Advertisement

Single-Subject Structural Networks with Closed-Form Rotation Invariant Matching Improve Power in Developmental Studies of the Cortex

  • Benjamin M. Kandel
  • Danny JJ Wang
  • James C. Gee
  • Brian B. Avants
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

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.

Keywords

Cortical Thickness Default Mode Network Cortical Structure Pediatric Data Graph Metrics 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Alexander-Bloch, A., Giedd, J.N., Bullmore, E.: Imaging structural co-variance between human brain regions. Nature Reviews Neuroscience 14(5), 322–336 (2013)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Csardi, G., Nepusz, T.: The igraph software package for complex network research. InterJournal 2006. Complex Systems 1695, 1–9 (2006)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Kabsch, W.: A solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A 32(5), 922–923 (1976)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Varoquaux, G., Craddock, R.C.: Learning and comparing functional connectomes across subjects. NeuroImage 80, 405–415 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Benjamin M. Kandel
    • 1
  • Danny JJ Wang
    • 2
  • James C. Gee
    • 1
  • Brian B. Avants
    • 1
  1. 1.Penn Image Computing and Science LaboratoryUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of NeurologyUniversity of CaliforniaLos AngelesUSA

Personalised recommendations