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Dijital Arşivi

Surface based morphometry in Alzheimer's disease

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dc.contributor Graduate Program in Biomedical Engineering.
dc.contributor.advisor Ademoğlu, Ahmet.
dc.contributor.author Uluğ, Esma Ece.
dc.date.accessioned 2023-03-16T13:13:36Z
dc.date.available 2023-03-16T13:13:36Z
dc.date.issued 2019.
dc.identifier.other BM 2019 U68
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/18936
dc.description.abstract Alzheimer’s disease( AD) is aneurodegenerativedisorderespeciallyaffectingthe elderlypopulationwhichisgrowingworldwide. Inthisstudy, surface-basedmorphometry analysis was performed on anatomical MR images of patients with Alzheimer’s Disease (AD) and healthy control (HC) subjects using a computational anatomy toolbox called CAT(Computational Anatomy Toolbox) on SPM (Statistical Parameter Mapping) platform. MR images were obtained from a database named Minimal Interval Resonance Imaging in Alzheimer’s Disease (MIRIAD) consisting of 46 AD patients and 23 HC subjects. The cortical thickness measurements were performed over 34 different regions on each hemisphere defined by Desikan-Killiany anatomical atlas. The t-statistics parameters of the cortical thickness values were found to be decreased in 24 regions in AD patients compared with the HC subjects. Additionally, the linear correlation values between the MMSE scores and cortical thickness values of AD and HC individuals were estimated for each atlas region. Accordingly, 28 regions exhibited a significant correlation between MMSE(Mini Mental State Examination) scores and cortical thickness values. Significant regions that were affected by AD were observed to be as parietal, temporal, frontal, cingulate and occipital lobes as reported in previous studies.|Keywords : Alzheimer’s Disease, Magnetic Resonans Imaging, Surface Based Morphometry, Cortical Thickness, Computational Anatomy Toolbox, Desikan-Killiany Atlas, Mini Mental State Examination.
dc.format.extent 30 cm.
dc.publisher Thesis (M.S.)-Bogazici University. Institute of Biomedical Engineering, 2019.
dc.subject.lcsh Alzheimer's disease.
dc.title Surface based morphometry in Alzheimer's disease
dc.format.pages xiii, 27 leaves ;


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