Evaluation of the MCFLIRT Correction Algorithm in Head Motion from Resting State fMRI Data
References:
[1] Wang, K., Liang, M., Wang, L., Tian, L., Zhang, X., Li, K., & Jiang, T. (2007). Altered functional connectivity in early Alzheimer's disease: a resting‐state fMRI study. Human brain mapping, 28(10), 967-978.
[2] Di Martino, A., Scheres, A., Margulies, D. S., Kelly, A. M. C., Uddin, L. Q., Shehzad, Z., et al. (2008). Functional connectivity of human striatum: a resting state FMRI study. Cerebral cortex, 18(12), 2735-2747.
[3] Van Den Heuvel, M. P., Pol, H. E. H. (2010). Exploring the brain network: a review on resting-state fMRI functional connectivity. European neuropsychopharmacology, 20(8), 519-534.
[4] Van Dijk, KR, Sabuncu, M. R., & Buckner, R. L. (2012). The influence of head motion on intrinsic functional connectivity MRI. Neuroimage, 59(1), 431-438.
[5] Pruim, RH, Mennes, M, van Rooij, D, Llera, A, Buitelaar, J. K., & Beckmann, C. F. (2015). ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage, 112, 267-277.
[6] Ardekani, B. A., Bachman, A. H., & Helpern, J. A. (2001). A quantitative comparison of motion detection algorithms in fMRI. Magnetic resonance imaging, 19(7), 959-963.
[7] Jenkinson, M., Bannister, P., Brady, J. M. and Smith, S. M. Improved Optimisation for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage, 17(2), 825-841, 2002.
[8] M. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith. FSL. NeuroImage, 62:782-90, 2012.
[9] Polman, C. H., Reingold, S. C., Banwell, B., Clanet, M., Cohen, J. A., Filippi, M., et al., (2011). Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Annals of neurology, 69(2), 292-302.
