Functional volumes modeling: Scaling for group size in averaged images

Peter T. Fox, Aileen Y. Huang, Lawrence M. Parsons, Jin Hu Xiong, Lacey Rainey, Jack L. Lancaster

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)143-150
Number of pages8
JournalHuman Brain Mapping
Issue number2-3
StatePublished - 1999


  • Brain
  • FVM
  • M1-mouth

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Anatomy


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