Dynamic Bayesian network modeling for longitudinal brain morphometry

Rong Chen, Susan M. Resnick, Christos Davatzikos, Edward H. Herskovits

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)2330-2338
Number of pages9
JournalNeuroImage
Volume59
Issue number3
DOIs
StatePublished - Feb 1 2012

Keywords

  • Dynamic Bayesian network
  • Longitudinal morphometry

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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