Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies

Pantelis Samartsidis, Claudia R. Eickhoff, Simon B. Eickhoff, Tor D. Wager, Lisa Feldman Barrett, Shir Atzil, Timothy D. Johnson, Thomas E. Nichols

פרסום מחקרי: פרסום בכתב עתמאמרביקורת עמיתים

תקציר

Working memory (WM) was one of the first cognitive processes studied with functional magnetic resonance imaging. With now over 20 years of studies on WM, each study with tiny sample sizes, there is a need for meta-analysis to identify the brain regions that are consistently activated by WM tasks, and to understand the interstudy variation in those activations. However, current methods in the field cannot fully account for the spatial nature of neuroimaging meta-analysis data or the heterogeneity observed among WM studies. In this work, we propose a fully Bayesian random-effects metaregression model based on log-Gaussian Cox processes, which can be used for meta-analysis of neuroimaging studies. An efficient Markov chain Monte Carlo scheme for posterior simulations is presented which makes use of some recent advances in parallel computing using graphics processing units. Application of the proposed model to a real data set provides valuable insights regarding the function of the WM.

שפה מקוריתאנגלית אמריקאית
עמודים (מ-עד)217-234
מספר עמודים18
כתב עתJournal of the Royal Statistical Society. Series C: Applied Statistics
כרך68
מספר גיליון1
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - ינו׳ 2019

ASJC Scopus subject areas

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