Prediction of Alzheimer's disease pathophysiology based on cortical thickness patterns
- Title
- Prediction of Alzheimer's disease pathophysiology based on cortical thickness patterns
- Author
- 이종민
- Keywords
- Alzheimer's disease; Alzheimer's Disease Neuroimaging Initiative; Cortical thickness; Magnetic resonance imaging; Positron emission tomography
- Issue Date
- 2015-12
- Publisher
- Elsevier
- Citation
- Alzheimer's and Dementia, v. 2, Page. 58-67
- Abstract
- Introduction: Recent studies have shown that pathologically defined subtypes of Alzheimer's disease (AD) represent distinctive atrophy patterns and clinical characteristics. We investigated whether a cortical thickness-based clustering method can reflect such findings. Methods: A total of 77 AD subjects from the Alzheimer's Disease Neuroimaging Initiative 2 data set who underwent 3-T magnetic resonance imaging, [18F]-fluorodeoxyglucose-positron emission tomography (PET), [18F]-Florbetapir PET, and cerebrospinal fluid (CSF) tests were enrolled. After clustering based on cortical thickness, diverse imaging and biofluid biomarkers were compared between these groups. Results: Three cortical thinning patterns were noted: medial temporal (MT; 19.5%), diffuse (55.8%), and parietal dominant (P; 24.7%) atrophy subtypes. The P subtype was the youngest and represented more glucose hypometabolism in the parietal and occipital cortices and marked amyloid-beta accumulation in most brain regions. The MT subtype revealed more glucose hypometabolism in the left hippocampus and bilateral frontal cortices and less performance in memory tests. CSF test results did not differ between the groups. Discussion: Cortical thickness patterns can reflect pathophysiological and clinical changes in AD. © 2016 The Authors.
- URI
- http://www.sciencedirect.com/science/article/pii/S2352872915000937?via%3Dihubhttp://hdl.handle.net/20.500.11754/30184
- ISSN
- 2352-8729
- DOI
- 10.1016/j.dadm.2015.11.008
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRICAL AND BIOMEDICAL ENGINEERING(전기·생체공학부) > Articles
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