Multi-Table Differential Correlation Analysis of Neuroanatomical and Cognitive Interactions in Turner Syndrome
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.
KeywordsPermutation 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Wechsler, D. (2002). Wechsler preschool and primary scale of intelligence (WPPSI-III), 3rd Edn. San Antonio: The Psychological Corporation.Google Scholar
- Wechsler, D. (2003). Wechsler intelligence scale for children (WISC-IV), 4th Edn. San Antonio: The Psychological Corporation.Google Scholar
- Wold, H. (1966). Estimation of principal components and related models by iterative least squares (pp. 391–420). New York: Academic Press.Google Scholar