Bibliography for ANIMGN11 / ANIMGN20: Advanced Imaging BETA

Andersson, J.L.R. et al. (2001) ‘Modeling Geometric Deformations in EPI Time Series’, NeuroImage, 13(5), pp. 903–919. Available at: https://doi.org/10.1006/nimg.2001.0746.

‘Artifacts in Diffusion MRI’ (no date). Available at: http://stbb.nichd.nih.gov/pdf/9780195369779_Jone-Pierpaoli.pdf.

Ashburner, J. (2007) ‘A fast diffeomorphic image registration algorithm’, NeuroImage, 38(1), pp. 95–113. Available at: https://doi.org/10.1016/j.neuroimage.2007.07.007.

Ashburner, J. (2009) ‘Computational anatomy with the SPM software’, Magnetic Resonance Imaging, 27(8), pp. 1163–1174. Available at: https://doi.org/10.1016/j.mri.2009.01.006.

Ashburner, J. and Friston, K.J. (2000a) ‘Voxel-Based Morphometry—The Methods’, NeuroImage, 11(6), pp. 805–821. Available at: https://doi.org/10.1006/nimg.2000.0582.

Ashburner, J. and Friston, K.J. (2000b) ‘Voxel-Based Morphometry—The Methods’, NeuroImage, 11(6), pp. 805–821. Available at: https://doi.org/10.1006/nimg.2000.0582.

Ashburner, J. and Friston, K.J. (2005) ‘Unified segmentation’, NeuroImage, 26(3), pp. 839–851. Available at: https://doi.org/10.1016/j.neuroimage.2005.02.018.

Ashburner, J. and Friston, K.J. (2009) ‘Computing average shaped tissue probability templates’, NeuroImage, 45(2), pp. 333–341. Available at: https://doi.org/10.1016/j.neuroimage.2008.12.008.

Ashburner, J. and Klöppel, S. (2011) ‘Multivariate models of inter-subject anatomical variability’, NeuroImage, 56(2), pp. 422–439. Available at: https://doi.org/10.1016/j.neuroimage.2010.03.059.

Attwell, D. and Iadecola, C. (2002) ‘The neural basis of functional brain imaging signals’, Trends in Neurosciences, 25(12), pp. 621–625. Available at: https://doi.org/10.1016/S0166-2236(02)02264-6.

Barnes, J. et al. (2008) ‘A comparison of methods for the automated calculation of volumes and atrophy rates in the hippocampus’, NeuroImage, 40(4), pp. 1655–1671. Available at: https://doi.org/10.1016/j.neuroimage.2008.01.012.

Buxton, R.B. (2002) Introduction to Functional Magnetic Resonance Imaging: Principles and Techniques. Cambridge: Cambridge University Press. Available at: http://dx.doi.org/10.1017/CBO9780511549854.

Buxton, R.B. et al. (2004) ‘Modeling the hemodynamic response to brain activation’, NeuroImage, 23, pp. S220–S233. Available at: 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). Available at: http://apps.webofknowledge.com/full_record.do?product=UA&search_mode=GeneralSearch&qid=3&SID=S12r93sw8L3b7BInz7B&page=1&doc=1.

Chupin, M. et al. (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), pp. 996–1019. Available at: https://doi.org/10.1016/j.neuroimage.2006.10.035.

Daunizeau, J. et al. (2013) ‘An electrophysiological validation of stochastic DCM for fMRI’, Frontiers in Computational Neuroscience, 6. Available at: https://doi.org/10.3389/fncom.2012.00103.

‘Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation.’ (15AD) Proceedings of the National Academy of Sciences of the United States of America, 89(12). Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC49355/.

Edelman, R.R., Hesselink, J.R. and Zlatkin, M.B. (1996) MRI: clinical magnetic resonance imaging volume 1. 2nd ed. Philadelphia: Saunders.

FIRST - FslWiki (no date). Available at: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIRST.

Fischl, B. and 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), pp. 11050–11055. Available at: https://doi.org/10.1073/pnas.200033797.

Friston, K. and Penny, W. (2011) ‘Post hoc Bayesian model selection’, NeuroImage, 56(4), pp. 2089–2099. Available at: https://doi.org/10.1016/j.neuroimage.2011.03.062.

Friston, K.J., Harrison, L. and Penny, W. (2003) ‘Dynamic causal modelling’, NeuroImage, 19(4), pp. 1273–1302. Available at: https://doi.org/10.1016/S1053-8119(03)00202-7.

Glover, G.H., Li, T.-Q. and Ress, D. (2000) ‘Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR’, Magnetic Resonance in Medicine, 44(1), pp. 162–167. Available at: https://doi.org/10.1002/1522-2594(200007)44:1<162::AID-MRM23>3.0.CO;2-E.

Golay, Xavier PhD* (no date) ‘Perfusion Imaging Using Arterial Spin Labeling’, Topics in Magnetic Resonance Imaging, 15(1), pp. 10–27. Available at: 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. et al. (2001) ‘A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains’, NeuroImage, 14(1), pp. 21–36. Available at: https://doi.org/10.1006/nimg.2001.0786.

Hobbs, N.Z. et al. (2011) ‘The structural involvement of the cingulate cortex in premanifest and early Huntington’s disease’, Movement Disorders, 26(9), pp. 1684–1690. Available at: https://doi.org/10.1002/mds.23747.

Huettel, S.A., Song, A.W. and McCarthy, G. (2014) Functional magnetic resonance imaging. Third edition. Sunderland, Massachusetts, U.S.A.: Sinauer Associates, Inc., Publishers.

Human Brain Function (no date). Available at: http://www.fil.ion.ucl.ac.uk/spm/doc/books/hbf1/.

Jezzard, P. and Balaban, R.S. (1995) ‘Correction for geometric distortion in echo planar images from B0 field variations’, Magnetic Resonance in Medicine, 34(1), pp. 65–73. Available at: https://doi.org/10.1002/mrm.1910340111.

Jezzard, P., Matthews, P.M. and Smith, S.M. (2001) Functional magnetic resonance imaging: an introduction to methods. Oxford: Oxford University Press.

Johansen-Berg, H. and Behrens, T.E.J. (eds) (2014) Diffusion MRI: from quantitative measurement to in vivo neuroanatomy. Second edition. Amsterdam: Academic Press. Available at: http://www.sciencedirect.com/science/book/9780123964601.

John Detre’s slides on ASL fMRI (no date). Available at: https://cfn.upenn.edu/perfusion/index.htm.

Johnson, G. (no date) ‘Absolute Beginners Guide to Perfusion MRI’. Available at: http://cds.ismrm.org/ismrm-2008/files/Syllabus-036.pdf.

Jones, D.K. (2011) Diffusion MRI: theory, methods, and applications. New York: Oxford University Press.

Kahan, J. and Foltynie, T. (2013) ‘Understanding DCM: Ten simple rules for the clinician’, NeuroImage, 83, pp. 542–549. Available at: 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), pp. 469–480. Available at: https://doi.org/10.1038/nrn1119.

Li, B. et al. (2011) ‘Generalised filtering and stochastic DCM for fMRI’, NeuroImage, 58(2), pp. 442–457. Available at: 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), pp. 869–878. Available at: https://doi.org/10.1038/nature06976.

Logothetis, N.K. (2008b) ‘What we can do and what we cannot do with fMRI’, Nature, 453(7197), pp. 869–878. Available at: https://doi.org/10.1038/nature06976.

Marreiros, A.C., Kiebel, S.J. and Friston, K.J. (2008) ‘Dynamic causal modelling for fMRI: A two-state model’, NeuroImage, 39(1), pp. 269–278. Available at: https://doi.org/10.1016/j.neuroimage.2007.08.019.

Mechelli, A. (2005) ‘Structural Covariance in the Human Cortex’, Journal of Neuroscience, 25(36), pp. 8303–8310. Available at: https://doi.org/10.1523/JNEUROSCI.0357-05.2005.

Mechelli, A. et al. (2005) ‘Voxel-Based Morphometry of the Human Brain: Methods and Applications’, Current Medical Imaging Reviews, 1(2), pp. 105–113. Available at: 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), pp. 794–807. Available at: https://doi.org/10.1002/jmri.20587.

Parkes, L.M. and Detre, J.A. (2003) ‘ASL: Blood Perfusion Measurements Using Arterial Spin Labelling’, in P. Tofts (ed.) Quantitative MRI of the Brain. Chichester, UK: John Wiley & Sons, Ltd, pp. 455–473. Available at: https://doi.org/10.1002/0470869526.ch13.

Pennec, X., Cachier, P. and Ayache, N. (1999) ‘Understanding the "Demon’s Algorithm”: 3D Non-rigid Registration by Gradient Descent’, in C. Taylor and A. Colchester (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI’99. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 597–605. Available at: https://doi.org/10.1007/10704282_64.

Questions and Answers in MRI (no date). Available at: http://mri-q.com/index.html.

Razi, A. et al. (2015) ‘Construct validation of a DCM for resting state fMRI’, NeuroImage, 106, pp. 1–14. Available at: 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), pp. 153–163. Available at: https://doi.org/10.1109/TMI.2011.2163944.

Rosa, M.J., Friston, K. and Penny, W. (2012) ‘Post-hoc selection of dynamic causal models’, Journal of Neuroscience Methods, 208(1), pp. 66–78. Available at: https://doi.org/10.1016/j.jneumeth.2012.04.013.

Rueckert, D. et al. (1999) ‘Nonrigid registration using free-form deformations: application to breast MR images’, IEEE Transactions on Medical Imaging, 18(8), pp. 712–721. Available at: https://doi.org/10.1109/42.796284.

Schmitz, C. and Hof, P.R. (2005) ‘Design-based stereology in neuroscience’, Neuroscience, 130(4), pp. 813–831. Available at: 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), pp. 443–470. Available at: https://doi.org/10.1111/j.0021-8782.2004.00359.x.

Stephan, K.E. et al. (2008) ‘Nonlinear dynamic causal models for fMRI’, NeuroImage, 42(2), pp. 649–662. Available at: https://doi.org/10.1016/j.neuroimage.2008.04.262.

Stephan, K.E. et al. (2010) ‘Ten simple rules for dynamic causal modeling’, NeuroImage, 49(4), pp. 3099–3109. Available at: https://doi.org/10.1016/j.neuroimage.2009.11.015.

Studholme, C., Hill, D.L.G. and Hawkes, D.J. (1999) ‘An overlap invariant entropy measure of 3D medical image alignment’, Pattern Recognition, 32(1), pp. 71–86. Available at: https://doi.org/10.1016/S0031-3203(98)00091-0.

Tofts, P. and John Wiley & Sons, Ltd (2003) Quantitative MRI of the brain: measuring changes caused by disease. Chichester, West Sussex: Wiley. Available at: http://dx.doi.org/10.1002/0470869526.

Triantafyllou, C. et al. (2005) ‘Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters’, NeuroImage, 26(1), pp. 243–250. Available at: https://doi.org/10.1016/j.neuroimage.2005.01.007.

Weiskopf, N. et al. (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), pp. 493–504. Available at: https://doi.org/10.1016/j.neuroimage.2006.07.029.

Wiggins, G.C. et al. (2006) ‘32-channel 3 Tesla receive-only phased-array head coil with soccer-ball element geometry’, Magnetic Resonance in Medicine, 56(1), pp. 216–223. Available at: https://doi.org/10.1002/mrm.20925.

Wright, I.C. et al. (1995) ‘A Voxel-Based Method for the Statistical Analysis of Gray and White Matter Density Applied to Schizophrenia’, NeuroImage, 2(4), pp. 244–252. Available at: https://doi.org/10.1006/nimg.1995.1032.