title: Dynamic Bayesian network modeling for longitudinal brain morphometry.
abstract: Identifying interactions among brain regions from structural magnetic-resonance images presents one of the major challenges in computational neuroanatomy. We propose a Bayesian data-mining approach to the detection of longitudinal morphological changes in the human brain. Our method uses a dynamic Bayesian network to represent evolving inter-regional dependencies. The major advantage of dynamic Bayesian network modeling is that it can represent complicated interactions among temporal processes. We validated our approach by analyzing a simulated atrophy study, and found that this approach requires only a small number of samples to detect the ground-truth temporal model. We further applied dynamic Bayesian network modeling to a longitudinal study of normal aging and mild cognitive impairment--the Baltimore Longitudinal Study of Aging. We found that interactions among regional volume-change rates for the mild cognitive impairment group are different from those for the normal-aging group.
authors.affiliation: Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, USA. rongchen@uphs.upenn.edu
authors.forename: Rong
authors.initials: R
authors.lastname: Chen
authors.forename: Susan M
authors.initials: SM
authors.lastname: Resnick
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
authors.forename: Edward H
authors.initials: EH
authors.lastname: Herskovits
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology; D000465:Algorithms; D001284:Atrophy; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology* / Q000254:growth & development* / Q000473:pathology; D001931:Brain Mapping; D060825:Cognitive Dysfunction / Q000473:pathology; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D018450:Disease Progression; D017352:Echo-Planar Imaging; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D018401:Sample Size; D013238:Stereotaxic Techniques