Abstract
Establishment of structural and functional correspondences across different brains is one of the most fundamental issues in the human brain mapping field. Recently, several multimodal DTI/fMRI studies have demonstrated that consistent white matter fiber connection patterns can predict brain function and represent common brain architectures across individuals and populations, and along this direction, several approaches have been proposed to discover large-scale cortical landmarks with common structural connection profiles. However, an important limitation of previous approaches is that the rich anatomical information such as gyral/sulcal folding patterns has not been incorporated into the landmark discovery procedure yet. In this paper, we present a novel anatomy-guided discovery framework that defines and optimizes a dense map of cortical landmarks that possess group-wise consistent anatomical and fiber connectional profiles. This framework effectively integrates reliable and rich anatomical, morphological, and fiber connectional information for landmark initialization, optimization and prediction, which are formulated and solved as an energy minimization problem. Validation results based on fMRI data demonstrate that the identified 555 cortical landmarks are producible, predictable and exhibit accurate structural and functional correspondences across individuals and populations, offering a universal and individualized brain reference system for neuroimaging research.
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Thompson, P., Toga, A.W.: A surface-based technique for 1336 warping 3-dimensional images of the brain. IEEE Trans. Med. Imaging 15(4), 402–417 (1996)
Fischl, B., et al.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3), 341–355 (2002)
Shen, D., Davatzikos, C.: HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Trans. Med. Imaging 21(11), 1421–1439 (2002)
Johansen-Berg, et al.: Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. Proceedings of the National Academy of Sciences of the United States of America (PNAS) 101(36), 13335–13340 (2004)
Jbabdi, S., et al.: Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models. NeuroImage 44(2), 373–384 (2009)
Poldrack, R.A.: The future of fMRI in cognitive neuroscience. NeuroImage (2011), doi:10.1016/j.neuroimage.2011.08.007
Liu, T.: A few thoughts on brain ROIs. Brain Imaging and Behavior (2011), doi:10.1007/s11682-011-9123-6
Zhu, D., et al.: Discovering Dense and Consistent Landmarks in the Brain. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 97–110. Springer, Heidelberg (2011)
Zhang, T., et al.: Predicting functional cortical landmarks via DTI-derived fiber shape models. Cerebral Cortex (2011)
Zhu, D., et al.: DICCCOL: Dense Individualized and Common Connectivity-based Cortical Landmarks. Cerebral Cortex (2012), doi:10.1093/cercor/bhs072
Li, K., et al.: Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles. In: Advances in Neural Information Processing Systems, NIPS (2010)
Honey, C.J., et al.: Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences of the United States of America (PNAS) 106(6), 2035–2040 (2009)
Siegel, S., Castellan Jr., N.J.: Nonparametric Statistics for the Behavioral Sciences, 2nd edn., p. 266. McGraw-Hill, New York (1988)
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Jiang, X. et al. (2013). Anatomy-Guided Discovery of Large-Scale Consistent Connectivity-Based Cortical Landmarks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_77
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DOI: https://doi.org/10.1007/978-3-642-40760-4_77
Publisher Name: Springer, Berlin, Heidelberg
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