People may be interested to know that a technical report on the
implementation of group statistics in FEAT is now available:
TR03MW1 : Multi-Level Linear Modelling for FMRI Group Analysis Using
Bayesian Inference.
and is available from:
http://www.fmrib.ox.ac.uk/analysis/techrep/
abstract:
Functional magnetic resonance imaging studies often involve the
acquisition of data from multiple sessions and/or multiple subjects. A
hierarchical approach can be taken to modelling such data with a General
Linear Model at each level of the hierarchy introducing different random
effects variance components. Inferring on these models is non-trivial with
frequentist solutions being unavailable. A solution is to use a Bayesian
framework. One important ingredient in this is the choice of prior on the
variance components and top-level regression parameters. Due to the
typically small numbers of sessions or subjects in neuro-imaging the
choice of prior is critical. To alleviate this problem we introduce to
neuro-image modelling the approach of reference priors, which drives the
choice of prior such that it is non-informative in an
information-theoretic sense. We propose two inference techniques at the
top-level for multi-level hierarchies (a fast approach and a slower more
accurate approach). We also demonstrate that we can infer on the top-level
of multi-level hierarchies by inferring on the levels of the hierarchy
separately and passing summary statistics of a non-central multivariate
t-distribution between them.
Cheers, Mark.
Mark Woolrich.
Oxford University Centre for Functional MRI of the Brain (FMRIB),
John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK.
Work: +44-(0)-1865-222782, Mobile: +44-(0)-7808-727745
|