Distinguishing Natural Language Processes on the Basis of fMRI-Measured Brain Activation

  • Francisco Pereira
  • Marcel Just
  • Tom Mitchell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2168)


We present a method for distinguishing two subtly different mental states, on the basis of the underlying brain activation measured with fMRI. The method uses a classifier to learn to distinguish between brain activation in a set of selected voxels (volume elements) during the processing of two types of sentences, namely ambiguous versus unambiguous sentences. The classifier is then used to distinguish the two states in untrained instances. The method can be generalized to accomplish knowledge discovery in cases where the contrasting brain activation profiles are not known a priori.


Support Vector Machine Null Model Blood Oxygen Level Dependent Sentence Processing Ambiguous Sentence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    “Images of Mind”, Posner, M.I, Raichle, M.E., W H Freeman (1997)Google Scholar
  2. 2.
    “Functional MRI”, Moonen, C.T.W., Bandettini, P.A. (Eds.), Springer Verlag (2000)Google Scholar
  3. 3.
    “Ambiguity in the brain: How syntactically ambiguous sentences are processed.”, Mason, R. A., Just, M. A., Keller, T. A., Carpenter, P. A., Manuscript submitted for publication (2001)Google Scholar
  4. 4.
    “Statistical Inference in Functional Magnetic Resonance Imaging”, Genovese, C.R., Technical Report 674 (1999), Statistics Department, Carnegie Mellon UniversityGoogle Scholar
  5. 5.
    “Making large-Scale SVM Learning Practical. Advances in Kernel Methods-Support Vector Learning”, Joachims, T., B. Schölkopf and C. Burges and A. Smola (ed.) MIT Press (1999)Google Scholar
  6. 6.
    “Machine Learning”, Mitchell, T., McGraw Hill (1997)Google Scholar
  7. 7.
    “On clustering fMRI time series”, Goutte, C., Toft, P., Rostrup, E., Nielsen, F. Å., and Hansen, L. K., NeuroImage, 9(3):298–310.Google Scholar
  8. 8.
    “A neural network classifier for cerebral perfusion imaging”, Chan KH, Johnson KA, Becker JA, Satlin A, Mendelson J, Garada B, Holman BL., Journal of Nuclear Medicine 35(5):771–4. (1994)Google Scholar
  9. 9.
    “Discrete Multivariate Analysis: Theory and Practice”, Bishop, Y.M., Fienberg, S.E., Holland, P.W., MIT Press (1975)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Francisco Pereira
    • 1
  • Marcel Just
    • 2
  • Tom Mitchell
    • 1
  1. 1.Computer Science DepartmentUSA
  2. 2.Psychology DepartmentCarnegie Mellon UniversityPittsburgh

Personalised recommendations