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Bayesian log-Gaussian Cox process regression with applications to fMRI meta-analysis | | Pantelis Samartsidis
; Tor D. Wager
; Lisa Feldman Barrett
; Shir Atzil
; Simon B. Eickhoff
; Thomas E. Nichols
; Timothy D. Johnson
; | | Date: |
10 Jan 2017 | | Abstract: | A typical neuroimaging study will produce a 3D brain statistic image that
summarises the evidence for activation during the experiment. However, for
practical reasons those images are rarely published; instead, authors only
report the (x,y,z) locations of local maxima in the statistic image.
Neuroimaging meta-analyses use these foci from multiple studies to find areas
of consistent activation across the human brain. However, current methods in
the field do not account for study-specific characteristics. Therefore, we
propose a fully Bayesian model based on log-Gaussian Cox processes that allows
for the inclusion of study-specific variables. We present an efficient MCMC
scheme based on the Hamiltonian Monte Carlo algorithm to simulate draws from
the posterior. Computational time is significantly reduced through a parallel
implementation using a graphical processing unit card. We evaluate the method
on both simulated and real data. | | Source: | arXiv, 1701.2643 | | Services: | Forum | Review | PDF | Favorites | |
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