The most popular way of determining language dominance is to use fMRI to calculate a laterality index (LI), which quantifies the hemispheric asymmetry in task-evoked activity. Participants with a large LI can readily be classified as left or right dominant, but consensus has yet to be reached as to how “symmetrical” activity (small LI) should be treated, a decision virtually every study on language dominance has to face. The resulting heterogeneity in strategies to do so makes it challenging to compare findings between studies. This problem is perpetuated by 1) the difficulty to systematically investigate small LI's, as they usually account for less than 10% of the sample, 2) a lack of reliability data from which recommendations could be drawn and 3) uncertainty regarding the nature of small LI’s, i.e. whether they indicate language is actually organized bilaterally. I will address these issues by 1) examining the use of a spatially less detailed but cheaper alternative hemodynamic method (fTCDS) to screen for individuals with small LI's, 2) assess the test-retest reliability of small LI's, and 3) investigate the functional significance of small LI’s by relating them to hemispheric asymmetry in susceptibility to TMS-induced naming errors. In addition, I will evaluate the performance of a recently developed machine learning fMRI technique that allows a more fine-grained classification of bilateral language dominance than the conventional LI method.