Resting State fMRIguided Fiber Clustering Method

Authors: Bao Ge Lei Guo Tuo Zhang Xintao Hu Junwei Han Tianming Liu

Publish Date: 2012/10/12

Volume: 11, Issue: 1, Pages: 119-133

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Abstract

Clustering streamline fibers derived from diffusion tensor imaging DTI data into functionally meaningful bundles with groupwise correspondences across individuals and populations has been a fundamental step for tractbased 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 DTIderived streamline fibers into bundles Currently we use the corpus callosum CC fibers which are the largest fiber bundle in the brain as a testbed for methodology development and validation Our experimental results have shown that the proposed rsfMRIguided 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 taskbased 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 obtainedT Liu was supported by the NIH Career Award EB 006878 NIH R01 HL08792303S2 NIH R01 R01DA033393 NSF CAREER Award IIS1149260 and The University of Georgia startup 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 NAMIC The authors would like to thank the anonymous reviewers for their constructive and helpful comments

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