title: The Relevance Voxel Machine (RVoxM): a Bayesian method for image-based prediction.
abstract: This paper presents the Relevance Voxel Machine (RVoxM), a Bayesian multivariate pattern analysis (MVPA) algorithm that is specifically designed for making predictions based on image data. In contrast to generic MVPA algorithms that have often been used for this purpose, the method is designed to utilize a small number of spatially clustered sets of voxels that are particularly suited for clinical interpretation. RVoxM automatically tunes all its free parameters during the training phase, and offers the additional advantage of producing probabilistic prediction outcomes. Experiments on age prediction from structural brain MRI indicate that RVoxM yields biologically meaningful models that provide excellent predictive accuracy.
authors: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, MGH, Harvard Medical School, USA.
authors: Mert R
authors: MR
authors: Sabuncu
authors: Koen
authors: K
authors: Van Leemput
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D016011:Normal Distribution; D015203:Reproducibility of Results