Functional volumes modeling: scaling for group size in averaged images.
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.
Fox
P T
PT
Research Imaging Center, University of Texas Health Science Center, San Antonio, Texas 78284-6240, USA.
Huang
A Y
AY
Parsons
L M
LM
Xiong
J H
JH
Rainey
L
L
Lancaster
J L
JL
D001931:Brain Mapping; D006801:Humans; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D009044:Motor Cortex / Q000033:anatomy & histology / Q000000981:diagnostic imaging / Q000502:physiology; D011237:Predictive Value of Tests; D011597:Psychomotor Performance / Q000502:physiology; D015203:Reproducibility of Results; D014055:Tomography, Emission-Computed