8.4 INDIRECT VISUALIZATION OF BRAIN DAMAGE THROUGH THE EYES – THE RATIONALE
The retina and the optic nerve stretch from the disencephalon during embryonic development and can be thus considered as a part of the CNS (4). The retinal anatomy and function are well known, arguably better than the brain´s. The transparency of the retina enables noninvasive anatomical inspection and ERG can be used to record the function of the retina safely and economically. Although ERG records integrated activity of all retinal cells classes, substantial basic research on the ERG technique enables us to make conclusions about cellular contributions of detected abnormalities. ERG measurements, especially the PERG, can detect even subclinical changes in retinal function (403‐406).
Likely due to the excessive energy demand in the retina, its function is very sensitive to changes in body homeostasis (including e.g. oxygen saturation and pH of the blood, and energy metabolism) (407,408). The high sensitivity of ERG can be even a downside, since experimental conditions ruthlessly affect the results. According to our experience, potential main sources for within‐subjects variability in animal experiments are anesthesia‐related factors. In human experiment, alertness of subject and ability to concentrate on the stimulation may be prominent factors for variability, however, depending largely on stimulus parameters. This may partially explain why human experiments seeking eye‐
derived biomarkers for CNS diseases have seldom incorporated the ERG. Also, the specificity of the ERG is rather limited since several factors affect retinal function in a
similar manner.
In animal experiments, the whole retina can be isolated from the eye and used for transretinal recording (ex vivo ERG) (127). Recordings across distinct retinal layers (134), or single‐cell recordings (409), provide very detailed functional information. Likewise, the whole‐mount retinal specimen is ideal for immunohistochemical morphological inspection maintaining the normal architecture of the tissue. The well‐defined layered anatomy of the retina enables straightforward functional‐anatomical correlations in pathologies (see e.g.
Figs. 28 and 29) and detection of even minuscule distortions to the retinal structure (Publication III). Furthermore, the eye structures (the retina in the frontline) are ideal for research of locally admininistered drugs, stem cells or viral vectors due to accessibility via intravitreal or subretinal injections. A great deal of our current understanding of the processess of neuroprotection, axonal regeneration and cell renewal has been provided by investigations of the retina and the optic nerve (410‐413).
The theory of retina´s usefulness for prognosis of brain pathology has not yet turned into reality. However, the fast development of optical imaging methods is pushing retinal imaging in CNS diseases more common. Some latest highlights in this context include imaging of retinal microvasculature and choroid changes in stroke, AD and HD patients (258,414‐416), as well as imaging of amyloid plaques in retinas in alive AD model mice (325,326). Retinal microvasculature changes seem to occur in parallel with cerebrovascular changes (258,414‐416). This is an important notation since microvasculature changes and the supposedly related break of blood‐brain barrier likely play a significant role in CNS disorders (417,418). Cerebral microvasculature changes can be imaged in vivo but is neither simple nor economic (414,419). Thus retinal imaging may provide an indirect but sufficiently sensitive alternative for detection of microvascular pathology in CNS diseases. Furhermore, retinal hypoperfusion should be readily detectable by ERG. It would
be interesting to test if multifocal ERG could detect hypoperfused location in the retina/choroid before they are anatomically obvious.
Brain and retina pathology in CNS diseases is most essentially, however, linked through the RGCs. This is not far‐fetched since RGCs display the typical properties of brain neurons, and they directly contact brain targets via their long axons. It is good to bear in mind that movement in axons is not one‐way only; in addition to anterograde neuronal signal, also retrograde transport occurs (420). The RGCs are very sensitive for defects in retrograde axonal transport and die without trophic support from the brain (172).
Therefore, RGCs may be damaged due to direct harmful effect of the disease pathology onto them, but also secondarily due to a brain insult. The RGCs (and their axons which form the optic nerve) seem to be affected by disease progression of several CNS diseases (295,393,421,422). Most of the recent evidence was collected using OCT, which can be readily used to detect thinning of retinal nerve fiber layer among several other anatomical endpoints (423). In the future, researcher may be able to obtain images of human RGC structures in vivo using two‐photon imaging (424). In addition, the PERG is very sensitive for RGC dysfunction and thus RGC imbalance can be tracked by PERG already before cell death (194‐196). Precise PERG can be conveniently recorded with skin electrodes in humans (425). However, there are some downsides in PERG. Stable PERG recording require precise fixation of the sight to the stimulus for some time (tens of seconds). This might be impossible in patients who do not cooperate, and therefore PERG might be challenging in patients with advanced neurodegenerative disease state. Similarly, eye opacities preclude PERG recording. The same limitations apply to pattern VEP and retinal imaging, especially OCT. Therefore, the examination of the retina is somewhat limited for early to intermediate disease stages. Also, the subjects should not display eye opacities.
To conclude, exploring the retina in CNS diseases for screening, or even diagnostic, purposes is becoming a hot topic. Most of the studies are conducted using OCT exclusively. Incorporating ERG or PERG, or even VEP (when primary damage expected at the optic nerve), would increase power of imaging studies and could enable detection of anomalities earlier than anatomical imaging methods alone. Valid animal models might be useful in optimizing ERG/PERG/VEP paradigms for a given task before human experiments, helping to avoid disappointments and unnecessary use of resources. If sufficient screening criteria would arise from human experimental data, the next step would be to collect large databases from control recordings and to create reference values for standardized parameters. Needless to say, reference values are mandatory for screening purposes. This step is very critical and challenging for the functional parameters, but also applies for anatomical parameters. Developing eye‐derived screening biomarkers for early detection of CNS pathology will not be easy, but the potential gain is so remarkable that it justifies the resources spent. Some success in related context is the established diagnostic utility of the VEP in multiple schlerosis (426) and the ERG in NCLs (331). It should be noted, however, that the specificity of eye‐derived biomarkers is not, and will not be, sufficient enough for differential diagnosis for most CNS disorders. Therefore, we suggest that the retina could work as a “biosensor” of CNS health status and eye‐derived biomarkers could be used for early detection of compromised CNS health in general. More traditional diagnostic methods would still be needed for final diagnosis.
9 Conclusions
Our results indicate that neurodegenerative changes may commonly happen in the brain and the eye simultaneously. In all of the neurodegenerative disease models studied during this project, we found retinal pathology that has similarities with corresponding human condition. In two disease models, retinal dysfunction was the first clearly detectable disease manifestation. Our findings support the idea that for neurodegenerative diseases, which span from the retina to the rest of the CNS, the eye examination serves as a potential screening tool. Advancements in this context are important, because diagnosis of neurodegenerative disease is commonly obtained too late when the pathology is already too advanced for any remedy. In order for the remedies to be effective, the intervention should be started already before clinical symptoms, or at the latest in the early disease stage. Therefore, development of efficient, safe and economic screening methods for neurodegenerative diseases is imperative. As eye examinations are completely safe and proportionally economic (examinations are possible outside the clinics, even at home), we believe that eye‐derived biomarkers will be developed and used in the screening and follow‐up of neurodegenerative diseases in the near future.
10 References
25
22. Ramkumar HL, Zhang J, Chan CC. Retinal ultrastructure of murine models of dry age‐related macular degeneration (AMD). Prog Retin Eye Res.2010 May;29(3):169‐90.
23. Fletcher EL, Jobling AI, Greferath U, et al. Studying age‐related macular degeneration using animal models. Optom Vis Sci.2014 Aug;91(8):878‐86.
24. Jeon CJ, Strettoi E, Masland RH. The major cell populations of the mouse retina. J Neurosci.1998
Nov 1;18(21):8936‐46.
25. Stone J. The number and distribution of ganglion cells in the catʹs retina. J Comp Neurol.1978 Aug 15;180(4):753‐71.
26. Drager UC, Olsen JF. Ganglion cell distribution in the retina of the mouse. Invest Ophthalmol
Vis Sci.1981 Mar;20(3):285‐93.
27. Curcio CA, Allen KA. Topography of ganglion cells in human retina. J Comp Neurol.1990 Oct 1;300(1):5‐25.
28. Artal P, Herreros de Tejada P, Munoz Tedo C, Green DG. Retinal image quality in the rodent eye. Vis Neurosci.1998 Jul‐Aug;15(4):597‐605.
29. Gianfranceschi L, Fiorentini A, Maffei L. Behavioural visual acuity of wild type and bcl2 transgenic mouse. Vision Res.1999 Feb;39(3):569‐74.
30. Barten BG. Contrast sensitivity of the human eye and its effect on image quality. Bellingham, Washington, USA: Optical Engireening Press; 1999.
31. Histed MH, Carvalho LA, Maunsell JH. Psychophysical measurement of contrast sensitivity in the behaving mouse. J Neurophysiol.2012 Feb;107(3):758‐65.
32. Kolb H. Simple anatomy of the retina BTI ‐ webvision: The organization of the retina and visual
system.
33. Henkind P, Hansen RI, Szalay J. Ocular circulation. In: Records RE, ed. Physiology of the human
eye and visual system. New York, USA: Harper & Row; 1979:98‐155.
34. Gioffi GA, Grandtam E, Alm A. Ocular circulation. In: Kaufman PL, Alm A, eds. Adler´s
(Bethesda).2010 Feb;25(1):8‐15.
42. LaVail MM. Rod outer segment disk shedding in rat retina: Relationship to cyclic lighting.
Science.1976 Dec 3;194(4269):1071‐4.
43. Besharse JC, Hollyfield JG, Rayborn ME. Photoreceptor outer segments: Accelerated membrane
Biol.2014;801:77‐83.
damage. Mol Vis.2012;18:1021‐30.
49. Vinberg F, Turunen TT, Heikkinen H, Pitkanen M, Koskelainen A. A novel Ca2+‐feedback mechanism extends the operating range of mammalian rods to brighter light. J Gen Physiol.2015 Oct;146(4):307‐21.
50. Reingruber J, Pahlberg J, Woodruff ML, et al. Detection of single photons by toad and mouse rods. Proc Natl Acad Sci U S A.2013 Nov 26;110(48):19378‐83.
51. Wang JS, Estevez ME, Cornwall MC, Kefalov VJ. Intra‐retinal visual cycle required for rapid and complete cone dark adaptation. Nat Neurosci.2009 Mar;12(3):295‐302.
52. Hoon M, Okawa H, Della Santina L, Wong RO. Functional architecture of the retina:
Development and disease. Prog Retin Eye Res.2014 Sep;42:44‐84.
53. Witkovsky P. Dopamine and retinal function. Doc Ophthalmol.2004 Jan;108(1):17‐40. cells in the mouse retina. J Neurosci.2009 Jan 7;29(1):106‐17.
58. Helmstaedter M, Briggman KL, Turaga SC, et al. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature.2013 Aug 8;500(7461):168‐74.
59. DeVries SH. Bipolar cells use kainate and AMPA receptors to filter visual information into separate channels. Neuron.2000 Dec;28(3):847‐56.
60. Marc RE, Murry RF, Basinger SF. Pattern recognition of amino acid signatures in retinal neurons.
J Neurosci.1995 Jul;15(7 Pt 2):5106‐29.
61. Kolb H, Linberg KA, Fisher SK. Neurons of the human retina: A golgi study. J Comp
Neurol.1992 Apr 8;318(2):147‐87.
62. Vaney DI. Morphological identification of serotonin‐accumulating neurons in the living retina.
Science.1986 Jul 25;233(4762):444‐6.
63. Famiglietti EV Jr. ʹStarburstʹ amacrine cells and cholinergic neurons: Mirror‐symmetric on and off amacrine cells of rabbit retina. Brain Res.1983 Feb 14;261(1):138‐44.
64. Nguyen‐Legros J, Simon A, Caille I, Bloch B. Immunocytochemical localization of dopamine D1 receptors in the retina of mammals. Vis Neurosci.1997 May‐Jun;14(3):545‐51.
65. Doi M, Yujnovsky I, Hirayama J, et al. Impaired light masking in dopamine D2 receptor‐null mice. Nat Neurosci.2006 Jun;9(6):732‐4.Epub 2006 May 21.
66. Lavoie J, Illiano P, Sotnikova TD, Gainetdinov RR, Beaulieu JM, Hebert M. The
electroretinogram as a biomarker of central dopamine and serotonin: Potential relevance to psychiatric disorders. Biol Psychiatry.2014 Mar 15;75(6):479‐86.
67. Jurklies B, Kaelin‐Lang A, Niemeyer G. Cholinergic effects on cat retina in vitro: Changes in rod‐
and ganglion cells in the vertebrate retina. Histol Histopathol.1995 Oct;10(4):947‐68.
71. Sagar SM. Somatostatin‐like immunoreactive material in the rabbit retina: Immunohistochemical staining using monoclonal antibodies. J Comp Neurol.1987 Dec 8;266(2):291‐9.
72. Sinclair JR, Jacobs AL, Nirenberg S. Selective ablation of a class of amacrine cells alters spatial processing in the retina. J Neurosci.2004 Feb 11;24(6):1459‐67.
73. Vecino E, Rodriguez FD, Ruzafa N, Pereiro X, Sharma SC. Glia‐neuron interactions in the mammalian retina. Prog Retin Eye Res.2015 Jun 23.pii: S1350‐9462(15)00045‐2.
74. Bergman J. Is the inverted human eye a poor design. Journal of the American Scientific
Affiliation. 2000;52(1):18‐30.
75. Labin AM, Safuri SK, Ribak EN, Perlman I. Muller cells separate between wavelengths to nucleus. J Comp Neurol.1993 Dec 8;338(2):289‐303.
79. Hofbauer A, Drager UC. Depth segregation of retinal ganglion cells projecting to mouse superior colliculus. J Comp Neurol.1985 Apr 22;234(4):465‐74.
80. Wang L, Sarnaik R, Rangarajan K, et al. Visual receptive field properties of neurons in the lesions. Brain Res.1978 Nov 3;156(1):17‐31.
84. Douglas RM, Alam NM, Silver BD, et al. Independent visual threshold measurements in the two circadian clock. Science.2002 Feb 8;295(5557):1070‐3.
92. Berson DM. Strange vision: Ganglion cells as circadian photoreceptors. Trends Neurosci.2003
Jun;26(6):314‐20. neural circuits. Front Neural Circuits.2014 Oct 2;8:123.
100. Coleman JE, Law K, Bear MF. Anatomical origins of ocular dominance in mouse primary visual cortex. Neuroscience.2009 Jun 30;161(2):561‐71.
101. Sawtell NB, Frenkel MY, Philpot BD, Nakazawa K, Tonegawa S, Bear MF. NMDA receptor‐
dependent ocular dominance plasticity in adult visual cortex. Neuron. 2003;38(6):977‐985.
102. Frenkel MY, Bear MF. How monocular deprivation shifts ocular dominance in visual cortex of
young mice. Neuron. 2004;44(6):917‐923.
103. Callaway EM. Structure and function of parallel pathways in the primate early visual system. J
Physiol.2005 Jul 1;566(Pt 1):13‐9.
104. Percival KA, Koizumi A, Masri RA, et al. Identification of a pathway from the retina to koniocellular layer K1 in the lateral geniculate nucleus of marmoset. J Neurosci.2014 Mar
12;34(11):3821‐5.
105. Xu X, Ichida JM, Allison JD, et al. A comparison of koniocellular, magnocellular and
parvocellular receptive field properties in the lateral geniculate nucleus of the owl monkey (aotus trivirgatus). J Physiol.2001 Feb 15;531(Pt 1):203‐18.
106. Sincich LC, Park KF, Wohlgemuth MJ, Horton JC. Bypassing V1: A direct geniculate input to
area MT. Nat Neurosci.2004 Oct;7(10):1123‐8.
107. Cowey A. Visual system: How does blindsight arise? Curr Biol.2010 Sep 14;20(17):R702‐4.
108. Zeater N, Cheong SK, Solomon SG, Dreher B, Martin PR. Binocular visual responses in the primate lateral geniculate nucleus. Curr Biol.2015 Nov 18.pii: S0960‐9822(15)01295‐6.
109. Gao E, DeAngelis GC, Burkhalter A. Parallel input channels to mouse primary visual cortex. J
Neurosci.2010 Apr 28;30(17):5912‐26.
110. Marshel JH, Garrett ME, Nauhaus I, Callaway EM. Functional specialization of seven mouse visual cortical areas. Neuron.2011 Dec 22;72(6):1040‐54.
111. Wang Q, Sporns O, Burkhalter A. Network analysis of corticocortical connections reveals catʹs visual cortex. J Physiol.1962 Jan;160:106‐54.
114. Hubel DH, Wiesel TN. Receptive fields and functional architecture of monkey striate cortex. J
Physiol.1968 Mar;195(1):215‐43.
115. Wurtz RH. Recounting the impact of hubel and wiesel. J Physiol.2009 Jun 15;587(Pt 12):2817‐23.
116. Bear MF, Connors BW, Paradiso MA. Neuroscience : Exploring the brain. 3rd ed. ed.
nucleus. J Neurosci.2013 Jul 31;33(31):12751‐63.
120. Pinto LH, Enroth‐Cugell C. Tests of the mouse visual system. Mamm Genome.2000
Accessed February, 6th, 2016.
124. Gotch F. The time relations of the photo‐electric changes in the eyeball of the frog. J
Physiol.1903 Jun 15;29(4‐5):388‐410.
125. Einthoven W, Jolly W. The form and magnitude of the electrical response of the eye to stimulation by light at various intensities. Q J Exp Physiol. 1908(1):373–416.
126. Armington JC. The electroretinogram. New York: Academic Press, Inc,; 1974.
127. Granit R. The components of the retinal action potential in mammals and their relation to the discharge in the optic nerve. J Physiol.1933 Feb 8;77(3):207‐39.
128. McCulloch DL, Marmor MF, Brigell MG, et al. ISCEV standard for full‐field clinical
USA: The MIT press; 2006:139‐183.
131. Perlman I. The electroretinogram: ERG BTI ‐ webvision: The organization of the retina and
visual system.
132. Kofuji P, Ceelen P, Zahs KR, et al. Genetic inactivation of an inwardly rectifying potassium channel (Kir4.1 subunit) in mice: Phenotypic impact in retina. J Neurosci.2000 Aug 1;20(15):5733‐40.
133. Robson JG, Frishman LJ. The rod‐driven a‐wave of the dark‐adapted mammalian
electroretinogram. Prog Retin Eye Res.2014 Mar;39:1‐22.
134. Penn RD, Hagins WA. Signal transmission along retinal rods and the origin of the
electroretinographic a‐wave. Nature.1969 Jul 12;223(5202):201‐4.
135. Brown K, Wiesel T. Analysis of the intraretinal electroretinogram in the intact cat eye. J Physiol.1961 Sep;158:229‐56.
136. Arden G, Brown K. Some properties of components of the cat electroretinogram revealed by local recording under oil. J Physiol.1965 Feb;176:429‐61.
137. Tomita T, Funaishi A. Studies on intraretinal action potential with low‐resistance
microelectrode. J Neurophysiol.1952 Jan;15(1):75‐84.
138. Hanitzsch R. Dependence of the b‐wave on the potassium concentration in the isolated superfused rabbit retina. Doc Ophthalmol.1981 Jul 15;51(3):235‐40.
139. Stockton RA, Slaughter MM. B‐wave of the electroretinogram. A reflection of ON bipolar cell activity. J Gen Physiol.1989 Jan;93(1):101‐22.
140. Knapp AG, Schiller PH. The contribution of on‐bipolar cells to the electroretinogram of rabbits and monkeys. A study using 2‐amino‐4‐phosphonobutyrate (APB). Vision Res.1984;24(12):1841‐6.
141. Smith BJ, Wang X, et al. Contribution of retinal ganglion cells to the mouse electroretinogram.
Doc Ophthalmol.2014 Jun;128(3):155‐68.
142. Zhang K, Yao G, Gao Y, et al. Frequency spectrum and amplitude analysis of dark‐ and light‐
adapted oscillatory potentials in albino mouse, rat and rabbit. Doc Ophthalmol.2007 Sep;115(2):85‐
93.
143. Wachtmeister L. Oscillatory potentials in the retina: What do they reveal. Prog Retin Eye
Res.1998 Oct;17(4):485‐521.
144. Rangaswamy NV, Hood DC, Frishman LJ. Regional variations in local contributions to the primate photopic flash ERG: Revealed using the slow‐sequence mfERG. Invest Ophthalmol Vis
Sci.2003 Jul;44(7):3233‐47.
145. Korol S, Leuenberger PM, et al. In vivo effects of glycine on retinal ultrastructure and averaged electroretinogram. Brain Res.1975 Oct 31;97(2):235‐51.
146. Yonemura D, Kawasaki K. New approaches to ophthalmic electrodiagnosis by retinal
oscillatory potential, drug‐induced responses from retinal pigment epithelium and cone potential.
Doc Ophthalmol.1979 Dec 14;48(1):163‐222.
147. Alarcon‐Martinez L, Aviles‐Trigueros M, Galindo‐Romero C, et al. ERG changes in albino and pigmented mice after optic nerve transection. Vision Res.2010 Oct 12;50(21):2176‐87.
148. Heiduschka P, Julien S, et al. Loss of retinal function in aged DBA/2J mice ‐ new insights into retinal neurodegeneration. Exp Eye Res.2010 Nov;91(5):779‐83.
149. Kong YX, Crowston JG, Vingrys AJ, et al. Functional changes in the retina during and after acute intraocular pressure elevation in mice. Invest Ophthalmol Vis Sci.2009 Dec;50(12):5732‐40.
150. Vigh J, Solessio E, Morgans CW, Lasater EM. Ionic mechanisms mediating oscillatory membrane potentials in wide‐field retinal amacrine cells. J Neurophysiol.2003 Jul;90(1):431‐43.
151. Djamgoz MB. Common features of light‐evoked amacrine cell responses in vertebrate retina.
Neurosci Lett.1986 Nov 11;71(2):187‐91.
152. Steinberg RH, Schmidt R, Brown KT. Intracellular responses to light from cat pigment ERG of mice lacking b‐waves. Exp Eye Res.2008 Jun;86(6):914‐28.
156. Sharma S, Ball SL, Peachey NS. Pharmacological studies of the mouse cone electroretinogram.
Vis Neurosci.2005 Sep‐Oct;22(5):631‐6.
157. Xu L, Ball SL, Alexander KR, Peachey NS. Pharmacological analysis of the rat cone
electroretinogram. Vis Neurosci.2003 May‐Jun;20(3):297‐306.
158. Miura G, Wang MH, Ivers KM, Frishman LJ. Retinal pathway origins of the pattern ERG of the
mouse. Exp Eye Res. 2009;89(1):49‐62.
159. Wu J, Peachey NS, Marmorstein AD. Light‐evoked responses of the mouse retinal pigment epithelium. J Neurophysiol.2004 Mar;91(3):1134‐42.
160. Strauss O. The retinal pigment epithelium in visual function. ‐ Physiol Rev.2005 Jul;85(3):845‐
81. 161. Sieving PA, Frishman LJ, Steinberg RH. Scotopic threshold response of proximal retina in
cat. J Neurophysiol.1986 Oct;56(4):1049‐61.
162. Naarendorp F, Sato Y, Cajdric A, Hubbard NP. Absolute and relative sensitivity of the scotopic system of rat: Electroretinography and behavior. Vis Neurosci.2001 Jul‐Aug;18(4):641‐56.
163. Saszik SM, Robson JG, Frishman LJ. The scotopic threshold response of the dark‐adapted electroretinogram of the mouse. J Physiol.2002 Sep 15;543(Pt 3):899‐916.
164. Bui BV, Fortune B. Origin of electroretinogram amplitude growth during light adaptation in pigmented rats. Vis Neurosci.2006 Mar‐Apr;23(2):155‐67.
165. Saszik SM, Robson JG, Frishman LJ. Contributions of spiking and non‐spiking inner retinal neurons to the scotopic ERG of the mouse. Invest Ophthalmol Vis Sci. 2002;47:1817.
166. Frishman LJ, Steinberg RH. Intraretinal analysis of the threshold dark‐adapted ERG of cat retina. J Neurophysiol.1989 Jun;61(6):1221‐32.
167. Viswanathan S, Frishman LJ. Evidence that negative potentials in photopic ERGs of cats and primates depend upon spiking activity of retinal ganglion cell axons. Soc Neurosci Abstr.
1997;23:1024.
168. Chrysostomou V, Crowston JG. The photopic negative response of the mouse
electroretinogram: Reduction by acute elevation of intraocular pressure. Invest Ophthalmol Vis
Sci.2013 Jul 12;54(7):4691‐7.
169. Barnard AR, Charbel Issa P, et al. Specific deficits in visual electrophysiology in a mouse model
169. Barnard AR, Charbel Issa P, et al. Specific deficits in visual electrophysiology in a mouse model