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Prognostic classification of mild cognitive impairment and Alzheimer's disease: MRI independent component analysis.
Psychiatry Res. 2014 Nov 30; 224(2):81-8.PR

Abstract

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-tau(181p), and ApoE ε4 status. For CN vs. AD, 94.8% accuracy was achieved based on cognitive performance measures, ICs, and p-tau(181p). 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-tau(181p). ICA-derived MRI biomarkers achieve excellent diagnostic accuracy for MCI conversion, which is little improved by CSF biomarkers and ApoE ε4 status.

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Authors+Show Affiliations

Willette AA
Laboratory of Neurosciences, National Institute on Aging, Biomedical Research Center, 251 Bayview Boulevard, Baltimore, MD 21224, USA.
Calhoun VD
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA; The Mind Research Network, Albuquerque, NM 87131, USA.
Egan JM
Laboratory of Clinical Investigation, National Institute on Aging, 3001 S. Hanover Street, Baltimore, MD 21225, USA.
Kapogiannis D
Laboratory of Neurosciences, National Institute on Aging, Biomedical Research Center, 251 Bayview Boulevard, Baltimore, MD 21224, USA. Electronic address: kapogiannisd@mail.nih.gov.
Alzheimer׳s Disease Neuroimaging Initiative
No affiliation info available

MeSH

AgedAlzheimer DiseaseApolipoproteins EBiomarkersCognitionCognition DisordersCognitive DysfunctionDisease ProgressionFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedPeptide FragmentsPrognosisReproducibility of ResultsSensitivity and Specificitytau Proteins

Pub Type(s)

Comparative Study
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

25194437

Citation

Willette, Auriel A., et al. "Prognostic Classification of Mild Cognitive Impairment and Alzheimer's Disease: MRI Independent Component Analysis." Psychiatry Research, vol. 224, no. 2, 2014, pp. 81-8.
Willette AA, Calhoun VD, Egan JM, et al. Prognostic classification of mild cognitive impairment and Alzheimer's disease: MRI independent component analysis. Psychiatry Res. 2014;224(2):81-8.
Willette, A. A., Calhoun, V. D., Egan, J. M., & Kapogiannis, D. (2014). Prognostic classification of mild cognitive impairment and Alzheimer's disease: MRI independent component analysis. Psychiatry Research, 224(2), 81-8. https://doi.org/10.1016/j.pscychresns.2014.08.005
Willette AA, et al. Prognostic Classification of Mild Cognitive Impairment and Alzheimer's Disease: MRI Independent Component Analysis. Psychiatry Res. 2014 Nov 30;224(2):81-8. PubMed PMID: 25194437.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Prognostic classification of mild cognitive impairment and Alzheimer's disease: MRI independent component analysis. AU - Willette,Auriel A, AU - Calhoun,Vince D, AU - Egan,Josephine M, AU - Kapogiannis,Dimitrios, AU - ,, Y1 - 2014/08/17/ PY - 2014/02/11/received PY - 2014/06/06/revised PY - 2014/08/10/accepted PY - 2014/9/8/entrez PY - 2014/9/10/pubmed PY - 2014/12/19/medline KW - AD KW - Alzheimer׳s disease neuroimaging initiative KW - Data reduction KW - MCI SP - 81 EP - 8 JF - Psychiatry research JO - Psychiatry Res VL - 224 IS - 2 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-tau(181p), and ApoE ε4 status. For CN vs. AD, 94.8% accuracy was achieved based on cognitive performance measures, ICs, and p-tau(181p). 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-tau(181p). ICA-derived MRI biomarkers achieve excellent diagnostic accuracy for MCI conversion, which is little improved by CSF biomarkers and ApoE ε4 status. SN - 1872-7123 UR - https://www.unboundmedicine.com/medline/citation/25194437/Prognostic_classification_of_mild_cognitive_impairment_and_Alzheimer's_disease:_MRI_independent_component_analysis_ DB - PRIME DP - Unbound Medicine ER -