Modeling of activation data in the BrainMap database: detection of outliers.
We describe a system for meta-analytical modeling of activation foci from functional neuroimaging studies. Our main vehicle is a set of density models in Talairach space capturing the distribution of activation foci in sets of experiments labeled by lobar anatomy. One important use of such density models is identification of novelty, i.e., low probability database events. We rank the novelty of the outliers and investigate the cause for 21 of the most novel, finding several outliers that are entry and transcription errors or infrequent or non-conforming terminology. We briefly discuss the use of atlases for outlier detection.
Nielsen
Finn Arup
FA
Informatics and Mathematical Modelling, Building 321, Technical University of Denmark, DK-8200 Lyngby, Denmark. fn@imm.dtu.dk
Hansen
Lars Kai
LK
D001931:Brain Mapping / Q000379:methods; D016208:Databases, Factual / Q000706:statistics & numerical data*; D015201:Meta-Analysis as Topic; D008959:Models, Neurological; D011338:Probability Theory