Abstract
Although much attention has recently been focused on single-subject functional networks, using methods such as resting-state functional MRI, methods for constructing single-subject structural networks are in their infancy. Single-subject cortical networks aim to describe the self-similarity across the cortical structure, possibly signifying convergent developmental pathways. Previous methods for constructing single-subject cortical networks have used patch-based correlations and distance metrics based on curvature and thickness. We present here a method for constructing similarity-based cortical structural networks that utilizes a rotation-invariant representation of structure. The resulting graph metrics are closely linked to age and indicate an increasing degree of closeness throughout development in nearly all brain regions, perhaps corresponding to a more regular structure as the brain matures. The derived graph metrics demonstrate a four-fold increase in power for detecting age as compared to cortical thickness. This proof of concept study indicates that the proposed metric may be useful in identifying biologically relevant cortical patterns.
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Alexander-Bloch, A., Giedd, J.N., Bullmore, E.: Imaging structural co-variance between human brain regions. Nature Reviews Neuroscience 14(5), 322–336 (2013)
Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54(3), 2033–2044 (2011)
Bassett, D.S., Bullmore, E., Verchinski, B.A., Mattay, V.S., Weinberger, D.R., Meyer-Lindenberg, A.: Hierarchical organization of human cortical networks in health and schizophrenia. The Journal of Neuroscience 28(37), 9239–9248 (2008)
Csardi, G., Nepusz, T.: The igraph software package for complex network research. InterJournal 2006. Complex Systems 1695, 1–9 (2006)
Dai, D., He, H., Vogelstein, J., Hou, Z.: Network-based classification using cortical thickness of AD patients. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds.) MLMI 2011. LNCS, vol. 7009, pp. 193–200. Springer, Heidelberg (2011)
Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V.: Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences 100(1), 253–258 (2003)
Greicius, M.D., Supekar, K., Menon, V., Dougherty, R.F.: Resting-state functional connectivity reflects structural connectivity in the default mode network. Cerebral Cortex 19(1), 72–78 (2009)
Jafari-Khouzani, K., Soltanian-Zadeh, H.: Radon transform orientation estimation for rotation invariant texture analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 1004–1008 (2005)
Kabsch, W.: A solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A 32(5), 922–923 (1976)
Raj, A., Mueller, S.G., Young, K., Laxer, K.D., Weiner, M.: Network-level analysis of cortical thickness of the epileptic brain. NeuroImage 52(4), 1302–1313 (2010)
Segall, J.M., Allen, E.A., Jung, R.E., Erhardt, E.B., Arja, S.K., Kiehl, K., Calhoun, V.D.: Correspondence between structure and function in the human brain at rest. Frontiers in Neuroinformatics 6, 10 (2012)
Tijms, B.M., Seris, P., Willshaw, D.J., Lawrie, S.M.: Similarity-based extraction of individual networks from gray matter MRI scans. Cerebral Cortex 22(7), 1530–1541 (2012)
Varoquaux, G., Craddock, R.C.: Learning and comparing functional connectomes across subjects. NeuroImage 80, 405–415 (2013)
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Kandel, B.M., Wang, D.J., Gee, J.C., Avants, B.B. (2014). Single-Subject Structural Networks with Closed-Form Rotation Invariant Matching Improve Power in Developmental Studies of the Cortex. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8675. Springer, Cham. https://doi.org/10.1007/978-3-319-10443-0_18
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DOI: https://doi.org/10.1007/978-3-319-10443-0_18
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