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[P4–503]: IMPROVED PREDICTION OF PROGRESSION TO CLINICAL STAGES OF ALZHEIMER's DISEASE USING MULTIVARIATE SURFACE MORPHOMETRY OF MRI BIOMARKERS AND PATCH‐BASED SPARSE CODING
Author(s) -
Zhang Jie,
Wang Yalin,
Li Qingyang,
Shi Jie,
Bauer Robert,
Reiman Eric M.,
Caselli Richard J.,
Chen Kewei,
Stonnington Cynthia M.
Publication year - 2017
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.2017.07.665
Subject(s) - multivariate statistics , artificial intelligence , dementia , pattern recognition (psychology) , computer science , psychology , medicine , pathology , machine learning , disease
and resting-state dynamic functional connectivity. The connectogram schematically depicts connections with a significant association, line thickness is proportional to regression t-values (p<0.05 FWE corrected). The scatterplot shows the partial regression, hence, values are mean centered after correction for sex, age, APOe4 status and clinical diagnose. Poster Presentations: Wednesday, July 19, 2017 P1532