title: Significance of normalization on anatomical MRI measures in predicting Alzheimer's disease.
abstract: This study establishes a new approach for combining neuroimaging and neuropsychological measures for an optimal decisional space to classify subjects with Alzheimer's disease (AD). This approach relies on a multivariate feature selection method with different MRI normalization techniques. Subcortical volume, cortical thickness, and surface area measures are obtained using MRIs from 189 participants (129 normal controls and 60 AD patients). Statistically significant variables were selected for each combination model to construct a multidimensional space for classification. Different normalization approaches were explored to gauge the effect on classification performance using a support vector machine classifier. Results indicate that the Mini-mental state examination (MMSE) measure is most discriminative among single-measure models, while subcortical volume combined with MMSE is the most effective multivariate model for AD classification. The study demonstrates that subcortical volumes need not be normalized, whereas cortical thickness should be normalized either by intracranial volume or mean thickness, and surface area is a weak indicator of AD with and without normalization. On the significant brain regions, a nearly perfect symmetry is observed for subcortical volumes and cortical thickness, and a significant reduction in thickness is particularly seen in the temporal lobe, which is associated with brain deficits characterizing AD.
authors.affiliation: Department of Electrical Engineering at the Florida International University, Miami, FL 33174, USA.
authors.forename: Qi
authors.initials: Q
authors.lastname: Zhou
authors.affiliation: Department of Electrical Engineering at the Florida International University, Miami, FL 33174, USA.
authors.forename: Mohammed
authors.identifier: 0000-0002-6875-2191
authors.initials: M
authors.lastname: Goryawala
authors.affiliation: Department of Electrical Engineering at the Florida International University, Miami, FL 33174, USA.
authors.forename: Mercedes
authors.identifier: 0000-0002-0302-7300
authors.initials: M
authors.lastname: Cabrerizo
authors.affiliation: Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL 33140, USA.
authors.forename: Warren
authors.initials: W
authors.lastname: Barker
authors.affiliation: Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL 33140, USA.
authors.forename: Ranjan
authors.initials: R
authors.lastname: Duara
authors.affiliation: Department of Electrical Engineering at the Florida International University, Miami, FL 33174, USA ; Florida International University, 10555 West Flagler Street, EC 2672, Miami, FL 33174, USA.
authors.forename: Malek
authors.identifier: 0000-0001-5380-3155
authors.initials: M
authors.lastname: Adjouadi
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis*; D001921:Brain / Q000473:pathology / Q000503:physiopathology; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008962:Models, Theoretical; D059906:Neuroimaging; D009483:Neuropsychological Tests; D009929:Organ Size