@article{ref1,
title="Functional volumes modeling: scaling for group size in averaged images",
journal="Human brain mapping",
year="1999",
author="Fox, P. T. and Huang, A. Y. and Parsons, L. M. and Xiong, J. H. and Rainey, L. and Lancaster, J. L.",
volume="8",
number="2-3",
pages="143-150",
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.<p /><p>Language: en</p>",
language="en",
issn="1065-9471",
doi="",
url="http://dx.doi.org/"
}