Decoding gender dimorphism of the human brain using multimodal anatomical and diffusion MRI data

NeuroImage

Volumn 70, Issue , 2013, Pages 250-257

  Feis, Delia Lisa ^a   Brodersen, Kay H ^b,c   von Cramon, D Yves ^a   Luders, Eileen ^d   Tittgemeyer, Marc ^a  

^a MAX PLANCK INSTITUTE FOR NEUROLOGICAL RESEARCH   (Germany)

^b UNIVERSITY OF ZURICH   (Switzerland)

^c ETH ZURICH   (Switzerland)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Classification; Decoding; Feature selection; Gender dimorphism; Multimodal imaging; Multivariate pattern analysis

Indexed keywords

ACCURACY; AREA UNDER THE CURVE; ARTICLE; CONTROLLED STUDY; DECISION MAKING; DIFFUSION WEIGHTED IMAGING; FEMALE; GRAY MATTER; HUMAN; HUMAN EXPERIMENT; HUMAN TISSUE; IMAGE ANALYSIS; MALE; MORPHOMETRICS; NEUROANATOMY; NEUROIMAGING; NORMAL HUMAN; PREDICTIVE VALUE; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SEX DIFFERENCE; SOCIAL COGNITION;

ADULT; BRAIN; BRAIN MAPPING; DIFFUSION MAGNETIC RESONANCE IMAGING; FEMALE; HUMANS; MALE; SEX CHARACTERISTICS; YOUNG ADULT;

EID: 84872731969     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2012.12.068     Document Type: Article

References (34)

1. Allen J.S., Damasio H., Grabowski T.J., Bruss J., Zhang W. Sexual dimorphism and asymmetries in the gray-white composition of the human cerebrum. NeuroImage 2003, 18:880-894.
2. Bendfeldt K., Klöppel S., Nichols T.E., Smieskova R., Kuster P., Traud S., Mueller-Lenke N., Naegelin Y., Kappos L., Radue E.-W., Borgwardt S.J. Multivariate pattern classification of gray matter pathology in multiple sclerosis. NeuroImage 2012, 60:400-408.
3. Brodersen K.H., Mathys C., Chumbley J.R., Daunizeau J. Bayesian mixed-effects inference on classification performance in hierarchical datasets. J. Mach. Learn. Res. 2012, 13:3133-3176.
4. Brodersen K.H., Ong C.S., Stephan K.E., Buhmann J.M. The balanced accuracy and its posterior distribution. Pattern Recognition (ICPR), 2010 20th International Conference on 2010, 3121-3124.
5. Brodersen K.H., Schofield T.M., Leff A.P., Ong C.S., Lomakina E.I., Buhmann J.M., Stephan K.E. Generative embedding for model-based classification of fMRI data. PLoS Comput. Biol. 2011, 7:e1002079.
6. Chang C.C., Lin C.J. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2:27.
7. Cuadra M.B., Cammoun L., Butz T., Cuisenaire O., Thiran J.-P. Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images. IEEE Trans. Med. Imaging 2005, 24:1548-1565.
8. Davatzikos C. Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. NeuroImage 2004, 23:17-20.
9. Davatzikos C., Fan Y., Wu X., Shen D., Resnick S.M. Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiol. Aging 2008, 29:514-523.
10. Ecker C., Rocha-Rego V., Johnston P., Mourao-Miranda J., Marquand A., Daly E.M., Brammer M.J., Murphy C., Murphy D.G., Consortium M.A. Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. NeuroImage 2010, 49:44-56.
11. Filipek P.A., Richelme C., Kennedy D.N., Caviness V.S. The young adult human brain: an MRI-based morphometric analysis. Cereb. Cortex 1994, 4:344-360.
12. Furey T.S., Cristianini N., Duffy N., Bednarski D.W., Schummer M., Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16:906-914.
13. Giedd J.N., Raznahan A., Mills K.L., Lenroot R.K. Review: magnetic resonance imaging of male/female differences in human adolescent brain anatomy. Biol. Sex Differ. 2012, 3:19.
14. Giedd J.N., Snell J.W., Lange N., Rajapakse J.C., Casey B.J., Kozuch P.L., Vaituzis A.C., Vauss Y.C., Hamburger S.D., Kaysen D., Rapoport J.L. Quantitative magnetic resonance imaging of human brain development: ages 4-18. Cereb. Cortex 1996, 6:551-560.
15. Goldstein J.M., Seidman L.J., Horton N.J., Makris N., Kennedy D.N., Caviness V.S., Faraone S.V., Tsuang M.T. Normal sexual dimorphism of the adult human brain assessed by in vivo magnetic resonance imaging. Cereb. Cortex 2001, 11:490-497.
16. Good C.D., Johnsrude I., Ashburner J., Henson R.N., Friston K.J., Frackowiak R.S. Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains. NeuroImage 2001, 14:685-700.
17. Gur R.C., Turetsky B.I., Matsui M., Yan M., Bilker W., Hughett P., Gur R.E. Sex differences in brain gray and white matter in healthy young adults: correlations with cognitive performance. J. Neurosci. 1999, 19:4065-4072.
18. Jones D.K., Cercignani M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed. 2010, 23:803-820.
19. Klöppel S., Stonnington C.M., Chu C., Draganski B., Scahill R.I., Rohrer J.D., Fox N.C., Jack C.R., Ashburner J., Frackowiak R.S.J. Automatic classification of MR scans in Alzheimer's disease. Brain 2008, 131:681-689.
20. Koutsouleris N., Davatzikos C., Bottlender R., Patschurek-Kliche K., Scheuerecker J., Decker P., Gaser C., Möller H.-J., Meisenzahl E.M. Early recognition and disease prediction in the at-risk mental states for psychosis using neurocognitive pattern classification. Schizophr. Bull. 2011, 38:1200-1215.
21. Lao Z., Shen D., Xue Z., Karacali B., Resnick S.M., Davatzikos C. Morphological classification of brains via high-dimensional shape transformations and machine learning methods. NeuroImage 2004, 21:46-57.
22. Luders E., Gaser C., Narr K.L., Toga A.W. Why sex matters: brain size independent differences in gray matter distributions between men and women. J. Neurosci. 2009, 29:14265-14270.
23. Luders E., Toga A.W. Sex differences in brain anatomy. Prog. Brain Res. 2010, 186:3-12.
24. Marzelli M.J., Hoeft F., Hong D.S., Reiss A.L. Neuroanatomical spatial patterns in Turner syndrome. NeuroImage 2011, 55:439-447.
25. Müller K.-R., Krauledat M., Dornhege G., Curio G., Blankertz B. Machine Learning Techniques for Brain-Computer Interfaces 2004.
26. Müller K.-R., Mika S., Rätsch G., Tsuda K., Schölkopf B. An introduction to kernel-based learning algorithms. IEEE Trans. Neural Networks 2001, 12:181-201.
27. Oldfield R.C. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 1971, 9:97-113.
28. Pereira F., Botvinick M. Information mapping with pattern classifiers: a comparative study. NeuroImage 2011, 56:476-496.
29. Rajapakse J.C., Giedd J.N., Rapoport J.L. Statistical approach to segmentation of single-channel cerebral MR images. IEEE Trans. Med. Imaging 1997, 16:176-186.
30. Teipel S.J., Born C., Ewers M., Bokde A.L.W., Reiser M.F., Möller H.-J., Hampel H. Multivariate deformation-based analysis of brain atrophy to predict Alzheimer's disease in mild cognitive impairment. NeuroImage 2007, 38:13-24.
31. Tohka J., Zijdenbos A., Evans A. Fast and robust parameter estimation for statistical partial volume models in brain MRI. NeuroImage 2004, 23:84-97.
32. Vapnik V.N. Statistical Learning Theory 1998, Wiley-Interscience.
33. Witelson S.F., Beresh H., Kigar D.L. Intelligence and brain size in 100 postmortem brains: sex, lateralization and age factors. Brain 2006, 129:386-398.
34. Zhang D., Wang Y., Zhou L., Yuan H., Shen D., Initiative, A.a.s.D.N. Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage 2011, 55:856-867.