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"Some new spatial point process models for functional neuroimaging meta analysis"
 

Summary: "Some new spatial point process models for
functional neuroimaging meta analysis"
Jian Kang
Emory University
The University of Georgia
Department of Statistics
Colloquium Series
In this talk, we focus on some new spatial point process models with their applications
to meta analysis of functional neuroimaging data. We propose a Bayesian spatial hier-
archical model using a marked independent cluster process for functional neuroimag-
ing meta analysis. In contrast to the current approaches, our hierarchical model ac-
counts for intra-study variation in location (if any), inter-study variation, and idiosyn-
cratic foci that do not cluster between studies. A defining feature of our model is its
ability to dissociate inter-study spread of foci from the spatial uncertainty in population
centers. Our model is illustrated on a meta analysis consisting of 437 studies from 164
publications.
Another interesting topic is "reverse inference" on psychological states given function-
al neuroimaging meta analysis data. Given type labels that classify each study, we con-
struct a Bayesian spatial point process classifier based on the posterior predictive prob-
ability of class membership. We measure performance via leave-one-out cross valida-

  

Source: Arnold, Jonathan - Nanoscale Science and Engineering Center & Department of Genetics, University of Georgia

 

Collections: Biotechnology