Investigating Functional Cooperation in the Human Brain Using Simple Graph-Theoretic Methods

  • Michael L. Anderson
  • Joan Brumbaugh
  • Aysu Şuben
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 38)

Abstract

This chapter introduces a very simple analytic method for mining large numbers of brain imaging experiments to discover functional cooperation between regions. We then report some preliminary results of its application, illustrate some of the many future projects in which we expect the technique will be of considerable use (including a way to relate fMRI to EEG), and describe a research resource for investigating functional cooperation in the cortex that will be made publicly available through the lab web site. One significant finding is that differences between cognitive domains appear to be attributable more to differences in patterns of cooperation between brain regions, rather than to differences in which brain regions are used in each domain. This is not a result that is predicted by prevailing localization-based and modular accounts of the organization of the cortex.

References

  1. 1.
    Abello, J., Pardalos, P.M., Resende, M.G.C. On maximum clique problems in very large graphs in external memory algorithms. In: Abello, J., Vitter, J. (eds.) AMS-DIMACS Series on Discrete Mathematics and Theoretical Computer Science, Vol. 50 (1999)Google Scholar
  2. 2.
    Alba, R.D. A graph-theoretic definition of a sociometric clique. J Math Sociol 3, 113–126 (1973)MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Anderson, J.R., Qin, Y., Jung, K.J., Carter, C.S. Information processing modules and their relative domain specificity. Cogn Psychol 54, 185–217 (2007)CrossRefGoogle Scholar
  4. 4.
    Anderson, M.L. Evolution of cognitive function via redeployment of brain areas. Neuroscientist 131, 13–21 (2007)CrossRefGoogle Scholar
  5. 5.
    Anderson, M.L. Massive redeployment, exaptation, and the functional integration of cognitive operations. Synthese 159(3), 329–345 (2007)CrossRefGoogle Scholar
  6. 6.
    Anderson, M.L. The massive redeployment hypothesis and the functional topography of the brain. Philos Psychol 21(2), 143–174 (2007)CrossRefGoogle Scholar
  7. 7.
    Attwell, D., Iadecola, C. The neural basis of functional brain imaging signals. Trends Neurosci 25(12), 621–25 (2002)CrossRefGoogle Scholar
  8. 8.
    Bock, R.D., Husain, S.Z. An adaptation of Holzinger's b-coefficients for the analysis of sociometric data. Sociometry 13, 146–53 (1950)CrossRefGoogle Scholar
  9. 9.
    Bonacich, P. Factoring and weighting approaches to status scores and clique identification. J Math Sociol 2, 113–20 (1972)CrossRefGoogle Scholar
  10. 10.
    Brannen, J.H., Badie, B., Moritz, C.H., Quigley, M., Meyerand, M.E., Haughton, V.M. Reliability of functional MR imaging with word-generation tasks for mapping Broca's area. Am J Neuroradiol 22, 1711–1718 (2001)Google Scholar
  11. 11.
    Cabeza, R., Nyberg, L. Imaging cognition II: An empirical review of 275 PET and fMRI studies. J Cogn Neurosci 12, 1–47 (2000)CrossRefGoogle Scholar
  12. 12.
    Chaovalitwongse, W., Fan, Y.J., Sachdeo, R. On the k-nearest dynamic time warping neighbor for abnormal brain activity classification. IEEE Trans Syst Man Cybern A Syst Hum 37(6), 1005–1016 (2007). To appearCrossRefGoogle Scholar
  13. 13.
    Chaovalitwongse, W., Iasemidis, L.D., Pardalos, P.M., Carney, P.R., Shiau, D.S., Sackellares, J.C. Performance of a seizure warning algorithm based on the dynamics of intracranial EEG. Epilepsy Res 64, 93–133 (2005)CrossRefGoogle Scholar
  14. 14.
    Chaovalitwongse, W., Pardalos, P.M., Prokopyev, O.A. Electroencephalogram (EEG) time series classification: Applications in epilepsy. Ann Operations Res 148, 227–250 (2006)MATHCrossRefGoogle Scholar
  15. 15.
    Diestel, R. Graph Theory, 3rd edn. Springer-Verlag, Heidelberg (2005)MATHGoogle Scholar
  16. 16.
    Gross, J.L., Yellen, J. Graph Theory and its Applications, [ed]2nd edn. Discrete Mathematics and Its Applications. Chapman & Hall/CRC, London (2005)Google Scholar
  17. 17.
    Han X., Xu, C., Braga-Neto, U., Prince, J.L. Topology correction in brain cortex segmentation using a multiscale, graph-based approach. IEEE Trans Med Imaging 21, 109–121 (2002)CrossRefGoogle Scholar
  18. 18.
    Hayes, B. Graph theory in practice: Part I. Am Sci 88(1), 9–13 (2000)Google Scholar
  19. 19.
    Hayes, B. Graph theory in practice: Part II. Am Sci 88(2), 104–109 (2000)Google Scholar
  20. 20.
    Horwitz, B., Poeppel, D. How can EEG/MEG and fMRI/PET data be combined? Hum. Brain Mapp. 17, 1–3 (2002)CrossRefGoogle Scholar
  21. 21.
    Laird, A.R., Fox, M., Prince, C.J., Glahn, D.C., Uecker, A.M., Lancaster, J.L., Turkeltaub, P.E., Kochunov, P., Fox, P.T. Ale meta-analysis: Controlling the false discovery rate and performing statistical contrasts. Hum Brain Mapp 25, 155–164 (2005)CrossRefGoogle Scholar
  22. 22.
    Laird, A.R., Lancaster, J.L., Fox, P.T. Brainmap: The social evolution of a functional neuroimaging database. Neuroinformatics 3, 65–78 (2005)CrossRefGoogle Scholar
  23. 23.
    Lancaster, J., Laird, A., Fox, M., Glahn, D., Fox, P. Automated analysis of meta-analysis networks. Hum Brain Mapp 25, 174–184 (2005)CrossRefGoogle Scholar
  24. 24.
    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. Hum Brain Mapp 10, 120–131 (2000)CrossRefGoogle Scholar
  25. 25.
    Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001)CrossRefGoogle Scholar
  26. 26.
    Nunez, P.L., Silberstein, R.B. On the relationship of synaptic activity to macroscopic measurements: Does co-registration of EEG with fMRI make sense? Brain Topogr 13, 79–96 (2000)CrossRefGoogle Scholar
  27. 27.
    Sporns, O., Ktter, R. Motifs in brain networks. PLoS Biol 2, e369 (2004)Google Scholar
  28. 28.
    Özcan, M., Baumgärtner, U., Vucurevic G. Stoeter, P., Treede, R.D. Spatial resolution of fMRI in the human parasylvian cortex: Comparison of somatosensory and auditory activation. NeuroImage 25(3), 877–887 (2005)CrossRefGoogle Scholar
  29. 29.
    Grave de Peralta Menendez, R., Gonzales Andino, S., Morand, S., Michel, C., Landis, T. Imaging the electrical activity of the brain. Electra Hum Brain Mapp 9, 1–12 (2000)CrossRefGoogle Scholar
  30. 30.
    Grave de Peralta Menendez, R., Murray, M.M., Michel, C., Martuzzi, R., Gonzales Andino, S.L. Electrical neuroimaging based on biophysical constraints. NeuroImage 21, 527–539 (2004)CrossRefGoogle Scholar
  31. 31.
    Sporns, O., Tononi, G., Edelman, G.M. Theoretical neuroanatomy: Relating anatomical and functional connectivity in graphs and cortical connection matrices. Cereb Cortex 10, 127–141 (2000)CrossRefGoogle Scholar
  32. 32.
    Suharitdamrong, W., Chaovalitwongse, A., Pardalos, P.M. Graph theory-based data mining techniques to study similarity of epileptic brain network. In: Proceedings of DIMACS Workshop on Data Mining, Systems Analysis, and Optimization in Neuroscience (2006)Google Scholar
  33. 33.
    Turkeltaub, P.E., Eden, G.F., Jones, K.M., Zeffiro, T.A. Meta-analysis of the functional neuroanatomy of single-word reading: Method and validation. Neuroimage 16, 765–780 (2002)CrossRefGoogle Scholar
  34. 34.
    Ugurbil, K., Toth, L., Kim, D.S. How accurate is magnetic resonance imaging of brain function? Trends Neurosci. 26(2), 108–114 (2003)CrossRefGoogle Scholar
  35. 35.
    Viswanathan, A., Freeman, R.D. Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity. Nat Neurosci 10(10), 1308–1312 (2007)CrossRefGoogle Scholar
  36. 36.
    Vitacco, D., Brandeis, D., Pasual-Marqui, R., Martin, E. Correspondence of event-related potential tomography and functional magnetic resonance imaging during language processing. Hum Brain Mapp 17, 4–12 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Michael L. Anderson
    • 1
    • 2
  • Joan Brumbaugh
    • 1
  • Aysu Şuben
    • 1
  1. 1.Department of PsychologyFranklin and Marshall CollegeLancasterUSA
  2. 2.Institute for Advanced Computer StudiesUniversity of MarylandCollege ParkUSA

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