Functional volumes modeling: Scaling for group size in averaged images

Functional volumes modeling (FVM) is a statistical construct for metanalyti c modeling of the locations of brain functional areas as spatial probabilit y distributions. FV models have a variety of applications, in particular, t o serve as spatially explicit predictions of the Talairach-space locations of functional activations, thereby allowing voxel-based analyses to be hypo thesis testing rather than hypothesis generating. As image averaging is oft en applied in the analysis of functional images, an important feature of FV M is that a model can be scaled to accommodate any degree of intersubject i mage averaging in the data set to which the model is applied. In this repor t, the group-size scaling properties of FVM were tested. This was done by: (1) scaling a previously constructed FV model of the mouth representation o f primary motor cortex (M1-mouth) to accommodate various degrees of averagi ng (number of subjects per image = n = 1, 2, 5, 10), and (2) comparing FVM- predicted spatial probability contours to location-distributions observed i n averaged images of varying n composed from randomly sampling a 30-subject validation data set. (C) 1999 Wiley-Liss, Inc.