Network analysis of mild cognitive impairment.
We present a network analysis of a cross-sectional study of mild cognitive impairment (MCI). Network analysis, as opposed to univariate analysis, accounts for interactions among brain structures in explaining a clinical outcome. In this context, we analyze structural magnetic resonance (MR) data based on a Bayesian network representation of variables in the problem domain. The Bayesian network resulting from this analysis reveals complex, nonlinear multivariate associations among morphological changes in the left hippocampus and in the right thalamus and the presence of mild cognitive impairment. This Bayesian network could be used to predict the presence of mild cognitive impairment from structural MR scans.
Chen
Rong
R
Department of Radiology, University of Pennsylvania, Philadelphia 19104, USA. rong.chen@uphs.upenn.edu
Herskovits
Edward H
EH
D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000473:pathology*; D000679:Amygdala / Q000473:pathology; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping; D003072:Cognition Disorders / Q000175:diagnosis / Q000473:pathology*; D003430:Cross-Sectional Studies; D004292:Dominance, Cerebral / Q000502:physiology; D018728:Entorhinal Cortex / Q000473:pathology; D005260:Female; D006624:Hippocampus / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009415:Nerve Net / Q000473:pathology*; D009483:Neuropsychological Tests; D015203:Reproducibility of Results; D013788:Thalamus / Q000473:pathology