G. Fichtinger, A. Martel, and T. Peters (Eds.): MICCAI 2011, Part II, LNCS 6892, pp. 149–156, 2011. © Springer-Verlag Berlin Heidelberg 2011 Resting State fMRI-Guided Fiber Clustering Bao Ge 1 , Lei Guo 1 , Jinglei Lv 1 , Xintao Hu 1 , Junwei Han 1 , Tuo Zhang 1,2 , and Tianming Liu 2 1 School of Automation, Northwestern Polytechnical University, Xi’an, China {oct.bob,guolei.npu,lvjinglei,xintao.hu, junweihan2010,zhangtuo.npu}@gmail.com 2 Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA tliu@cs.uga.edu Abstract. Fiber clustering is a prerequisite step towards tract-based analysis of white mater integrity via diffusion tensor imaging (DTI) in various clinical neu- roscience applications. Many methods reported in the literature used geometric or anatomic information for fiber clustering. This paper proposes a novel me- thod that uses functional coherence as the criterion to guide the clustering of fi- bers derived from DTI tractography. Specifically, we represent the functional identity of a white matter fiber by two resting state fMRI (rsfMRI) time series extracted from the two gray matter voxels to which the fiber connects. Then, the functional coherence or similarity between two white matter fibers is defined as their rsfMRI time series’ correlations, and the data-driven affinity propagation (AP) algorithm is used to cluster fibers into bundles. At current stage, we use the corpus callosum (CC) fibers that are the largest fiber bundle in the brain as an example. Experimental results show that the proposed fiber clustering me- thod can achieve meaningful bundles that are reasonably consistent across dif- ferent brains, and part of the clustered bundles was validated via the benchmark data provided by task-based fMRI data. Keywords: Resting state fMRI, DTI, fiber clustering. 1 Introduction Diffusion tensor imaging (DTI), as a powerful tool to image the axonal fibers in vivo, provides rich structural connectivity information that is believed to be closely related to brain function. In order to infer meaningful and comparable information from DTI data of different brains, the large number of fiber trajectories produced by DTI tracto- graphy need to be grouped into appropriate fiber bundles for tract-based analysis [4]. Many approaches reported in the literature used geometric, anatomical or structural features, e.g., fiber’s Euclidean distances [3, 4], fiber shape information [11], or fi- ber’s end point positions [10], to cluster fiber bundles. Though these methods have their own advantages in clustering meaningful bundles, the functional interpretation of the clustering results remains to be elucidated.