Basic NeuroscienceAveLI: A robust lateralization index in functional magnetic resonance imaging using unbiased threshold-free computation

The laterality index (LI) is often applied in functional magnetic resonance imaging (fMRI) studies to determine functional hemispheric lateralization. A difficulty in using conventional LI methods lies in ensuring a legitimate computing procedure with a clear rationale. Another problem with LI is dealing with outliers and noise. We propose a method called AveLI that follows a simple and unbiased computational principle using all voxel t-values within regions of interest (ROIs). This method first computes subordinate LIs (sub-LIs) using each of the task-related positive voxel t-values in the ROIs as the threshold as follows: sub-LI = (Lt − Rt)/(Lt + Rt), where Lt and Rt are the sums of the t-values at and above the threshold in the left and right ROIs, respectively. The AveLI is the average of those sub-LIs and indicates how consistently lateralized the performance of the subject is across the full range of voxel t-value thresholds. Its intrinsic weighting of higher t-value voxels in a data-driven manner helps to reduce noise effects. The resistance against outliers is demonstrated using a simulation. We applied the AveLI as well as other “non-thresholding” and “thresholding” LI methods to two language tasks using participants with right- and left-hand preferences. The AveLI showed a moderate index value among 10 examined indices. The rank orders of the participants did not vary between indices. AveLI provides an index that is not only comprehensible but also highly resistant to outliers and to noise, and it has a high reproducibility between tasks and the ability to categorize functional lateralization.

► We have developed a new laterality index for functional magnetic resonance imaging. ► This theory-based index provides a clear indication of overall asymmetry. ► Degrees of lateralization at all voxel t-value thresholds are averaged within regions. ► The computation algorithm is simple and robust against noise and outliers. ► High reproducibility and laterality categorization are shown using two language tasks.