Structural correlates of functional language dominance: a voxel-based morphometry study

J Neuroimaging. 2010 Apr;20(2):148-156. doi: 10.1111/j.1552-6569.2009.00367.x. Epub 2009 May 7.


Background and purpose: The goal of this study was to explore the structural correlates of functional language dominance by directly comparing the brain morphology of healthy subjects with left- and right-hemisphere language dominance.

Methods: Twenty participants were selected based on their language dominance from a cohort of subjects with known language lateralization. Structural differences between both groups were assessed by voxel-based morphometry, a technique that automatically identifies differences in the local gray matter volume between groups using high-resolution T1-weighted magnetic resonance images.

Results: The main findings can be summarized as follows: (1) Subjects with right-hemisphere language dominance had significantly larger gray matter volume in the right hippocampus than subjects with left-hemisphere language dominance. (2) Leftward structural asymmetries in the posterior superior temporal cortex, including the planum temporale (PT), were observed in both groups.

Conclusions: Our study does not support the still prevalent view that asymmetries of the PT are related in a direct way to functional language lateralization. The structural differences found in the hippocampus underline the importance of the medial temporal lobe in the neural language network. They are discussed in the context of recent findings attributing a critical role of the hippocampus in the development of language lateralization.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Brain / cytology*
  • Brain / physiology*
  • Dominance, Cerebral / physiology*
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted / methods
  • Imaging, Three-Dimensional / methods
  • Language*
  • Magnetic Resonance Imaging / methods*
  • Male
  • Neurons / cytology*
  • Neurons / physiology*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Statistics as Topic