December 2010
Volume 10, Issue 15
OSA Fall Vision Meeting Abstract  |   December 2010
Intrinsic optical signal monitoring of visual signal propagation from the photoreceptor to inner retina
Author Affiliations
  • Xincheng Yao
    Department of Biomedical Engineering University of Alabama at Birmingham
Journal of Vision December 2010, Vol.10, 11. doi:
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      Xincheng Yao; Intrinsic optical signal monitoring of visual signal propagation from the photoreceptor to inner retina. Journal of Vision 2010;10(15):11.

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      © ARVO (1962-2015); The Authors (2016-present)

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We recently demonstrated intrinsic optical signal (IOS) imaging of frog retinal slices that allowed simultaneous observation of stimulus-evoked responses propagating from the photoreceptors to inner retinal neurons. High-resolution images revealed three robust IOS sources, i.e., photoreceptors, inner plexiform layer (IPL) and ganglion cells. These IOSs showed complex but consistent spatiotemporal dynamics during the retinal activation. While IOSs of the photoreceptor layer were mainly confined to the area directly stimulated by the visible light; IOSs of inner retinal layers spread from the stimulus site into relatively large areas with distinct time courses. At the photoreceptor and ganglion layers, positive (increasing) and negative (decreasing) IOSs were mixed together at a sub-cellular scale. In contrast, the IPL was predominated by positive IOSs. It is well established that the bipolar, amacrine and ganglion cells interact in the IPL through nerve terminals, and thus we hypothesize that the IOSs from the IPL might be related to light scattering changes due to light-regulated release of synaptic vesicles at nerve terminals. We expect that IOS imaging of retinal slices will provide a simple but valuable platform for advanced study of the functional connectivity of retinal neurons, particularly the interactions among IPL cell dendrites that cannot be simultaneously recorded in multiple cells by conventional electrophysiological techniques. A better understanding of the retinal neural network not only will advance our understanding of visual information processing mechanisms of the retina, but also can provide valuable information to improve retinal disease diagnosis and treatment outcome evaluation.


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