TY - JOUR
T1 - Prognostic classification of mild cognitive impairment and Alzheimer's disease
T2 - MRI independent component analysis
AU - Alzheimer's Disease Neuroimaging Initiative
AU - Willette, Auriel A.
AU - Calhoun, Vince D.
AU - Egan, Josephine M.
AU - Kapogiannis, Dimitrios
N1 - Funding Information:
Data collection and sharing for this project was funded by the Alzheimer׳s Disease Neuroimaging Initiative (ADNI) ( National Institutes of Health Grant U01 AG024904 ) and DOD ADNI ( Department of Defense award number W81XWH-12-2-0012 ). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer׳s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging.
Publisher Copyright:
© 2014.
PY - 2014/11/30
Y1 - 2014/11/30
N2 - Identifying predictors of mild cognitive impairment (MCI) and Alzheimer's disease (AD) can lead to more accurate diagnosis and facilitate clinical trial participation. We identified 320 participants (93 cognitively normal or CN, 162 MCI, 65 AD) with baseline magnetic resonance imaging (MRI) data, cerebrospinal fluid biomarkers, and cognition data in the Alzheimer's Disease Neuroimaging Initiative database. We used independent component analysis (ICA) on structural MR images to derive 30 matter covariance patterns (ICs) across all participants. These ICs were used in iterative and stepwise discriminant classifier analyses to predict diagnostic classification at 24 months for CN vs. MCI, CN vs. AD, MCI vs. AD, and stable MCI (MCI-S) vs. MCI progression to AD (MCI-P). Models were cross-validated with a "leave-10-out" procedure. For CN vs. MCI, 84.7% accuracy was achieved based on cognitive performance measures, ICs, p-tau181p, and ApoE ε4 status. For CN vs. AD, 94.8% accuracy was achieved based on cognitive performance measures, ICs, and p-tau181p. For MCI vs. AD and MCI-S vs. MCI-P, models achieved 83.1% and 80.3% accuracy, respectively, based on cognitive performance measures, ICs, and p-tau181p. ICA-derived MRI biomarkers achieve excellent diagnostic accuracy for MCI conversion, which is little improved by CSF biomarkers and ApoE ε4 status.
AB - Identifying predictors of mild cognitive impairment (MCI) and Alzheimer's disease (AD) can lead to more accurate diagnosis and facilitate clinical trial participation. We identified 320 participants (93 cognitively normal or CN, 162 MCI, 65 AD) with baseline magnetic resonance imaging (MRI) data, cerebrospinal fluid biomarkers, and cognition data in the Alzheimer's Disease Neuroimaging Initiative database. We used independent component analysis (ICA) on structural MR images to derive 30 matter covariance patterns (ICs) across all participants. These ICs were used in iterative and stepwise discriminant classifier analyses to predict diagnostic classification at 24 months for CN vs. MCI, CN vs. AD, MCI vs. AD, and stable MCI (MCI-S) vs. MCI progression to AD (MCI-P). Models were cross-validated with a "leave-10-out" procedure. For CN vs. MCI, 84.7% accuracy was achieved based on cognitive performance measures, ICs, p-tau181p, and ApoE ε4 status. For CN vs. AD, 94.8% accuracy was achieved based on cognitive performance measures, ICs, and p-tau181p. For MCI vs. AD and MCI-S vs. MCI-P, models achieved 83.1% and 80.3% accuracy, respectively, based on cognitive performance measures, ICs, and p-tau181p. ICA-derived MRI biomarkers achieve excellent diagnostic accuracy for MCI conversion, which is little improved by CSF biomarkers and ApoE ε4 status.
KW - AD
KW - Alzheimer's disease neuroimaging initiative
KW - Data reduction
KW - MCI
UR - http://www.scopus.com/inward/record.url?scp=84907997970&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907997970&partnerID=8YFLogxK
U2 - 10.1016/j.pscychresns.2014.08.005
DO - 10.1016/j.pscychresns.2014.08.005
M3 - Article
C2 - 25194437
AN - SCOPUS:84907997970
SN - 0925-4927
VL - 224
SP - 81
EP - 88
JO - Psychiatry Research - Neuroimaging
JF - Psychiatry Research - Neuroimaging
IS - 2
ER -