  Purpose: to compare different methods to calculate Laterality Index (LI), a
metric which allows to evaluate hemispheric brain language dominance in
functional MRI examinations (fMRI).
  Methods: Two methods were considered for calculating LI: LI AVE and LI VOL ,
respectively based on the differences between measurements of average and
volume of fMRI signal in brain hemispheres. Laterality curves were obtained
calculating values of LI VOL with increasing thresholds of fMRI signal and
fitted with sigmoidal functions. A model for dominant and co-dominant
classification based on fit parameters has been developed. The two methods and
the sigmoidal model were applied to two cohorts of 93 epileptic patients and 27
healthy subjects undergoing language fMRI examinations with association,
understanding and fluency tasks.
  Results: Despite the different definitions, LI AVE and LI VOL resulted in
equivalent classification of language lateralization. The agreement of
neuroradiological clinical reports with classification of language
lateralization resulting from the proposed methods ranged from 94.6% to 89.2%
for LI metrics and up to 100% for the sigmoidal model. The fit parameters of
the sigmoidal function defined empirical thresholds useful for classification
between dominant and co-dominant, providing similar values for subjects and
epileptic patients for fluency and association tasks. This result supports the
idea of a unique model for language lateralization classification in epileptic
patients and healthy subjects.
  Conclusions: Language lateralization in fMRI can be effectively assessed by
objective metrics. A novel approach based on sigmoidal fit of laterality curves
resulted in higher agreement with clinical reports providing further
information about the strength of language lateralization.
