• David C Zhu and Shantanu Majumdar.
• Departments of Psychology and Radiology, and Cognitive Imaging Research Center, Michigan State University.
• J Neuroimaging. 2014 Mar 1;24(2):176-86.

BackgroundIntegration 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.MethodsWe 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.ResultsThe 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.ConclusionsThis 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.Copyright © 2012 by the American Society of Neuroimaging.

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