Functional volumes modeling: scaling for group size in averaged images

Hum Brain Mapp. 1999;8(2-3):143-50. doi: 10.1002/(sici)1097-0193(1999)8:2/3<143::aid-hbm12>;2-9.


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.

Publication types

  • Meta-Analysis
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Brain Mapping*
  • Humans
  • Magnetic Resonance Imaging
  • Models, Neurological*
  • Motor Cortex / anatomy & histology
  • Motor Cortex / diagnostic imaging
  • Motor Cortex / physiology
  • Predictive Value of Tests
  • Psychomotor Performance / physiology
  • Reproducibility of Results
  • Tomography, Emission-Computed