title: Predictive structural dynamic network analysis.
abstract: BACKGROUND
  Classifying individuals based on magnetic resonance data is an important task in neuroscience. Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data. We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data.
  
  
  METHODS
  Our method generates a dynamic Bayesian network model for each group which represents complex spatiotemporal interactions among brain regions, and then calculates a score representing that subject's deviation from expected network patterns. This network-derived score, along with other candidate predictors, are used to construct predictive models.
  
  
  RESULTS
  We validated the proposed method based on simulated data and the Alzheimer's Disease Neuroimaging Initiative study. For the Alzheimer's Disease Neuroimaging Initiative study, we built a predictive model based on the baseline biomarker characterizing the baseline state and the network-based score which was constructed based on the state transition probability matrix. We found that this combined model achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity.
  
  
  METHODS
  For the Alzheimer's Disease Neuroimaging Initiative study, the model based on the baseline biomarkers achieved 0.77 accuracy. The accuracy of our model is significantly better than the model based on the baseline biomarkers (p-value=0.002).
  
  
  CONCLUSIONS
  We have presented a method to classify subjects based on structural dynamic network model based scores. This method is of great importance to distinguish subjects based on structural network dynamics and the understanding of the network architecture of brain processes and disorders.
authors.affiliation: Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, School of Medicine, 100N. Greene St, 4th Floor, 22 S. Greene St., Baltimore, MD 21201, USA. Electronic address: rchen@umm.edu.
authors.forename: Rong
authors.initials: R
authors.lastname: Chen
authors.affiliation: Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, School of Medicine, 100N. Greene St, 4th Floor, 22 S. Greene St., Baltimore, MD 21201, USA.
authors.forename: Edward H
authors.initials: EH
authors.lastname: Herskovits
mesh_terms: D000544:Alzheimer Disease / Q000473:pathology; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping; D003198:Computer Simulation; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D017711:Nonlinear Dynamics
keywords: Alzheimer's disease; Brain network; Dynamic Bayesian network; Magnetic resonance imaging; Predictive modeling