Statistical Approaches to Functional Neuroimaging Data

• Type of Material: Article
• Authors: F Dubois Bowman, Emory University; Ying Guo, Emory University; Gordana Derado, Emory University
• Language: English
• Date: 2007-11
• Publisher: Elsevier: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2007 Elsevier Inc. All rights reserved.
• License: Creative Commons Attribution-NonCommerical-NoDerivs 3.0 Unported
• Final Published Version (URL): http://dx.doi.org/10.1016/j.nic.2007.09.002
• Title of Journal or Parent Work: Neuroimaging Clinics of North America
• ISSN: 1052-5149
• Volume: 17
• Issue: 4
• Start Page: 441
• End Page: viii
• Grant/Funding Information: Funded by: National Institutes of Health; Grant number: K25-MH65473 and R01 MH079251-01A1
• Keywords: functional connectivity; Linear model; spatial model; Bayesian hierarchical model; independent component analysis; prediction; clustering
• Research Categories: Biology, Neuroscience; Health Sciences, Radiology

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.