Document Detail


Resting state fMRI-guided fiber clustering: methods and applications.
MedLine Citation:
PMID:  23065648     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Bao Ge; Lei Guo; Tuo Zhang; Xintao Hu; Junwei Han; Tianming Liu
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Neuroinformatics     Volume:  11     ISSN:  1559-0089     ISO Abbreviation:  Neuroinformatics     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-01-07     Completed Date:  2013-06-17     Revised Date:  2014-02-04    
Medline Journal Info:
Nlm Unique ID:  101142069     Medline TA:  Neuroinformatics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  119-33     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Brain Mapping / methods
Corpus Callosum / anatomy & histology,  physiology*
Diffusion Tensor Imaging / methods*
Functional Neuroimaging / methods
Humans
Image Processing, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods
Nerve Fibers / physiology*
Neural Pathways / anatomy & histology,  physiology*
Reproducibility of Results
Grant Support
ID/Acronym/Agency:
EB 006878/EB/NIBIB NIH HHS; R01 DA033393/DA/NIDA NIH HHS; R01 HL087923-03S2/HL/NHLBI NIH HHS; R01 R01DA033393/DA/NIDA NIH HHS
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