Abstract
Study of structural and functional connectivities of the human brain has received significant interest and effort recently. A fundamental question arises when attempting to measure the structural and/or functional connectivities of specific brain networks: how to best identify possible Regions of Interests (ROIs)? In this paper, we present a novel ROI prediction framework that localizes ROIs in individual brains based on learned fiber shape models from multimodal task-based fMRI and diffusion tensor imaging (DTI) data. In the training stage, ROIs are identified as activation peaks in task-based fMRI data. Then, shape models of white matter fibers emanating from these functional ROIs are learned. In addition, ROIs’ location distribution model is learned to be used as an anatomical constraint. In the prediction stage, functional ROIs are predicted in individual brains based on DTI data. The ROI prediction is formulated and solved as an energy minimization problem, in which the two learned models are used as energy terms. Our experiment results show that the average ROI prediction error is 3.45 mm, in comparison with the benchmark data provided by working memory task-based fMRI. Promising results were also obtained on the ADNI-2 longitudinal DTI dataset.
Chapter PDF
Similar content being viewed by others
References
Biswal, B.B., Mennes, M., Zuo, X.N., Gohel, S., Kelly, C., Smith, S.M., Beckmann, C.F., Adelstein, J.S., Buckner, R.L., Colcombe, S., Dogonowski, A.M., Ernst, M., Fair, D., Hampson, M., Hoptman, M.J., Hyde, J.S., Kiviniemi, V.J., Kötter, R., Li, S.J., Lin, C.P., Lowe, M.J., Mackay, C., Madden, D.J., Madsen, K.H., Margulies, D.S., Mayberg, H.S., McMahon, K., Monk, C.S., Mostofsky, S.H., Nagel, B.J., Pekar, J.J., Peltier, S.J., Petersen, S.E., Riedl, V., Rombouts, S.A., Rypma, B., Schlaggar, B.L., Schmidt, S., Seidler, R.D., Siegle, G.J., Sorg, C., Teng, G.J., Veijola, J., Villringer, A., Walter, M., Wang, L., Weng, X.C., Whitfield-Gabrieli, S., Williamson, P., Windischberger, C., Zang, Y.F., Zhang, H.Y., Castellanos, F.X., Milham, M.P.: Toward discovery science of human brain function. PNAS 107(10), 4734–4739 (2010)
Sporns, O., Tononi, G., Kötter, R.: The human connectome: A structural description of the human brain. PLoS Comput. Biol. 1(4), e42 (2005)
Van Dijk, K.R., Hedden, T., Venkataraman, A., Evans, K.C., Lazar, S.W., Buckner, R.L.: Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J. Neurophysiol. 103(1), 297–321 (2010)
Hagmann, P., Cammoun, L., Gigandet, X., Gerhard, S., Grant, P.E., Wedeen, V., Meuli, R., Thiran, J.P., Honey, C.J., Sporns, O.: MR connectomics: Principles and challenges. J. Neurosci. Methods 194(1), 34–45 (2010)
Human Connectome Project, http://www.humanconnectomeproject.org/overview/
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: NIPS (2010)
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)
Passingham, R.E., Stephan, K.E., Kötter, R.: The anatomical basis of functional localization in the cortex. Nat. Rev. Neurosci. 3(8), 606–616 (2002)
Liu, T., Li, H., Wong, K., Tarokh, A., Guo, L., Wong, S.T.: Brain Tissue Segmentation Based on DTI Data. NeuroImage 38(1), 114–123 (2007)
Hu, X., Guo, L., Zhang, T., Li, G., Nie, J., Jiang, X., Zhang, D., Liu, T.: Joint analysis of fiber shape and cortical folding patterns. In: ISBI (2010)
Shen, D., Davatzikos, C.: HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Trans. Med. Imaging 21(11), 1421–1439 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, T., Guo, L., Li, K., Zhu, D., Cui, G., Liu, T. (2011). Predicting Functional Brain ROIs via Fiber Shape Models. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23629-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-23629-7_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23628-0
Online ISBN: 978-3-642-23629-7
eBook Packages: Computer ScienceComputer Science (R0)