Skip to main content
SpringerLink
Log in

Information Decoding from fMRI Images

  • Chapter
  • First Online:
fMRI: From Nuclear Spins to Brain Functions

Part of the book series: Biological Magnetic Resonance ((BIMR,volume 30))

  • 3343 Accesses

Abstract

Conventional analysis of functional magnetic resonance imaging (fMRI) time series is based on univariate statistical analysis. In this approach, a spatially invariant model of the expected blood oxygenation level-dependent (BOLD) response is fitted independently at each voxel’s time course, and the differences between estimated activation levels during two or more experimental conditions are tested. Together with methods for mitigating the problem of performing a large number of tests, this massively univariate analysis produces statistical maps of response differences, highlighting brain locations that are “selective” or “specialized” for a certain stimulus dimension, that is, voxels or regions of interest (ROI) that respond more vigorously to a sensory, motor, or cognitive stimulus compared to one or more appropriate control conditions. This approach is not appropriate when the relevant question is what is the “information” content of a certain network of brain regions rather than which is its “activation” level.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover 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

  • Allwein EL, Schapire RE, Singer Y (2000) Reducing multiclass to binary: a unifying approach for margin classifiers. J Mach Learn Res 1:113–141

    Google Scholar 

  • Beauchamp MS, Laconte S, Yasar N (2009) Distributed representation of single touches in somatosensory and visual cortex. Hum Brain Mapp 30(10):3163–3171

    Article  PubMed  Google Scholar 

  • Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

    Google Scholar 

  • Björnsdotter M, Wessberg J (2008) An evolutionary approach to the identification of informative voxel clusters for brain state discrimination. IEEE J Sle. Topics Signal Process 2(6):919–928

    Article  Google Scholar 

  • Björnsdotter M, Löken L, Olausson H, Vallbo A, Wessberg J (2009) Somatotopic organization of gentle touch processing in the posterior insular cortex. J Neurosci 29(29):9314–9320

    Article  PubMed  Google Scholar 

  • Björnsdotter M, Rylander K, Wessberg J (2011) A Monte Carlo method for locally multivariate brain mapping. Neuroimage 56(2):508–516

    Article  PubMed  Google Scholar 

  • Borg I, Groenen P (2005) Modern multidimensional scaling: theory and applications, 2nd edn. Springer, New York

    Google Scholar 

  • Carroll MK, Cecchi GC, Rish I, Garg R, Rao AR (2009) Prediction and interpretation of distributed neural activity with sparse models. Neuroimage 44(1):112–122

    Article  PubMed  Google Scholar 

  • Chaimow D, Yacoub E, Uğurbil K, Shmuel A (2011) Modeling and analyzing mechanisms underlying fMRI-based decoding of information conveyed in cortical columns. Neuroimage 56(2):627–642

    Article  PubMed Central  PubMed  Google Scholar 

  • Chu C, Ni Y, Tan G, Saunders CJ, Ashburner J (2011) Kernel regression for fMRI pattern prediction. NeuroImage 56(2):662–673

    Article  PubMed Central  PubMed  Google Scholar 

  • Cox D, Savoy R (2003) Functional magnetic resonance (fMRI) “Brain Reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage 19(2):261–270

    Article  PubMed  Google Scholar 

  • Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, New York

    Google Scholar 

  • De Martino F, Moerel M, van de Moortele PF, Uğurbil K, Goebel R, Yacoub E, Formosane E (2013) Spatial organization of frequency preference and selectivity in the huan inferior colliculus. Nat Commun 4:1386

    Google Scholar 

  • De Martino F, Valente G, Staëren N, Ashburner J, Goebel R, Formisano E (2008) Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. Neurimage 43(1):44–58

    Article  Google Scholar 

  • De Martino F, Valente G, de Borst AW, Esposito F, Roebroeck A, Goebel R, Formisano E (2010) Multimodal imaging: an evaluation of univariate and multivariate methods for simultaneous EEG/fMRI. Magn Reson Imaging 28(8):1104–1112

    Article  PubMed  Google Scholar 

  • De Martino F, de Borst AW, Valente G, Goebel R, Formisano E (2011) Predicting EEG single trial responses with simultaneous fMRI and relevance vector machine regression. Neuroimage 56(2):826–836

    Article  PubMed  Google Scholar 

  • Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York

    Google Scholar 

  • Formisano E, De Martino F, Bonte M, Goebel R (2008a) “Who” is saying “What”? Brain-based decoding of human voice and speech. Science 322(5903):970–973

    Article  CAS  PubMed  Google Scholar 

  • Formisano E, De Martino F, Valente G (2008b) Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning. Magn Reson Imaging 26(7):921–934

    Article  PubMed  Google Scholar 

  • Friston KJ, Holmes AP, Worsley KJ, Poline JB, Frith CD, Frackowiak RSJ (1995) Statistical parametric maps in functional imaging: a general linear approach. Hum Brain Mapp 2:189–210

    Article  Google Scholar 

  • Furmanski CS, Engel SA (2000) An oblique effect in human primary visual cortex. Nat Neurosci 3(6):535–536

    Article  CAS  PubMed  Google Scholar 

  • Ganesh G, Burdet E, Haruno M, Kawato M (2008) Sparse linear regression for reconstructing muscle activity from human cortical fMRI. Neuroimagen 42(4):1463–1472

    Article  CAS  Google Scholar 

  • Gardner JL (2010) Is cortical vasculature functionally organized? Neuroimage 49(3):1953–1956

    Article  PubMed  Google Scholar 

  • Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    Google Scholar 

  • Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422

    Article  Google Scholar 

  • Hanson SJ, Halchenko YO (2008) Brain reading using full brain support vector machines for object recognition: there is no face identification area. Neural Comput 20(2):486–503

    Article  PubMed  Google Scholar 

  • Haushofer J, Livingstone MS, Kanwisher N (2008) Multivariate patterns in object-selective cortex dissociate perceptual and physical shape similarity. PLoS Biol 6(7):187

    Article  Google Scholar 

  • Haxby JV, Gobbini MI, Furey ML, Ishai A, Aschouten JL, Pietrini P (2001) Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293(5539):2425–2430

    Article  CAS  PubMed  Google Scholar 

  • Haynes JD, Rees G (2005) Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nat Neurosci 8(5):686–691

    Article  CAS  PubMed  Google Scholar 

  • Kamitani Y, Sawahata Y (2010) Spatial smoothing hurts localization but not information: pitfalls for brain mappers. Neuroimage 49(3):1949–1952

    Article  PubMed  Google Scholar 

  • Kamitani Y, Tong F (2005) Decoding the visual and subjective contents of the human brain. Nat Neurosci 8(5):679–685

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kay KN, Naselaris T, Prenger RJ, Gallant JL (2008) Identifying natural images from human brain activity. Nature 452(7185):352–355

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kohonen T (2001) Self-organizing maps, 3rd edn. Springer, New York

    Book  Google Scholar 

  • Kriegeskorte N, Bandettini P (2007) Analyzing for information, not activation, to exploit high-resolution fMRI. Neuroimage 38(4):649–662

    Article  PubMed Central  PubMed  Google Scholar 

  • Kriegeskorte N, Goebel R, Bandettini P (2006) Information-based functional brain mapping. Proc Natl Acad Sci U S A 103(10):3863–3868

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kriegeskorte N, Mur M, Ruff DA, Kiani R, Bodurka J, Esteky H, Tanaka K, Bandettini PA (2008a) Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron 60(6):1126–1141

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kriegeskorte N, Mur M, Bandettini P (2008b) Representational similarity analysis—connecting the branches of systems neuroscience. Front Syst Neurosci 2:4.

    Article  PubMed Central  PubMed  Google Scholar 

  • Kriegeskorte N, Cusack R, Bandettini P (2010) How does an fMRI voxel sample the neuronal activity pattern: compact-kernel or complex spatiotemporal filter? Neuroimage 49(3):1965–1976

    Article  PubMed Central  PubMed  Google Scholar 

  • Kuncheva LI, Rodriguez JJ, Plumpton CO, Linden DE, Johnston SJ (2010) Random subspace ensembles for FMRI classification. IEEE Trans Med Imaging 29(2):531–542

    Article  PubMed  Google Scholar 

  • Langs G, Menze BH, Lashkari D, Golland P (2011) Detecting stable distributed patterns of brain activation using Gini contrast. Neuroimage 56 (2):497–507

    Article  PubMed Central  PubMed  Google Scholar 

  • MacKay DJC (1994) Bayesian methods for backpropagation networks. In: Domany E, van Hemmen JL, Schulten K (eds) Models of neural networks III, chap 6. Springer, New York, pp 211–254

    Google Scholar 

  • Marquand A, Howard M, Brammer M, Chu C, Coen S, Mourão-Miranda J (2010) Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. Neuroimage 49:2178–2189

    Article  PubMed  Google Scholar 

  • Martinez-Ramon M, Koltchinskii V, Heileman L, Posse S (2006) fMRI pattern classification using neuroanatomically constrained boosting. Neiroimage 31:1129–1141

    Article  Google Scholar 

  • McKeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, Bell AJ, Sejnowski TJ (1998) Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp 6(3):160–188

    Article  CAS  PubMed  Google Scholar 

  • Misaki M, Kim Y, Bandettini PA, Kriegeskorte N (2010) Comparison of multivariate classifiers and response normalizations for pattern-information fMRI. Neuroimage 53(1):103–118

    Article  PubMed Central  PubMed  Google Scholar 

  • Mitchell TM, Hutchinson R, Niculescu RS, Pereira F, Wang X (2004) Learning to decode cognitive states from brain images. Mach Learn 57:145–175

    Article  Google Scholar 

  • Mitchell TM, Shinkareva SV, Carlson A, Chang KM, Malave VL, Mason RA, Just MA (2008) Predicting human brain activity associated with the meanings of nouns. Science 320(5880):1191–1195

    Article  CAS  PubMed  Google Scholar 

  • Miyawaki Y, Uchida H, Yamashita O, Sato MA, Morito Y, Tanabe HC, Sadato N, Kamitani Y (2008) Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. Neuron 60(5):915–929

    Article  CAS  PubMed  Google Scholar 

  • Moerel M, De Martino F, Formosano E (2012) Processing of natural sounds in human auditory cortex: tonotopy, spectral tuning, and relation to voice sensitivity. J Neurosci 32(41):14205–14216

    Article  PubMed  Google Scholar 

  • Moerel M, De Martino F, Santoro R, Uğurbil K, Goebel R, Yacoub E, Formisano E (2013) Processing of natural sounds: charachterization of multipeak spectral tuning in human auditory cortex. J Neurosci 33(29):11888–11898

    Google Scholar 

  • Moerel M, De Martino F, Satoro R, Yacoub E, Formisano E (2015) Representation of pitch chroma by multi-peak spectral tuning in human auditory cortex. Neuroimage 106:161–169

    Google Scholar 

  • Mourão-Miranda J, Bokde AL, Born C, Hampel H, Stetter M (2005) Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. Neuroimage 28(4):980–995

    Google Scholar 

  • Mourão-Miranda J, Reynaud E, McGlone F, Calvert G, Brammer M (2006) The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data. Neuroimage 33(4):1055–1065

    Article  PubMed  Google Scholar 

  • Mourão-Miranda J, Friston KJ, Brammer M (2007) Dynamic discrimination analysis: a spatial-temporal SVM. Neuroimage 36(1):88–99

    Article  PubMed  Google Scholar 

  • Mur M, Bandettini PA, Kriegeskorte N (2009) Revealing representational content with pattern-information fMRI—an introductory guide. Soc Cogn Affect Neurosci 4(1):101–109

    Article  PubMed Central  PubMed  Google Scholar 

  • Naselaris T, Prenger RJ, Kay KN, Oliver M, Gallant JL (2009) Bayesian reconstruction of natural images from human brain activity. Neuron 63(6):902–915

    Article  CAS  PubMed  Google Scholar 

  • Neal RM (1996) Bayesian learning for neural networks. Springer, New York

    Google Scholar 

  • Norman KA, Polyn SM, Detre GJ, Haxby JV (2006) Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn Sci 10(9):424–430

    Article  PubMed  Google Scholar 

  • Op de Beeck HP (2010a) Against hyperacuity in brain reading: spatial smoothing does not hurt multivariate fMRI analyses? Neuroimage 49(3):1943–1948

    Article  PubMed  Google Scholar 

  • Op de Beeck HP (2010b) Probing the mysterious underpinnings of multi-voxel fMRI analyses. Neuroimage 50(2):567–571

    Article  PubMed  Google Scholar 

  • O’Toole A, Jiang F, Abdi H, P’enard N, Dunlop J, Parent M (2007) Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. J Cogn Neurosci 19(11):1735–1752

    Article  PubMed  Google Scholar 

  • Pereira F, Botvinick M (2011) Information mapping with pattern classifiers: a comparative study. Neuroimage 56(2):476–496

    Article  PubMed Central  PubMed  Google Scholar 

  • Pereira F, Mitchell T, Botvinick M (2009) Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45:199–209

    Article  Google Scholar 

  • Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge MA

    Google Scholar 

  • Rasmussen PM, Madsen KH, Lund TE, Hansen LK (2011) Visualization of non linear kernel models in neuroimaging by sensitivity maps. Neuroimage 55(3):1120–1131

    Article  PubMed  Google Scholar 

  • Ryali S, Supekar K, Abrams DA, Menon V (2010) Sparse logistic regression for whole-brain classification of fMRI data. Neuroimage 51:752–764

    Article  PubMed Central  PubMed  Google Scholar 

  • Santoro R, Morel M, De Martino F, Goebel R, Uğurbil K, Yacoub E, Formisano E (2014) Encoding of naturla sounds at multiple spectral and temporal resolutions in the human auditory cortex. PLOS Comput Biol 10(1):e1003412

    Google Scholar 

  • Sasaki Y, Rajimehr R, Kim BW, Ekstrom LB, Vanduffel W, Tootell RB (2006) The radial bias: a different slant on visual orientation sensitivity in human and nonhuman primates. Neuron 51(5):661–670

    Article  CAS  PubMed  Google Scholar 

  • Shmuel A, Chaimow D, Raddatz G, Uğurbil K, Yacoub E (2010) Mechanisms underlying decoding at 7 T: ocular dominance columns, broad structures, and macroscopic blood vessels in V1 convey information on the stimulated eye. Neuroimage 49(3):1957–1964

    Article  PubMed  Google Scholar 

  • Smolders A, De Martino F, Staeren N, Scheunders P, Sijbers J, Goebel R, Formisano E (2007) Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis. Magn Reson Imaging 25(6):860–868

    Article  PubMed  Google Scholar 

  • Staëren N, Renvall H, De Martino F, Goebel R, Formisano E (2009) Sound categories are represented as distributed patterns in the human auditory cortex. Curr Biol 19(6):498–502

    Article  PubMed  Google Scholar 

  • Suykens JAK, Van Gestel T, De Barbanter J, De Moor B, Vanderwalle J (2002) Least squares support vector machines. World Scientific Publishing, Singapore

    Google Scholar 

  • Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Lear Res 1:211–244

    Google Scholar 

  • Valente G, De Martino F, Goebel R, Formisano E (2008) A comparison of feature selection strategies for classification of fMRI activation patterns. Poster at the organization on human brain mapping. Melbourne, Australia

    Google Scholar 

  • Valente G, De Martino F, Esposito F, Goebel R, Formisano E (2011) Predicting subject-driven actions and sensory experience in a virtual world with relevance vector machine regression of fMRI data. Neuroimage 56(2):651–661

    Article  PubMed  Google Scholar 

  • van Gerven MA, Cseke B, de Lange FP, Heskes T (2010) Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior. Neuroimage 50(1):150–161

    Article  PubMed  Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  • Yamashita O, Sato M, Yoshioka T, Tong F, Kamitani Y (2008) Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns. Neuroimage 42:1414–1429

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for MR Research, University of Minnesota, Minneapolis, MN, USA

    Cheryl Olman

  2. Department of Cognitive Neurosciences, Maastricht University, P.O. Box 616, 6200, Maastricht, MD, The Netherlands

    Federico De Martino & Giancarlo Valente

Authors
  1. Federico De Martino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cheryl Olman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giancarlo Valente
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Federico De Martino .

Editor information

Editors and Affiliations

  1. Maastricht Brain Imaging Centre, Maastricht University, Maastricht, The Netherlands

    Kamil Uludag

  2. University of Minnesota Medical School, Minneapolis, Minnesota, USA

    Kamil Ugurbil

  3. Department of Chemistry and Biochemistry, University of Denver, Denver, Colorado, USA

    Lawrence Berliner

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer New York

About this chapter

Cite this chapter

De Martino, F., Olman, C., Valente, G. (2015). Information Decoding from fMRI Images. In: Uludag, K., Ugurbil, K., Berliner, L. (eds) fMRI: From Nuclear Spins to Brain Functions. Biological Magnetic Resonance, vol 30. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7591-1_23

Download citation

Publish with us

Policies and ethics

Search

Navigation