1
Attwell D, Iadecola C. The neural basis of functional brain imaging signals. Trends in Neurosciences 2002;25:621–5. doi:10.1016/S0166-2236(02)02264-6
2
Buxton RB, Uludağ K, Dubowitz DJ, et al. Modeling the hemodynamic response to brain activation. NeuroImage 2004;23:S220–33. doi:10.1016/j.neuroimage.2004.07.013
3
Logothetis NK. What we can do and what we cannot do with fMRI. Nature 2008;453:869–78. doi:10.1038/nature06976
4
Edelman RR, Hesselink JR, Zlatkin MB. MRI: clinical magnetic resonance imaging volume 1. 2nd ed. Philadelphia: : Saunders 1996.
5
Logothetis NK. What we can do and what we cannot do with fMRI. Nature 2008;453:869–78. doi:10.1038/nature06976
6
Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proceedings of the National Academy of Sciences of the United States of America 15AD;89.http://www.ncbi.nlm.nih.gov/pmc/articles/PMC49355/
7
Norris DG. Principles of magnetic resonance assessment of brain function. Journal of Magnetic Resonance Imaging 2006;23:794–807. doi:10.1002/jmri.20587
8
Jezzard P, Matthews PM, Smith SM. Functional magnetic resonance imaging: an introduction to methods. Oxford: : Oxford University Press 2001.
9
Buxton RB. Introduction to Functional Magnetic Resonance Imaging: Principles and Techniques. Cambridge: : Cambridge University Press 2002. http://dx.doi.org/10.1017/CBO9780511549854
10
Huettel SA, Song AW, McCarthy G. Functional magnetic resonance imaging. Third edition. Sunderland, Massachusetts, U.S.A.: : Sinauer Associates, Inc., Publishers 2014.
11
Human Brain Function. http://www.fil.ion.ucl.ac.uk/spm/doc/books/hbf1/
12
Triantafyllou C, Hoge RD, Krueger G, et al. Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters. NeuroImage 2005;26:243–50. doi:10.1016/j.neuroimage.2005.01.007
13
Wiggins GC, Triantafyllou C, Potthast A, et al. 32-channel 3 Tesla receive-only phased-array head coil with soccer-ball element geometry. Magnetic Resonance in Medicine 2006;56:216–23. doi:10.1002/mrm.20925
14
Weiskopf N, Hutton C, Josephs O, et al. Optimal EPI parameters for reduction of susceptibility-induced BOLD sensitivity losses: A whole-brain analysis at 3 T and 1.5 T. NeuroImage 2006;33:493–504. doi:10.1016/j.neuroimage.2006.07.029
15
Glover GH, Li T-Q, Ress D. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine 2000;44:162–7. doi:10.1002/1522-2594(200007)44:1<162::AID-MRM23>3.0.CO;2-E
16
Jezzard P, Balaban RS. Correction for geometric distortion in echo planar images from B0 field variations. Magnetic Resonance in Medicine 1995;34:65–73. doi:10.1002/mrm.1910340111
17
Andersson JLR, Hutton C, Ashburner J, et al. Modeling Geometric Deformations in EPI Time Series. NeuroImage 2001;13:903–19. doi:10.1006/nimg.2001.0746
18
Friston KJ, Harrison L, Penny W. Dynamic causal modelling. NeuroImage 2003;19:1273–302. doi:10.1016/S1053-8119(03)00202-7
19
Stephan KE. On the role of general system theory for functional neuroimaging. Journal of Anatomy 2004;205:443–70. doi:10.1111/j.0021-8782.2004.00359.x
20
Kahan J, Foltynie T. Understanding DCM: Ten simple rules for the clinician. NeuroImage 2013;83:542–9. doi:10.1016/j.neuroimage.2013.07.008
21
Stephan KE, Penny WD, Moran RJ, et al. Ten simple rules for dynamic causal modeling. NeuroImage 2010;49:3099–109. doi:10.1016/j.neuroimage.2009.11.015
22
Marreiros AC, Kiebel SJ, Friston KJ. Dynamic causal modelling for fMRI: A two-state model. NeuroImage 2008;39:269–78. doi:10.1016/j.neuroimage.2007.08.019
23
Stephan KE, Kasper L, Harrison LM, et al. Nonlinear dynamic causal models for fMRI. NeuroImage 2008;42:649–62. doi:10.1016/j.neuroimage.2008.04.262
24
Li B, Daunizeau J, Stephan KE, et al. Generalised filtering and stochastic DCM for fMRI. NeuroImage 2011;58:442–57. doi:10.1016/j.neuroimage.2011.01.085
25
Daunizeau J, Lemieux L, Vaudano AE, et al. An electrophysiological validation of stochastic DCM for fMRI. Frontiers in Computational Neuroscience 2013;6. doi:10.3389/fncom.2012.00103
26
Friston K, Penny W. Post hoc Bayesian model selection. NeuroImage 2011;56:2089–99. doi:10.1016/j.neuroimage.2011.03.062
27
Rosa MJ, Friston K, Penny W. Post-hoc selection of dynamic causal models. Journal of Neuroscience Methods 2012;208:66–78. doi:10.1016/j.jneumeth.2012.04.013
28
Razi A, Kahan J, Rees G, et al. Construct validation of a DCM for resting state fMRI. NeuroImage 2015;106:1–14. doi:10.1016/j.neuroimage.2014.11.027
29
Jones DK. Diffusion MRI: theory, methods, and applications. New York: : Oxford University Press 2011.
30
Johansen-Berg H, Behrens TEJ, editors. Diffusion MRI: from quantitative measurement to in vivo neuroanatomy. Second edition. Amsterdam: : Academic Press 2014. http://www.sciencedirect.com/science/book/9780123964601
31
Le Bihan D. Looking into the functional architecture of the brain with diffusion MRI. Nature Reviews Neuroscience 2003;4:469–80. doi:10.1038/nrn1119
32
Questions and Answers in MRI. http://mri-q.com/index.html
33
Artifacts in Diffusion MRI. http://stbb.nichd.nih.gov/pdf/9780195369779_Jone-Pierpaoli.pdf
34
Golay, Xavier PhD*. Perfusion Imaging Using Arterial Spin Labeling. Topics in Magnetic Resonance Imaging;15:10–27.http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&AN=00002142-200402000-00003&LSLINK=80&D=ovft
35
Parkes LM, Detre JA. ASL: Blood Perfusion Measurements Using Arterial Spin Labelling. In: Tofts P, ed. Quantitative MRI of the Brain. Chichester, UK: : John Wiley & Sons, Ltd 2003. 455–73. doi:10.1002/0470869526.ch13
36
Johnson G. Absolute Beginners Guide to Perfusion MRI. http://cds.ismrm.org/ismrm-2008/files/Syllabus-036.pdf
37
John Detre’s slides on ASL fMRI. https://cfn.upenn.edu/perfusion/index.htm
38
Tofts P, John Wiley & Sons, Ltd. Quantitative MRI of the brain: measuring changes caused by disease. Chichester, West Sussex: : Wiley 2003. http://dx.doi.org/10.1002/0470869526
39
By:van Buchem, MA (van Buchem, MA); Tofts, PS (Tofts, PS). Magnetization transfer imaging. NEUROIMAGING CLINICS OF NORTH AMERICA NEUROIMAGING CLINICS OF NORTH AMERICA 2000;10.http://apps.webofknowledge.com/full_record.do?product=UA&search_mode=GeneralSearch&qid=3&SID=S12r93sw8L3b7BInz7B&page=1&doc=1
40
Schmitz C, Hof PR. Design-based stereology in neuroscience. Neuroscience 2005;130:813–31. doi:10.1016/j.neuroscience.2004.08.050
41
Hobbs NZ, Pedrick AV, Say MJ, et al. The structural involvement of the cingulate cortex in premanifest and early Huntington’s disease. Movement Disorders 2011;26:1684–90. doi:10.1002/mds.23747
42
Chupin M, Mukuna-Bantumbakulu AR, Hasboun D, et al. 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 2007;34:996–1019. doi:10.1016/j.neuroimage.2006.10.035
43
Ashburner J, Friston KJ. Voxel-Based Morphometry—The Methods. NeuroImage 2000;11:805–21. doi:10.1006/nimg.2000.0582
44
Barnes J, Foster J, Boyes RG, et al. A comparison of methods for the automated calculation of volumes and atrophy rates in the hippocampus. NeuroImage 2008;40:1655–71. doi:10.1016/j.neuroimage.2008.01.012
45
Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences 2000;97:11050–5. doi:10.1073/pnas.200033797
46
FIRST - FslWiki. http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIRST
47
Ashburner J, Klöppel S. Multivariate models of inter-subject anatomical variability. NeuroImage 2011;56:422–39. doi:10.1016/j.neuroimage.2010.03.059
48
Mechelli A. Structural Covariance in the Human Cortex. Journal of Neuroscience 2005;25:8303–10. doi:10.1523/JNEUROSCI.0357-05.2005
49
Wright IC, McGuire PK, Poline J-B, et al. A Voxel-Based Method for the Statistical Analysis of Gray and White Matter Density Applied to Schizophrenia. NeuroImage 1995;2:244–52. doi:10.1006/nimg.1995.1032
50
Ashburner J, Friston KJ. Voxel-Based Morphometry—The Methods. NeuroImage 2000;11:805–21. doi:10.1006/nimg.2000.0582
51
Good CD, Johnsrude IS, Ashburner J, et al. A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains. NeuroImage 2001;14:21–36. doi:10.1006/nimg.2001.0786
52
Ashburner J, Friston KJ. Unified segmentation. NeuroImage 2005;26:839–51. doi:10.1016/j.neuroimage.2005.02.018
53
Mechelli A, Price C, Friston K, et al. Voxel-Based Morphometry of the Human Brain: Methods and Applications. Current Medical Imaging Reviews 2005;1:105–13. doi:10.2174/1573405054038726
54
Ashburner J. A fast diffeomorphic image registration algorithm. NeuroImage 2007;38:95–113. doi:10.1016/j.neuroimage.2007.07.007
55
Ashburner J, Friston KJ. Computing average shaped tissue probability templates. NeuroImage 2009;45:333–41. doi:10.1016/j.neuroimage.2008.12.008
56
Ashburner J. Computational anatomy with the SPM software. Magnetic Resonance Imaging 2009;27:1163–74. doi:10.1016/j.mri.2009.01.006
57
Studholme C, Hill DLG, Hawkes DJ. An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 1999;32:71–86. doi:10.1016/S0031-3203(98)00091-0
58
Pennec X, Cachier P, Ayache N. Understanding the "Demon’s Algorithm”: 3D Non-rigid Registration by Gradient Descent. In: Taylor C, Colchester A, eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI’99. Berlin, Heidelberg: : Springer Berlin Heidelberg 1999. 597–605. doi:10.1007/10704282_64
59
Rueckert D, Sonoda LI, Hayes C, et al. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Transactions on Medical Imaging 1999;18:712–21. doi:10.1109/42.796284
60
Rohlfing T. Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable. IEEE Transactions on Medical Imaging 2012;31:153–63. doi:10.1109/TMI.2011.2163944
