Hostname: page-component-848d4c4894-ttngx Total loading time: 0 Render date: 2024-05-10T21:08:25.128Z Has data issue: false hasContentIssue false
  • English
  • Français

Hierarchical decomposition of dichoptic multifocal visual evoked potentials

Published online by Cambridge University Press:  04 October 2006

TED MADDESS ,
ANDREW C. JAMES ,
RASA RUSECKAITE  and
ELIZABETH A. BOWMAN
TED MADDESS
Affiliation:
Centre for Visual Sciences and the ARC Centre of Excellence in Vision Science, Research School of Biological Sciences, Australian National University, Canberra, Australia
ANDREW C. JAMES
Affiliation:
Centre for Visual Sciences and the ARC Centre of Excellence in Vision Science, Research School of Biological Sciences, Australian National University, Canberra, Australia
RASA RUSECKAITE
Affiliation:
Centre for Visual Sciences and the ARC Centre of Excellence in Vision Science, Research School of Biological Sciences, Australian National University, Canberra, Australia
ELIZABETH A. BOWMAN
Affiliation:
Centre for Visual Sciences and the ARC Centre of Excellence in Vision Science, Research School of Biological Sciences, Australian National University, Canberra, Australia John Curtin School of Medical Research, Australian National University, Canberra, Australia
Get access Rights & Permissions [Opens in a new window]

Abstract

Visual evoked responses to dichoptically presented multifocal stimuli were recorded for 92 eyes. Two stimulus variants were explored: temporally sparse and rapidly contrast reversing. We used hierarchal decomposition (HD) to represent the multifocal responses in terms of a small number of potentially unique component waveforms that are interrelated in a multivariate linear autoregressive (MLAR) relationship. The HD method exploits temporal correlations over a range of delays in the responses to estimate parallel, feedforward and feedback relationships between the HD components. Three HD components having temporal interrelationships constrained (at P < 0.05) to a moving ∼20 ms window could describe the multifocal responses well (median r2-values up to 90%). HD components were similar for both stimulus types and the component waveforms were temporally correlated, especially the first and third components. The data set was large enough to estimate separate HD components for each multifocal stimulus region. The component waveforms differed somewhat by region but the MLAR relationships were similar. At short delays parallel processing dominated. At longer delays the proportion of response drives that were attributed to feedback and feedforward relationships grew. Overall HD analysis seems to provide an informed summary of multifocal responses and insights into their sources.

Keywords

MultifocalSparseVisual evoked potentialsmfVEPHierarchical decompositionPrincipal components
Type
Research Article
Information
Visual Neuroscience , Volume 23 , Issue 5 , September 2006 , pp. 703 - 712
Copyright
2006 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Baseler, H., Sutter, E., Klein, S., & Carney, T. (1994). The topography of visual evoked response properties across the visual field. Electroencephalogram Clinical Neurophysiology 90, 65–81.CrossRefGoogle Scholar
Baseler, H.A. & Sutter, E.E. (1997). M and P components of the VEP and their visual field distribution. Vision Research 37, 675–690.CrossRefGoogle Scholar
Di Russo, F., Martinez, A., Sereno, M.I., Pitzalis, S., & Hillyard, S.A. (2002). Cortical sources of the early components of the visual evoked potential. Human Brain Mapping 15, 95–111.CrossRefGoogle Scholar
Fortune, B. & Hood, D. (2003). Conventional pattern-reversal VEPs are not equivalent to summed multifocal VEPs. Investigative Ophthalmology & Visual Science 44, 1364–1375.CrossRefGoogle Scholar
Gersch, W. & Yonemoto, J. (1977). Parametric time-series models for multivariate EEG analysis. Computers in Biology and Medicine 10, 113–125.CrossRefGoogle Scholar
Goldberg, I., Graham, S., & Klistorner, A. (2002). Multifocal objective perimetry in the detection of glaucomatous field loss. American Journal of Ophthalmology 133, 29–39.CrossRefGoogle Scholar
Hood, D., Odel, J., & Zhang, X. (2000a). Tracking the recovery of local optic nerve function after optic neuritis: A multifocal VEP study. Investigative Ophthalmology & Visual Science 41, 4032–4038.Google Scholar
Hood, D., Zhang, X., Greenstein, V., Kangovi, S., Odel, J., Liebmann, M., & Ritch, R. (2000b). An interocular comparison of the multifocal VEP: A possible technique for detecting local damage to the optic nerve. Investigative Ophthalmology & Visual Science 41, 1580–1587.Google Scholar
Hupe, J.-M., James, A.C., Girard, P., & Bullier, J. (2001). Response modulations by static texture surround in area V1 of the macaque monkey do not depend on feedback connections from V2. American Journal of Ophthalmology 85, 146–163.Google Scholar
James, A.C. (2003). The pattern pulse multifocal visual evoked potential. Investigative Ophthalmology & Visual Science 44, 879–890.CrossRefGoogle Scholar
James, A.C., Maddess, T., Goh, X.-L., & Winkles, N. (2005a). Spatially sparse pattern-pulse stimulation enhances multifocal visual evoked potential analysis. [ARVO Abstract]. Investigative Ophthalmology and Vision Science 46, Abstract No. 3602.Google Scholar
James, A.C., Ruseckaite, R., & Maddess, T. (2005b). Effect of temporal sparseness and dichoptic presentation on multifocal visual evoked potentials. Visual Neuroscience (In press).Google Scholar
James, A.C., Winkles, N., & Maddess, T. (2004). Comparison of contrast reversing and pattern-pulse stimulation for multifocal visual evoked potential analysis. [ARVO Abstract]. Investigative Ophthalmology and Vision Science 45, Abstract No. 5485.Google Scholar
Jeffreys, D.A. (1971). Cortical source locations of pattern-related visual evoked potentials recorded from the human scalp. Nature 229, 502–504.CrossRefGoogle Scholar
Jeffreys, D.A. & Axford, J.G. (1972). Source locations of pattern-specific components of human visual evoked potentials. I. Component of striate cortical origin. Experimental Brain Research 16, 1–21.Google Scholar
Klistorner, A., Graham, S., Grigg, J., & Billson, F. (1998). Electrode position and the multi-focal visual-evoked potential: Role in objective visual field assessment. Australian and New Zealand Journal of Ophthalmology 26, 91–94.CrossRefGoogle Scholar
Lankheet, M.J., Lennie, P., & Krauskopf, J. (1998). Distinctive characteristics of subclasses of red-green P-cells in LGN of macaque. Visual Neuroscience 15, 37–46.Google Scholar
Maddess, T., James, A.C., & Bowman, E.A. (2005). Contrast response of temporally sparse and contrast reversing dichoptic multifocal evoked potentials. Visual Neuroscience 22, 153–162.CrossRefGoogle Scholar
Repucci, M.A., Schiff, N.D., & Victor, J.D. (2001). General strategy for hierarchical decomposition of multivariate time series: Implications for temporal lobe seizures. Annals of Biomedical Engineering 29, 1135–1149.CrossRefGoogle Scholar
Reyment, R. & Jöreskog, K.G. (1996). Applied factor analysis in the natural sciences. Cambridge: Cambridge University Press.
Ruseckaite, R., Maddess, T., & James, A.C. (2005). Sparse multifocal stimuli for the detection of multiple sclerosis. Annals of Neurology 57, 904–913.CrossRefGoogle Scholar
Slotnick, S., Klein, S., Carney, T., Sutter, E., & Dastmalchi, S. (1999). Using multi-stimulus VEP source localization to obtain a retinotopic map of human primary visual cortex. Clinical Neurophysiology 110, 1793–1800.CrossRefGoogle Scholar
Victor, J.D. & Canel, A. (1992). A relation between the Akaike criterion and reliability of parameter estimates, with application to nonlinear autoregressive modelling of ictal EEG. Annals of Biomedical Engineering 20, 167–180.CrossRefGoogle Scholar
Wachtler, T., Lee, T.W., & Sejnowski, T.J. (2001). Chromatic structure of natural scenes. Journal of the Optical Society of America A 18, 65–77.CrossRefGoogle Scholar
Zhang, X. & Hood, D.C. (2004). Increasing the sensitivity of the multifocal visual evoked potential (mfVEP) technique: Incorporating information from higher order kernels using a principal component analysis method. Documenta Ophthalmologica 108, 211–222.CrossRefGoogle Scholar