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Abstract #2451

Bayesian Hierarchical Modelling for Analyzing Neuroimaging Results

Christopher Hammill1, Benjamin C Darwin1, Darren Fernandes2, and Jason Lerch1,2

1Neuroscience and Mental Health, The Hospital For Sick Children, Toronto, ON, Canada, 2Medical Biophysics, University of Toronto, Toronto, ON, Canada

The most common analysis of structural brain MRIs involves massively univariate modelling. Such analyses separately approach different levels of resolution (whole brain, regional, and voxel) and do not provide an easy solution to understanding whether some areas of the brain are more or less affected than others. Here we explore applying hierarchical bayesian modelling to simultaneously analyze brain MRI studies at multiple levels of resolution while allowing for the explicit interrogation of whether brain areas are differentially affected. In addition, we show that hierarchical modelling provides improved parameter recapture, sign error rate, and model fit.

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