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P3–079: Sparse Bayesian learning for identifying the neuroanatomical basis of cognitive impairment in Alzheimer's disease
Author(s) -
Wan Jing,
Zhang Zhilin,
Fang Shiaofen,
Risacher Shan,
Saykin Andrew,
Shen Li
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.2013.05.1149
Subject(s) - alzheimer's disease neuroimaging initiative , multivariate statistics , bayesian probability , regression , voxel , neuroimaging , artificial intelligence , cognition , computer science , psychology , pattern recognition (psychology) , machine learning , cognitive impairment , statistics , mathematics , neuroscience
a neurodegenerative disease. Three main variants have been described: nonfluent/agrammatic, semantic and logopenic. Each variant is more closely related to the involvement of defined parts of the language network, usually in the left hemisphere. Diffusion tensor images allow study the different anatomical tracts invivopatients.Objective:Toevaluate the potential ofDiffusion tensor tractography in the diagnosis of variants of primary progressive aphasia. Methods: Fifteen patients with clinical diagnosis of PPA were enrolled in this study. They were evaluated by a neurologist and the diagnosis or PPA and its variants was determined by clinical and neurolinguistic features following the diagnostic process according Gorno Tempini et al (2011). We selected the most paradigmatic cases for each variant according to the criteria above. The selected cases underwent 3T MRI with diffusion tensor images (DTI) and tractography was performed to evaluate the inferior longitudinal, uncinate and superior longitudinal fasciculus. Results: The patient with the non-fluent/agrammatic variant showed differences in the superior longitudinal and uncinate fasciculus, logopenic variant showed fiber dispersion in