title: Prognostic classification of mild cognitive impairment and Alzheimer's disease: MRI independent component analysis.
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.
authors.affiliation: Laboratory of Neurosciences, National Institute on Aging, Biomedical Research Center, 251 Bayview Boulevard, Baltimore, MD 21224, USA.
authors.forename: Auriel A
authors.initials: AA
authors.lastname: Willette
authors.affiliation: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA; The Mind Research Network, Albuquerque, NM 87131, USA.
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
authors.affiliation: Laboratory of Clinical Investigation, National Institute on Aging, 3001 S. Hanover Street, Baltimore, MD 21225, USA.
authors.forename: Josephine M
authors.initials: JM
authors.lastname: Egan
authors.affiliation: Laboratory of Neurosciences, National Institute on Aging, Biomedical Research Center, 251 Bayview Boulevard, Baltimore, MD 21224, USA. Electronic address: kapogiannisd@mail.nih.gov.
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Kapogiannis
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000134:cerebrospinal fluid / Q000175:diagnosis* / Q000209:etiology / Q000473:pathology; D001057:Apolipoproteins E / Q000134:cerebrospinal fluid; D015415:Biomarkers / Q000134:cerebrospinal fluid; D003071:Cognition; D003072:Cognition Disorders / Q000134:cerebrospinal fluid / Q000175:diagnosis* / Q000473:pathology; D060825:Cognitive Dysfunction / Q000134:cerebrospinal fluid / Q000150:complications / Q000175:diagnosis* / Q000473:pathology; D018450:Disease Progression; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D010446:Peptide Fragments / Q000134:cerebrospinal fluid; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016875:tau Proteins / Q000134:cerebrospinal fluid
chemical_list: D001057:Apolipoproteins E; D015415:Biomarkers; D010446:Peptide Fragments; D016875:tau Proteins
keywords: AD; Alzheimer׳s disease neuroimaging initiative; Data reduction; MCI