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Scientific Background

 

The NeuroVision technology is a non-invasive, patient-specific, perceptual learning program based on visual stimulation. It facilitates neural connections at the cortical level through a computerized visual training regime, using Gabor patches, to improve contrast sensitivity and visual acuity. As visual perception depends on both the optical input received from the eye and the neural processing of that input in the visual cortex, NeuroVision technology improves quality of vision (contrast sensitivity and visual acuity) by enhancing neural processing in the primary visual cortex.

Several studies have shown that the noise of individual cortical neurons can be modulated by appropriate choice of stimulus conditions and that contrast sensitivity at low levels can be increased through control of stimulus parameters. The typical building block of the visual stimulus in the field of visual neuroscience is a Gabor patch (Figure 1), which efficiently activates and matches the shape of receptive fields in the visual cortex. Polat and colleagues had demonstrated that contrast sensitivity at low levels can be increased dramatically through a "lateral masking" technique, where collinearly oriented flanking Gabors are displayed in addition to the target Gabor image. 2,8-10 (Figure 2).


Figure 1: Receptive Field Structure and the Gabor Patch



Figure 2. The software analyses the contrast threshold of a Gabor target with the presence of flankers. The patient is exposed to two short displays in succession and the patient identifies which display contains three Gabors

This lateral masking technique is tailored to a individual computerized training regimen using various parameters of the stimulus (Gabors) such as spatial frequencies, spatial arrangement of the Gabor patches, contrast level, orientation (local and global), tasks order, context, exposure duration.1 (Figure 3) It improves neuronal efficiency and induced improvement of contrast sensitivity function (CSF) by reducing the noise to signal ratio of neural activity in the primary visual cortex.

Spatial Frequency
Local Orientation
Contrast
Global Orientation
Target-Flankers Separation
Target Displacement
Figure 3. Manipulation of Gabors

Figures 4 demonstrates contrast response derived from subjects with normal vision, who were exposed to psychophysical tasks using the lateral masking technique. When subjects are practicing contrast modulation under a very precise and subject-specific stimuli regimen, a dramatic improvement in contrast sensitivity is achieved.


Figure 4: Practicing Contrast Modulation


This precise control of stimulus conditions leading to increased neuronal efficiency is fundamental in initiating the neural modifications that are the basis for brain plasticity.

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 tasks, with evidence pointing to physical modifications in the adult cortex during repetitive performance.

The NeuroVision technology probes specific neuronal interactions, using a set of patient-specific stimuli that improve neuronal efficiency 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 such as letter recognition and visual acuity.

The human visual system consists of a highly sophisticated optical processing system. Optical images from the retina travels through a hierarchy of progressive levels of visual processing including photoreceptors and several stages of spatial integration, each forming receptive fields of increasing complexity.
 

Contrast is one of the most important parameters triggering the neural activity in the visual cortex.1 Neural interactions determine contrast sensitivity at each spatial frequency. The combination of neural interactions at various spatial frequencies derives individual CSF.2 Experiments have shown that the response of individual neurons to repeated stimulus is highly variable (noise), this high noise level imposes a fundamental limitation on the reliable detection and discrimination of visual signals by individual cortical neurons.1,3-4 The brain pools response across many neurons to average out noise activity of single cells. This creates a signal to noise ratio (S/N ration) that determines detection and limits the CSF. Thus improvement of the signal-to-noise ratio, leads to substantially improved visual performance.5

The term perceptual learning describes a process whereby practicing certain visual tasks leads to an improvement in visual performance. Brain plasticity in visual functions of adults has been shown in various studies.11-17 It was shown that visual performance improves with repetitive practice on specific controlled visual tasks. Through these precise controlled conditions, repetitive practice initiates neural modifications that lead to improvement in neuronal efficiency. These neural modifications indicate the presence of brain plasticity.
 

The technology has been clinically proven in the treatment of adult amblyopia which until now has been considered untreatable.18 In recent years the technology has been used in Asia and Europe where clinical studies showed efficacy in the treatment of amblyopia, low myopia and early presbyopia.18-19

Variability in efficacy has been found and may be a reflection of different individuals' "final cortical potential" that could be achieved through the perceptual learning process, in turn dependent on the state of inherent neural plasticity. Follow up studies may be designed to address these issues and find optimal "exposure dosing". However, the individual effort invested and motivation will be expected to be different with resultant impact on variability of final result. 
 

Further studies may be designed to explore the role of this novel computerized visual cortex training program in the area of rehabilitation for the low vision with various ocular conditions, and its prospective role in the enhancement of visual potential for the better quality of vision.

References:
 

  1. Albrecht, D. G. & Hamilton, D. B. Striate cortex of monkey and cat: contrast response function. Journal of Neurophysiology. 1982; 48;217-37.
  2. Polat U. Functional architecture of long-range perceptual interactions. Spat Vis. 1999;12:143-162.
  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. 1983; 23:775-85.
  4. Geisler, W. S. & Albrecht, D. G. Bayesian analysis of identification performance in monkey visual cortex: nonlinear mechanisms and stimulus certainty. Vision Res. 1995; 35: 2723-30.
  5. Geisler, W. S. & Albrecht, D. G. Visual cortex neurons in monkeys and cats: detection, discrimination, and identification. Vis Neurosci. 1997;14: 897-919.
  6. Gabor D. Theory of communication. Journal of the Institute of Electrical Engineer. 1946;93: 429-452.
  7. Daugman. Two-dimensional spectral analysis of critical receptive field profile. Vision Res. 1980;20: 847-56.
  8. 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. 2001;138: 163-72.
  9. Polat, U., Mizobe, K., Pettet, M. W., Kasamatsu, T. & Norcia, A. M. Collinear stimuli regulate visual responses depending on cell's contrast threshold. Nature. 1998;391: 580-4.
  10. Polat, U. & Sagi, D. Lateral interactions between spatial channels: suppression and facilitation revealed by lateral masking experiments. Vision Res. 1993; 33: 993-9.
  11. Polat, U. & Sagi, D. The architecture of perceptual spatial interactions. Vision Res. 1994;34: 73-8
  12. Levi D.M., Polat U. Neural plasticity in adults with amblyopia. Proc. Natl. Acad. Sci. USA. 1996; 93: 6830-6834
  13. Levi D.M., Polat U., Hu YS, Visual improvement in adults with amblyopia. Invest. Ophthalmol. Vis. Sci., 1997; 38(8): 1493-1510.
  14. Kaarniranta K, Kontkanen M. Visual recovery of the amblyopic eye in an adult patient after loss of the dominant eye. Acta Ophthalmol Scand. 2003;81:539
  15. Simmers AJ, Gray LS. Improvement of visual function in an adult amblyope. Optom Vis Sci. 1999;76:82-87.
  16. Fronius M, Cirina L, Cordey A, Ohrloff C. Visual improvement during psychophysical training in an adult amblyopic eye following visual loss in the contralateral eye. Graefes Arch Clin Exp Ophthalmol. 2005;243:278-280.
  17. Rahi JS, Logan S, Borja MC, Timms C, Russell-Eggitt I, Taylor D. Prediction of improved vision in the amblyopic eye after visual loss in the non-amblyopic eye. Lancet. 2002;36:621-22.
  18. Polat U, Naim TM, Belkin M, Sagi D. PNAS 2004;101:17:6692-6697.
  19. Tan DT, Fong A. Efficacy of neural vision therapy to enhance contrast sensitivity function and visual acuity in low myopia. J Cataract Refract Surg. 2008;Apr;34(4):570-7.
  20. Polat U, Ma-Naim T, Belkin M, Sagi D. Improving vision in adult amblyopia by perceptual learning. Proc Natl Acad Sci USA. 2004;101:6692-6697
  21. Polat U, Sagi D, Norcia AM. Abnormal long-range spatial interactions in amblyopia. Vision Res. 1997;37:737-744.
  22. Durrie D, McMinn PS. Computer-based primary visual cortex training for treatment of low myopia and early presbyopia. Trans Am Ophthalmol Soc. 2007;105:132-8; discussion 138-40.
  23. Lim KL, Fam HB. NeuroVision treatment for low myopia following LASIK regression. J Refract Surg. 2006 Apr;22(4):406-8.
  24. George O. Waring IV, MD, Daniel S. Durrie, MD. "NeuroLASIK - Can surgeons improve LASIK outcomes by training the visual cortex?" Cataract and Refractive Surgery Today, Aug 2008; 52-53.
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