Affiliations: [a] NIF – Neuroimagem Funcional, Departamento de Radiologia da Faculdade de Medicina do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Brazil | [b] Departamento de Neurologia da Faculdade de Medicina do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Brazil | [c] LIM21, Instituto de Psiquiatria da Faculdade de Medicina do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Brazil | [d] Medicina Nuclear, Departamento de Radiologia da Faculdade de Medicina do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Brazil
Correspondence:
[*]
Correspondence to: Pedro Paulo de Magalhães Oliveira Jr., Rua Dr. Enéas de Carvalho Aguiar, s/n, InRad – Instituto de Radiologia – Setor de RM, São Paulo – SP, CEP 05403-900, Brazil. Tel.: +55 11 3069 7919; E-mail: ppj@netfilter.com.br.
Abstract: Here, we examine morphological changes in cortical thickness of patients with Alzheimer's disease (AD) using image analysis algorithms for brain structure segmentation and study automatic classification of AD patients using cortical and volumetric data. Cortical thickness of AD patients (n = 14) was measured using MRI cortical surface-based analysis and compared with healthy subjects (n = 20). Data was analyzed using an automated algorithm for tissue segmentation and classification. A Support Vector Machine (SVM) was applied over the volumetric measurements of subcortical and cortical structures to separate AD patients from controls. The group analysis showed cortical thickness reduction in the superior temporal lobe, parahippocampal gyrus, and enthorhinal cortex in both hemispheres. We also found cortical thinning in the isthmus of cingulate gyrus and middle temporal gyrus at the right hemisphere, as well as a reduction of the cortical mantle in areas previously shown to be associated with AD. We also confirmed that automatic classification algorithms (SVM) could be helpful to distinguish AD patients from healthy controls. Moreover, the same areas implicated in the pathogenesis of AD were the main parameters driving the classification algorithm. While the patient sample used in this study was relatively small, we expect that using a database of regional volumes derived from MRI scans of a large number of subjects will increase the SVM power of AD patient identification.
Keywords: Alzheimer's disease, FreeSurfer, magnetic resonance imaging, support vector machine, surface based methods
