The effect of fMRI task combinations on determining the hemispheric dominance of language functions

Neuroradiology. 2012 Apr;54(4):393-405. doi: 10.1007/s00234-011-0959-7. Epub 2011 Sep 20.


Introduction: The purpose of this study is to establish the most suitable combination of functional magnetic resonance imaging (fMRI) language tasks for clinical use in determining language dominance and to define the variability in laterality index (LI) and activation power between different combinations of language tasks.

Methods: Activation patterns of different fMRI analyses of five language tasks (word generation, responsive naming, letter task, sentence comprehension, and word pair) were defined for 20 healthy volunteers (16 right-handed). LIs and sums of T values were calculated for each task separately and for four combinations of tasks in predefined regions of interest. Variability in terms of activation power and lateralization was defined in each analysis. In addition, the visual assessment of lateralization of language functions based on the individual fMRI activation maps was conducted by an experienced neuroradiologist.

Results: A combination analysis of word generation, responsive naming, and sentence comprehension was the most suitable in terms of activation power, robustness to detect essential language areas, and scanning time. In general, combination analyses of the tasks provided higher overall activation levels than single tasks and reduced the number of outlier voxels disturbing the calculation of LI.

Conclusions: A combination of auditory and visually presented tasks that activate different aspects of language functions with sufficient activation power may be a useful task battery for determining language dominance in patients.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Brain Mapping / methods*
  • Female
  • Functional Laterality / physiology*
  • Humans
  • Image Interpretation, Computer-Assisted
  • Language*
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Software
  • Task Performance and Analysis