Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Simple group fMRI modeling and inference

Tools
- Tools
+ Tools

Mumford, Jeanette A. and Nichols, Thomas E. (2009) Simple group fMRI modeling and inference. NeuroImage, 47 (4). pp. 1469-1475. doi:10.1016/j.neuroimage.2009.05.034 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.

Official URL: http://dx.doi.org/10.1016/j.neuroimage.2009.05.034

Request Changes to record.

Abstract

While many advanced mixed-effects models have been proposed and are used in fMRI, the simplest, ordinary least squares (OLS), is still the one that is most widely used. A survey of 90 papers found that 92% of group fMRI analyses used OLS. Despite the widespread use, this simple approach has never been thoroughly justified and evaluated; for example, the typical reference for the method is a conference abstract, (Holmes, A., Friston, K., 1998. Generalisability, random effects & population inference. NeuroImage 7 (4 (2/3)), S754, proceedings of Fourth International Conference on Functional Mapping of the Human Brain, June 7-12, 1998, Montreal, Canada.), which has been referenced over 400 times. In this work we fully derive the simplified method in a general setting and carefully identify the homogeneity assumptions it is based on. We examine the specificity (Type I error rate) of the OLS method under heterogeneity in the one-sample case and find that the OLS method is valid, with only slight conservativeness. Surprisingly, a Satterthwaite approximation for effective degrees of freedom only makes the method more conservative, instead of more accurate. While other authors have highlighted the inferior power of the OLS method relative to optimal mixed-effects methods under heterogeneity, we revisit these results and find the power differences very modest. While statistical methods that make the best use of the data are always to be preferred, software or other practical concerns may require the use of the simple OLS group modeling. In such cases, we find that group mean inferences will be valid under the null hypothesis and will have nearly optimal sensitivity under the alternative.

Item Type: Journal Article
Divisions: Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Journal or Publication Title: NeuroImage
Publisher: Elsevier
ISSN: 1053-8119
Official Date: 1 October 2009
Dates:
DateEvent
1 October 2009Published
Volume: 47
Number: 4
Page Range: pp. 1469-1475
DOI: 10.1016/j.neuroimage.2009.05.034
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item
twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us