Expert Opinion

"At the moment, those undergoing the treatment are between 16 and 53. On average, after only 10 sessions, not only is there great improvement in vision but it is also maintained for at least two years."
Prof Donald Tan, Director Singapore Eye Research Institute and Deputy Director, Singapore National Eye Centre
"This treatment helps the brain to better understand the images the eyes are sending it, rather than altering the images the eyes receive by using corrective lenses or surgically altering the eye itself."
Dr Chan Wing Kwong, Senior Consultant and Head of Refractive Surgery Centre, Singapore National Eye Centre
"For example, select an item in your house that you cannot see clearly. After that, every five sessions, take a look at the object again and you will notice that your vision has become sharper. These are testimonials from patients who have experienced this."
Prof Donald Tan, Director Singapore Eye Research Institute and Deputy Director, Singapore National Eye Centre
"Vision is dependent on two things, how your eye receives the image and how your brain interprets the image. NeuroVision helps the brain to interpret sharper images."
Dr Chan Wing Kwong, Senior Consultant and Head of Refractive Surgery Centre, Singapore National Eye Centre
"Naturally we were quite skeptical about the whole thing, because traditionally, ophthalmologists thought that apart from glasses and surgery, other methods wouldn't work for myopia. But we tried it out, and it did work."
Dr Chan Wing Kwong, Senior Consultant and Head of Refractive Surgery Centre, Singapore National Eye Centre
Neuroplasticity, Perceptual Learning and Contrast Sensitivity Function

The visual system is classically described as a hierarchy of visual processing stages, starting from light detection and transduction in the eye through several stages of spatial integration, each stage forming receptive fields of increasing complexity.

An important stage in image analysis, in the primary visual cortex, includes cortical cells (neurons) which are highly specialized and optimized as image analyzers (filters), so they respond only to a limited range of parameters of the visual image such as orientation, location in the visual field and spatial frequency1. Thus, to characterize an image, visual processing involves the cooperative activity of many neurons, these neural interactions contributing both excitation and inhibition.

Contrast is one of the most important parameters activating cortical cells involved in vision processing2. Responses of individual neurons to repeated presentations of the same stimulus are highly variable (noisy)2-4. Noise may impose a fundamental limit on the reliable detection and discrimination of visual signals by individual cortical neurons5,6. Neural interactions determine the sensitivity for contrast at each spatial frequency, and the combination of neural activities set the Contrast Sensitivity Function (CSF)7.

Theory suggests that the relationship between neuronal responses and perception are mainly determined by the signal-to-noise ratio (S/N ratio) of the neuronal activity. The brain pools responses across many neurons to average out noisy activity of single cells, thus improving S/N ratio, leading to substantially improved visual performance8.

In several studies it has been shown that the noise of individual cortical neurons can be brought under experimental control by appropriate choice of stimulus conditions9,10. Scientists 9-13, have demonstrated that contrast sensitivity at low levels can be increased dramatically through control of stimulus parameters. This precise control of stimulus conditions leading to increased neuronal efficiency is fundamental in initiating the neural modifications that are the basis for brain plasticity14,15.

Brain plasticity relates to the ability of the nervous system to adapt to changed conditions, sometimes after injury or strokes, but more commonly in acquiring new skills. Brain plasticity has been demonstrated in many basic tasks16, with evidence pointing to physical modifications in the adult cortex during repetitive performance16,17.

NeuroVision™'s technology probes specific neuronal interactions, using a set of patient specific stimuli that improve neuronal efficiency11,21 and induce improvement of CSF due to a reduction of noise and increase in signal strength. The improved lower level processing (Contrast Sensitivity and Lateral Interactions) produces an improvement in higher level processing (i.e. Visual Acuity - Letter recognition).

NeuroVision™ has proved these principles in the clinical applications of amblyopia, low myopia and post LASIK vision enhancement24.

As visual perception quality depends both on the input received through the eye and the processing in the visual cortex, NeuroVision™'s technology is able to compensate for blurred (myopic) inputs, coming from the retina, by enhancing neural processing in the brain.

References

  1. Hubel, D. H. & Wiesel, T. N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.
    J. Physiol. (Lond.) 160, 106-154 (1962).
  2. Albrecht, D. G. & Hamilton, D. B. Striate cortex of monkey and cat: contrast response function.
    Journal of Neurophysiology 48, 217-37 (1982).
  3. Tolhurst, D. J., Movshon, J. A. & Dean, A. F. The statistical reliability of signals in single neurons in cat and monkey visual cortex.
    Vision Res 23, 775-85 (1983).
  4. Geisler, W. S. & Albrecht, D. G. Bayesian analysis of identification performance in monkey visual cortex: nonlinear mechanisms and stimulus certainty.
    Vision Res 35, 2723-30 (1995).
  5. Shadlen, M. N. & Newsome, W. T. Noise, neural codes and cortical organization.
    Curr Opin Neurobiol 4, 569-79 (1994).
  6. Shadlen, M. N. & Newsome, W. T. The variable discharge of cortical neurons: implications for connectivity, computation, and information coding.
    J Neurosci 18, 3870-96 (1998).
  7. Polat, U. Functional architecture of long-range perceptual interactions.
    Spat Vis 12, 143-62 (1999).
  8. Geisler, W. S. & Albrecht, D. G. Visual cortex neurons in monkeys and cats: detection, discrimination, and identification.
    Vis Neurosci 14, 897-919 (1997).
  9. Kasamatsu, T., Polat, U., Pettet, M. W. & Norcia, A. M. Colinear facilitation promotes reliability of single-cell responses in cat striate cortex.
    Exp Brain Res 138, 163-72. (2001).
  10. Polat, U., Mizobe, K., Pettet, M. W., Kasamatsu, T. & Norcia, A. M. Collinear stimuli regulate visual responses depending on cell's contrast threshold.
    Nature 391, 580-4 (1998).
  11. Polat, U. & Sagi, D. Spatial interactions in human vision: from near to far via experience- dependent cascades of connections.
    Proc Natl Acad Sci U S A 91, 1206-9 (1994).
  12. Polat, U. & Sagi, D. Lateral interactions between spatial channels: suppression and facilitation revealed by lateral masking experiments.
    Vision Res 33, 993-9 (1993).
  13. Polat, U. & Sagi, D. The architecture of perceptual spatial interactions.
    Vision Res 34, 73-8 (1994).
  14. Dosher, B. A. & Lu, Z. L. Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting.
    Proc Natl Acad Sci U S A 95, 13988-93. (1998).
  15. Dosher, B. A. & Lu, Z. L. Mechanisms of perceptual learning.
    Vision Res 39, 3197- 221. (1999).
  16. Sagi, D. & Tanne, D. Perceptual learning: learning to see.
    Curr Opin Neurobiol 4, 195-9 (1994).
  17. Gilbert, C. D. Adult Cortical Dynamics.
    Physiological Reviews 78, 467-485 (1998).
  18. Zenger, B. & Sagi, D. Isolating excitatory and inhibitory nonlinear spatial interactions involved in contrast detection.
    Vision Research 36, 2497-2513 (1996).
  19. Crist, R. E., Li, W. & Gilbert, C. D. Learning to see: experience and attention in primary visual cortex.
    Nat Neurosci 4, 519-25. (2001).
  20. Gilbert, C. D., Sigman, M. & Crist, R. E. The neural basis of perceptual learning.
    Neuron 31, 681-97. (2001).
  21. Polat, U. & Sagi, D. in Maturational Windows and Adult Cortical Plasticity
    (eds. Julesz, B. & Kovācs, I.) 1-15 (Addison-Wesley, 1995).
  22. Polat, U., Sagi, D. & Norcia, A. M. Abnormal long-range spatial interactions in amblyopia.
    Vision Res 37, 737-44 (1997).
  23. Solomon, J. A. & Pelli, D. G. The visual filter mediating letter identification.
    Nature 369, 395-7 (1994).
  24. Polat, U., Ma-Naim, T. Belkin, M. Sagi, D. Improving vision in adult amblyopia by perceptual learning.
    PNAS 101, 6692-97 (2004).