Andersson, J. L. R., Hutton, C., Ashburner, J., Turner, R., & Friston, K. (2001). Modeling Geometric Deformations in EPI Time Series. NeuroImage, 13(5), 903–919. https://doi.org/10.1006/nimg.2001.0746
Artifacts in Diffusion MRI. (n.d.). http://stbb.nichd.nih.gov/pdf/9780195369779_Jone-Pierpaoli.pdf
Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38(1), 95–113. https://doi.org/10.1016/j.neuroimage.2007.07.007
Ashburner, J. (2009). Computational anatomy with the SPM software. Magnetic Resonance Imaging, 27(8), 1163–1174. https://doi.org/10.1016/j.mri.2009.01.006
Ashburner, J., & Friston, K. J. (2000a). Voxel-Based Morphometry—The Methods. NeuroImage, 11(6), 805–821. https://doi.org/10.1006/nimg.2000.0582
Ashburner, J., & Friston, K. J. (2000b). Voxel-Based Morphometry—The Methods. NeuroImage, 11(6), 805–821. https://doi.org/10.1006/nimg.2000.0582
Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839–851. https://doi.org/10.1016/j.neuroimage.2005.02.018
Ashburner, J., & Friston, K. J. (2009). Computing average shaped tissue probability templates. NeuroImage, 45(2), 333–341. https://doi.org/10.1016/j.neuroimage.2008.12.008
Ashburner, J., & Klöppel, S. (2011). Multivariate models of inter-subject anatomical variability. NeuroImage, 56(2), 422–439. https://doi.org/10.1016/j.neuroimage.2010.03.059
Attwell, D., & Iadecola, C. (2002). The neural basis of functional brain imaging signals. Trends in Neurosciences, 25(12), 621–625. https://doi.org/10.1016/S0166-2236(02)02264-6
Barnes, J., Foster, J., Boyes, R. G., Pepple, T., Moore, E. K., Schott, J. M., Frost, C., Scahill, R. I., & Fox, N. C. (2008). A comparison of methods for the automated calculation of volumes and atrophy rates in the hippocampus. NeuroImage, 40(4), 1655–1671. https://doi.org/10.1016/j.neuroimage.2008.01.012
Buxton, R. B. (2002). Introduction to Functional Magnetic Resonance Imaging: Principles and Techniques. Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511549854
Buxton, R. B., Uludağ, K., Dubowitz, D. J., & Liu, T. T. (2004). Modeling the hemodynamic response to brain activation. NeuroImage, 23, S220–S233. https://doi.org/10.1016/j.neuroimage.2004.07.013
By:van Buchem, MA (van Buchem, MA); Tofts, PS (Tofts, PS). (2000). Magnetization transfer imaging. NEUROIMAGING CLINICS OF NORTH AMERICA NEUROIMAGING CLINICS OF NORTH AMERICA, 10(4). http://apps.webofknowledge.com/full_record.do?product=UA&search_mode=GeneralSearch&qid=3&SID=S12r93sw8L3b7BInz7B&page=1&doc=1
Chupin, M., Mukuna-Bantumbakulu, A. R., Hasboun, D., Bardinet, E., Baillet, S., Kinkingnéhun, S., Lemieux, L., Dubois, B., & Garnero, L. (2007). Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer’s disease. NeuroImage, 34(3), 996–1019. https://doi.org/10.1016/j.neuroimage.2006.10.035
Daunizeau, J., Lemieux, L., Vaudano, A. E., Friston, K. J., & Stephan, K. E. (2013). An electrophysiological validation of stochastic DCM for fMRI. Frontiers in Computational Neuroscience, 6. https://doi.org/10.3389/fncom.2012.00103
Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. (15 C.E.). Proceedings of the National Academy of Sciences of the United States of America, 89(12). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC49355/
Edelman, R. R., Hesselink, J. R., & Zlatkin, M. B. (1996). MRI: clinical magnetic resonance imaging volume 1 (2nd ed). Saunders.
FIRST - FslWiki. (n.d.). http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIRST
Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences, 97(20), 11050–11055. https://doi.org/10.1073/pnas.200033797
Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302. https://doi.org/10.1016/S1053-8119(03)00202-7
Friston, K., & Penny, W. (2011). Post hoc Bayesian model selection. NeuroImage, 56(4), 2089–2099. https://doi.org/10.1016/j.neuroimage.2011.03.062
Glover, G. H., Li, T.-Q., & Ress, D. (2000). Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine, 44(1), 162–167. https://doi.org/10.1002/1522-2594(200007)44:1<162::AID-MRM23>3.0.CO;2-E
Golay, Xavier PhD*. (n.d.). Perfusion Imaging Using Arterial Spin Labeling. Topics in Magnetic Resonance Imaging, 15(1), 10–27. http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&AN=00002142-200402000-00003&LSLINK=80&D=ovft
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001). A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains. NeuroImage, 14(1), 21–36. https://doi.org/10.1006/nimg.2001.0786
Hobbs, N. Z., Pedrick, A. V., Say, M. J., Frost, C., Dar Santos, R., Coleman, A., Sturrock, A., Craufurd, D., Stout, J. C., Leavitt, B. R., Barnes, J., Tabrizi, S. J., & Scahill, R. I. (2011). The structural involvement of the cingulate cortex in premanifest and early Huntington’s disease. Movement Disorders, 26(9), 1684–1690. https://doi.org/10.1002/mds.23747
Huettel, S. A., Song, A. W., & McCarthy, G. (2014). Functional magnetic resonance imaging (Third edition). Sinauer Associates, Inc., Publishers.
Human Brain Function. (n.d.). http://www.fil.ion.ucl.ac.uk/spm/doc/books/hbf1/
Jezzard, P., & Balaban, R. S. (1995). Correction for geometric distortion in echo planar images from B0 field variations. Magnetic Resonance in Medicine, 34(1), 65–73. https://doi.org/10.1002/mrm.1910340111
Jezzard, P., Matthews, P. M., & Smith, S. M. (2001). Functional magnetic resonance imaging: an introduction to methods. Oxford University Press.
Johansen-Berg, H., & Behrens, T. E. J. (Eds.). (2014). Diffusion MRI: from quantitative measurement to in vivo neuroanatomy (Second edition). Academic Press. http://www.sciencedirect.com/science/book/9780123964601
John Detre’s slides on ASL fMRI. (n.d.). https://cfn.upenn.edu/perfusion/index.htm
Johnson, G. (n.d.). Absolute Beginners Guide to Perfusion MRI. http://cds.ismrm.org/ismrm-2008/files/Syllabus-036.pdf
Jones, D. K. (2011). Diffusion MRI: theory, methods, and applications. Oxford University Press.
Kahan, J., & Foltynie, T. (2013). Understanding DCM: Ten simple rules for the clinician. NeuroImage, 83, 542–549. https://doi.org/10.1016/j.neuroimage.2013.07.008
Le Bihan, D. (2003). Looking into the functional architecture of the brain with diffusion MRI. Nature Reviews Neuroscience, 4(6), 469–480. https://doi.org/10.1038/nrn1119
Li, B., Daunizeau, J., Stephan, K. E., Penny, W., Hu, D., & Friston, K. (2011). Generalised filtering and stochastic DCM for fMRI. NeuroImage, 58(2), 442–457. https://doi.org/10.1016/j.neuroimage.2011.01.085
Logothetis, N. K. (2008a). What we can do and what we cannot do with fMRI. Nature, 453(7197), 869–878. https://doi.org/10.1038/nature06976
Logothetis, N. K. (2008b). What we can do and what we cannot do with fMRI. Nature, 453(7197), 869–878. https://doi.org/10.1038/nature06976
Marreiros, A. C., Kiebel, S. J., & Friston, K. J. (2008). Dynamic causal modelling for fMRI: A two-state model. NeuroImage, 39(1), 269–278. https://doi.org/10.1016/j.neuroimage.2007.08.019
Mechelli, A. (2005). Structural Covariance in the Human Cortex. Journal of Neuroscience, 25(36), 8303–8310. https://doi.org/10.1523/JNEUROSCI.0357-05.2005
Mechelli, A., Price, C., Friston, K., & Ashburner, J. (2005). Voxel-Based Morphometry of the Human Brain: Methods and Applications. Current Medical Imaging Reviews, 1(2), 105–113. https://doi.org/10.2174/1573405054038726
Norris, D. G. (2006). Principles of magnetic resonance assessment of brain function. Journal of Magnetic Resonance Imaging, 23(6), 794–807. https://doi.org/10.1002/jmri.20587
Parkes, L. M., & Detre, J. A. (2003). ASL: Blood Perfusion Measurements Using Arterial Spin Labelling. In P. Tofts (Ed.), Quantitative MRI of the Brain (pp. 455–473). John Wiley & Sons, Ltd. https://doi.org/10.1002/0470869526.ch13
Pennec, X., Cachier, P., & Ayache, N. (1999). Understanding the "Demon’s Algorithm”: 3D Non-rigid Registration by Gradient Descent. In C. Taylor & A. Colchester (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI’99 (Vol. 1679, pp. 597–605). Springer Berlin Heidelberg. https://doi.org/10.1007/10704282_64
Questions and Answers in MRI. (n.d.). http://mri-q.com/index.html
Razi, A., Kahan, J., Rees, G., & Friston, K. J. (2015). Construct validation of a DCM for resting state fMRI. NeuroImage, 106, 1–14. https://doi.org/10.1016/j.neuroimage.2014.11.027
Rohlfing, T. (2012). Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable. IEEE Transactions on Medical Imaging, 31(2), 153–163. https://doi.org/10.1109/TMI.2011.2163944
Rosa, M. J., Friston, K., & Penny, W. (2012). Post-hoc selection of dynamic causal models. Journal of Neuroscience Methods, 208(1), 66–78. https://doi.org/10.1016/j.jneumeth.2012.04.013
Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999). Nonrigid registration using free-form deformations: application to breast MR images. IEEE Transactions on Medical Imaging, 18(8), 712–721. https://doi.org/10.1109/42.796284
Schmitz, C., & Hof, P. R. (2005). Design-based stereology in neuroscience. Neuroscience, 130(4), 813–831. https://doi.org/10.1016/j.neuroscience.2004.08.050
Stephan, K. E. (2004). On the role of general system theory for functional neuroimaging. Journal of Anatomy, 205(6), 443–470. https://doi.org/10.1111/j.0021-8782.2004.00359.x
Stephan, K. E., Kasper, L., Harrison, L. M., Daunizeau, J., den Ouden, H. E. M., Breakspear, M., & Friston, K. J. (2008). Nonlinear dynamic causal models for fMRI. NeuroImage, 42(2), 649–662. https://doi.org/10.1016/j.neuroimage.2008.04.262
Stephan, K. E., Penny, W. D., Moran, R. J., den Ouden, H. E. M., Daunizeau, J., & Friston, K. J. (2010). Ten simple rules for dynamic causal modeling. NeuroImage, 49(4), 3099–3109. https://doi.org/10.1016/j.neuroimage.2009.11.015
Studholme, C., Hill, D. L. G., & Hawkes, D. J. (1999). An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition, 32(1), 71–86. https://doi.org/10.1016/S0031-3203(98)00091-0
Tofts, P. & John Wiley & Sons, Ltd. (2003). Quantitative MRI of the brain: measuring changes caused by disease. Wiley. http://dx.doi.org/10.1002/0470869526
Triantafyllou, C., Hoge, R. D., Krueger, G., Wiggins, C. J., Potthast, A., Wiggins, G. C., & Wald, L. L. (2005). Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters. NeuroImage, 26(1), 243–250. https://doi.org/10.1016/j.neuroimage.2005.01.007
Weiskopf, N., Hutton, C., Josephs, O., & Deichmann, R. (2006). Optimal EPI parameters for reduction of susceptibility-induced BOLD sensitivity losses: A whole-brain analysis at 3 T and 1.5 T. NeuroImage, 33(2), 493–504. https://doi.org/10.1016/j.neuroimage.2006.07.029
Wiggins, G. C., Triantafyllou, C., Potthast, A., Reykowski, A., Nittka, M., & Wald, L. L. (2006). 32-channel 3 Tesla receive-only phased-array head coil with soccer-ball element geometry. Magnetic Resonance in Medicine, 56(1), 216–223. https://doi.org/10.1002/mrm.20925
Wright, I. C., McGuire, P. K., Poline, J.-B., Travere, J. M., Murray, R. M., Frith, C. D., Frackowiak, R. S. J., & Friston, K. J. (1995). A Voxel-Based Method for the Statistical Analysis of Gray and White Matter Density Applied to Schizophrenia. NeuroImage, 2(4), 244–252. https://doi.org/10.1006/nimg.1995.1032