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Klinische Neurophysiologie 2024; 55(03): 139-146
DOI: 10.1055/a-2331-0668
Übersicht

Das visuelle System als Modell in der translationalen Forschung

The Visual System as a Model in Translational Research
Mustafa Sindi
1   Klinik für Neurologie, Universitätsklinikum Düsseldorf, Düsseldorf, Germany
,
Frederike Cosima Oertel
2   Körperschaftsmitglied der Freien Universität Berlin und der Humboldt-Universität zu Berlin, Max Delbrück Centrum für Molekulare Medizin Experimental and Clinical Research Center, Berlin, Germany
3   Neurologisches Klinisches Forschungszentrum, Charite Universitatsmedizin Berlin, Berlin, Germany
4   Abteilung für Neurologie, Charite Universitatsmedizin Berlin, Berlin, Germany
,
Friedemann Paul
2   Körperschaftsmitglied der Freien Universität Berlin und der Humboldt-Universität zu Berlin, Max Delbrück Centrum für Molekulare Medizin Experimental and Clinical Research Center, Berlin, Germany
3   Neurologisches Klinisches Forschungszentrum, Charite Universitatsmedizin Berlin, Berlin, Germany
4   Abteilung für Neurologie, Charite Universitatsmedizin Berlin, Berlin, Germany
,
Sven G Meuth
1   Klinik für Neurologie, Universitätsklinikum Düsseldorf, Düsseldorf, Germany
,
Philipp Albrecht
5   Klinik für Neurologie, Kliniken Maria Hilf GmbH, Monchengladbach, Germany
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Zusammenfassung

Das visuelle System bietet einzigartige Einblicke in die komplexen Mechanismen neurologischer Erkrankungen und stellt daher ein zentrales Modell in der translationalen Forschung dar. Die Netzhaut, als Teil des zentralen Nervensystems, dient als präzises Fenster, das es ermöglicht, neurodegenerative und neuroinflammatorische Prozesse zu untersuchen. Dieser Artikel beleuchtet die Anwendung des visuellen Systems in der translationalen Erforschung neurologischer Erkrankungen durch verschiedene experimentelle Modelle und Analysemethoden. Besonderes Augenmerk liegt auf der Untersuchung entzündlicher Modelle wie der Experimentellen Autoimmunen Enzephalomyelitis Optikusneuritis (EAEON), nicht-entzündlichen degenerativen Modellen wie dem Optic Nerve Crush und dem lichtinduzierten Photorezeptorverlust sowie demyelinisierenden Modellen wie dem Cuprizone-Modell sowie neurodegenerative Erkrankungen wie Demenz vom Alzheimer-Typ und idiopathisches Parkinson-Syndrom. Der Artikel stellt zudem diagnostische und funktionelle Evaluierungsmethoden wie die Optische Kohärenztomographie (OCT), konfokale Scanning Laser Ophthalmoskopie (cSLO), optomotorische Reaktions-Messung (OMR) und die Messung Visuell Evozierter Potentiale (VEP) vor. Abschließend werden ein kurzer Ausblick gegeben und die Limitationen, insbesondere bezüglich der Übertragbarkeit der Ergebnisse zwischen Tiermodellen und dem Menschen, erläutert.

Abstract

The visual system provides unique insights into the complex mechanisms of neurological diseases, thus serving as a central model in translational research. The retina, as part of the central nervous system, acts as a precise window that enables the study of neurodegenerative and neuroinflammatory processes. This article highlights the application of the visual system in the translational research of neurological diseases through various experimental models and analytical methods. Special emphasis is placed on the examination of inflammatory models such as Experimental Autoimmune Encephalomyelitis Optic Neuritis (EAEON), non-inflammatory degenerative models like Optic Nerve Crush and light-induced photoreceptor loss, as well as demyelinating models like the Cuprizone model, in addition to neurodegenerative diseases such as Alzheimer's type dementia and idiopathic Parkinson's syndrome. The article also presents diagnostic and functional evaluation methods such as Optical Coherence Tomography (OCT), confocal Scanning Laser Ophthalmoscopy (cSLO), optomotor response (OMR) measurements, and the measurement of Visually Evoked Potentials (VEP). Furthermore, a brief outlook is provided, as well as the limitations, especially regarding the extrapolatability of results from animal models to humans and vice versa.

Schlüsselwörter

OCT - VEP - Visuelles System - Retina - Translationale Evaluierungsmethoden

Keywords

OCT - VEP - visual system - retina - translational evaluation methods


Publication History

Article published online:
09 September 2024

© 2024. Thieme. All rights reserved.

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  • Literatur

  • 1 Oertel FC, Hastermann M, Paul F. Delimiting MOGAD as a disease entity using translational imaging. Front Neurol 2023; 14: 1216477
    CrossrefPubMedSearch in Google Scholar
  • 2 Dietrich M, Koska V, Hecker C. et al. Protective effects of 4-aminopyridine in experimental optic neuritis and multiple sclerosis. Brain 2020; 143: 1127-1142
    CrossrefPubMedSearch in Google Scholar
  • 3 Ingwersen J, De Santi L, Wingerath B. et al. Nimodipine confers clinical improvement in two models of experimental autoimmune encephalomyelitis. J Neurochem 2018;
    CrossrefPubMedSearch in Google Scholar
  • 4 Levy H, Assaf Y, Frenkel D. Characterization of brain lesions in a mouse model of progressive multiple sclerosis. Exp Neurol 2010; 226: 148-158
    CrossrefPubMedSearch in Google Scholar
  • 5 Rothhammer V, Kenison JE, Tjon E. et al. Sphingosine 1-phosphate receptor modulation suppresses pathogenic astrocyte activation and chronic progressive CNS inflammation. Proc Natl Acad Sci USA 2017; 114: 2012-2017
    CrossrefPubMedSearch in Google Scholar
  • 6 Dietrich M, Hecker C, Nasiri M. et al. Neuroprotective Properties of Dimethyl Fumarate Measured by Optical Coherence Tomography in Non-inflammatory Animal Models. Front Neurol 2020; 11: 601628
    CrossrefPubMedSearch in Google Scholar
  • 7 Hilla AM, Diekmann H, Fischer D. Microglia Are Irrelevant for Neuronal Degeneration and Axon Regeneration after Acute Injury. J Neurosci 2017; 37: 6113-6124
    CrossrefPubMedSearch in Google Scholar
  • 8 Meyer R, Weissert R, Diem R. et al. Acute neuronal apoptosis in a rat model of multiple sclerosis. J Neurosci 2001; 21: 6214-6220
    CrossrefPubMedSearch in Google Scholar
  • 9 Sindi M, Hecker C, Issberner A. et al. S1PR-1/5 modulator RP-101074 shows beneficial effects in a model of central nervous system degeneration. Front Immunol 2023; 14: 1234984
    CrossrefPubMedSearch in Google Scholar
  • 10 Cordano C, Sin JH, Timmons G. et al. Validating visual evoked potentials as a preclinical, quantitative biomarker for remyelination efficacy. Brain 2022; 145: 3943-3952
    CrossrefPubMedSearch in Google Scholar
  • 11 Marenna S, Huang S-C, Dalla Costa G. et al. Visual Evoked Potentials to Monitor Myelin Cuprizone-Induced Functional Changes. Front Neurosci 2022; 16: 820155
    CrossrefPubMedSearch in Google Scholar
  • 12 Parisi V, Restuccia R, Fattapposta F. et al. Morphological and functional retinal impairment in Alzheimer’s disease patients. Clin Neurophysiol 2001; 112: 1860-1867
    CrossrefPubMedSearch in Google Scholar
  • 13 Loh EH-T, Ong Y-T, Venketasubramanian N. et al. Repeatability and Reproducibility of Retinal Neuronal and Axonal Measures on Spectral-Domain Optical Coherence Tomography in Patients with Cognitive Impairment. Front Neurol 2017; 8: 359
    CrossrefPubMedSearch in Google Scholar
  • 14 Garcia-Martin E, Larrosa JM, Polo V. et al. Distribution of retinal layer atrophy in patients with Parkinson disease and association with disease severity and duration. Am J Ophthalmol 2014; 157: 470-478.e2
    CrossrefPubMedSearch in Google Scholar
  • 15 Satue M, Obis J, Alarcia R. et al. Retinal and Choroidal Changes in Patients with Parkinson’s Disease Detected by Swept-Source Optical Coherence Tomography. Curr Eye Res 2018; 43: 109-115
    CrossrefPubMedSearch in Google Scholar
  • 16 Simonett J, Chou J, Siddique N. et al. Ocular Manifestations and Optic Nerve Changes in Patients with Amyotrophic Lateral Sclerosis (ALS). Investigative Ophthalmology & Visual Science 2013; 54: 4382-4382
    PubMedSearch in Google Scholar
  • 17 Mukherjee N, McBurney-Lin S, Kuo A. et al. Retinal thinning in amyotrophic lateral sclerosis patients without ophthalmic disease. PLoS One 2017; 12: e0185242
    CrossrefPubMedSearch in Google Scholar
  • 18 Kersten HM, Danesh-Meyer HV, Kilfoyle DH. et al. Optical coherence tomography findings in Huntington’s disease: a potential biomarker of disease progression. J Neurol 2015; 262: 2457-2465
    CrossrefPubMedSearch in Google Scholar
  • 19 Gatto E, Parisi V, Persi G. et al. Optical coherence tomography (OCT) study in Argentinean Huntington’s disease patients. Int J Neurosci 2018; 128: 1157-1162
    CrossrefPubMedSearch in Google Scholar
  • 20 Tsokolas G, Tsaousis KT, Diakonis VF. et al. Optical Coherence Tomography Angiography in Neurodegenerative Diseases: A Review. Eye Brain 2020; 12: 73-87
    CrossrefPubMedSearch in Google Scholar
  • 21 Albrecht P, Müller A-K, Südmeyer M. et al. Optical coherence tomography in parkinsonian syndromes. PLoS One 2012; 7: e34891
    CrossrefPubMedSearch in Google Scholar
  • 22 Wagner SK, Romero-Bascones D, Cortina-Borja M. et al. Retinal Optical Coherence Tomography Features Associated With Incident and Prevalent Parkinson Disease. Neurology 2023; 101: e1581-e1593
    CrossrefPubMedSearch in Google Scholar
  • 23 Tran KKN, Wong VHY, Lim JKH. et al. Characterization of retinal function and structure in the MPTP murine model of Parkinson’s disease. Sci Rep 2022; 12: 7610
    CrossrefPubMedSearch in Google Scholar
  • 24 Harper DJ, Augustin M, Lichtenegger A. et al. Retinal analysis of a mouse model of Alzheimer’s disease with multicontrast optical coherence tomography. Neurophotonics 2020; 7: 015006
    CrossrefPubMedSearch in Google Scholar
  • 25 Groh J, Stadler D, Buttmann M. et al. Non-invasive assessment of retinal alterations in mouse models of infantile and juvenile neuronal ceroid lipofuscinosis by spectral domain optical coherence tomography. Acta Neuropathol Commun 2014; 2: 54
    CrossrefPubMedSearch in Google Scholar
  • 26 Huang D, Swanson EA, Lin CP. et al. Optical coherence tomography. Science 1991; 254: 1178-1181
    CrossrefPubMedSearch in Google Scholar
  • 27 Parisi V, Manni G, Spadaro M. et al. Correlation between morphological and functional retinal impairment in multiple sclerosis patients. Invest Ophthalmol Vis Sci 1999; 40: 2520-2527
    PubMedSearch in Google Scholar
  • 28 Albrecht P, Fröhlich R, Hartung H-P. et al. Optical coherence tomography measures axonal loss in multiple sclerosis independently of optic neuritis. J Neurol 2007; 254: 1595-1596
    CrossrefPubMedSearch in Google Scholar
  • 29 Costello F, Hodge W, Pan YI. et al. Differences in retinal nerve fiber layer atrophy between multiple sclerosis subtypes. J Neurol Sci 2009; 281: 74-79
    CrossrefPubMedSearch in Google Scholar
  • 30 Albrecht P, Müller A-K, Ringelstein M. et al. Retinal neurodegeneration in Wilson’s disease revealed by spectral domain optical coherence tomography. PLoS One 2012; 7: e49825
    CrossrefPubMedSearch in Google Scholar
  • 31 Albrecht P, Ringelstein M, Müller AK. et al. Degeneration of retinal layers in multiple sclerosis subtypes quantified by optical coherence tomography. Mult Scler 2012; 18: 1422-1429
    CrossrefPubMedSearch in Google Scholar
  • 32 Saidha S, Sotirchos ES, Ibrahim MA. et al. Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: a retrospective study. Lancet Neurol 2012; 11: 963-972
    CrossrefPubMedSearch in Google Scholar
  • 33 Dietrich M, Hecker C, Hilla A. et al. Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats. J Vis Exp 2019;
    CrossrefPubMedSearch in Google Scholar
  • 34 Fischer MD, Huber G, Beck SC. et al. Noninvasive, in vivo assessment of mouse retinal structure using optical coherence tomography. PLoS One 2009; 4: e7507
    CrossrefPubMedSearch in Google Scholar
  • 35 Sotirchos ES, Saidha S. OCT is an alternative to MRI for monitoring MS – YES. Mult Scler 2018; 24: 701-703
    CrossrefPubMedSearch in Google Scholar
  • 36 Saidha S, Calabresi PA. Optical coherence tomography should be part of the routine monitoring of patients with multiple sclerosis: yes. Mult Scler 2014; 20: 1296-1298
    CrossrefPubMedSearch in Google Scholar
  • 37 Castonguay A, Lefebvre J, Lesage F. et al. Comparing three-dimensional serial optical coherence tomography histology to MRI imaging in the entire mouse brain. J Biomed Opt 2018; 23: 1-9
    CrossrefPubMedSearch in Google Scholar
  • 38 Manogaran P, Hanson JVM, Olbert ED. et al. Optical Coherence Tomography and Magnetic Resonance Imaging in Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorder. Int J Mol Sci 2016; 17: 1894
    CrossrefPubMedSearch in Google Scholar
  • 39 Brandt AU, Martinez-Lapiscina EH, Nolan R. et al. Monitoring the Course of MS With Optical Coherence Tomography. Curr Treat Options Neurol 2017; 19: 15
    CrossrefPubMedSearch in Google Scholar
  • 40 Oh J, Sotirchos ES, Saidha S. et al. Relationships between quantitative spinal cord MRI and retinal layers in multiple sclerosis. Neurology 2015; 84: 720-728
    CrossrefPubMedSearch in Google Scholar
  • 41 Frenger MJ, Hecker C, Sindi M. et al. Semi-Automated Live Tracking of Microglial Activation in CX3CR1GFP Mice During Experimental Autoimmune Encephalomyelitis by Confocal Scanning Laser Ophthalmoscopy. Front Immunol 2021; 12: 761776
    CrossrefPubMedSearch in Google Scholar
  • 42 Hecker C, Dietrich M, Issberner A. et al. Comparison of different optomotor response readouts for visual testing in experimental autoimmune encephalomyelitis-optic neuritis. J Neuroinflammation 2020; 17: 216
    CrossrefPubMedSearch in Google Scholar
  • 43 Marenna S, Rossi E, Huang S-C. et al. Visual evoked potentials waveform analysis to measure intracortical damage in a preclinical model of multiple sclerosis. Front Cell Neurosci 2023; 17: 1186110
    CrossrefPubMedSearch in Google Scholar
  • 44 Li M, Wan C. The use of deep learning technology for the detection of optic neuropathy. Quant Imaging Med Surg 2022; 12: 2129-2143
    CrossrefPubMedSearch in Google Scholar
  • 45 Ortiz M, Mallen V, Boquete L. et al. Diagnosis of multiple sclerosis using optical coherence tomography supported by artificial intelligence. Mult Scler Relat Disord 2023; 74: 104725
    CrossrefPubMedSearch in Google Scholar
  • 46 Przybyszewski AW, Śledzianowski A, Chudzik A. et al. Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms. Sensors (Basel) 2023; 23: 2145
    CrossrefPubMedSearch in Google Scholar
  • 47 Volland S, Esteve-Rudd J, Hoo J. et al. A comparison of some organizational characteristics of the mouse central retina and the human macula. PLoS One 2015; 10: e0125631
    CrossrefPubMedSearch in Google Scholar
  • 48 Lama J, Buhidma Y, Fletcher EJR. et al. Animal models of Parkinson’s disease: a guide to selecting the optimal model for your research. Neuronal Signal 2021; 5: NS20210026
    CrossrefPubMedSearch in Google Scholar