Integration of Resting-State FMRI and Diffusion-Weighted MRI Connectivity Analyses of the Human Brain: Limitations and Improvement
| Autor: | Shantanu Majumdar, David C. Zhu |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
medicine.diagnostic_test
Artificial neural network Resting state fMRI business.industry Functional connectivity Pattern recognition Human brain Independent component analysis medicine.anatomical_structure medicine Radiology Nuclear Medicine and imaging Neurology (clinical) Artificial intelligence Sensitivity (control systems) Functional magnetic resonance imaging business Diffusion MRI |
| Zdroj: | Journal of Neuroimaging. 24:176-186 |
| ISSN: | 1051-2284 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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