Anatomical global spatial normalization

Jack L. Lancaster, Matthew D. Cykowski, David Reese McKay, Peter V. Kochunov, Peter T. Fox, William Rogers, Arthur W. Toga, Karl Zilles, Katrin Amunts, John Mazziotta

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


Anatomical global spatial normalization (aGSN) is presented as a method to scale high-resolution brain images to control for variability in brain size without altering the mean size of other brain structures. Two types of mean preserving scaling methods were investigated, "shape preserving" and "shape standardizing". aGSN was tested by examining 56 brain structures from an adult brain atlas of 40 individuals (LPBA40) before and after normalization, with detailed analyses of cerebral hemispheres, all gyri collectively, cerebellum, brainstem, and left and right caudate, putamen, and hippocampus. Mean sizes of brain structures as measured by volume, distance, and area were preserved and variance reduced for both types of scale factors. An interesting finding was that scale factors derived from each of the ten brain structures were also mean preserving. However, variance was best reduced using whole brain hemispheres as the reference structure, and this reduction was related to its high average correlation with other brain structures. The fractional reduction in variance of structure volumes was directly related to ρ 2, the square of the reference-to-structure correlation coefficient. The average reduction in variance in volumes by aGSN with whole brain hemispheres as the reference structure was approximately 32%. An analytical method was provided to directly convert between conventional and aGSN scale factors to support adaptation of aGSN to popular spatial normalization software packages.

Original languageEnglish (US)
Pages (from-to)171-182
Number of pages12
Issue number3
StatePublished - Oct 2010


  • AGSN
  • Area
  • GSN
  • Linear distance
  • Mean volume
  • Size preservation
  • Variance

ASJC Scopus subject areas

  • Software
  • Information Systems
  • General Neuroscience


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