Interindividual Variation in Fornix Microstructure and Macrostructure Is Related to Visual Discrimination Accuracy for Scenes But Not Faces

Mark Postans, Carl J Hodgetts, Matthew E Mundy, Derek K Jones, Andrew D Lawrence, Kim S Graham

Research output: Contribution to journalArticlepeer-review


Transection of the nonhuman primate fornix has been shown to impair learning of configurations of spatial features and object-in-scene memory. Although damage to the human fornix also results in memory impairment, it is not known whether there is a preferential involvement of this white-matter tract in spatial learning, as implied by animal studies. Diffusion-weighted MR images were obtained from healthy participants who had completed versions of a task in which they made rapid same/different discriminations to two categories of highly visually similar stimuli: (1) virtual reality scene pairs; and (2) face pairs. Diffusion-MRI measures of white-matter microstructure [fractional anisotropy (FA) and mean diffusivity (MD)] and macrostructure (tissue volume fraction, f) were then extracted from the fornix of each participant, which had been reconstructed using a deterministic tractography protocol. Fornix MD and f measures correlated with scene, but not face, discrimination accuracy in both discrimination tasks. A complementary voxelwise analysis using tract-based spatial statistics suggested the crus of the fornix as a focus for this relationship. These findings extend previous reports of spatial learning impairments after fornix transection in nonhuman primates, critically highlighting the fornix as a source of interindividual variation in scene discrimination in humans.

Original languageEnglish
Pages (from-to)12121-12126
Number of pages6
JournalThe Journal of Neuroscience
Issue number36
Publication statusPublished - 3 Sept 2014


  • Brain Mapping
  • Discrimination (Psychology)
  • Face/anatomy & histology
  • Female
  • Fornix, Brain/anatomy & histology
  • Humans
  • Learning
  • Magnetic Resonance Imaging
  • Male
  • Pattern Recognition, Visual
  • Young Adult

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