Skip to main content

Advertisement

SpringerLink
Log in

Resting State fMRI-guided Fiber Clustering: Methods and Applications

  • Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

Clustering streamline fibers derived from diffusion tensor imaging (DTI) data into functionally meaningful bundles with group-wise correspondences across individuals and populations has been a fundamental step for tract-based analysis of white matter integrity and brain connectivity modeling. Many approaches of fiber clustering reported in the literature so far used geometric and/or anatomic information derived from structural MRI and/or DTI data only. In this paper, we take a novel, alternative multimodal approach of combining resting state fMRI (rsfMRI) and DTI data, and propose to use functional coherence as the criterion to guide the clustering of fibers derived from DTI tractography. Specifically, the functional coherence between two streamline fibers is defined as their rsfMRI time series’ correlations, and the affinity propagation (AP) algorithm is used to cluster DTI-derived streamline fibers into bundles. Currently, we use the corpus callosum (CC) fibers, which are the largest fiber bundle in the brain, as a test-bed for methodology development and validation. Our experimental results have shown that the proposed rsfMRI-guided fiber clustering method can achieve functionally homogeneous bundles that are reasonably consistent across individuals and populations, suggesting the close relationship between structural connectivity and brain function. The clustered fiber bundles were evaluated and validated via the benchmark data provided by task-based fMRI, via reproducibility studies, and via comparison with other methods. Finally, we have applied the proposed framework on a multimodal rsfMRI/DTI dataset of schizophrenia (SZ) and reproducible results were obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Basser, P. J., & Pierpaoli, C. (1996). Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. Journal of Magnetic Resonance Series B, 111(3), 209–219.

    Article  PubMed  CAS  Google Scholar 

  • Basser, P. J., Pjevic, S., Pierpaoli, C., et al. (2000). In vitro fiber tractography using DT-MRI data. Magnetic Resonance in Medicine, 44(4), 625–632.

    Article  PubMed  CAS  Google Scholar 

  • Behrens, T. E. J., Johansen-Berg, H., Woolrich, M. W., Smith, S. M., Wheeler-Kingshott, C. A. M., Boulby, P. A., et al. (2003). Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nature Neuroscience, 6(7), 750–757.

    Article  PubMed  CAS  Google Scholar 

  • Behrens, J.-B. H., Robson, T. E. J., Drobnjak, M. D., Rushworth, I., Brady, M. F. S., Smith, J. M., 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, 13335–13340.

    Article  PubMed  Google Scholar 

  • Brun, A., Knutsson, H., Park, H. J., Shenton, M. E., & Westin, C.-F., (2004). Clustering fiber traces using normalized cuts (pp. 368–75). Proceedings of the 7th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI).

  • Cohen, A. L., Fair, D. A., Dosenbach, N. U. F., Miezin, F. M., Dierker, D., Van Essen, D. C., et al. (2008). Defining functional areas in individual human brains using resting functional connectivity MRI. NeuroImage, 41(1), 45–57.

    Article  PubMed  Google Scholar 

  • Corouge, I., Gouttard, S., & Gerig, G. (2004). Towards a shape model of white matter fiber bundles using diffusion tensor MRI (pp. 344–347), ISBI.

  • Downhill, J. E., Jr., Buchsbaum, M. S., Wei, T., Spiegel-Cohen, J., Hazlett, E. A., Haznedar, M. M., et al. (2000). Shape and size of the corpus callosum in schizophrenia and schizotypal personality disorder. Schizophrenia Research, 42(3), 193–208.

    Article  PubMed  Google Scholar 

  • Faraco, C. C., Unsworth, N., Langley, J., Terry, D., Li, K., Zhang, D., et al. (2011). Complex span tasks and hippocampal recruitment during working memory. NeuroImage, 55(2), 773–787.

    Article  PubMed  Google Scholar 

  • Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience, 8, 700–711.

    Article  PubMed  CAS  Google Scholar 

  • Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315, 972–976.

    Article  PubMed  CAS  Google Scholar 

  • Ge, B., Guo, L., Li, K., Li, H., Faraco, C., Zhao, Q., et al. (2010). Automatic clustering of white matter fibers via symbolic sequence analysis. SPIE Medical Image, 7623, 762327.1–762327.8.

    Google Scholar 

  • Ge, B., Guo, L., Hu, X., Han, J., & Liu, T. (2011). Resting state fMRI-guided fiber clustering. Medical Image Computing and Computer-Assisted Intervention (MICCAI).

  • Gerig, G., Gouttard, S., & Corouge, I. (2004). Analysis of brain white matter via fiber tract modeling. IEEE EMBS, 2, 4421–4424.

    Google Scholar 

  • van den Heuvel, M., Mandl, R., & Pol, H. H. (2008). Normalized cut group clustering of resting-state fMRI data. PLoS One, 3(4), e2001.

    Article  PubMed  Google Scholar 

  • Honey, C., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J., Meuli, R., et al. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences of the United States of America, 106(6), 2035–2040.

    Article  PubMed  CAS  Google Scholar 

  • Innocenti, G. M., Ansermet, F., & Parnas, J. (2003). Schizophrenia, neurodevelopment and corpus callosum. Molecular Psychiatry, 8, 261–274.

    Article  PubMed  CAS  Google Scholar 

  • Kanaan, R. A., Kim, J. S., Kaufmann, W. E., Pearlson, G. D., Barker, G. J., & McGuire, P. K. (2005). Diffusion tensor imaging in schizophrenia. Biological Psychiatry, 58(12), 921–929.

    Article  PubMed  Google Scholar 

  • Kerchner, G. A. (2011). Ultra-high field 7 T MRI: a new tool for studying Alzheimer’s disease. Journal of Alzheimer’s Disease, 26(Suppl 3), 91–95.

    PubMed  Google Scholar 

  • Kyriakopoulos, M., Bargiotas, T., Barker, G. J., & Frangou, S. (2008). Diffusion tensor imaging in schizophrenia. European Psychiatry, 23(4), 255–273.

    Article  PubMed  Google Scholar 

  • Kubicki, M., McCarley, R., Westin, C. F., Park, H. J., Maier, S., Kikinis, R., et al. (2007). A review of diffusion tensor imaging studies in schizophrenia. Journal of Psychiatric Research, 41(1–2), 15–30.

    Article  PubMed  Google Scholar 

  • Li, K., Guo, L., Li, G., Nie, J., Faraco, C., Zhao, Q., et al. (2010a). Cortical surface based identification of brain networks using high spatial resolution resting state FMRI data. ISBI, (pp. 657–659).

  • Li, H., Xue, Z., Guo, L., Liu, T., Hunter, J., & Wong, S. (2010b). A hybrid approach to automatic clustering of white matter fibers. NeuroImage, 49(2), 1249–1258.

    Article  Google Scholar 

  • Liu, T., Shen, D., & Davatzikos, C. (2004). Deformable registration of cortical structures via hybrid volumetric and surface warping. NeuroImage, 22(4), 1790–1801.

    Article  PubMed  Google Scholar 

  • Liu, T., Young, G., Huang, L., Chen, N.-K., & Wong, S. (2006). 76-space analysis of grey matter diffusivity: methods and applications. NeuroImage, 15(31), 51–65.

    Article  Google Scholar 

  • Liu, T. (2011). A few thoughts on brain ROIs, Brain imaging and behavior, in press.

  • Liu, T., Li, H., Wong, K., Tarokh, A., Guo, L., & Wong, S. (2007). Brain tissue segmentation based on DTI data. NeuroImage, 38(1), 114–123.

    Article  PubMed  CAS  Google Scholar 

  • Liu, T., Nie, J., Tarokh, A., Guo, L., & Wong, S. (2008). Reconstruction of central cortical surface from MRI brain images: method and application. NeuroImage, 40(3), 991–1002.

    Article  PubMed  Google Scholar 

  • Maddah, M., & Mewes, A. U. J. et al. (2005). Automated atlas-based clustering of white matter fiber tracts form DTMRI. MICCAI2005, (pp. 188–195).

  • Maddah, M., Grimson, W., & Warfield, S. (2006). Statistical modeling and EM clustering of white matter fiber tracts. ISBI, 1, 53–56.

    Google Scholar 

  • Mezer, A., Yovel, Y., Pasternak, O., Gorfine, T., & Assaf, Y. (2009). Cluster analysis of resting-state fMRI time series. NeuroImage, 45(4), 1117–1125.

    Article  PubMed  Google Scholar 

  • Mori, S. (2006). Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron, 51(5), 527–539.

    Article  PubMed  CAS  Google Scholar 

  • Mori, S., Crain, B. J., Chacko, V. P., & van Zijl, P. C. M. (1999). Three dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Annals of Neurology, 45(2), 265–269.

    Article  PubMed  CAS  Google Scholar 

  • Nie, J., Guo, L., Li, K., Wang, Y., Chen, G., Li, L., et al. (2011). Axonal fiber terminations concentrate on gyri, accepted, Cerebral Cortex.

  • O’Donnell, L. J., Kubicki, M., Shenton, M. E., Dreusicke, M. H., Grimson, W. E., & Westin, C. F. (2006). A method for clustering white matter fiber tracts. AJNR American Journal of Neuroradiology, 27, 1032–1036.

    PubMed  Google Scholar 

  • Paul, L. K., et al. (2007). Agenesis of the corpus callosum: genetic, developmental and functional aspects of connectivity. Nature Reviews Neuroscience, 8(4), 288.

    Article  Google Scholar 

  • Rotarska-Jagiela, A., Schönmeyer, R., Oertel, V., Haenschel, C., Vogeley, K., & Linden, D. E. (2008). The corpus callosum in schizophrenia-volume and connectivity changes affects specific regions. NeuroImage, 39(4), 1522–1532.

    Article  PubMed  Google Scholar 

  • Skudlarski, P., Jagannathan, K., Calhoun, V. D., Hampson, M., Skudlarski, B. A., & Pearlson, G. D. (2008). Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations. NeuroImage, 43, 554–561.

    Article  PubMed  Google Scholar 

  • Tuch, D. S., Reese, T. G., Wiegell, M. R., Makris, N., Belliveau, J. W., & Wedeen, V. J. (2002). High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magnetic Resonance in Medicine, 48(4), 577–582.

    Article  PubMed  Google Scholar 

  • Wakana, S., Caprihan, A., et al. (2007). Reproducibility of quantitative tractography methods applied to cerebral white matter. NeuroImage, 36, 630–644.

    Article  PubMed  Google Scholar 

  • Westin, C. F., Maier, S. E., Mamata, H., Nabavi, A., Jolesz, F. A., & Kikinis, R. (2002). Processing and visualization of diffusion tensor MRI. Medical Image Analysis, 6(2), 93–108.

    Article  PubMed  Google Scholar 

  • Xia, Y., Turken, U., Whitfield-Gabrieli, S. L., & Gabrieli, J. D. (2005). Knowledge-based classification of neuronal fibers in entire brain. MICCAI, 3479, 205–212.

    Google Scholar 

  • Zhang, T., Guo, L., Hu, X., Li, G., Nie, J., Jiang, X., et al. (2010). Joint analysis of fiber shape and cortical folding patterns. ISBI, 1165–1168.

  • Zhang, T., Guo, L., Hu, X., Li, K., Jin, C., Cui, G., et al. (2011a). Predicting functional cortical rois based on fiber shape models. Cerebral Cortex, in press.

  • Zhang, D., Guo, L., Hu, X., Li, K., Zhao, Q., & Liu, T. (2011b). Increased cortico-subcortical functional connectivity in schizophrenia, accepted, Brain Imaging and Behavior.

  • Zhu, D., Li, K, Faraco, C., Deng, F., Zhang, D., Jiang, X., et al. (2011). Optimization of functional brain ROIs via maximization of consistency of structural connectivity profiles, NeuroImage, in press.

Download references

Acknowledgements

T Liu was supported by the NIH Career Award EB 006878, NIH R01 HL087923-03S2, NIH R01 R01DA033393, NSF CAREER Award IIS-1149260, and The University of Georgia start-up research funding. B Ge was supported by the Fundamental Research Funds for the Central Universities from China (No. GK201001005). The authors would like to thank Carlos Faraco and L Stephen Miller for providing the working memory fMRI paradigm used in this paper. The SZ dataset was provided by the NA-MIC. The authors would like to thank the anonymous reviewers for their constructive and helpful comments.

Author information

Authors and Affiliations

  1. School of Automation, Northwestern Polytechnic University, Xian, China

    Bao Ge, Lei Guo, Tuo Zhang, Xintao Hu & Junwei Han

  2. Bioimaging Research Center, The University of Georgia, Athens, GA, USA

    Tuo Zhang & Tianming Liu

  3. Department of Computer Science & Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, Athens, GA, 30602, USA

    Tianming Liu

Authors
  1. Bao Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tuo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xintao Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Junwei Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tianming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tianming Liu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ge, B., Guo, L., Zhang, T. et al. Resting State fMRI-guided Fiber Clustering: Methods and Applications. Neuroinform 11, 119–133 (2013). https://doi.org/10.1007/s12021-012-9169-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-012-9169-7

Keywords

Search

Navigation