, Volume 16, Issue 1, pp 81–93 | Cite as

Multi-Table Differential Correlation Analysis of Neuroanatomical and Cognitive Interactions in Turner Syndrome

  • Christof SeilerEmail author
  • Tamar Green
  • David Hong
  • Lindsay Chromik
  • Lynne Huffman
  • Susan Holmes
  • Allan L. Reiss
Original Article


Girls and women with Turner syndrome (TS) have a completely or partially missing X chromosome. Extensive studies on the impact of TS on neuroanatomy and cognition have been conducted. The integration of neuroanatomical and cognitive information into one consistent analysis through multi-table methods is difficult and most standard tests are underpowered. We propose a new two-sample testing procedure that compares associations between two tables in two groups. The procedure combines multi-table methods with permutation tests. In particular, we construct cluster size test statistics that incorporate spatial dependencies. We apply our new procedure to a newly collected dataset comprising of structural brain scans and cognitive test scores from girls with TS and healthy control participants (age and sex matched). We measure neuroanatomy with Tensor-Based Morphometry (TBM) and cognitive function with Wechsler IQ and NEuroPSYchological tests (NEPSY-II). We compare our multi-table testing procedure to a single-table analysis. Our new procedure reports differential correlations between two voxel clusters and a wide range of cognitive tests whereas the single-table analysis reports no differences. Our findings are consistent with the hypothesis that girls with TS have a different brain-cognition association structure than healthy controls.


Permutation tests Multi-table analysis Sparse canonical correlation analysis Turner syndrome Tensor-based morphometry Cognitive abilities 



The Turner Syndrome Society and the Turner Syndrome Foundation made this work possible. The authors would like to sincerely thank all of the families who kindly volunteered to participate.

Christof Seiler was supported by two postdoctoral fellowships from the Swiss National Science Foundation (146281 and 158500) and a travel grant from the France-Stanford Center for Interdisciplinary Studies. Tamar Green was supported by a grant from the Gazit-Globe Post-Doctoral Fellowship Award. Allan L. Reiss is supported by grants from the NICHD (HD049653), NIMH (MH099630), and the Sharon Levine Foundation. Dr. Reiss is an unpaid medical advisor for the Turner Syndrome Society and Turner Syndrome Foundation. The funding sources mentioned above had no role in the study design; in the collection, analysis and interpretation of the data. Susan Holmes is supported by NICHD (HD049653).

We would like to thank two anonymous reviewers for their helpful input that greatly contributed to improved clarity and quality of this manuscript.

Supplementary material

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  1. Avants, B.B., Cook, P.A., Ungar, L., Gee, J.C., & Grossman, M. (2010). Dementia induces correlated reductions in white matter integrity and cortical thickness: A multivariate neuroimaging study with sparse canonical correlation analysis. NeuroImage, 50(3), 1004–1016.CrossRefPubMedPubMedCentralGoogle Scholar
  2. Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., & Gee, J.C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage, 54(3), 2033–2044.CrossRefPubMedGoogle Scholar
  3. Avants, B.B., Libon, D.J., Rascovsky, K., Boller, A., McMillan, C.T., Massimo, L., Coslett, H.B., Chatterjee, A., Gross, R.G., & Grossman, M. (2014). Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population. NeuroImage, 84, 698–711.CrossRefPubMedGoogle Scholar
  4. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, 57(1), 289–300.Google Scholar
  5. Bookstein, F.L. (1994). Partial least squares: a dose–response model for measurement in the behavioral and brain sciences. Psycoloquy, 5(23), 1.Google Scholar
  6. Bray, S., Dunkin, B., Hong, D.S., & Reiss, A.L. (2011). Reduced functional connectivity during working memory in Turner syndrome. Cerebral Cortex, 21(11), 2471–2481.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Brooks, B.L., Sherman, E.M., & Strauss, E. (2009). NEPSY-II: a developmental neuropsychological assessment, 2nd Edn. Child Neuropsychology, 16(1), 80–101.CrossRefGoogle Scholar
  8. Brown, W.E., Kesler, S.R., Eliez, S., Warsofsky, I.S., Haberecht, M., & Reiss, A.L. (2004). A volumetric study of parietal lobe subregions in Turner syndrome. Developmental Medicine & Child Neurology, 46(9), 607–609.CrossRefGoogle Scholar
  9. Chi, E., Allen, G., Zhou, H., Kohannim, O., Lange, K., & Thompson, P. (2013). Imaging genetics via sparse canonical correlation analysis. In International symposium on biomedical imaging – ISBI (pp. 740–743).Google Scholar
  10. Chung, M., Worsley, K., Paus, T., Cherif, C., Collins, D., Giedd, J., Rapoport, J., & Evans, A. (2001). A unified statistical approach to deformation-based morphometry. NeuroImage, 14(3), 595–606.CrossRefPubMedGoogle Scholar
  11. Davatzikos, C., Vaillant, M., Resnick, S.M., Prince, J.L., Letovsky, S., & Bryan, R.N. (1996). A computerized approach for morphological analysis of the corpus callosum. Journal of Computer Assisted Tomography, 20(1), 88–97.CrossRefPubMedGoogle Scholar
  12. Duda, J.T., Detre, J.A., Kim, J., Gee, J.C., & Avants, B.B. (2013). Fusing functional signals by sparse canonical correlation analysis improves network reproducibility. In Mori, K., Sakuma, I., Sato, Y., Barillot, C., & Navab, N. (Eds.) Medical image computing and computer-assisted intervention – MICCAI, vol. 8151 of lecture notes in computer science (pp. 635–642). Springer. Google Scholar
  13. Fornell, C., & Bookstein, F.L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research pp. 440–452.Google Scholar
  14. Freeborough, P.A., & Fox, N.C. (1998). Modeling brain deformations in Alzheimer disease by fluid registration of serial 3D MR images. Journal of Computer Assisted Tomography, 22(5), 838–843.CrossRefPubMedGoogle Scholar
  15. Gee, J.C., & Bajcsy, R.K. (1998). Elastic matching: Continuum mechanical and probabilistic analysis. In Toga, A.W. (Ed.) Brain warping. Academic Press.Google Scholar
  16. Gravholt, C.H. (2005). Clinical practice in Turner syndrome. Nature Reviews Endocrinology, 1(1), 41–52.CrossRefGoogle Scholar
  17. Green, T., Chromik, L.C., Mazaika, P.K., Fierro, K., Raman, M.M., Lazzeroni, L.C., Hong, D.S., & Reiss, A.L. (2014). Aberrant parietal cortex developmental trajectories in girls with Turner syndrome and related visual–spatial cognitive development: A preliminary study. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 165(6), 531–540.CrossRefGoogle Scholar
  18. Hart, S.J., Davenport, M.L., Hooper, S.R., & Belger, A. (2006). Visuospatial executive function in Turner syndrome: functional MRI and neurocognitive findings. Brain, 129(5), 1125–1136.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Hong, D., Scaletta Kent, J., & Kesler, S. (2009). Cognitive profile of Turner syndrome. Developmental Disabilities Research Reviews, 15(4), 270–278.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Hong, D.S., Hoeft, F., Marzelli, M.J., Lepage, J.-F., Roeltgen, D., Ross, J., & Reiss, A.L. (2014). Influence of the X-chromosome on neuroanatomy: evidence from turner and Klinefelter syndromes. The Journal of Neuroscience, 34(10), 3509–3516.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3/4), 321–377.CrossRefGoogle Scholar
  22. Izenman, A.J. (1975). Reduced-rank regression for the multivariate linear model. Journal of Multivariate Analysis, 5(2), 248–264.CrossRefGoogle Scholar
  23. Kesler, S.R. (2007). Turner syndrome. Child and Adolescent Psychiatric Clinics of North America, 16(3), 709–722.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Kesler, S.R., Haberecht, M.F., Menon, V., Warsofsky, I.S., Dyer-Friedman, J., Neely, E.K., & Reiss, A.L. (2004). Functional neuroanatomy of spatial orientation processing in Turner syndrome. Cerebral Cortex, 14 (2), 174–180.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Krishnan, A., Williams, L.J., McIntosh, A.R., & Abdi H. (2011). Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. NeuroImage, 56(2), 455–475.CrossRefPubMedGoogle Scholar
  26. Lê Cao, K.-A., Martin, P.G., Robert-Granié, C., & Besse, P. (2009). Sparse canonical methods for biological data integration: application to a cross-platform study. BMC Bioinformatics, 10(1), 34.CrossRefPubMedPubMedCentralGoogle Scholar
  27. Leow, A., Yanovsky, I., Chiang, M.-C., Lee, A., Klunder, A., Lu, A., Becker, J., Davis, S., Toga, A., & Thompson, P. (2007). Statistical properties of Jacobian maps and the realization of unbiased large-deformation nonlinear image registration. IEEE Transactions on Medical Imaging, 26(6), 822–832.CrossRefPubMedGoogle Scholar
  28. Lorenzi, M., Gutman, B., Hibar, D.P., Altmann, A., Jahanshad, N., Thompson, P.M., & Ourselin, S. (2016a). Partial least squares modelling for imaging-genetics in Alzheimer’s disease: Plausibility and generalization. In 13th International symposium on biomedical imaging (ISBI), IEEE (pp. 838–841).Google Scholar
  29. Lorenzi, M., Simpson, I.J., Mendelson, A.F., Vos, S.B., Cardoso, M.J., Modat, M., Schott, J.M., & Ourselin, S. (2016b). Multimodal image analysis in Alzheimer’s disease via statistical modelling of non-local intensity correlations. Scientific Reports, 6, 22161.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Marshall, W.A., & Tanner, J.M. (1969). Variations in pattern of pubertal changes in girls. Archives of Disease in Childhood, 44(235), 291.CrossRefPubMedPubMedCentralGoogle Scholar
  31. Mazzocco, M.M. (1998). A process approach to describing mathematics difficulties in girls with Turner syndrome. Pediatrics, 102(Supplement 3), 492–496.PubMedGoogle Scholar
  32. McIntosh, A.R., & Lobaugh, N.J. (2004). Partial least squares analysis of neuroimaging data: applications and advances. NeuroImage, 23, S250–S263.CrossRefPubMedGoogle Scholar
  33. McIntosh, A., Bookstein, F., Haxby, J.V., & Grady, C. (1996). Spatial pattern analysis of functional brain images using partial least squares. Neuroimage, 3(3), 143–157.CrossRefPubMedGoogle Scholar
  34. Molko, N., Cachia, A., Rivière, D., Mangin, J.-F., Bruandet, M., Le Bihan, D., Cohen, L., & Dehaene, S. (2003). Functional and structural alterations of the intraparietal sulcus in a developmental dyscalculia of genetic origin. Neuron, 40(4), 847–858.CrossRefPubMedGoogle Scholar
  35. Nichols, T.E., & Holmes, A.P. (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping, 15(1), 1–25.CrossRefPubMedGoogle Scholar
  36. Parkhomenko, E., Tritchler, D., & Beyene, J. (2007). Genome-wide sparse canonical correlation of gene expression with genotypes. In BMC proceedings (Vol. 1, p. S119).Google Scholar
  37. Parkhomenko, E., Tritchler, D., & Beyene, J. (2009). Sparse canonical correlation analysis with application to genomic data integration. Statistical Applications in Genetics and Molecular Biology, 8(1), 1–34.CrossRefGoogle Scholar
  38. Poline, J.-B., & Mazoyer, B.M. (1993). Analysis of individual positron emission tomography activation maps by detection of high signal-to-noise-ratio pixel clusters. Journal of Cerebral Blood Flow & Metabolism, 13(3), 425–437.CrossRefGoogle Scholar
  39. Roland, P., Levin, B., Kawashima, R., & Åkerman, S. (1993). Three-dimensional analysis of clustered voxels in 15O-butanol brain activation images. Human Brain Mapping, 1(1), 3–19.CrossRefGoogle Scholar
  40. Rovet, J.F. (1993). The psychoeducational characteristics of children with Turner syndrome. Journal of Learning Disabilities, 26(5), 333–341.CrossRefPubMedGoogle Scholar
  41. Smith, S.M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155.CrossRefPubMedGoogle Scholar
  42. Smith, S.M., Nichols, T.E., Vidaurre, D., Winkler, A.M., Behrens, T.E., Glasser, M.F., Ugurbil, K., Barch, D.M., Van Essen, D.C., & Miller, K.L. (2015). A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nature Neuroscience, 18(11), 1565–1567.CrossRefPubMedPubMedCentralGoogle Scholar
  43. Streissguth, A.P., Bookstein, F.L., Sampson, P.D., & Barr, H.M. (1993). The enduring effects of prenatal alcohol exposure on child development: Birth through seven years, a partial least squares solution. Ann Arbor: The University of Michigan Press.Google Scholar
  44. Sybert, V.P., & McCauley, E. (2004). Turner’s syndrome. New England Journal of Medicine, 351(12), 1227–1238.CrossRefPubMedGoogle Scholar
  45. Tucker, L.R. (1958). An inter-battery method of factor analysis. Psychometrika, 23, 111–136. ISSN 0033-3123.CrossRefGoogle Scholar
  46. Tustison, N., Avants, B., Cook, P., Zheng, Y., Egan, A., Yushkevich, P., & Gee, J. (2010). N4ITK: improved N3 bias correction. IEEE Transactions on Medical Imaging, 29(6), 1310–1320.CrossRefPubMedPubMedCentralGoogle Scholar
  47. Waaijenborg, S., Verselewel de Witt Hamer, P.C., & Zwinderman, A.H. (2008). Quantifying the association between gene expressions and DNA-markers by penalized canonical correlation analysis. Statistical Applications in Genetics and Molecular Biology 7(1).Google Scholar
  48. Wechsler, D. (2002). Wechsler preschool and primary scale of intelligence (WPPSI-III), 3rd Edn. San Antonio: The Psychological Corporation.Google Scholar
  49. Wechsler, D. (2003). Wechsler intelligence scale for children (WISC-IV), 4th Edn. San Antonio: The Psychological Corporation.Google Scholar
  50. Witten, D.M., Tibshirani, R., & Hastie, T. (2009). A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics, 10(3), 515–534.CrossRefPubMedPubMedCentralGoogle Scholar
  51. Wold, H. (1966). Estimation of principal components and related models by iterative least squares (pp. 391–420). New York: Academic Press.Google Scholar
  52. Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45–57.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Christof Seiler
    • 1
    Email author
  • Tamar Green
    • 2
    • 3
  • David Hong
    • 2
  • Lindsay Chromik
    • 2
  • Lynne Huffman
    • 4
  • Susan Holmes
    • 1
  • Allan L. Reiss
    • 2
    • 5
  1. 1.Department of StatisticsStanford UniversityStanfordUSA
  2. 2.Center for Interdisciplinary Brain Sciences ResearchStanford University School of MedicineStanfordUSA
  3. 3.Sackler Faculty of MedicineTel Aviv UniversityTel AvivIsrael
  4. 4.Department of PediatricsStanford University School of MedicineStanfordUSA
  5. 5.Departments of Radiology, Psychiatry and Behavioral SciencesStanford University School of MedicineStanfordUSA

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