Skip to main content

An Introduction to fMRI

  • Chapter
  • First Online:
An Introduction to Model-Based Cognitive Neuroscience

Abstract

Functional magnetic resonance imaging (fMRI) provides an opportunity to indirectly observe neural activity noninvasively in the human brain as it changes in near real time. Most fMRI experiments measure the blood oxygen-level dependent (BOLD) signal, which rises to a peak several seconds after a brain area becomes active. Several experimental designs are common in fMRI research. Block designs alternate periods in which subjects perform some task with periods of rest, whereas event-related designs present the subject with a set of discrete trials. After the fMRI experiment is complete, pre-processing analyses prepare the data for task-related analyses. The most popular task-related analysis uses the General Linear Model to correlate a predicted BOLD response with the observed activity in each brain region. Regions where this correlation is high are identified as task related. Connectivity analysis then tries to identify active regions that belong to the same functional network. In contrast, multivariate methods, such as independent component analysis and multi-voxel pattern analysis identify networks of event-related regions, rather than single regions, so they simultaneously address questions of functional connectivity.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dobbs D (2005) Fact or phrenology? Scientific American Mind

    Google Scholar 

  2. Waldschmidt JG, Ashby FG (2011) Cortical and striatal contributions to automaticity in information-integration categorization. Neuroimage 56:1791 -1802

    Google Scholar 

  3. Ashby FG, O’Brien JB (2005) Category learning and multiple memory systems. Trends Cogn Sci 2:83–89

    Google Scholar 

  4. Lopez-Paniagua D, Seger CA (2011) Interactions within and between corticostriatal loops during component processes of category learning. J Cogn Neurosci 23:3068 -3083

    Google Scholar 

  5. Seger CA, Cincotta CM (2005) The roles of the caudate nucleus in human classification learning. J Neurosci 25:2941–2951

    Google Scholar 

  6. Seger CA, Peterson EJ, Cincotta CM, Lopez-Paniagua D, Anderson CW (2010) Dissociating the contributions of independent corticostriatal systems to visual categorization learning through the use of reinforcement learning modeling and Granger causality modeling. NeuroImage 50:644–656

    Google Scholar 

  7. Pauling L, Coryell CD (1936) The magnetic properties and structure of hemoglobin, oxygenated hemoglobin, and carbonmonoxygenated hemoglobin. Proc Nat Acad Sci U S A 22:21022:210–236

    Google Scholar 

  8. Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE, Penny WD (eds) (2007) Statistical parametric mapping: the analysis of functional brain images. Academic, London

    Google Scholar 

  9. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy R, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23:208–219

    Google Scholar 

  10. Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, Beckmann C, Jenkinson M, Smith SM (2009) Bayesian analysis of neuroimaging data in FSL. NeuroImage 45:173–186

    Google Scholar 

  11. Ogawa S, Lee TM, Kay AR, Tank DW (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Nat Acad Sci 87:9868–9872

    Google Scholar 

  12. Ogawa S, Lee TM, Nayak AS, Glynn P (1990) Oxygenation-sensitive contrast in magnetic resonance imaging of rodent brain at high magnetic fields. Magn Reson Med 16:9–18

    Google Scholar 

  13. Logothetis NK (2003) The underpinnings of the BOLD functional magnetic resonance imaging signal. J Neurosci 23:3963–3971

    Google Scholar 

  14. Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A (2001) Neurophysiological investigation of the basis of the fMRI signal. Nature 412:150–157

    Google Scholar 

  15. Boynton GM, Engel SA, Glover GH, Heeger DJ (1996) Linear systems analysis of functional magnetic resonance imaging in human V1. J Neurosci 16:4207–4221

    Google Scholar 

  16. Buxton RB, Frank LR (1998) A model for coupling between cerebral blood flow and oxygen metabolism during neural stimulation. J Cerebral Blood Flow Metab 17:64–72

    Google Scholar 

  17. Vazquez AL, Noll DC (1998) Non-linear aspects of the blood oxygenation response in functional MRI. NeuroImage 8:108–118

    Google Scholar 

  18. Huettel SA, Singerman JD, McCarthy G (2001) The effects of aging upon the hemodynamic response measured by functional MRI. NeuroImage 13:161–175

    Google Scholar 

  19. Richter W, Richter M (2003) The shape of the fMRI BOLD response in children and adults changes systematically with age. NeuroImage 20:1122–1131

    Google Scholar 

  20. Huettel SA, Song AW, McCarthy G (2004) Functional magnetic resonance imaging. Sinauer, Sunderland

    Google Scholar 

  21. Ashburner J, Friston K (2007) Rigid body registration. In: Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE, Penny WD (eds) Statistical parametric mapping: the analysis of functional brain images. Academic, London, pp 49–62

    Google Scholar 

  22. Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, Chiang M-C, Christensen GE, Collins DL, Gee J, Hellier P, Song JH, Jenkinson M, Lepage C, Rueckert D, Thompson P, Vercauteren T, Woods RP, Mann JJ, Parsey RV (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46:786 -802

    Google Scholar 

  23. Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain: 3-Dimensional proportional system—an approach to cerebral imaging. Thieme Medical Publishers, NewYork

    Google Scholar 

  24. Friston KJ, Frith CD, Liddle PF, Frackowiak RS (1991) Comparing functional (PET) images: the assessment of significant change. J Cerebral Blood Flow Metab 11:690–699

    Google Scholar 

  25. Friston K, Holmes A, Worsley K, Poline J, Frith C, Frackowiak R (1995) Statistical parametric maps in functional imaging: a general linear approach. Hum Brain Mapp 2:189–210

    Google Scholar 

  26. O’Doherty JP, Hampton A, Kim H (2007) Model-based fMRI and its application to reward learning and decision making. Ann NY Acad Sci 1104:35–53

    Google Scholar 

  27. O’Doherty J, Dayan P, Schultz J, Deichmann R, Friston K, Dolan RJ (2004) Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science 304:452–454

    Google Scholar 

  28. Worsley KJ (1995) Estimating the number of peaks in a random field using the Hadwiger characteristic of excursion sets with applications to medical images. Ann Stat 23:640–669

    Google Scholar 

  29. Worsley KJ, Marrett S, Neelin P, Vandal AC, Friston KJ, Evans AC (1996) A unified statistical approach for determining significant signals in images of cerebral activation. Hum Brain Mapp 4:58–73

    Google Scholar 

  30. Nichols TE, Holmes AP (2001) Nonparametric permutation tests for functional neuroimaging: A primer with examples. Hum Brain Mapp 15:1–25

    Google Scholar 

  31. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Methodol 57:289–300

    Google Scholar 

  32. Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424–438

    Google Scholar 

  33. Ashby FG (2011) Statistical analysis of fMRI data. MIT Press, Boston

    Google Scholar 

  34. Sun FT, Miller LM, D’Esposito M (2004) Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. NeuroImage 21:647–658

    Google Scholar 

  35. Hasson U, Nir Y, Levy I, Fuhrmann G, Malach R (2004) Intersubject synchronization of cortical activity during natural vision. Science 303:1634–1640

    Google Scholar 

  36. Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms. Hum Brain Mapp 13:43–53

    Google Scholar 

  37. McKeown MJ, Makeig S, Brown GG, Jung T-P, Kindermann SS, Bell AJ, Sejnowski TJ (1998) Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp 6:160–188

    Google Scholar 

  38. Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain's default network: Anatomy, function, and relevance to disease. Ann NY Acad Sci 1124:1–38

    Google Scholar 

  39. Haynes J, Rees G (2006) Decoding mental states from brain activity in humans. Nat Rev Neurosci 7:523–534

    Google Scholar 

  40. Norman K, Polyn SM, Detre G, Haxby JV (2006) Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends Cogn Sci 10:424–430

    Google Scholar 

  41. Pereira F, Mitchell T, Botvinick M (2009) Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 45(1 Suppl):S199–209

    Google Scholar 

  42. Mumford JA, Turner BO, Ashby FG, Poldrack RA (2012) Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. NeuroImage 59:2636–2643

    Google Scholar 

  43. Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P (2001) Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293:2425–2430

    Google Scholar 

  44. Buxton RB (2002) Introduction to functional magnetic resonance imaging: principles and techniques. Cambridge University Press, NewYork

    Google Scholar 

  45. Hashemi RH, Bradley WG Jr, Lisanti CJ (2004) MRI: the basics, 2nd Ed. Lippincott Williams & Wilkins, Philadelphia

    Google Scholar 

  46. Haacke EM, Brown RW, Thompson MR, Venkatesan R (1999) Magnetic resonance imaging: physical principles and sequence design. Wiley, NewYork

    Google Scholar 

  47. Poldrack RA, Mumford JA, Nichols TE (2011) Handbook of fMRI data analysis. Cambridge University Press, NewYork

    Google Scholar 

Download references

Acknowledgments

This research was supported in part by AFOSR grant FA9550-12-1-0355 and by the U.S. Army Research Office through the Institute for Collaborative Biotechnologies under grant W911NF-07-1-0072.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to F. Gregory Ashby .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Ashby, F. (2015). An Introduction to fMRI. In: Forstmann, B., Wagenmakers, EJ. (eds) An Introduction to Model-Based Cognitive Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2236-9_5

Download citation

Publish with us

Policies and ethics