Functional Connectivity in the Cortical Circuits Subserving Eye Movements

  • Christopher R. Genovese
  • John A. Sweeney
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 140)

Abstract

The eyes move continually during visual processing, usually without explicit control or awareness, to scan the key features of a scene. This is essential to our ability to attend to multiple features of our environment and to extract useful information from complex visual stimuli. Eye movement abnormalities are a significant and reliable neurobehavioral marker for a number of major neurological, developmental, and psychiatric disorders, so enhanced understanding of how the brain controls eye movements can yield insights into the brain abnormalities at the root of these conditions.

Eye movements have been studied extensively in both humans and monkeys. A general picture has emerged from these data regarding what areas of the brain subserve eye-movement processes. Although there is a strong homology between humans and monkeys, there are also notable differences, and a detailed delineation of the system in the human brain is still needed. To develop and test a complete theory of how eye movements are implemented requires study of the component sub-processes of the human system, of the interactions among these sub-processes, and of their functional connectivity. The advent of neuroimaging has opened the door to exciting new advances in this area.

We use functional Magnetic Resonance Imaging (fMRI) to study the eye-movement system in humans. Among neuroimaging techniques, fMRI offers a superior combination of spatial and temporal resolution. Each experiment yields as data the realization of a complicated spatio-temporal process that contains information about the dynamics of neural processing during eye movements. We apply a Bayesian hierarchical model for these data to make inferences about the system. In particular, we address an open question: What is the functional relationship between the neural systems subserving saccadic eye movements (rapid repositionings) and smooth visual pursuit of a target? We also illustrate several computational and statistical issues that arise in making inferences from these data.

Keywords

Schizophrenia Respiration Retina Neurol Autocorrelation 

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References

  1. Baker, J.R., Weisskoff, R.M., Stern, C.E., Kennedy, D.N., Jiang, A., Kwong, K.K., Kolodny, L.B., Davis, T.L. Boxerman, J.L., Buchbinder, B.R., Weeden, V.J., Belliveau, J.W. and Rosen, B.R. (1994). Statistical assessment of functional MRI signal change. In Proceedings of the Society for Magnetic Resonance, Second Annual Meeting, volume 2, 626. SMR.Google Scholar
  2. Bandettini, P.A., Jesmanowicz, A., Wong, E.C. and Hyde, J. (1993). Processing strategies for time-course data sets in functional MRI ofthe human brain. Magnetic Resonance in Medicine, 30: 161–173.CrossRefGoogle Scholar
  3. Becker, W. (1989). Metrics. In R.H. Wurtz and M.E. Goldberg, editorsThe Neurobiology of Saccidic Eye Movements, pages 13–39. Elsevier, New York.Google Scholar
  4. Belliveau, J.W., Kennedy, D.N., McKinstry, R.C., Buchbinder, B.R., Weisskoff, R.M., Cohen, M.S., Vevea, J.M., Brady, T.J. and Rosen, B.R. (1992). Functional mapping of the human visual cortex by magnetic resonance imaging. Science, 254: 716–719.CrossRefGoogle Scholar
  5. Berman, R.A., Luna, B., McCurtain, B.J., Strojwas, M.H., Voyvodic, J.T., Thulborn, K.R. and Sweeney, J.A. (1996). fmri studies of human frontal eye fields [abstract]. InSociety for Neuroscience, volume 22, p 1687.Google Scholar
  6. Bronstein, A.M. and Kennard, C. (1985). Predicitve ocular motor control in Parkinson’s disease. Brain, 108: 925–940.CrossRefGoogle Scholar
  7. Brown, M.A. and Semelka, R.C. (1995). MRI: Basic Principles and Applications., John Wiley and Sons, New York.Google Scholar
  8. Bruce, C.J. and Goldberg, M.E. (1985). Primate frontal eye fields. I. single neurons discharging before saccades. Journal of Neurophysiology, 53: 603–635.Google Scholar
  9. Carl, J.R. and Gellman, R.S. (1987). Human smooth pursuit: Stimulus-dependent responses. Journal of Neurophysiology, 57: 1446–1463.Google Scholar
  10. Clementz, B.A. and Sweeney, J.A. (1990). Is eye movement dysfunction a biological marker for schizophrenia? A methodological review. Psychology Bulletin, 108: 77–92.CrossRefGoogle Scholar
  11. Cohen, J.D., Forman, S.D., Braver, T.S., Casey, B.J., Servan-Schreiber, D. and Noll, D.C. (1994). Activation of prefrontal cortex in a non-spatial working memory task with functional MRI. Human Brain Mapping, 1:, 293–304.CrossRefGoogle Scholar
  12. Cohen, J.D., Noll, D.C. and Schneider, W. (1993). Functional magnetic resonance imaging: Overview nad methods for psychological research. Behavior Research Methods Instruments Computers, 25(2): 101–113.CrossRefGoogle Scholar
  13. Colby, C.L., Duhamel, J.R. and Goldberg, M.E. (1996). Visual, presaccadic, and cognitive activation of single neurons in monkey lateral intraparietal area. Journal of Neurophysiology, 75(5): 2841–2852.Google Scholar
  14. de Boor, C. (1978). A Practical Guide to Splines. Springer-Verlag.CrossRefGoogle Scholar
  15. DiCiccio, T.J., Kass, R.E., Raftery, A. and Wasserman, L. (1997). Computing bayes Factors by combining simulation and asymptotic approximations. J. Amer. Statist. Assoc., 903–915.Google Scholar
  16. Eddy, W.F. (1997). Comment on Lange and Zeger. Journal of the Royal Statisitical Society C, 46: 19–20.Google Scholar
  17. Eddy, W.F., Fitzgerald, M. and Noll, D.C. (1996). Improved image registration using Fourier interpolation. Magn. Reson. Med., 36: 923–931.CrossRefGoogle Scholar
  18. Forman, S., Cohen, J.C., Fitzgerald, M., Eddy, W.F., Mintun, M.A. and Noll, D.C. (1995). Improved assessment of significant change in fucntional magnetic resonsance fMRI: Use of a cluster size threshold. Magn. Reson. Med., 33: 636–647.CrossRefGoogle Scholar
  19. Friedman, L., Jesberger, J.A. and Meltzer, H.Y. (1991). A model of smooth pursuit performance illustrates the relationship between gain, catch-up saccade rate, and catch-up saccade amplitude in normal controls and patients with schizophrenia. Biological Psychiatry, 30: 537–556.CrossRefGoogle Scholar
  20. Friston, K.J., Frith, C.D. and Frackowiak, R.S.J. (1994). Human Brain Mapping, 1:, 69–79.CrossRefGoogle Scholar
  21. Friston, K.J., Jezzard, P. and Turner, R. (1994). Analysis of functional MRI time-series. Human Brian Mapping, 1:, 153–171.CrossRefGoogle Scholar
  22. Friston, K.J., Holmes, A.P., Poline, J.B., Grasby, P.J., Williams, S.C.R., Frackowiak, R.S.J. and Turner, R. (1995). Analysis of fMRI time series revisited. Neurolmage, 2: 45–53.CrossRefGoogle Scholar
  23. Funahasi, S., Bruce, C.J. and Goldman-Rakic,S. (1989). Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. Journal fo Neurophysiology, 61: 331–349.Google Scholar
  24. Gaymard, B. and Pierrot-Deseilligny, C. (1990). Impairment of sequences of memory-guided sccades after supplementary motor area lesions. Annals of Neurology, 28: 622–626.CrossRefGoogle Scholar
  25. Genovese, C.R. (1997). Statistical inference in functional Magnetic Resonance Imaging. Technical Report 674, Department of Statistics, Carnegie Mellon University.Google Scholar
  26. Gottlieb, J.P., MacAvoy, G. and Bruce, C.J. (1994). Neural responses related to smooth-pursuit eye movements and their correspondence with electrically elicited smooth eye movements in the primate frontal eye field. Journal fo Neurophysiology, 72(4): 1634–1653.Google Scholar
  27. Green, P.J. (1995). Reversible jump MCMC computation and Bayesian moel determination. Biometrika, 82: 711–732.MathSciNetMATHCrossRefGoogle Scholar
  28. Hastie, T.J. and Tibshirani, R.J. (1990). Generalized Additive Models, Chapman and Hall.Google Scholar
  29. Haxby, J.V. Grady, C.L., Horwitz, B., Ungerleider, L.G., Mishkin, M., Carson, R.E., Herscovitch, P., Schapiro, M.B. and Rapoport, S.I. (1991). Dissociation of object and spatial visual processing pathways in human extrastriate cortex. Neurobiology, 88: 1621–1625.Google Scholar
  30. Holzman, P.S., Proctor, L.R., Levy, D.L., Yasilo, N.J., Meltzer, H.Y. and Hirt, S.W. (1974). Eye-tracking dysfunctions in schizophrenic patients and their relatives. Archives of General Psychiatry, 31 143–151.CrossRefGoogle Scholar
  31. Just, M.A. and Carpenter, P.A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87: 329–354.CrossRefGoogle Scholar
  32. Kennard, C. and Rose, F.C. (1988). Physiological Aspects of Clinical Neuro-ophthalmology., Chicago Year Book Medical Publishers, Chicago, IL.Google Scholar
  33. Kwon, K.K., Belliveau, J.W., Chesler, D.A., I.E. Goldberg, Weisskoff, R.M., Poncelet, B.P., Kennedy, D.N., Hoppel, B.E., Cohen, M.S., Turner, R., Cheng, H., Brady, T.J. and Rosen, B.R. (1992). Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl. Acad. Sci. U.S.A., 89: 5675.CrossRefGoogle Scholar
  34. Lange, N. and Zeger, S. (1997). Non-linear Fourier time series analysis for human brain mapping by functional magnetic resonance imaging. Journal of the Royal Statistical Society C Applied Statistics, 46: 1–29.MathSciNetMATHCrossRefGoogle Scholar
  35. Lehmann, E.L. (1975). Nonparametrics: Statistical Methods Based on Ranks., Holden Day, Oakland, CA.Google Scholar
  36. Leigh, R.J. and Zee, D.S. (1991). The Neurology of Eye Movements (2nd ed.)., F.A. Davis, Philadelphia, PA.Google Scholar
  37. Lueck, C.J., Tanyeri, S., Crawford, T.J., Henderson, L. and Kennard, C. (1990). Journal of Neurology, Neurosurgery and Psychiatry, 53: 284–288.CrossRefGoogle Scholar
  38. Luna, B., Thulborn, K.R., Strojwas, M.H., McCurtain, B.J., Berman, R.A., Genovese, C.R. and Sweeney, J.A. (1998). Dorsal cortical regions subserving visually-guided saccades in humans: An fmri study. Cerebral Cortez, 8: 40–47.CrossRefGoogle Scholar
  39. Marr, D. (1982). Vision, W.H. Freeman and Co., New York.Google Scholar
  40. Minshew, N.J., Sweeney, J.A. and Bauman, M.L. (1998). Neurologic aspects of autism. In D.J. Cohen and F.R. Volkmar, editorsHandbook of Autism and Pervasive Developmental Disorders, p. 344–369. John Wiley and Sons, second edition.Google Scholar
  41. Minshew, N.J., Sweeney, J.A. and Furman, J.M. (1995). Evidence for a primary neocortical system abnormality in autism [abstract]. InSociety for Neuroscience Abstracts, volume 21, page 293.13.Google Scholar
  42. Mintun, M.A., Richle, M.E., Martin, W.R.W and Herscovitch, P. (1984). Brain oxygen utilization measured with 0–15 radiotrcers and positron emission tomography. Journal of Nuclear Medicine, 25: 177–187.Google Scholar
  43. Munoz, D.P. and Wurtz, R.H. (1992). Role of the rostral superior colliculus in active visual fixation and execution of express saccades. Journal of Neurophysiology, 67: 1000–1002.Google Scholar
  44. Ogawa, S., Tank, D.W., Menon, D.W., Ellermann, J.M., Kim, S., Merkle, H. and Ugurbil, K. (1992). Intrinsic signal changes acompanying sensory stimulation: Functional brain mapping using MRI. Proc. Natl. Acad. Sci. U.S.A., 89: 5951–5955.CrossRefGoogle Scholar
  45. Pauler, D. (1996). The Schwarz Criteion for Mixed Effects Models, Ph.D. thesis, Carnegie Mellon Unviersity.Google Scholar
  46. Petit, L., Clark, V.P., Ingeholm, J. and Haxby, J.V. (1997). Dissociation of sacccaderelated and pursuit-related activation in human frontal eye fields as revealed by fmri. Journal of Neurophysiology, 77(6): 3386–3390.Google Scholar
  47. Poline, J.B. and Mazoyer, B. (1994). Cluster analysis in individaul functional brain images: Some new techniques to enhance the sensitivity of activation detection methods. Human Brain Mapping, 2: 103–111.CrossRefGoogle Scholar
  48. Posner, M.I. and Raichle, M.E. (1994). IMages of Mind., Scientific American Library.Google Scholar
  49. Rakic, P. (1995). The development of the frontal lobe: A view from the rear of the brain. In H.H. Jasper, Riggio, S., and P.S. Goldman-Rakic, editorsEpilepsy and the Functional Anatomy of the Frontal Lobe, pages 1–8. Raven Press, New York.Google Scholar
  50. Rosenberg, D.R., Sweeney, J.A., Squires-Wheeler, E., Keshavan, M.S., Cornblatt, B.A. and Erlenmeyer-Kimling, L. (1997). Eye-tracking dysfunction in offspring from the New York high-risk project: Diagnostic specificity and the role of attention. Psychiatry Research, 66: 121–130.CrossRefGoogle Scholar
  51. Schervish, M.J. (1995). Theory of Statistics., Springer-Verlag.Google Scholar
  52. Schlag, J. and Schlag-Rey, M. (1987). Evidence for a supplementary eye field. Journal of Neurophysicology, 57:, 179–200.Google Scholar
  53. Schwarz, G. (1978). Estimating the dimension of a model. Ann. Stat., 6(2): 461–464.MATHCrossRefGoogle Scholar
  54. Sommer, M.A. and Tehovnik, E.J. Reversible inactivation of macaque frontal eye field. Experimental Brain Research, in press.Google Scholar
  55. Sweeney, J.A., Clementz, B.A., Haas, G.L. Escobar, M.D., Drake, K. and Frances, A.J. (1994). Eye tracking dysfunction in schizophrenia: Characterization of component eye movement abnormalities, diagnostic specificy, and the role of attention. Journal of Abnormal Psychology, 103: 222–230.CrossRefGoogle Scholar
  56. Sweeny, J.A., Luna, B.,Berman, R.A., McCurtain, B.J., Strojwas, M.H., Voyvodic, J.T., Genovese, C.R. and Thulborn, K.R. (1996a). fmri studies of spatial working memory [abstract]. In Society for Neuroscience, 22: 1688.Google Scholar
  57. Sweeney, J.A., Mintun, Kwee, S., Wiseman, M.B., Brown, D.L., Rosenberg, D.R. and Carl, J.R. (1996b). A positron emission tomography study of voluntary saccadic eye movements and spatial working memory. Journal of Neurophysiology, 75(1):, 545–468.Google Scholar
  58. Talairach, J. and Tounoux, P. (1988). Coplanar Stereotaxic Atlas of the Human Brain. Three-imensional Proportional System: An Approach to Cerebral Imaging., Thieme.Google Scholar
  59. Thulborn, K.R. Personal communication.Google Scholar
  60. Thulborn, K.R., Waterton, J.C., Matthews, P.M. and Radda, G.K. (1982). Oxygenation dependence of the transverse relaxation time of water protons in whole blood at high field. Biochem. Biophys. Acta, 714: 265–2770.CrossRefGoogle Scholar
  61. Tian, J.R. and Lynch, J.C. (1996). Functionally defined smooth and saccadic eye movement subregions in the frontaleye, field of cebus monkeys. Journal of Neurophysiology, 7694): 2740–5753.Google Scholar
  62. Tierney, L. (1994). Marov chains for exploring posterior distributions. The Annals of Statistics, 22(4): 1701–1727.MathSciNetMATHCrossRefGoogle Scholar
  63. Tootell, R.B.H., Reppas, J.B., Kwong, K.K., Malach, R., Born, R.T., Brady, T.J., Rosen, B.R. and Belliveau, J.W. (1995). Functional analysis of human mt and related visual cortical areas using magnetic resonance imaging. Journal of Neuroscience, 15: 3215–3230.Google Scholar
  64. Vardi, Y., Shepp, L.A. and Kaufman, L. (1985). A statistical model fo rpositron emission tomography. J. Amer. Statist. Assoc., 80: 8–20.MathSciNetMATHCrossRefGoogle Scholar
  65. Weaver, J.B., Saykin, A.Y., Burr, R.B., Riordan, H. and Maerlender, A. (1994). Principal component analysis of functional MRI of memory. InProceeding sof the Society for Magnetic Resonance, Second Annual Meeting, p. 808. SMR.Google Scholar
  66. Weisskoff, R.M. (1997). Personal communication.Google Scholar
  67. Weisskoff, R.M., Baker, J.R., Belliveau, J., Davis, T.L., Kwong, K.K., Cohen, M.S. and Rosen, B.R. (1993). Power spectrum analysis of functionally-weighted MR data: What’s in the noise? InProceedings of the Society for Magnetic Resonance in Medicine, Twelfth Annual Meeting, p. 7. MRM.Google Scholar
  68. Worsley, K.J. and Friston, K.J. (1995). Analysis of fMRI time series revisited - again. Neurolmage, 2:173–181.CrossRefGoogle Scholar
  69. Wurtz, R.H. and Goldberg, M.E. (1972). Activity of superior colliculus in behaving monkey.III., cells discharging before eye movements. Journal of Neurophysiology, 35: 575–586.Google Scholar
  70. Wurtz, R.H. and Goldberg, M.E. (1989). The Neumbiology of Saccadic Eye Movements, Elsevier, New York.Google Scholar

References

  1. Dale, A.M. and Buckner, R.L. (1997). “Selective averaging of rapidly presented individual trials using fMRI,” Human Brain Mapping, 5, 329–340.CrossRefGoogle Scholar
  2. Eddy, W.F., Fitzgerald, M., Genovese, C.R., Mockus, A., and Noll, D.C. (1996). “Functional imaging analysis software—computational Olio, inProceedings in Computational Statistics, Prat, A., ed., Physica-Verlag, Heidelberg, 3949.Google Scholar
  3. Friston, K.J., Frith, C.D., Liddle, P.F., and Frackowiak, R.S.J. (1991). “Comparing functional (PET) images: The assessment of significantchange, ” Journal of Cerebral Blood Flow and Metabolism, 11, 690–699.CrossRefGoogle Scholar
  4. Friston, K.J., Holmes, A.P., Poline, J.-B., Grasby, P.-J., Williams, S.C.R., Frackowiak, R.S.J., and Turner, R. (1995). `Analysis of fMRI time-series revisited,“Neuroimage, 2, 45–53.CrossRefGoogle Scholar
  5. Genovese, C.R. (1997). “Statistical inference in functional magnetic resonance imaging,” Technical Report 674, Carnegie Mellon Department of Statistics.Google Scholar
  6. Hu, X., Le, T.H., and Ugurbil, K. (1997). “Evaluation of the early response in fMRI in individual subjects using short stimulus duration,”Magnetic Resonance in Medicine, 37, 877–884.CrossRefGoogle Scholar
  7. Lange, N. and Zeger, S.L. (1997). “Non-linear Fourier time series analysis for human brain mapping by functional magnetic resonanceimaging, ” Applied Statistics, 46, 1–29.MathSciNetMATHGoogle Scholar
  8. Nielsen, F.A., Hansen, L.K., Toft, P., Goutte, C., Lange, N., Strother, S.C., Morch, N., Svarer, C., Savoy, R., Rosen, B., Rostrup, E., and Born, P. (1997). “Comparison of two convolution methods for fMRI timeseries., ”Neurolmage Supplement, 5, S473.Google Scholar
  9. Tootell, R.B.H., Reppas, J.B., Date, A.M., Look, R.B., Sereno, M.I., Malach, R., Brady, T.J., and Rosen, B.R. (1995). “Visual motion aftereffect in human cortical area MT revealed by functional magnetic resonance imaging,”Nature, 375, 139–141.CrossRefGoogle Scholar
  10. Worsley, K.J., Evans, A.C., Marrett, S., and Neelin, P. (1992). “A three-dimensional statistical analysis for CBF activation studies in human brain,”Journal of Cerebral Blood Flow and Metabolism, 12, 900–918.CrossRefGoogle Scholar
  11. Worsley, K.J. and Friston, K.J. (1997). `Analysis of fMRI time-series revisited — again,“Neuroimage, 2, 173–181.CrossRefGoogle Scholar
  12. Zarahn, E., Aguirre, G. and D’Esposito, M. (1997). “A trial-based experimental design for fMRI,” Neuroimage, 6, 122–138.CrossRefGoogle Scholar

Additional references

  1. Gelman, A., Meng, X.-L., and Stern, H. (1996) Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statistica Sinica, 6, 733–807.MathSciNetMATHGoogle Scholar
  2. Rubin, D. B. (1984) Bayesianly justifiable and relevant frequency calculations for the applied statistician. Ann. of Statist., 12, 1151–1172.MATHCrossRefGoogle Scholar
  3. Rubin, K.B. and Wu, Y.(1997) Modeling schizophrenic behavior using general mixture components, Biometrics, 53, 243–261.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Christopher R. Genovese
  • John A. Sweeney

There are no affiliations available

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