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SOME USEFUL REFERENCES mentioned in the NITP course

For MRI Physics (Ennis, Cohen)

  1. Online MRI Physics interactive book. Joseph Hornak

For Liu, Neurovascular Coupling

  1. Liu, T. T., G. H. Glover, B. A. Mueller, D. N. Greve and G. G. Brown (2013). "An introduction to normalization and calibration methods in functional MRI." Psychometrika 78(2): 308-321.
  2. Liu, T. T. (2013). "Neurovascular factors in resting-state functional MRI." Neuroimage 80: 339-348.
  3. Wong, C. W., V. Olafsson, O. Tal and T. T. Liu (2013). "The amplitude of the resting-state fMRI global signal is related to EEG vigilance measures." Neuroimage 83: 983-990.

For Monti, General Linear Model

  1. Monti, M. M. (2011). "Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach." Front Hum Neurosci 5: 28.
  2. Poldrack R.A., Mumford J.A., Nichols T.E. (2011) Handbook of Functional MRI Analysis, Cambridge University Press.
  3. Lazar, N. (2008). The statistical analysis of functional MRI data. Springer.
  4. Kiebel S.J., Holmes T.E. (2007) The general linear model. In Friston K.J., et al Statistical Parametric Mapping: The Analysis of Functional Brain Images, chapter 8.

For Rissman, Group Analysis

  1. Vul, E., C. Harris, P. Winkielman and H. Pashler (2009). "Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition." Perspect Psychol Sci 4(3): 274-290.
  2. Kriegeskorte, N., W. K. Simmons, P. S. Bellgowan and C. I. Baker (2009). "Circular analysis in systems neuroscience: the dangers of double dipping." Nat Neurosci 12(5): 535-540.
  3. Kriegeskorte, N., M. A. Lindquist, T. E. Nichols, R. A. Poldrack and E. Vul (2010). "Everything you never wanted to know about circular analysis, but were afraid to ask." J Cereb Blood Flow Metab 30(9): 1551-1557.
  4. Vul, E. and H. Pashler (2012). "Voodoo and circularity errors." Neuroimage 62(2): 945-948.
For Lindquist
  1. Coursera: Statistical Analysis of fMRI Data
For Cohen, MRI Safety
  1. MRISafety.org
  2. MRI Safety selected links and references

For McIntosh, Virtual brain

  1. Schirner, M., S. Rothmeier, V. K. Jirsa, A. R. McIntosh and P. Ritter (2015). "An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data." Neuroimage 117: 343-357.
  2. The Virtual Brain web site

For Snyder, Resting State

  1. Snyder, A. Z. and M. E. Raichle (2012). "A brief history of the resting state: the Washington University perspective." Neuroimage 62(2): 902-910.
  2. Yeo, B. T., F. M. Krienen, J. Sepulcre, M. R. Sabuncu, D. Lashkari, M. Hollinshead, J. L. Roffman, J. W. Smoller, L. Zollei, J. R. Polimeni, B. Fischl, H. Liu and R. L. Buckner (2011). "The organization of the human cerebral cortex estimated by intrinsic functional connectivity." J Neurophysiol 106(3): 1125-1165.
  3. Power, J. D., A. Mitra, T. O. Laumann, A. Z. Snyder, B. L. Schlaggar and S. E. Petersen (2014). "Methods to detect, characterize, and remove motion artifact in resting state fMRI." Neuroimage 84: 320-341.
  4. Tagliazucchi, E. and H. Laufs (2014). "Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep." Neuron 82(3): 695-708.

For Sadighiani, Graph Analysis

  1. Rubinov, M. and O. Sporns (2010). "Complex network measures of brain connectivity: uses and interpretations." Neuroimage 52(3): 1059-1069.

For Boas, fNIRS

  1. Ferrari, M. and V. Quaresima (2012). "A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application." Neuroimage 63(2): 921-935.
  2. Boas, D. A., C. E. Elwell, M. Ferrari and G. Taga (2014). "Twenty years of functional near-infrared spectroscopy: introduction for the special issue." Neuroimage 85 Pt 1: 1-5.

Van Wedeen's suggested readings

  1. Debanne, D., E. Campanac, A. Bialowas, E. Carlier and G. Alcaraz (2011). "Axon physiology." Physiol Rev 91(2): 555-602.
  2. Pollock, J. D., D. Y. Wu and J. S. Satterlee (2014). "Molecular neuroanatomy: a generation of progress." Trends Neurosci 37(2): 106-123.
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The UCLA Neuroimaging Training Program is funded by generous awards from the National Institutes of Health, grant numbers R90 DA022768 and T90 DA023422