Resting State fMRI-guided Fiber Clustering: Methods and Applications
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.
resting state fmri fiber clustering dti tract-based analysis