nach oben

Neuroinformatics

Erschienen in:

21.12.2017 | Original Article

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

Erschienen in: Neuroinformatics | Ausgabe 1/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

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.

Vorheriger Artikel larvalign: Aligning Gene Expression Patterns from the Larval Brain of Drosophila melanogaster

Nächster Artikel NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

https://github.com/ChristofSeiler/braincog

https://github.com/ChristofSeiler/braincog_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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMed

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.

Bookstein, F.L. (1994). Partial least squares: a dose–response model for measurement in the behavioral and brain sciences. Psycoloquy, 5(23), 1.

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.CrossRefPubMedPubMedCentral

Brooks, B.L., Sherman, E.M., & Strauss, E. (2009). NEPSY-II: a developmental neuropsychological assessment, 2nd Edn. Child Neuropsychology, 16(1), 80–101.CrossRef

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.CrossRef

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).

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.CrossRefPubMed

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.CrossRefPubMed

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.

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.

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.CrossRefPubMed

Gee, J.C., & Bajcsy, R.K. (1998). Elastic matching: Continuum mechanical and probabilistic analysis. In Toga, A.W. (Ed.) Brain warping. Academic Press.

Gravholt, C.H. (2005). Clinical practice in Turner syndrome. Nature Reviews Endocrinology, 1(1), 41–52.CrossRef

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.CrossRef

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.CrossRefPubMedPubMedCentral

Hong, D., Scaletta Kent, J., & Kesler, S. (2009). Cognitive profile of Turner syndrome. Developmental Disabilities Research Reviews, 15(4), 270–278.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3/4), 321–377.CrossRef

Izenman, A.J. (1975). Reduced-rank regression for the multivariate linear model. Journal of Multivariate Analysis, 5(2), 248–264.CrossRef

Kesler, S.R. (2007). Turner syndrome. Child and Adolescent Psychiatric Clinics of North America, 16(3), 709–722.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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).

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.CrossRefPubMedPubMedCentral

Marshall, W.A., & Tanner, J.M. (1969). Variations in pattern of pubertal changes in girls. Archives of Disease in Childhood, 44(235), 291.CrossRefPubMedPubMedCentral

Mazzocco, M.M. (1998). A process approach to describing mathematics difficulties in girls with Turner syndrome. Pediatrics, 102(Supplement 3), 492–496.PubMed

McIntosh, A.R., & Lobaugh, N.J. (2004). Partial least squares analysis of neuroimaging data: applications and advances. NeuroImage, 23, S250–S263.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

Nichols, T.E., & Holmes, A.P. (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping, 15(1), 1–25.CrossRefPubMed

Parkhomenko, E., Tritchler, D., & Beyene, J. (2007). Genome-wide sparse canonical correlation of gene expression with genotypes. In BMC proceedings (Vol. 1, p. S119).

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.CrossRef

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.CrossRef

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.CrossRef

Rovet, J.F. (1993). The psychoeducational characteristics of children with Turner syndrome. Journal of Learning Disabilities, 26(5), 333–341.CrossRefPubMed

Smith, S.M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.

Sybert, V.P., & McCauley, E. (2004). Turner’s syndrome. New England Journal of Medicine, 351(12), 1227–1238.CrossRefPubMed

Tucker, L.R. (1958). An inter-battery method of factor analysis. Psychometrika, 23, 111–136. ISSN 0033-3123.CrossRef

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.CrossRefPubMedPubMedCentral

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).

Wechsler, D. (2002). Wechsler preschool and primary scale of intelligence (WPPSI-III), 3rd Edn. San Antonio: The Psychological Corporation.

Wechsler, D. (2003). Wechsler intelligence scale for children (WISC-IV), 4th Edn. San Antonio: The Psychological Corporation.

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.CrossRefPubMedPubMedCentral

Wold, H. (1966). Estimation of principal components and related models by iterative least squares (pp. 391–420). New York: Academic Press.

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.CrossRefPubMed

Titel: Multi-Table Differential Correlation Analysis of Neuroanatomical and Cognitive Interactions in Turner Syndrome
Publikationsdatum: 21.12.2017
Erschienen in: Neuroinformatics / Ausgabe 1/2018
Print ISSN: 1539-2791
Elektronische ISSN: 1559-0089
DOI: https://doi.org/10.1007/s12021-017-9351-z

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1/2018

larvalign: Aligning Gene Expression Patterns from the Larval Brain of Drosophila melanogaster

NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis

Deep Learning and Computational Neuroscience

Soma Detection in 3D Images of Neurons using Machine Learning Technique

CHIPS: an Extensible Toolbox for Cellular and Hemodynamic Two-Photon Image Analysis

Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models