 1559-4076

ER -