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Abstract of NIH published article. Original source article: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2459257/?report=abstract

Bowman FD, Guo Y, Derado G.

Department of Biostatistics, Center for Biomedical Imaging Statistics, The Rollins School of Public Health, Emory University, 1518 Clifton Road, N.E., Atlanta, GA 30322

 

Abstract

The field of statistics makes valuable contributions to functional neuroimaging research by establishing procedures for the design and conduct of neuroimaging experiements and by providing tools for objectively quantifying and measuring the strength of scientific evidence provided by the data. Two common functional neuroimaging research objecitves include detecting brain regions that reveal task-related alterations in measured brain activity (activations) and identifying highly correlated brain regions that exhibit similar patterns of activity over time (functional connectivity). In this article, we highlight various statistical procedures for analyzing data from activation studies and from functional connectivity studies, focusing on functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) data. We also discuss emerging statistical methods for prediction using fMRI and PET data, which stand to increase the translational significance of functional neuroimaging data to clinical practice.
 
Keywords: Linear model, spatial model, functional connectivity, independent component analysis, clustering, prediction, Bayesian hierarchical model
 
 
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