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An efficient method for effective connectivity of brain regions

  1. Fayyaz Ahmad1,*,
  2. M. Maqbool2,
  3. Eunwoo Kim1,
  4. Hyunwook Park1,
  5. Dae-Shik Kim1

Article first published online: 30 JAN 2012

DOI: 10.1002/cmr.a.20230

Issue

Concepts in Magnetic Resonance Part A

Concepts in Magnetic Resonance Part A

Volume 40A, Issue 1, pages 14–24, January 2012

Additional Information

How to Cite

Ahmad, F., Maqbool, M., Kim, E., Park, H. and Kim, D.-S. (2012), An efficient method for effective connectivity of brain regions. Concepts Magn. Reson., 40A: 14–24. doi: 10.1002/cmr.a.20230

Author Information

  1. 1

    Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST) 273-1 Guseong-dong, Youseong-gu, Daejeon 305-701, Republic of Korea

  2. 2

    Department of Physics and Astronomy, Ball State University, Muncie, IN 47306

*Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST) 273-1 Guseong-dong, Youseong-gu, Daejeon 305-701, Republic of Korea

Publication History

  1. Issue published online: 30 JAN 2012
  2. Article first published online: 30 JAN 2012
  3. Manuscript Accepted: 25 NOV 2011
  4. Manuscript Revised: 27 OCT 2011
  5. Manuscript Received: 20 SEP 2011

Funded by

  • Brain Korea. Grant Number: 21 (BK21)
  • NAP of Korea Research Council of Fundamental Science &Technology P90015
  • National Research Foundation of Korea. Grant Numbers: 2010-0012185, 2010-0018837

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REFERENCES

  • 1
    Bach FR,Jordan, MI. 2004. Learning graphical models for stationary time series. IEEE Trans Signal Proc 52: 2189–2199.
  • 2
    Bollen KA. 1989. Structural Equations with Latent Variables. New York: Wiley Academic Press.
  • 3
    Brillinger DR,Bryant HL,Segundo JP. 1976. Identification of synaptic interactions. Biol Cybern 22: 213–228.
  • 4
    Buchel C,Friston, KJ. 1997. Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modeling and fMRI. Cereb Cortex 7: 768–778.
  • 5
    Horwitz B. 1990. Simulating functional interactions in the brain: a model for examining correlations between regional cerebral metabolic rates. Int J Biomed Comput 26: 149–170.
  • 6
    Bullmore E,Horwitz B,Honey G,Brammer M,Williams S,Sharma T. 2000. How good is good enough in path analysis of fMRI data? NeuroImage 11: 289–301.
  • 7
    Dahlhaus R. 1997. Fitting time series models to nonstationary processes. Ann Stat 25: 1–37.
  • 8
    Dahlhaus, R,Eichler M,Sandkuhler J. 1997. Identification of synaptic connections in neural ensembles by graphical models. J Neurosci 77: 93–107.
  • 9
    Douglas CM,Elizabeth AP,Geoffrey V. 2001. Introduction to Linear Regression Analysis, 3rd ed. New York: Elsevier Academic Press.
  • 10
    Friston KJ,Beuchel C,Fink GR,Morris J,Rolls E,Dolan RJ. 1997. Psychophysiological and modulatory interactions in neuroimaging. NeuroImage 6: 218–229.
  • 11
    Friston, KJ,Frith CD,Liddle PF,Frackowiak RS. 1993. Functional connectivity: the principal component analysis of large (PET) data sets. J Cereb Blood Flow Metab 13: 5–14.
  • 12
    Friston, KJ,Frith, CD,Frackowiak RS. 1993. Time-dependent changes in effective connectivity measured with PET. Hum Brain Mapp 1: 69–80.
    Direct Link:
  • 13
    Friston KJ. 1994. Functional and effective connectivity in neuroimaging: a synthesis. Hum Brain Mapp 2: 56–78.
    Direct Link:
  • 14
    Friston, KJ,Ashburner J,Stefan K,Thomas E,Penny WD. 2006. Statistical Parametric Mapping: The Analysis of Functional Brain Images. London: Elsevier Academic Press.
  • 15
    Friston, KJ,Harrison L,Penny WD. 2003. Dynamic causal modelling. NeuroImage 19: 1273–1302.
  • 16
    Horwitz B. 2003. The elusive concept of brain connectivity. NeuroImage 19: 466–470.
  • 17
    Horwitz B,Grady CL,Haxby JV,Ungerleider LG,Schapiro MB,Mishkin M,Rapoport SI. 1992. Functional associations among human posterior extrastriate brain regions during object and spatial vision. J Cogn Neurosci 4: 311–322.
  • 18
    McIntosh AR,Gonzalez-Lima F. 1994. Structural equation modeling and its applications to network analysis in functional brain imaging. Hum Brain Mapp 2: 2–22.
    Direct Link:
  • 19
    Hebb DO. 1949. The Organization of Behavior: A Neurophysiological Theory. New York: Wiley Academic Press.
  • 20
    Sporns O,Toning G,Edelman GM. 2000. Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. Cereb Cortex 10: 127–141.
  • 21
    Tononi G,McIntosh AR,Russell DP,Edelman GM. 1998. Functional clustering: identifying strongly interactive brain regions in neuroimaging data. NeuroImage 7: 133–149.
  • 22
    Stone JV,Kotter R. 2002. Making connections about brain connectivity. Trends Cogn Sci 6: 327–328.
  • 23
    Sun FT,Miller LM,Esposito DM. 2004. Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. NeuroImage 21: 647–658.
  • 24
    Varela FJ,Lachaux JP,Podriguez E,Martinerie J. 2001. The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci 2: 229–239.
  • 25
    Salvador R,Suckling J,Schwarzbauer C,Bullmore E. 2005. Undirected graphs of frequency dependent functional connectivity in whole brain networks. Philos Trans R Soc B 360: 937–946.
  • 26
    Granger CWJ. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37: 414–420.
  • 27
    Kaminski M,Ding MZ,Truccolo WA,Bressler SL. 2001. Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance. Biol Cybern 85: 145–157.
  • 28
    Hesse W,Moller E,Arnold M,Schack B. 2003. The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies. J Neurosci Methods 124: 27–44.
  • 29
    Valdes-Sosa PA. 2004. Spatio-temporal autoregressive models defined over brain manifolds. Neuroinformatic 2: 239–250.
  • 30
    Eichler MA. 2005. Graphical approach for evaluating effective connectivity in neural systems. Philos Trans R Soc B 360: 953–967.
  • 31
    Pearl J. 1998. Graphs, causality, and structural equation models. Sociol Methods Res 27: 226–284.
  • 32
    Pearl J. 2000. Causality. Cambridge: Cambridge University Press.
  • 33
    Spirtes P,Glymour C,Scheines R. 1991. From probability to causality. Philos Stud 64: 1–36.
  • 34
    Spirtes P,Glymour C,Scheines R. 2000. Causation, Prediction, and Search. Cambridge: The MIT Press.
  • 35
    Eichler M. 2007. Granger causality and path diagrams for multivariate time series. J Econometrics 137: 334–353.
  • 36
    Pearl J. 2003. Statistics and causal inference: a review. Test 12: 281–318.
  • 37
    Spirtes, P,Richardson T,Meek C,Scheines R,Glymour C. 1998. Using path diagrams as a structural equation modeling tool. Sociol Methods Res 27: 182–225.
  • 38
    McIntosh AR,Gonzalez-Lima F. 1991. Structural modeling of functional neural pathways mapped with 2-deoxyglucose: effects of acoustic habituation on the auditory system. Brain Res 547: 295–302.
  • 39
    Penny WD,Stephan KE,Mechelli A,Friston KJ. 2004. Comparing dynamic causal models. NeuroImage 22: 1157–1172.
  • 40
    Ramsay J,Silverman B. 1997. Functional Data Analysis. New York: Springer-Verlag.
  • 41
    Golub G,Heath M,Wahba G. 1979. Generalized cross validation as a method for choosing a good ridge parameter. Technometrics 21: 215–224.
  • 42
    Schafer J,Strimmer K. 2005. An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21: 754–764.
  • 43
    Opgen-Rhein R,Strimmer K. 2006. Using regularized dynamic correlation to infer gene dependency networks from time-series microarray data. Fourth International Workshop on Computational Systems Biology WCSB 73–76.
  • 44
    Opgen-Rhein R,Strimmer K. 2007. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst Biol 1: 37–41.
  • 45
    Hoerl AE,Kennard RW. 2000. Ridge regression: biased estimation for non orthogonal problems. Technometrics 12: 55–67.
  • 46
    Whittaker J. 1990. Graphical Models in Applied Multivariate Statistics. Chichester, United Kingdom: Wiley Academic Press.
  • 47
    Lauritzen SL. 1996. Graphical Models. Oxford, United Kingdom: Clarendon Academic Press.
  • 48
    Kendall MG,Sturat A. 1961. The Advanced Theory of Statistics, Vol 2. New York: Hafner Publishing Company.
  • 49
    Morrison D. 1976. Multivariate Statistical Methods. New York: McGraw-Hill Publishing Company.
  • 50
    McNemar Q. 1966. Psychological Statistics. New York: Wiley Academic Press.
  • 51
    Kenneth JL,Subhash CN. 1978. Testing hypotheses concerning partial correlations: some methods and discussion. Int Stat Rev 46: 215–218.
  • 52
    Fayyaz A,Maqbool M,Lee N. 2011. Regularization of voxelwise autoregressive model for analysis of functional magnetic resonance Imaging data. Concepts Magn Reson A 38: 187–196.
  • 53
    McIntosh AR,Bookstein FL,Haxby JV,Grady CL. 1996. Spatial pattern analysis of functional brain images using partial least squares. NeuroImage 3: 143–157.
  • 54
    McIntosh AR,Lobaugh NJ. 2004. Partial least squares analysis of neuroimaging data: applications and advances. NeuroImage 23: 250–263.
  • 55
    Goebel R,Roebroeck A,Kim DS,Formisano E. 2003. Investigating directed cortical interactions in time-resolved fMRI data using vector auto regressive modeling and Granger causality mapping. Magn Reson Imaging 21: 1251–1261.
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