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