Joint representation of consistent structural and functional profiles for identification of common cortical landmarks
In the brain mapping field, there have been significant interests in representation of structural/functional profiles to establish structural/functional landmark correspondences across individuals and populations. For example, from the structural perspective, our previous studies have identified hundreds of consistent DICCCOL (dense individualized and common connectivity-based cortical landmarks) landmarks across individuals and populations, each of which possess consistent DTI-derived fiber connection patterns. From the functional perspective, a large collection of well-characterized HAFNI (holistic atlases of functional networks and interactions) networks based on sparse representation of whole-brain fMRI signals have been identified in our prior studies. However, due to the remarkable variability of structural and functional architectures in the human brain, it is challenging for earlier studies to jointly represent the connectome-scale structural and functional profiles for establishing a common cortical architecture which can comprehensively encode both structural and functional characteristics across individuals. To address this challenge, we propose an effective computational framework to jointly represent the structural and functional profiles for identification of consistent and common cortical landmarks with both structural and functional correspondences across different brains based on DTI and fMRI data. Experimental results demonstrate that 55 structurally and functionally common cortical landmarks can be successfully identified.
KeywordsCortical landmarks DTI fMRI Brain architecture
Compliance with ethical standards
This research was supported by the National Institutes of Health (DA-033393, AG-042599) and by the National Science Foundation (NSF CAREER Award IIS-1149260, CBET-1302089, BCS-1439051 and DBI-1564736).
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- Chen H, Zhang T, Liu T. Identifying group-wise consistent white matter landmarks via novel fiber shape descriptor. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Berlin, 2013: 66–73.Google Scholar
- Eickhoff, S. B., Laird, A. R., Grefkes, C., Wang, L. E., Zilles, K., & Fox, P. T. (2009). Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty. Human Brain Mapping, 30, 2907–2926.CrossRefPubMedPubMedCentralGoogle Scholar
- Jiang, X., Zhang, T., Zhao, Q., Lu, J., Guo, L., & Liu, T. (2015c). Fiber connection pattern-guided structured sparse representation of whole-brain FMRI signals for functional network inference. Medical Image Computing and Computer-Assisted Intervention., 9349, 133–141.Google Scholar
- Johansen-Berg, H., Behrens, T. E. J., Robson, M. D., et al. (2004). 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, 101(36), 13335–13340.CrossRefPubMedPubMedCentralGoogle Scholar
- Li, K., Guo, L., Faraco, C., et al. (2010). Individualized ROI optimization via maximization of group-wise consistency of structural and functional profiles. Advances in Neural Information Processing Systems., 1369–1377.Google Scholar
- Shen, D., Wong, W. H., & Ip, H. H. S. (1999). Affine-invariant image retrieval by correspondence matching of shapes. Image & Vision Computing, 17(7), 489–499.Google Scholar
- Tang, S., Fan, Y., Wu, G., Kim, M., & Shen, D. (2009). Rabbit: rapid alignment of brains by building intermediate templates. Neuroimage, 47(4), 1277–87.Google Scholar
- Yap, P. T., Wu, G., Zhu, H., Lin, W., & Shen, D. (2009). Timer: tensor image morphing for elastic registration. Neuroimage, 47(2), 549–563.Google Scholar
- Zhang, S., Li, X., Lv, J., et al. (2013). Sparse representation of higher-order functional interaction patterns in task-based FMRI data (pp. 626–634). Berlin: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer.Google Scholar
- Zhao, S., Han, J., Lv, J., et al. (2015). Supervised dictionary learning for inferring concurrent brain networks. IEEE Transactions on Medical Imaging, 34(10), 2036–2045.Google Scholar
- Zhu D, Li K, Guo L, et al. (2012b). DICCCOL: dense individualized and common connectivity-based cortical landmarks. Cerebral cortex: bhs072.Google Scholar