Rong Chen1, Edward H Herskovits2. 1. 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. 2. 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.
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. NEW METHOD: 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. COMPARISON WITH EXISTING 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.
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. NEW METHOD: 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. COMPARISON WITH EXISTING 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: Dominic Holland; James B Brewer; Donald J Hagler; Christine Fennema-Notestine; Christine Fenema-Notestine; Anders M Dale Journal: Proc Natl Acad Sci U S A Date: 2009-12-08 Impact factor: 11.205
Authors: Serge A Mitelman; Adam M Brickman; Lina Shihabuddin; Randall Newmark; King Wai Chu; Monte S Buchsbaum Journal: Schizophr Res Date: 2004-12-08 Impact factor: 4.939
Authors: Mark Jenkinson; Christian F Beckmann; Timothy E J Behrens; Mark W Woolrich; Stephen M Smith Journal: Neuroimage Date: 2011-09-16 Impact factor: 6.556
Authors: João Ricardo Sato; Marcelo Queiroz Hoexter; Pedro Paulo de Magalhães Oliveira; Michael John Brammer; Declan Murphy; Christine Ecker Journal: J Psychiatr Res Date: 2012-12-20 Impact factor: 4.791
Authors: Brandon A Zielinski; Jeffrey S Anderson; Alyson L Froehlich; Molly B D Prigge; Jared A Nielsen; Jason R Cooperrider; Annahir N Cariello; P Thomas Fletcher; Andrew L Alexander; Nicholas Lange; Erin D Bigler; Janet E Lainhart Journal: PLoS One Date: 2012-11-21 Impact factor: 3.240
Authors: Stephanie A Savage; Ben L Zarzaur; Greg E Gaski; Tyler McCarroll; Ruben Zamora; Rami A Namas; Yoram Vodovotz; Rachael A Callcut; Timothy R Billiar; Todd O McKinley Journal: Ann Transl Med Date: 2020-12