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Categorical laterality indices in fMRI: a parallel with classic similarity indices

  • Mohamed L. SeghierEmail author
Methods Paper
  • 66 Downloads

Abstract

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.

Keywords

Lateralisation Hemispheric dominance Laterality index Dice index Jaccard index Categorization cut-off Probability mass function 

Notes

Acknowledgements

This work was funded by ECAE’s Research Office.

Compliance with ethical standards

Conflict of interest

The author declares that he has no conflict of interest.

Ethical approval

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.

References

  1. Abbott DF, Waites AB, Lillywhite L, Jackson GD (2010) fMRI assessment of language lateralization: an objective approach. Neuroimage 50:1446–1455CrossRefGoogle Scholar
  2. Albatineh AN (2010) Means and variances for a family of similarity indices used in cluster analysis. J Stat Plan Inference 140:2828–2838CrossRefGoogle Scholar
  3. Albatineh AN, Khan HMR, Zogheib B, Kibria GBM (2017) Effects of some design factors on the distribution of similarity indices in cluster analysis. Commun Stat Simul Comput 46:4018–4034Google Scholar
  4. Barbosa AM (2015) fuzzySim: applying fuzzy logic to binary similarity indices in ecology. Methods Ecol Evol 6:853–858CrossRefGoogle Scholar
  5. Bauer PR, Reitsma JB, Houweling BM, Ferrier CH, Ramsey NF (2014) Can fMRI safely replace the Wada test for preoperative assessment of language lateralisation? A meta-analysis and systematic review. J Neurol Neurosurg Psychiatry 85:581–588CrossRefGoogle Scholar
  6. Binder JR, Rao SM, Hammeke TA, Frost JA, Bandettini PA, Jesmanowicz A, Hyde JS (1995) Lateralized human brain language systems demonstrated by task subtraction functional magnetic resonance imaging. Arch Neurol 52:593–601CrossRefGoogle Scholar
  7. Binder JR et al (1996) Determination of language dominance using functional MRI: a comparison with the Wada test. Neurology 46:978–984CrossRefGoogle Scholar
  8. Bishop DV (2013) Cerebral asymmetry and language development: cause, correlate, or consequence? Science 340:1230531CrossRefGoogle Scholar
  9. Bradshaw AR, Bishop DVM, Woodhead ZVJ (2017) Methodological considerations in assessment of language lateralisation with fMRI: a systematic review. PeerJ 5:e3557CrossRefGoogle Scholar
  10. Cai Q, Van der Haegen L, Brysbaert M (2013) Complementary hemispheric specialization for language production and visuospatial attention. Proc Natl Acad Sci USA 110:E322–E330CrossRefGoogle Scholar
  11. Cha S-H (2007) Comprehensive survey on distance/similarity measures between probability density functions. Int J Math Models Methods Appl Sci 1:300–307Google Scholar
  12. Chao A, Chazdon RL, Colwell RK, Shen TJ (2006) Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics 62:361–371CrossRefGoogle Scholar
  13. Cheetham AH, Hazel JE (1969) Binary (presence-absence) similarity coefficients. J Paleontol 43:1130–1136Google Scholar
  14. Chlebus P, Mikl M, Brazdil M, Pazourkova M, Krupa P, Rektor I (2007) fMRI evaluation of hemispheric language dominance using various methods of laterality index calculation. Exp Brain Res 179:365–374CrossRefGoogle Scholar
  15. Choi SS, Cha SH, Tappert C (2010) A survey of binary similarity and distance measures. J Syst Cybern Inf 8:43–48Google Scholar
  16. Corballis MC (2014) Left brain, right brain: facts and fantasies. PLoS Biol 12:e1001767CrossRefGoogle Scholar
  17. Desmond JE et al (1995) Functional MRI measurement of language lateralization in Wada-tested patients. Brain 118:1411–1419CrossRefGoogle Scholar
  18. Drane DL et al (2012) Cortical stimulation mapping and Wada results demonstrate a normal variant of right hemisphere language organization. Epilepsia 53:1790–1798CrossRefGoogle Scholar
  19. Fagard J, Chapelain A, Bonnet P (2015) How should “ambidexterity” be estimated? Laterality 20:543–570CrossRefGoogle Scholar
  20. Fligner MA, Verducci JS, Blower PE (2002) A modification of the Jaccard-Tanimoto similarity index for diverse selection of chemical compounds using binary strings. Technometrics 44:110–119CrossRefGoogle Scholar
  21. Geschwind N, Galaburda AM (1985) Cerebral lateralization: biological mechanisms, associations, and pathology: I. A hypothesis and a program for research. Arch Neurol 42:428–459CrossRefGoogle Scholar
  22. Gower JC, Legendre P (1986) Metric and Euclidean properties of dissimilarity coefficients. J Classif 3:5–48CrossRefGoogle Scholar
  23. Holliday JD, Hu CY, Willett P (2002) Grouping of coefficients for the calculation of inter-molecular similarity and dissimilarity using 2D fragment bit-strings. Comb Chem High Throughput Screen 5:155–166CrossRefGoogle Scholar
  24. Hubalek Z (1982) Coefficients of association and similarity, based on binary (presence-absence) data: an evaluation. Biol Rev 57:669–689CrossRefGoogle Scholar
  25. Hwang C-M, Yang M-S, Hung W-L (2018) New similarity measures of intuitionistic fuzzy sets based on the Jaccard index with its application to clustering. Int J Intell Syst 33:1672–1688CrossRefGoogle Scholar
  26. Ivchenko GI, Polpchuk OV, Khonov SA (1995) Concerning a class of similarity tests. Math Notes 58:1049–1056CrossRefGoogle Scholar
  27. Janecek JK, Swanson SJ, Sabsevitz DS, Hammeke TA, Raghavan ME, Rozman M, Binder JR (2013) Language lateralization by fMRI and Wada testing in 229 patients with epilepsy: rates and predictors of discordance. Epilepsia 54:314–322CrossRefGoogle Scholar
  28. Jansen A et al (2006) The assessment of hemispheric lateralization in functional MRI-robustness and reproducibility. Neuroimage 33:204–217CrossRefGoogle Scholar
  29. Johnston JW (1976) Similarity indices I: what do they measure? Battelle. Pacific Northwest Laboratories, VirginiaCrossRefGoogle Scholar
  30. Knecht S et al (2000) Handedness and hemispheric language dominance in healthy humans. Brain 123:2512–2518CrossRefGoogle Scholar
  31. Kong XZ et al (2018) Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium. Proc Natl Acad Sci USA 115:E5154–E5163CrossRefGoogle Scholar
  32. Kosub S (2019) A note on the triangle inequality for the Jaccard distance. Pattern Recog Lett 120:36–38CrossRefGoogle Scholar
  33. Lipkus AH (1999) A proof of the triangle inequality for the Tanimoto distance. J Math Chem 26:263–265CrossRefGoogle Scholar
  34. Mazoyer B et al (2014) Gaussian mixture modeling of hemispheric lateralization for language in a large sample of healthy individuals balanced for handedness. PLoS One 9:e101165CrossRefGoogle Scholar
  35. McCormick WP, Lyons NI, Hutcheson K (1992) Distributional properties of Jaccard’s index of similarity. Commun Stat 21:51–68CrossRefGoogle Scholar
  36. McHugh ML (2012) Interrater reliability: the kappa statistic. Biochem Med 22:276–282 (Zagreb)CrossRefGoogle Scholar
  37. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9:97–113CrossRefGoogle Scholar
  38. Paradowski M (2015) On the order equivalence relation of binary association measures. Int J Appl Math Comp Sci 25:645–657CrossRefGoogle Scholar
  39. Pinel P, Dehaene S (2010) Beyond hemispheric dominance: brain regions underlying the joint lateralization of language and arithmetic to the left hemisphere. J Cogn Neurosci 22:48–66CrossRefGoogle Scholar
  40. Real R (1999) Tables of significant values of Jaccard’s index of similarity. Misc Zool 22:29–40Google Scholar
  41. Real R, Vargas JM (1996) The probabilistic basis of Jaccard’s index of similarity. Syst Biol 45:380–385CrossRefGoogle Scholar
  42. Seghier ML (2008) Laterality index in functional MRI: methodological issues. Magn Res Imaging 26:594–601CrossRefGoogle Scholar
  43. Seghier ML, Kherif F, Josse G, Price CJ (2011) Regional and hemispheric determinants of language laterality: implications for preoperative fMRI. Hum Brain Mapp 32:1602–1614CrossRefGoogle Scholar
  44. Snijders TAB, Dormaar M, van Schuur WH, Dijkman-Caes C, Driessen G (1990) Distribution of some similarity coefficients for dyadic binary data in the case of associated attributes. J Classif 7:5–31CrossRefGoogle Scholar
  45. Stroobant N, Buijs D, Vingerhoets G (2009) Variation in brain lateralization during various language tasks: a functional transcranial Doppler study. Behav Brain Res 199:190–196CrossRefGoogle Scholar
  46. Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15:29CrossRefGoogle Scholar
  47. Todeschini R, Consonni V, Xiang H, Holliday J, Buscema M, Willett P (2012) Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets. J Chem Inf Model 52:2884–2901CrossRefGoogle Scholar
  48. Warrens MJ (2008) Similarity coefficients for binary data: properties of coefficients, coefficient matrices, multi-way metrics and multivariate coefficients. Leiden University, LeidenGoogle Scholar
  49. Whitehouse AJ, Bishop DV (2009) Hemispheric division of function is the result of independent probabilistic biases. Neuropsychologia 47:1938–1943CrossRefGoogle Scholar
  50. Wolda H (1981) Similarity indices, sample size and diversity. Oecologia 50:296–302CrossRefGoogle Scholar
  51. Zijdenbos AP, Dawant BM (1994) Brain segmentation and white matter lesion detection in MR images. Crit Rev Biomed Eng 22:401–465Google Scholar
  52. Zou KH et al (2004) Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 11:178–189CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Cognitive Neuroimaging UnitEmirates College for Advanced EducationAbu DhabiUnited Arab Emirates

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