Analysis of Regional Cerebral Blood Flow Data to Discriminate among Alzheimer's Disease, Frontotemporal Dementia, and Elderly Controls: A Multi-Block Barycentric Discriminant Analysis (MUBADA) Methodology

Issue title: Predictive Biomarkers for Alzheimer's Disease using State-of-the-Art Brain Imaging Techniques

Guest editors: Pravat K. Mandal

Article type: Research Article

Authors: Abdi, Hervé^{a; c; *} | Williams, Lynne J.^b | Beaton, Derek^a | Posamentier, Mette T.^a | Harris, Thomas S.^c | Krishnan, Anjali^d | Devous Sr, Michael D.^{a; c; *}

Affiliations: [a] Program in Cognition and Neuroscience, University of Texas at Dallas, Dallas, TX, USA | [b] Rotman Research Institute, Baycrest, Toronto, ON, Canada | [c] Nuclear Medicine Center, UT Southwestern Medical Center at Dallas, Dallas, TX, USA | [d] Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA

Correspondence: [*] Correspondence to:Dr. Michael D. Devous Sr, Professor of Radiology, Nuclear Medicine Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9061, USA. Tel.: +1 214 648 3315; Fax: +1 214 648 5641; E-mail: [email protected] and Dr. Hervé Abdi, Professor of Cognitive Neuroscience, Program in Cognition and Neuroscience, MS: Gr.4.1, The University of Texas at Dallas, Richardson, TX 75083-0688, USA. Tel.: +1 972 883 2065; Fax: +1 972 883 2491; E-mail: [email protected].

Abstract: 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.

Keywords: BADA, dementia, discriminant analysis, MUBADA, multiblock analysis, neuroimaging, partial least squares correlation, PET, PLS methods, SPECT

DOI: 10.3233/JAD-2012-112111

Journal: Journal of Alzheimer's Disease, vol. 31, no. s3, pp. S189-S201, 2012