Abstract
In task-based fMRI, the generalized linear model (GLM) is widely used to detect activated brain regions. A fundamental assumption in the GLM model for fMRI activation detection is that the brain’s response, represented by the blood-oxygenation level dependent (BOLD) signals of volumetric voxels, follows the shape of stimulus paradigm. Based on this same assumption, we use the dynamic functional connectivity (DFC) curves between two ends of a white matter fiber, instead of the BOLD signal, to represent the brain’s response, and apply the GLM to detect Activated Fibers (AFs). Our rational is that brain regions connected by white matter fibers tend to be more synchronized during stimulus intervals than during baseline intervals. Therefore, the DFC curves for fibers connecting active brain regions should be positively correlated with the stimulus paradigm, which is verified by our extensive experiments using multimodal task-based fMRI and diffusion tensor imaging (DTI) data. Our results demonstrate that the detected AFs connect not only most of the activated brain regions detected via traditional voxel-based GLM method, but also many other brain regions, suggesting that the voxel-based GLM method may be too conservative in detecting activated brain regions.
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References
Basser, P.J., Mattiello, J., LeBihan, D.: Estimation of the effective self-diffusion tensor from the NMR spin-echo. Journal of Magnetic Resonance Series B 103(3), 247–254 (1994)
Biswal, B.B., Mennes, M., et al.: M Toward discovery science of human brain function. PNAS 107(10), 4734–4739 (2010)
Fox, M.D., Raichle, M.E.: Spontaneous fluctuations in brain activity observed with functional magnetic resonance imagin. Nat. Rev. Neurosci. 8(9), 700–711 (2007)
Friston, K.: Modalities, modes, and models in functional neuroimaging. Science 326(5951), 399–403 (2009)
Hu, X., Deng, F., Li, K., et al.: Bridging Low-level Features and High-level Semantics via fMRI Brain Imaging for Video Classification. ACM Multimedia (2010)
Faraco, C.C., Smith, D., Langley, J., et al.: Mapping the Working Memory Network using the OSPAN Task. NeuroImage 47(suppl. 1), S105 (2009)
Li, K., Guo, L., Li, G., et al.: Cortical surface based identification of brain networks using high spatial resolution resting state FMRI data. In: IEEE International Conference on Biomedical Imaging: from Nano to Macro, Rotterdam, Netherlands, pp. 656–659 (2010)
Lv, J., Guo, L., Hu, X., Zhang, T., Li, K., Zhang, D., Yang, J., Liu, T.: Fiber-centered analysis of brain connectivities using DTI and resting state FMRI data. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 143–150. Springer, Heidelberg (2010)
Liu, T., Li, H., Wong, K., et al.: Brain tissue segmentation based on DTI data. NeuroImage 38(1), 114–123 (2007)
Liu, T., et al.: Deformable Registration of Cortical Structures via Hybrid Volumetric and Surface Warping. NeuroImage 22(4), 1790–1801 (2004)
MedINRIA, http://www-sop.inria.fr/asclepios/software/MedINRIA/
Zhang, D., et al.: Automatic cortical surface parcellation based on fiber density information. In: IEEE International Symposium on Biomedical Imaging (ISBI), Rotterdam, pp. 1133–1136 (2010)
Aviv, M., et al.: Cluster analysis of resting-state fMRI time series. NeuroImage 45, 1117–1125 (2009)
Honey, C.J., et al.: Predicting human resting-state functional connectivity from structural connectivity. PNAS 106(6), 2035–2040 (2009)
Worsley, K.J., et al.: A unified statistical approach for determining significant voxels in images of cerebral activation. Human Brain Mapping 4, 58–73 (1996)
Christopher, R., Genovese, N.A., Lazar, T.E.: Nichols: Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate. NeuroImage 15, 870–878 (2002)
Friston, K.J., et al.: Statistical Parametric Maps in Functional Imaging: A General Linear Approach. Human Brain Mapping 2, 189–210 (1995)
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Lv, J. et al. (2011). Activated Fibers: Fiber-Centered Activation Detection in Task-Based FMRI. In: Székely, G., Hahn, H.K. (eds) Information Processing in Medical Imaging. IPMI 2011. Lecture Notes in Computer Science, vol 6801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22092-0_47
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DOI: https://doi.org/10.1007/978-3-642-22092-0_47
Publisher Name: Springer, Berlin, Heidelberg
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