Abstract
The lack of consistent and reliable functionally meaningful landmarks in the brain has significantly hampered the advancement of brain imaging studies. In this paper, we use white matter fiber connectivity patterns, obtained from diffusion tensor imaging (DTI) data, as predictors of brain function, and to discover a dense, reliable and consistent map of brain landmarks within and across individuals. The general principles and our strategies are as follows. 1) Each brain landmark should have consistent structural fiber connectivity pattern across a group of subjects. We will quantitatively measure the similarity of the fiber bundles emanating from the corresponding landmarks via a novel trace-map approach, and then optimize the locations of these landmarks by maximizing the group-wise consistency of the shape patterns of emanating fiber bundles. 2) The landmark map should be dense and distributed all over major functional brain regions. We will initialize a dense and regular grid map of approximately 2000 landmarks that cover the whole brains in different subjects via linear brain image registration. 3) The dense map of brain landmarks should be reproducible and predictable in different datasets of various subject populations. The approaches and results in the above two steps are evaluated and validated via reproducibility studies. The dense map of brain landmarks can be reliably and accurately replicated in a new DTI dataset such that the landmark map can be used as a predictive model. Our experiments show promising results, and a subset of the discovered landmarks are validated via task-based fMRI.
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References
Van Essen, D.C., Dierker, D.L.: Surface-Based and Probabilistic Atlases of Primate Cerebral Cortex. Neuron. 56, 209–225 (2007)
Derrfuss, J., Mar, R.A.: Lost in localization: The need for a universal coordinate database. NeuroImage 48, 1–7 (2009)
Ashburner, J., Friston, K., Penny, W.: Human Brain Function. Academic Press, London (2004)
Friston, K.J.: Modalities, modes, and models in functional neuroimaging. Science 326(5951), 399–403 (2009)
Jack Jr., C.R., Bernstein, M.A., Borowski, B.J., Gunter, J.L., Fox, N.C., Thompson, P.M., Schuff, N., Krueger, G., Killiany, R.J., Decarli, C.S., Dale, A.M., Carmichael, O.W., Tosun, D., Weiner, M.W.: Update on the magnetic resonance imaging core of the Alzheimer’s disease neuroimaging initiative. Alzheimers Dement. 6(3), 212–220 (2010)
Li, Z., Santhanam, P., Coles, C.D., Lynch, M.E., Hamann, S., Peltier, S., Hu, X.: Increased “Default Mode” Activity in Adolescents Prenatally Exposed to Cocaine. Human Brain Mapping 32, 759–770 (2010)
Epstein, J.N., Casey, B.J., Tonev, S.T., Davidson, M., Reiss, A.L., Garrett, A., Hinshaw, S.P., Greenhill, L.L., Vitolo, A., Kotler, L.A., Jarrett, M.A., Spicer, J.: Assessment and Prevention of Head Motion During Imaging of Patients with Attention Deficit Hyperactivity Disorder. Psychiatry Res. 155(1), 75–82 (2007)
Li, K., Guo, L., Faraco, C., Zhu, D., Deng, F., Zhang, T., Jiang, X., Zhang, D., Chen, H., Hu, X., Miller, S., Liu, T.: Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles. In: Neural Information Processing Systems, NIPS (2010)
Passingham, R.E., Stephan, K.E., Kotter, R.: The anatomical basis of functional localization in the cortex. Nat. Rev. Neurosci. 3(8), 606–616 (2002)
Brett, M., Johnsrude, I.S., Owen, A.M.: The problem of functional localization in the human brain. Nat. Rev. Neurosci. 3(3), 243–249 (2002)
Honey, C.J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J.P., Meuli, R., Hagmann, P.: Predicting human resting-state functional connectivity from structural connectivity. PNAS 106(6), 2035–2040 (2009)
Faraco, C., Unsworth, N., Langley, J., Terry, D., Li, K., Zhang, D., Liu, T., Miller, S.: Complex span tasks and hippocampal recruitment during working memory. NeuroImage 55, 773–787 (2011)
Liu, T., Li, H., Wong, K., Tarokh, A., Guo, L., Wong, S.T.C.: Brain Tissue Segmentation Based on DTI Data. NeuroImage 38(1), 114–123 (2007)
Li, K., Guo, L., Li, G., Nei, J., Faraco, C., Zhao, Q., Miller, S., Liu, T.: Cortical surface based identification of brain networks using high spatial resolution resting state FMRI data. In: International Symposium on Biomedical Imaging, ISBI (2010)
O’Donnell, L.J., Kubicki, M., Shenton, M.E., Dreusicke, M.H., Grimson, W.E.L., Westin, C.F.: A method for clustering white matter fiber tracts. American Journal of Neuroradiology 27, 1032–1036 (2006)
Brun, A., Knutsson, H., Park, H.-J., Shenton, M.E., Westin, C.-F.: Clustering fiber traces using normalized cuts. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 368–375. Springer, Heidelberg (2004)
FreeSurfer, http://surfer.nmr.mgh.harvard.edu/
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)
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Zhu, D. et al. (2011). Discovering Dense and Consistent Landmarks in the Brain. In: Székely, G., Hahn, H.K. (eds) Information Processing in Medical Imaging. IPMI 2011. Lecture Notes in Computer Science, vol 6801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22092-0_9
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DOI: https://doi.org/10.1007/978-3-642-22092-0_9
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