Bayesian predictive modeling based on multidimensional connectivity profiling.
Dysfunction of brain structural and functional connectivity is increasingly being recognized as playing an important role in many brain disorders. Diffusion tensor imaging (DTI) and functional magnetic resonance (fMR) imaging are widely used to infer structural and functional connectivity, respectively. How to combine structural and functional connectivity patterns for predictive modeling is an important, yet open, problem. We propose a new method, called Bayesian prediction based on multidimensional connectivity profiling (BMCP), to distinguish subjects at the individual level based on structural and functional connectivity patterns. BMCP combines finite mixture modeling and Bayesian network classification. We demonstrate its use in distinguishing young and elderly adults based on DTI and resting-state fMR data.
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Baltimore, School of Medicine; Baltimore, Maryland, USA rchen@umm.edu.
Rong
R
Chen
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Baltimore, School of Medicine; Baltimore, Maryland, USA.
Edward
E
Herskovits
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology*; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D038524:Diffusion Magnetic Resonance Imaging; D056324:Diffusion Tensor Imaging; D005260:Female; D059907:Functional Neuroimaging; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D009434:Neural Pathways / Q000502:physiology; D055815:Young Adult
brain functional connectivity; brain structural connectivity; classification; magnetic resonance imaging; multimodality