Journal article
Functional volumes modeling: Scaling for group size in averaged images
Human brain mapping, Vol.8(2-3), pp.143-150
1999
DOI: 10.1002/(SICI)1097-0193(1999)8:2/3<143::AID-HBM12>3.0.CO;2-9
PMID: 10524606
Abstract
Functional volumes modeling (FVM) is a statistical construct for metanalytic modeling of the locations of brain functional areas as spatial probability distributions. FV models have a variety of applications, in particular, to serve as spatially explicit predictions of the Talairach-space locations of functional activations, thereby allowing voxel-based analyses to be hypothesis testing rather than hypothesis generating. As image averaging is often applied in the analysis of functional images, an important feature of FVM is that a model can be scaled to accommodate any degree of intersubject image averaging in the data set to which the model is applied. In this report, the group-size scaling properties of FVM were tested. This was done by: (1) scaling a previously constructed FV model of the mouth representation of primary motor cortex (M1-mouth) to accommodate various degrees of averaging (number of subjects per image = n = 1, 2, 5, 10), and (2) comparing FVM-predicted spatial probability contours to location-distributions observed in averaged images of varying n composed from randomly sampling a 30-subject validation data set.
Details
- Title: Subtitle
- Functional volumes modeling: Scaling for group size in averaged images
- Creators
- Peter T Fox - Research Imaging Center, University of Texas Health Science Center, San Antonio, TexasAileen Y Huang - Research Imaging Center, University of Texas Health Science Center, San Antonio, TexasLawrence M Parsons - Research Imaging Center, University of Texas Health Science Center, San Antonio, TexasJin-Hu Xiong - Research Imaging Center, University of Texas Health Science Center, San Antonio, TexasLacey Rainey - Research Imaging Center, University of Texas Health Science Center, San Antonio, TexasJack L Lancaster - Research Imaging Center, University of Texas Health Science Center, San Antonio, Texas
- Resource Type
- Journal article
- Publication Details
- Human brain mapping, Vol.8(2-3), pp.143-150
- DOI
- 10.1002/(SICI)1097-0193(1999)8:2/3<143::AID-HBM12>3.0.CO;2-9
- PMID
- 10524606
- NLM abbreviation
- Hum Brain Mapp
- ISSN
- 1065-9471
- eISSN
- 1097-0193
- Publisher
- John Wiley & Sons, Inc
- Number of pages
- 8
- Language
- English
- Date published
- 1999
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering
- Record Identifier
- 9984083882602771
Metrics
21 Record Views