TY - JOUR
T1 - Analysis of regional cerebral blood flow data to discriminate among Alzheimer's disease, frontotemporal dementia, and elderly controls
T2 - A multi-block barycentric discriminant analysis (MUBADA) methodology
AU - Abdi, Hervé
AU - Williams, Lynne J.
AU - Beaton, Derek
AU - Posamentier, Mette T.
AU - Harris, Thomas S.
AU - Krishnan, Anjali
AU - Devous, Michael D.
PY - 2012
Y1 - 2012
N2 - We present a generalization of mean-centered partial least squares correlation called multiblock barycentric discriminant analysis (MUBADA) that integrates multiple regions of interest (ROIs) to analyze functional brain images of cerebral blood flow or metabolism obtained with SPECT or PET. To illustrate MUBADA we analyzed data from 104 participants comprising Alzheimer's disease (AD) patients, frontotemporal dementia (FTD) patients, and elderly normal controls. Brain images were analyzed via 28 ROIs (59,845 voxels) selected for clinical relevance. This is a discriminant analysis (DA) question with several blocks (one per ROI) and with more variables than observations, a configuration that precludes using DA. MUBADA revealed two factors explaining 74% and 26% of the total variance: Factor 1 isolated FTD, and Factor 2 isolated AD. A random effects model correctly classified 64% (chance = 33%) of "new" participants (p < 0.0001). MUBADA identified ROIs that best discriminated groups: ROIs separating FTD were bilateral inferior, middle frontal, left inferior, and middle temporal gyri, while ROIs separating AD were bilateral thalamus, inferior parietal gyrus, inferior temporal gyrus, left precuneus, middle frontal, and middle temporal gyri. MUBADA classified participants at levels comparable to standard methods (i.e., SVM, PCA-LDA, and PLS-DA) but provided information (e.g., discriminative ROIs and voxels) not easily accessible to these methods.
AB - We present a generalization of mean-centered partial least squares correlation called multiblock barycentric discriminant analysis (MUBADA) that integrates multiple regions of interest (ROIs) to analyze functional brain images of cerebral blood flow or metabolism obtained with SPECT or PET. To illustrate MUBADA we analyzed data from 104 participants comprising Alzheimer's disease (AD) patients, frontotemporal dementia (FTD) patients, and elderly normal controls. Brain images were analyzed via 28 ROIs (59,845 voxels) selected for clinical relevance. This is a discriminant analysis (DA) question with several blocks (one per ROI) and with more variables than observations, a configuration that precludes using DA. MUBADA revealed two factors explaining 74% and 26% of the total variance: Factor 1 isolated FTD, and Factor 2 isolated AD. A random effects model correctly classified 64% (chance = 33%) of "new" participants (p < 0.0001). MUBADA identified ROIs that best discriminated groups: ROIs separating FTD were bilateral inferior, middle frontal, left inferior, and middle temporal gyri, while ROIs separating AD were bilateral thalamus, inferior parietal gyrus, inferior temporal gyrus, left precuneus, middle frontal, and middle temporal gyri. MUBADA classified participants at levels comparable to standard methods (i.e., SVM, PCA-LDA, and PLS-DA) but provided information (e.g., discriminative ROIs and voxels) not easily accessible to these methods.
KW - BADA
KW - MUBADA
KW - PET
KW - PLS methods
KW - SPECT
KW - dementia
KW - discriminant analysis
KW - multiblock analysis
KW - neuroimaging
KW - partial least squares correlation
UR - http://www.scopus.com/inward/record.url?scp=84866885744&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866885744&partnerID=8YFLogxK
U2 - 10.3233/JAD-2012-112111
DO - 10.3233/JAD-2012-112111
M3 - Article
C2 - 22785390
AN - SCOPUS:84866885744
SN - 1387-2877
VL - 31
SP - S189-S201
JO - Journal of Alzheimer's Disease
JF - Journal of Alzheimer's Disease
IS - SUPPL. 3
ER -