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Integration of Resting‐State FMRI and Diffusion‐Weighted MRI Connectivity Analyses of the Human Brain: Limitations and Improvement
Author(s) -
Zhu David C.,
Majumdar Shantanu
Publication year - 2012
Publication title -
journal of neuroimaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.822
H-Index - 64
eISSN - 1552-6569
pISSN - 1051-2284
DOI - 10.1111/j.1552-6569.2012.00768.x
Subject(s) - functional magnetic resonance imaging , default mode network , resting state fmri , diffusion mri , functional connectivity , neuroscience , pattern recognition (psychology) , medicine , artificial intelligence , magnetic resonance imaging , computer science , psychology , radiology
BACKGROUND Integration of functional connectivity analysis based on resting‐state functional Magnetic Resonance Imaging (fMRI) and structural connectivity analysis based on Diffusion‐Weighted Imaging (DWI) has shown great potential to improve understanding of the neural networks in the human brain. However, there are sensitivity and specificity‐related interpretation issues that must be addressed. METHODS We assessed the long‐range functional and structural connections of the default‐mode, attention, visual and motor networks on 25 healthy subjects. For each network, we first integrated these two analyses based on one common seed region. We then introduced a functional‐assisted fiber tracking strategy, where seed regions were defined based on independent component analysis of the resting‐state fMRI dataset. RESULTS The single‐seed based technique successfully identified the expected functional connections within these networks at both subject and group levels. However, the success rate of structural connectivity analysis showed a high level of variation among the subjects. The functional‐assisted fiber tracking strategy highly improved the rate of successful fiber tracking. CONCLUSIONS This fMRI/DWI integration study suggests that functional connectivity analysis might be a more sensitive and robust approach in understanding the connectivity between cortical regions, and can be used to improve DWI‐based structural connectivity analysis.