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P2–161: The number of dilated perivascular spaces: Hallmarks of mild cognitive impairment?
Author(s) -
Yun Young Hun
Publication year - 2013
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2013.05.806
Subject(s) - neuropsychology , medicine , cognition , fluid attenuated inversion recovery , cognitive impairment , hyperintensity , audiology , working memory , white matter , cardiology , magnetic resonance imaging , psychiatry , radiology
Constrained Laplacian-Based Automated Segmentation with Proximities algorithm from structural MRI and cortical thickness was defined using the tlink method. FDG uptake for each subject was registered to their structural MRI, interpolated onto their reconstructed cortical surfaces, normalized to average uptake of cerebellum and corrected partial volume effect by gray matter probability map. Feature selection The classification features were based on area under the receiver operating characteristic curve (AUC). We evaluated AUC of both of FDG uptake and cortical thickness in all nodes of cortical surface.We selected only node that demonstrated an AUC of 0.90 or better were retained. Classification using SVMFor classification, we used support vector machine (SVM) that find the maximum margin to optimally divide the NC and AD. The retained nodes were entered into SVM. Validation Classification performance was computed using leave-one-out crossvalidation. Results: In recent studies, each feature from FDG and cortical thickness demonstrated to inform diagnosis AD and NC. Therefore, combined features on this study will provide improvement of classification accuracy compared with results of previous single classifier. Conclusions: This study proposes a classification method for AD and NC based on combination of FDG-PETand cortical thickness. Our method will offer abilities using different data modalities. We believe that the different properties of multimodal images can provide understanding of AD pathology and more diagnostic accuracy than single classifier.