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Brain Imaging and Behavior

, Volume 8, Issue 2, pp 292–299 | Cite as

Local termination pattern analysis: a tool for comparing white matter morphology

  • M. CieslakEmail author
  • S. T. Grafton
SI: Genetic Neuroimaging in Aging and Age-Related Diseases

Abstract

Disconnections between structures in the brain have long been hypothesized to be the mechanism behind numerous disease states and pathological behavioral phenotypes. Advances in diffusion weighted imaging (DWI) provide an opportunity to study white matter, and therefore brain connectivity, in great detail. DWI-based research assesses white matter at two different scales: voxelwise indexes of anisotropy such as fractional anisotropy (FA) are used to compare small units of tissue and network-based methods compare tractography-based models of whole-brain connectivity. We propose a method called local termination pattern analysis (LTPA) that considers information about both local and global brain connectivity simultaneously. LTPA itemizes the subset of streamlines that pass through a small set of white matter voxels. The “local termination pattern” is a vector defined by counts of these streamlines terminating in pairs of cortical regions. To assess the reliability of our method we applied LTPA exhaustively over white matter voxels to produce complete maps of local termination pattern similarity, based on diffusion spectrum imaging (DSI) data from 11 individuals in triplicate. Here we show that local termination patterns from an individual are highly reproducible across the entire brain. We discuss how LTPA can be deployed into a clinical database and used to characterize white matter morphology differences due to disease, developmental or genetic factors.

Keywords

DSI Tractography Databases White matter Individual differences 

Notes

Acknowledgments

The authors thank Dani Bassett, Mario Mendoza, Philip Beach, Zsuzsi Fodor and Frank Yeh.

References

  1. Basser, P., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A. (2000). In vivo fiber tractography using DT-MRI data. Magnetic Resonance in Medicine, 44(4), 625–632.PubMedCrossRefGoogle Scholar
  2. Basser, P.J., & Jones, D.K. (2002). Diffusion-tensor MRI: theory, experimental design and data analysis. NMR in Biomedicine, 15, 456–467.PubMedCrossRefGoogle Scholar
  3. Bassett, D., Brown, J., Deshpande, V., Carlson, J., Grafton, S. (2011). Conserved and variable architecture of human white matter connectivity. NeuroImage, 54(2), 1262–1279.PubMedCrossRefGoogle Scholar
  4. Bullmore, E., & Bassett, D. (2011). Brain graphs: graphical models of the human brain connectome. Annual Review of Clinical Psychology. 7, 113–140.PubMedCrossRefGoogle Scholar
  5. Cammoun, L., Gigandet, X., Meskaldji, D., Thiran, J. P., Sporns, O., Do, K. Q., Maeder, P., Meuli, R., Hagmann, P. (2012). Mapping the human connectome at multiple scales with diffusion spectrum MRI. Journal of Neuroscience Methods, 203(2), 386–397.PubMedCrossRefGoogle Scholar
  6. Chiang, M.C., McMahon, K.L., de Zubicaray, G.I., Martin, N.G., Hickie, I., Toga, A.W., Wright, M.J., Thompson P.M. (2011). Genetics of white matter development: a DTI study of 705 twins and their siblings aged 12 to 29. NeuroImage, 54(3), 2308–2317.PubMedCentralPubMedCrossRefGoogle Scholar
  7. Dale, A.M., Fischl, B., Sereno, M.I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage, 9(2), 179–194.PubMedCrossRefGoogle Scholar
  8. Fernandez-Miranda, J.C., Pathak, S., Engh, J., Jarbo, K., Verstynen, T., Yeh, F.C., Wang, Y., Mintz, A., Boada, F., Schneider, W., Friedlander, R (2012). High-definition fiber tractography of the human brain. Neurosurgery, 71(2), 430–453.PubMedCrossRefGoogle Scholar
  9. Garyfallidis, E., Brett, M., Amirbekian, B., Nguyen, C., Yeh, F.C., Olivetti, E., Halchenko, Y., Nimmo-Smith, I. (2011). Dipy–a novel software library for diffusion MR and tractography. In 17th annual meeting of the organization for human brain mapping.Google Scholar
  10. Gerhard, S., Daducci, A., Lemkaddem, A., Meuli, R., Thiran, J.P., Hagmann, P. (2011). The connectome viewer toolkit: an open source framework to manage, analyze, and visualize connectomes. Frontiers in Neuroinformatics, 5(3), 1–15.Google Scholar
  11. Geschwind, N., & Kaplan, E. (1962). A human cerebral deconnection syndrome. Neurology, 12, 675–685.PubMedCrossRefGoogle Scholar
  12. Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V.J., Meuli, R., Thiran, J.P. (2007). Mapping human whole-brain structural networks with diffusion MRI. PLoS ONE, 2(7), e597.PubMedCentralPubMedCrossRefGoogle Scholar
  13. Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C.J., Wedeen, V.J., Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6(7), e159.PubMedCentralPubMedCrossRefGoogle Scholar
  14. Hagmann, P., Cammoun, L., Gigandet, X., Gerhard, S., Ellen Grant, P., Wedeen, V., Meuli, R., Thiran, J.P., Honey, C.J., Sporns, O. (2010). MR connectomics: principles and challenges. Journal of Neuroscience Methods, 194(1), 34–45.PubMedCrossRefGoogle Scholar
  15. Jones, D.K., Knösche, T.R., Turner, R. (2013). White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. NeuroImage, 73, 239–254.PubMedCrossRefGoogle Scholar
  16. Kreher, B.W., Mader, I., Kiselev, V.G. (2008). Gibbs tracking: a novel approach for the reconstruction of neuronal pathways. Magnetic Resonance in Medicine, 60(4), 953–963.PubMedCrossRefGoogle Scholar
  17. Kriegeskorte, N. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences, 103(10), 3863–3868.CrossRefGoogle Scholar
  18. Richiardi, J., Eryilmaz, H., Schwartz, S., Vuilleumier, P. (2011). Decoding brain states from fMRI connectivity graphs. NeuroImage, 56(2), 616–626.PubMedCrossRefGoogle Scholar
  19. Shi, F., Yap, P.T., Gao, W., Lin, W., Gilmore, J.H., Shen, D. (2012). Altered structural connectivity in neonates at genetic risk for schizophrenia: a combined study using morphological and white matter networks. NeuroImage, 62(3), 1622–1633.PubMedCentralPubMedCrossRefGoogle Scholar
  20. Sporns, O., Tononi, G., Kötter, R. (2005). The human connectome: a structural description of the human brain. PLoS Computational Biology, 1(4), e42.PubMedCentralPubMedCrossRefGoogle Scholar
  21. Tuch, D.S. (2004). Q-ball imaging. Magnetic Resonance in Medicine, 52(6), 1358–1372.PubMedCrossRefGoogle Scholar
  22. Van Essen, D.C., & Ugurbil, K. (2012). The future of the human connectome. NeuroImage, 62(2), 1299–1310.PubMedCentralPubMedCrossRefGoogle Scholar
  23. Viswanathan, S., Cieslak, M., Grafton, S.T. (2012). On the geometric structure of fMRI searchlight-based information maps. arXiv:1210.6317.
  24. Wang, R., Benner, T., Sorensen, A.G., Wedeen, V.J. (2007). Diffusion toolkit: a software package for diffusion imaging data processing and tractography. ISMRM, 15, 3720–3720.Google Scholar
  25. Wedeen, V.J., Hagmann, P., Tseng, W.Y.I., Reese, T.G., Weisskoff, R.M. (2005). Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magnetic Resonance in Medicine, 54(6), 1377–1386.PubMedCrossRefGoogle Scholar
  26. Yamagata, B., Barnea-Goraly, N., Marzelli, M.J., Park, Y., Hong, D.S., Mimura, M., Reiss, A.L. (2012). White matter aberrations in prepubertal estrogen-naive girls with monosomic turner syndrome. Cerebral Cortex, 22(12), 2761–2768.PubMedCentralPubMedCrossRefGoogle Scholar
  27. Yeh, F.C., Wedeen, V.J., Tseng, W. (2009). Dataset-independent reconstruction of high angular resolution diffusion sampling schemes by generalized q-space imaging. Proceedings International Society Magazine Resonance, 17, 3544.Google Scholar
  28. Yeh, F.C., & Tseng, W. (2011). NTU-90: a high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction. Neuroimage, 58(1), 91–99.PubMedCrossRefGoogle Scholar
  29. Zalesky, A., Fornito, A., Harding, I.H., Cocchi, L., Yücel, M., Pantelis, C., Bullmore, E. (2010). Whole-brain anatomical networks: does the choice of nodes matter?NeuroImage, 50(3), 970–983.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Psychological and Brain SciencesUCSBSanta BarbaraUSA

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