Categorical laterality indices in fMRI: a parallel with classic similarity indices
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FMRI-based laterality index (LI) is widely used to assess relative left–right differences in brain function. Here we investigated objective ways to generate categorical LI. By defining left and right hemisphere contributions as discrete random variables, it was possible to depict the probability mass function of LI. Its distribution has a shape of a symmetrical truncated exponential function. We demonstrate that LI = ± 0.2 is an objective cut-off to categorize classification of hemispheric dominance. We then searched for parallels between LI and classic similarity or association indices. A parallel between LI and Sorensen–Dice index can be established under maximal voxel-wise overlap between left and right hemispheres. To redefine LI as a proper distance metric, we suggest instead to relate LI to Jaccard–Tanimoto similarity index. Accordingly, a new LI formula can be derived: LInew = LH–RH/max(LH,RH). Using this new formula, all LInew values follow a uniform-like distribution, and optimal categorization of hemispheric dominance can be achieved at cut-off LInew = ± 1/3. Overall, this study investigated some statistical properties of LI and revealed interesting parallels with classic similarity indices in taxonomy. The theoretical distribution of LI should be taken into account when quantifying any existing bias in empirical distributions of lateralization in healthy or clinical populations.
KeywordsLateralisation Hemispheric dominance Laterality index Dice index Jaccard index Categorization cut-off Probability mass function
This work was funded by ECAE’s Research Office.
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Conflict of interest
The author declares that he has no conflict of interest.
For this study with synthetic/simulated data only, formal consent is not required.
Research involving human participants
This article does not contain any data from human participants or animals.
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