Mining Local Connectivity Patterns in fMRI Data

  • Kristian LoeweEmail author
  • Marcus Grueschow
  • Christian Borgelt
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 285)


A core task in the analysis of functional magnetic resonance imaging (fMRI) data is to detect groups of voxels that exhibit synchronous activity while the subject is performing a certain task. Synchronous activity is typically interpreted as functional connectivity between brain regions. We compare classical approaches like statistical parametric mapping (SPM) and some new approaches that are loosely based on frequent pattern mining principles, but restricted to the local neighborhood of a voxel. In particular, we examine how a soft notion of activity (rather than a binary one) can be modeled and exploited in the analysis process. In addition, we explore a fault-tolerant notion of synchronous activity of groups of voxels in both the binary and the soft/fuzzy activity setting. We apply the methods to fMRI data from a visual stimulus experiment to demonstrate their usefulness.


Functional Connectivity Blood Oxygen Level Dependent fMRI Data Statistical Parametric Mapping Local Connectivity 
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.


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  1. 1.
    Aguirre, G.K., Zarahn, E., D’Esposito, M.: The Variability of Human, BOLD Hemodynamic Responses. Neuroimage 8(4), 360–369 (1998)CrossRefGoogle Scholar
  2. 2.
    Cordes, D., Haughton, V.M., Arfanakis, K., Carew, J.D., Turski, P.A., Moritz, C.H., Quigley, M.A., Meyerand, M.E.: Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in “Resting-state” Data. American Journal of Neuroradiology 22(7), 1326–1333 (2001)Google Scholar
  3. 3.
    Eguiluz, V.M., Chialvo, D.R., Cecchi, G.A., Baliki, M., Apkarian, A.V.: Scale-free Brain Functional Networks. Physical Review Letters 94, 18102 (2005)CrossRefGoogle Scholar
  4. 4.
    Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, R.S.: Functional Connectivity: The Principal-component Analysis of Large (PET) Data Sets. Journal of Cerebral Blood Flow and Metabolism 13(1), 5–14 (1993)CrossRefGoogle Scholar
  5. 5.
    Handwerker, D.A., Ollinger, J.M., D’Esposito, M.: Variation of BOLD Hemodynamic Responses across Subjects and Brain Regions and Their Effects on Statistical Analyses. Neuroimage 21(4), 1639–1651 (2004)CrossRefGoogle Scholar
  6. 6.
    Hollmann, M., Mönch, T., Mulla-Osman, S., Tempelmann, C., Stadler, J., Bernarding, J.: A New Concept of a Unified Parameter Management, Experiment Control, and Data Analysis in fMRI: Application to Real-time fMRI at 3T and 7T. Journal of Neuroscience Methods 175(1), 154–162 (2008)CrossRefGoogle Scholar
  7. 7.
    Hollmann, M., Rieger, J.W., Baecke, S., Lützkendorf, R., Müller, C., Adolf, D., Bernarding, J.: Predicting Decisions in Human Social Interactions using Real-time fMRI and Pattern Classification. PloS ONE 6(10), e25304 (2011)CrossRefGoogle Scholar
  8. 8.
    Logothetis, N.K., Wandell, B.A.: Interpreting the BOLD Signal. Annual Review of Physiology 66, 735–769 (2004)CrossRefGoogle Scholar
  9. 9.
    Ogawa, S., Lee, T.M., Nayak, A.S., Glynn, P.: Oxygenation-sensitive Contrast in Magnetic Resonance Image of a Rodent Brain at High Magnetic Fields. Magnetic Resonance in Medicine 14(1), 68–78 (1990)CrossRefGoogle Scholar
  10. 10.
    Smith, A.M., Lewis, B.K., Ruttimann, U.E., Ye, F.Q., Sinnwell, T.M., Yang, Y., Duyn, J.H., Frank, J.A.: Investigation of Low Frequency Drift in fMRI signal. Neuroimage 9(5), 526–533 (1999)CrossRefGoogle Scholar
  11. 11.
    Tomasi, D., Volkow, N.D.: Functional Connectivity Density Mapping. Proceedings of the National Academy of Sciences of the USA 107(21), 9885 (2010)CrossRefGoogle Scholar
  12. 12.
    Tschukalin, A.: Noninvasive Lokalisation von magno- und parvozellulären Anteilen des humanen CGL mittels Hochfeld-MRT. Bachelor Thesis. Dept. of Computer Science, Otto-von-Guericke Universität Magdeburg, Germany (2011)Google Scholar
  13. 13.
    Van den Heuvel, M.P., Stam, C.J., Boersma, M., Hulshoff Pol, H.E.: Small-world and Scale-free Organization of Voxel-based Resting-state Functional Connectivity in the Human Brain. Neuroimage 43(3), 528–539 (2008)CrossRefGoogle Scholar
  14. 14.
    Zaitsev, M., Hennig, J., Speck, O.: Point Spread Function Mapping with Parallel Imaging Techniques and High Acceleration Factors: Fast, Robust, and Flexible Method for Echo-planar Imaging Distortion Correction. Magnetic Resonance in Medicine 52(5), 1156–1166 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Kristian Loewe
    • 1
    Email author
  • Marcus Grueschow
    • 2
  • Christian Borgelt
    • 3
  1. 1.Department of Knowledge and Language ProcessingUniversity of MagdeburgMagdeburgGermany
  2. 2.Laboratory for Social and Neural Systems Research, Department of EconomicsUniversity of ZürichZürichSwitzerland
  3. 3.European Centre for Soft ComputingMieresSpain

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