Salimi-Khorshidi, Gholamreza, Smith, Stephen M., Keltner, John R., Wager, Tor D. and Nichols, Thomas E. (2009) Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies. NeuroImage, Vol.45 (No.3). pp. 810-823. doi:10.1016/j.neuroimage.2008.12.039 ISSN 1053-8119.

Research output not available from this repository.

Request-a-Copy directly from author or use local Library Get it For Me service.

Request Changes to record.

Abstract

With the rapid growth of neuroimaging research and accumulation of neuroinformatic databases the synthesis of consensus findings using meta-analysis is becoming increasingly important. Meta-analyses pool data across many studies to identify reliable experimental effects and characterize the degree of agreement across studies. Coordinate-based meta-analysis (CBMA) methods are the standard approach, where each study entered into the meta-analysis has been summarized using only the (x, y, z) locations of peak activations (with or without activation magnitude) reported in published reports. Image-based meta-analysis (IBMA) methods use the full statistic images, and allow the use of hierarchical mixed effects models that account for differing intra-study variance and modeling of random inter-study variation. The purpose of this work is to compare image-based and coordinate-based meta-analysis methods applied to the same dataset, a group of 15 fMRI studies of pain, and to quantify the information lost by working only with the coordinates of peak activations instead of the full statistic images. We apply a 3-level IBMA mixed model for a "mega-analysis", and highlight important considerations in the specification of each model and contrast. We compare the IBMA result to three CBMA methods: ALE (activation likelihood estimation), KDA (kernel density analysis) and MKDA (multi-level kernel density analysis), for various CBMA smoothing parameters. For the datasets considered, we find that ALE at σ = 15 mm, KDA at ρ = 25–30 mm and MKDA at ρ = 15 mm give the greatest similarity to the IBMA result, and that ALE was the most similar for this particular dataset, though only with a Dice similarity coefficient of 0.45 (Dice measure ranges from 0 to 1). Based on this poor similarity, and the greater modeling flexibility afforded by hierarchical mixed models, we suggest that IBMA is preferred over CBMA. To make IBMA analyses practical, however, the neuroimaging field needs to develop an effective mechanism for sharing image data, including whole-brain images of both effect estimates and their standard errors.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics R Medicine > R Medicine (General) R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions:	Faculty of Science, Engineering and Medicine > Science > Statistics Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Brain -- Imaging -- Data processing, Magnetic resonance imaging -- Data processing, Tomography, Emission -- Data processing, Brain -- Imaging -- Statistical methods, Meta-analysis
Journal or Publication Title:	NeuroImage
Publisher:	Elsevier
ISSN:	1053-8119
Official Date:	15 April 2009
Dates:	Date Event 15 April 2009 Published
Volume:	Vol.45
Number:	No.3
Number of Pages:	14
Page Range:	pp. 810-823
DOI:	10.1016/j.neuroimage.2008.12.039
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Research Councils UK (RCUK), GlaxoSmithKline (GSK)

Data sourced from Thomson Reuters' Web of Knowledge

Request changes or add full text files to a record

Repository staff actions (login required)

View Item