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Looking Outside the Searchlight

  • Joset A. Etzel
  • Michael W. Cole
  • Todd S. Braver
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7263)

Abstract

Searchlight analysis (information mapping) with pattern classifiers is a popular method of multivariate fMRI analysis often interpreted as localizing informative voxel clusters. Applicability and utility of this method is limited, however, by its dependency on searchlight radius, the assumption that information is present at all spatial scales, and its susceptibility to overfitting. These problems are demonstrated in a dataset in which, contrary to common expectation, voxels identified as informative do not clearly contain more information than those not so identified.

Keywords

MVPA fMRI searchlight analysis information mapping 

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References

  1. 1.
    Poldrack, R.A.: Region of interest analysis for fMRI. Soc. Cogn. Affect Neurosci. 2, 67–70 (2007)CrossRefGoogle Scholar
  2. 2.
    Etzel, J.A., Gazzola, V., Keysers, C.: An introduction to anatomical ROI-based fMRI classification analysis. Brain Research 1282, 114–125 (2009)CrossRefGoogle Scholar
  3. 3.
    Kriegeskorte, N., Goebel, R., Bandettini, P.: Information-based functional brain mapping. PNAS 103, 3863–3868 (2006)CrossRefGoogle Scholar
  4. 4.
    Pereira, F., Botvinick, M.: Information mapping with pattern classifiers: A comparative study. NeuroImage 56, 476–496 (2011)CrossRefGoogle Scholar
  5. 5.
    Li, S., Mayhew, S.D., Kourtzi, Z.: Learning Shapes the Representation of Behavioral Choice in the Human Brain. Neuron 62, 441–452 (2009)CrossRefGoogle Scholar
  6. 6.
    Bode, S., Haynes, J.-D.: Decoding sequential stages of task preparation in the human brain. Neuroimage 45, 606–613 (2009)CrossRefGoogle Scholar
  7. 7.
    Eger, E., Michel, V., Thirion, B., Amadon, A., Dehaene, S., Kleinschmidt, A.: Deciphering Cortical Number Coding from Human Brain Activity Patterns. Current Biology 19, 1608–1615 (2009)CrossRefGoogle Scholar
  8. 8.
    Savine, A.C., Braver, T.S.: Motivated Cognitive Control: Reward Incentives Modulate Preparatory Neural Activity during Task-Switching. The Journal of Neuroscience 30, 10294–10305 (2010)CrossRefGoogle Scholar
  9. 9.
    Wellcome Trust Centre for Neuroimaging: SPM8 (2009)Google Scholar
  10. 10.
    R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2011) Google Scholar
  11. 11.
    Maldjian, J.A., Laurienti, P.J., Kraft, R.A., Burdette, J.H.: An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 19, 1233–1239 (2003)CrossRefGoogle Scholar
  12. 12.
    Maldjian, J.A., Laurienti, P.J., Burdette, J.H.: Precentral gyrus discrepancy in electronic versions of the Talairach atlas. Neuroimage 21, 450–455 (2004)CrossRefGoogle Scholar
  13. 13.
    Lancaster, J.L., Woldorff, M.G., Parsons, L.M., Liotti, M., Freitas, C.S., Rainey, L., Kochunov, P.V., Nickerson, D., Mikiten, S.A., Fox, P.T.: Automated Talairach Atlas labels for functional brain mapping. Human Brain Mapping 10, 120–131 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Joset A. Etzel
    • 1
  • Michael W. Cole
    • 1
  • Todd S. Braver
    • 1
  1. 1.Cognitive Control & Psychopathalogy LabWashington University in St. LouisSt. LouisUSA

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