Optical Coherence Tomography in Multiple Sclerosis: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Ioannis-Nikolaos Chalkias
,
Christos Bakirtzis
,
Demetrios Pirounides
,
Marina Kleopatra Boziki
,
Nikolaos Grigoriadis

Multiple sclerosis (MS) is an inflammatory and neurodegenerative, potentially disabling disease of the central nervous system. OCT (Optical Coherence Tomography) and OCT-A (Optical Coherence Tomography with Angiography) are imaging techniques for the retina and choroid that are used in the diagnosis and monitoring of ophthalmological conditions. 

  • multiple sclerosis
  • optical coherence tomography
  • optical coherence tomography with angiography

1. Introduction

Multiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system (CNS) characterised by inflammation, demyelination, and extensive axonal loss [1]. Although certain factors, such as Epstein–Barr virus (EBV) infection, smoking, childhood obesity, low levels of vitamin D, and ultraviolet B light (UVB) exposure, have been implicated in the pathogenesis of the disease, the definite underlying cause remains uncertain [2].
Visual symptoms and optic neuritis (ON) are the most common ocular manifestations, occurring in up to 25% of people with MS (pwMS) [3]. Post-mortem studies have shown that 90% of pwMS present degeneration and axonal loss in their optic nerves, regardless of ON history [4][5].
Given that the diagnosis of MS relies on a combination of clinical and magnetic resonance imaging (MRI) findings [6], an expensive and time-consuming method, the introduction of sensitive, low-cost biomarkers that can be used in the screening and monitoring of pwMS remains of vital importance.
The introduction of two commonly used ophthalmic imaging modalities, optical coherence tomography (OCT), and the more recent OCT with angiography (OCT-A) has revolutionised people's understanding of the underlying pathophysiological mechanisms in MS, as they can indirectly provide information about the CNS. The imaging techniques OCT and OCT-A are fast, non-invasive, and inexpensive and provide anatomical and microvascular cross-sectional images of the retina, respectively. Their main application in ophthalmology is in the diagnosis and monitoring of diseases, such as age-related macular degeneration, retinal vascular disorders, and glaucoma. However, given that the retina is an extension of the brain, its use has expanded in the study of neurological and neuro-ophthalmological conditions [7].
The ganglion cells of the retina are located in the ganglion cell layer (GCL) and their axons form the retinal nerve fibre layer (RNFL). Both layers can be easily assessed using the aforementioned techniques, thus allowing the evaluation of potential neuronal and axonal degeneration in people.

2. Optical Coherence Tomography in Multiple Sclerosis

The imaging technique OCT was first introduced in 1991 and uses light waves to obtain cross-sectional retinal images. The RNFL and the GCL-inner plexiform layer complex (GC-IPL) are the two most commonly studied layers in MS. According to the current literature, the thickness of the RNFL and GC-IPL is found reduced in pwMS, and there is an inverse relationship with the disease duration [8][9][10]. Despite several theories, the pathophysiological mechanisms underlying these findings remain unclear [11].
Furthermore, ON is the most typical ocular manifestation, characterized by gaze-evoked retro-orbital pain and reduced visual acuity, and occurs in a significant number of pwMS [12]. Retrograde degeneration leading to axonal loss is the most probable pathological mechanism causing RNFL and GC-IPL thinning. Nevertheless, in patients with no history of ON, these findings could be either due to primary degeneration caused by the disease itself or even to retrograde degeneration after one or more subclinical episodes of ON [13].
Since MRI is the most common imaging modality used in the diagnosis of MS, many studies have focused on associating the relevant findings with those of OCT [14][15][16]. In fact, in a study by Saidha et al., GC-IPL atrophy mirrored atrophy in the white matter, brainstem, grey matter, thalamus, and the whole brain. However, a recent study by Glasner et al. suggested no correlation between white matter plaques and RNFL or GC-IPL thickness [17]. Nevertheless, grey matter atrophy seems to be the most sensitive marker of progressive MS [14]. Additionally, studies have correlated the progression of GC-IPL thinning with the development of new T2 lesions [10], damage to the optic radiations [18], and the formation of contrast-enhancing lesions [14]. One study showed that there was greater thinning in the temporal RNFL in patients with newly formed lesions in the optic radiations [19].
Several studies suggest that OCT may be used as a marker for estimating disability [20][21]. More specifically, RNFL and GC-IPL thinning were independently associated with long-term disability worsening in a recent study by Lambe et al. [22]. These findings were recently supported by Skirkova et al., who found a correlation between peripapillary RNFL values, disability, and brain MRI volumetric parameters [23]. The risk of progression can be three times higher in patients with initial RNFL thickness < 88 μm [24].
Visual acuity and contrast sensitivity are affected in patients with ON and several studies have managed to show that there are in accordance with the OCT findings [8][25][26]. GC-IPL and RNFL thinning parallel low-contrast visual acuity, and according to Lampert et al., dyschromatopsia and GC-IPL thickness are interconnected in pwMS and no history of ON [27].
Thinning of the RNFL and GC-IPL is not specific to MS; however, OCT might be helpful in differentiating it from disorders of the same spectrum. In neuromyelitis optica spectrum disorders (NMOSD), RNFL thinning tends to be more evenly distributed, whereas the temporal quadrant seems to be more affected in patients with ON MS [28]. In addition, both RNFL and GC-IPL are much more severely reduced in NMOSD than in MS. According to Brandt et al., OCT parameters may be valuable in distinguishing MS from Susac syndrome, a rare condition characterized by the triad of branch retinal artery occlusion (BRAO), encephalopathy, and hearing loss [29]. Research has shown that in patients with Susac syndrome, OCT displays scar-like pathological patterns that are strictly confined to the inner retinal layers, sparing the outer nuclear layer and the photoreceptors, thus clearly differentiating it from MS [30].
In 2008, Toledo et al. were the first to correlate OCT findings with cognitive impairment. Researchers have linked reduced RNFL thickness to cognitive disability as measured by the symbol digit modality test, a gold standard measure of information processing speed [31]. These findings were later supported by Sedighi et al., who showed that only 20% of pwMS with cognitive impairment had normal OCT findings [32]. In another study, Coric et al. noted that cognitively impaired pwMS with no history of ON had significantly reduced mean peripapillary RNFL (pRNFL) and mean macular GC-IPL (mGC-IPL) thicknesses compared to cognitively healthy pwMS [33]. Although these results may seem promising, further studies are required to support these findings.
Moreover, OCT has been used to study responses to different therapeutic options. In 2016, Pul et al. found no significant association between interferon beta (IFNβ-1b) treatment and RNFL thinning [34]. However, when Button et al. compared the effect of glatiramer acetate, natalizumab, and interferon-β-1a with the rate of GC-IPL thinning, they found that the natalizumab-treated group had a significantly lower rate of GC-IPL reduction compared to the other two [35].
Visual evoked potentials (VEP) are electrical signals generated by the visual cortex of the occipital lobe in response to visual stimuli. Klistorner et al. studied the relationship between OCT parameters and multifocal VEP (mfVEP) in patients with acute ON and found a significant reduction in RNFL thickness and mean mfVEP, particularly in the temporal quadrant [36]. Additionally, in an earlier study, researchers associated the VEP amplitude with the loss in RNFL thickness in patients with ON and noted that the main mechanism behind this loss is axonal degeneration [37].
One study examined the pupillary light response in patients with MS and found that attenuation of the melanopsin-mediated sustained pupillary constriction response was significantly associated with thinning of the GCL-IPL in pwMS with a history of ON [38].
A recent paper described peripapillary hyper-reflective ovoid mass-like structures (PHOMS) as a novel OCT finding that might be noted in people with early MS. Their presence suggests disease progression; however, more research is needed to support these results [39].
In recent years, artificial intelligence (AI) and machine learning have proven useful in medicine. In a recent study, researchers developed a system based on a convolutional neural network that can classify the disease according to the thickness of the OCT scans, thus assisting in the early diagnosis of the disorder [40]. Machine learning has also been successfully used to predict disability progression in pwMS by analysing RNFL thickness [41].

This entry is adapted from the peer-reviewed paper 10.3390/healthcare10081386

References

  1. Compston, A.; Coles, A. Multiple sclerosis. Lancet 2008, 372, 1502–1517.
  2. Ascherio, A. Environmental factors in multiple sclerosis. Expert Rev. Neurother. 2013, 13, 3–9.
  3. Cennamo, G.; Romano, M.R.; Vecchio, E.C.; Minervino, C.; Della Guardia, C.; Velotti, N.; Carotenuto, A.; Montella, S.; Orefice, G. Anatomical and functional retinal changes in multiple sclerosis. Eye 2016, 30, 456–462.
  4. Ikuta, F.; Zimmerman, H.M. Distribution of plaques in seventy autopsy cases of multiple sclerosis in the United States. Neurology 1976, 26, 26–28.
  5. Toussaint, D.; Périer, O.; Verstappen, A.; Bervoets, S. Clinicopathological study of the visual pathways, eyes, and cerebral hemispheres in 32 cases of disseminated sclerosis. J. Clin. Neuroophthalmol. 1983, 3, 211–220.
  6. Thompson, A.J.; Banwell, B.L.; Barkhof, F.; Carroll, W.M.; Coetzee, T.; Comi, G.; Correale, J.; Fazekas, F.; Filippi, M.; Freedman, M.S.; et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018, 17, 162–173.
  7. Wang, L.; Murphy, O.; Caldito, N.G.; Calabresi, P.A.; Saidha, S. Emerging Applications of Optical Coherence Tomography Angiography (OCTA) in neurological research. Eye Vis. 2018, 5, 11.
  8. Saidha, S.; Syc, S.B.; Durbin, M.K.; Eckstein, C.; Oakley, J.D.; Meyer, S.A.; Conger, A.; Frohman, T.C.; Newsome, S.; Ratchford, J.N.; et al. Visual dysfunction in multiple sclerosis correlates better with optical coherence tomography derived estimates of macular ganglion cell layer thickness than peripapillary retinal nerve fiber layer thickness. Mult. Scler. J. 2011, 17, 1449–1463.
  9. González-López, J.J.; Rebolleda, G.; Leal, M.; Oblanca, N.; Muñoz-Negrete, F.J.; Costa-Frossard, L.; Álvarez-Cermeño, J.C. Comparative Diagnostic Accuracy of Ganglion Cell-Inner Plexiform and Retinal Nerve Fiber Layer Thickness Measures by Cirrus and Spectralis Optical Coherence Tomography in Relapsing-Remitting Multiple Sclerosis. BioMed Res. Int. 2014, 2014, 128517.
  10. Ratchford, J.N.; Saidha, S.; Sotirchos, E.S.; Oh, J.A.; Seigo, M.A.; Eckstein, C.; Durbin, M.K.; Oakley, J.D.; Meyer, S.A.; Conger, A.; et al. Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning. Neurology 2013, 80, 47–54.
  11. Petzold, A.; Balcer, L.J.; Calabresi, P.A.; Costello, F.; Frohman, T.C.; Frohman, E.M.; Martinez-Lapiscina, E.H.; Green, A.J.; Kardon, R.; Outteryck, O.; et al. Retinal layer segmentation in multiple sclerosis: A systematic review and meta-analysis. Lancet Neurol. 2017, 16, 797–812.
  12. Kale, N. Optic neuritis as an early sign of multiple sclerosis. Eye Brain 2016, 8, 195–202.
  13. Britze, J.; Pihl-Jensen, G.; Frederiksen, J.L. Retinal ganglion cell analysis in multiple sclerosis and optic neuritis: A systematic review and meta-analysis. J. Neurol. 2017, 264, 1837–1853.
  14. Saidha, S.; Al-Louzi, O.; Ratchford, J.N.; Bhargava, P.; Oh, J.; Newsome, S.D.; Prince, J.L.; Pham, D.; Roy, S.; van Zijl, P.; et al. Optical coherence tomography reflects brain atrophy in multiple sclerosis: A four-year study: Retinal Atrophy Reflects Brain Atrophy in MS. Ann. Neurol. 2015, 78, 801–813.
  15. Dörr, J.; Wernecke, K.D.; Bock, M.; Gaede, G.; Wuerfel, J.T.; Pfueller, C.F.; Bellmann-Strobl, J.; Freing, A.; Brandt, A.U.; Friedemann, P. Association of Retinal and Macular Damage with Brain Atrophy in Multiple Sclerosis. PLoS ONE 2011, 6, e18132.
  16. Cilingir, V.; Batur, M.; Bulut, M.D.; Milanlioglu, A.; Yılgor, A.; Yasar, T.; Tombul, T. The association between retinal nerve fibre layer thickness and corpus callosum index in different clinical subtypes of multiple sclerosis. Neurol. Sci. 2017, 38, 1223–1232.
  17. Glasner, P.; Sabisz, A.; Chylińska, M.; Komendziński, J.; Wyszomirski, A.; Karaszewski, B. Retinal nerve fiber and ganglion cell complex layer thicknesses mirror brain atrophy in patients with relapsing-remitting multiple sclerosis. Restor. Neurol. Neurosci. 2022, 40, 35–42.
  18. Balk, L.; Steenwijk, M.; Tewarie, P.; Daams, M.; Killestein, J.; Wattjes, M.; Vrenken, H.; Barkhof, F.; Polman, C.; Uitdehaag, B.; et al. Bidirectional trans-synaptic axonal degeneration in the visual pathway in multiple sclerosis. J. Neurol. Neurosurg. Psychiatry 2015, 86, 419–424.
  19. Klistorner, A.; Graham, E.C.; Yiannikas, C.; Barnett, M.; Parratt, J.; Garrick, R.; Wang, C.; You, Y.; Graham, S. Progression of retinal ganglion cell loss in multiple sclerosis is associated with new lesions in the optic radiations. Eur. J. Neurol. 2017, 24, 1392–1398.
  20. Knier, B.; Leppenetier, G.; Wetzlmair, C.; Aly, L.; Hoshi, M.-M.; Pernpeintner, V.; Biberacher, V.; Berthele, A.; Mühlau, M.; Zimmer, C.; et al. Association of Retinal Architecture, Intrathecal Immunity, and Clinical Course in Multiple Sclerosis. JAMA Neurol. 2017, 74, 847–856.
  21. Martinez-Lapiscina, E.H.; Arnow, S.; Wilson, J.A.; Saidha, S.; Preiningerova, J.L.; Oberwahrenbrock, T.; Brandt, A.U.; Pablo, L.E.; Guerrieri, S.; Gonzalez, I.; et al. Retinal thickness measured with optical coherence tomography and risk of disability worsening in multiple sclerosis: A cohort study. Lancet Neurol. 2016, 15, 574–584.
  22. Lambe, J.; Fitzgerald, K.C.; Murphy, O.C.; Filippatou, A.G.; Sotirchos, E.S.; Kalaitzidis, G.; Vasileiou, E.; Pellegrini, N.; Ogbuokiri, E.; Toliver, B.; et al. Association of Spectral-Domain OCT with Long-term Disability Worsening in Multiple Sclerosis. Neurology 2021, 96, e2058–e2069.
  23. Skirková, M.; Mikula, P.; Maretta, M.; Fedičová, M.; Vitková, M.; Frigová, L.; Szilasi, J.; Moravská, M.; Horňák, M.; Szilasiová, J. Associations of optical coherence tomography with disability and brain MRI volumetry in patients with multiple sclerosis. Neurol. Neurochir. Polska, 2022; in press.
  24. Bsteh, G.; Hegen, H.; Teuchner, B.; Amprosi, M.; Berek, K.; Ladstätter, F.; Wurth, S.; Auer, M.; Di Pauli, F.; Deisenhammer, F.; et al. Peripapillary retinal nerve fibre layer as measured by optical coherence tomography is a prognostic biomarker not only for physical but also for cognitive disability progression in multiple sclerosis. Mult. Scler. J. 2019, 25, 196–203.
  25. Walter, S.D.; Ishikawa, H.; Galetta, K.M.; Sakai, R.E.; Feller, D.J.; Henderson, S.B.; Wilson, J.A.; Maguire, M.G.; Galetta, S.L.; Frohman, E.; et al. Ganglion Cell Loss in Relation to Visual Disability in Multiple Sclerosis. Ophthalmology 2012, 119, 1250–1257.
  26. Sotirchos, E.; Seigo, M.A.; Calabresi, P.; Saidha, S. Comparison of Point Estimates and Average Thicknesses of Retinal Layers Measured Using Manual Optical Coherence Tomography Segmentation for Quantification of Retinal Neurodegeneration in Multiple Sclerosis. Curr. Eye Res. 2013, 38, 224–228.
  27. Lampert, E.J.; Andorra, M.; Torres-Torres, R.; Ortiz-Pérez, S.; Llufriu, S.; Sepúlveda, M.; Sola, N.; Saiz, A.; Sánchez-Dalmau, B.; Villoslada, P.; et al. Color vision impairment in multiple sclerosis points to retinal ganglion cell damage. J. Neurol. 2015, 262, 2491–2497.
  28. Lin, T.-Y.; Chien, C.; Lu, A.; Paul, F.; Zimmermann, H.G. Retinal optical coherence tomography and magnetic resonance imaging in neuromyelitis optica spectrum disorders and MOG-antibody associated disorders: An updated review. Expert Rev. Neurother. 2021, 21, 1101–1123.
  29. Brandt, A.U.; Zimmermann, H.; Kaufhold, F.; Promesberger, J.; Schippling, S.; Finis, D.; Aktas, O.; Geis, C.; Ringelstein, M.; Ringelstein, E.B.; et al. Patterns of Retinal Damage Facilitate Differential Diagnosis between Susac Syndrome and MS. PLoS ONE 2012, 11, e38741.
  30. Ringelstein, M.; Albrecht, P.; Kleffner, I.; Bühn, B.; Harmel, J.; Müller, A.-K.; Finis, D.; Guthoff, R.; Bergholz, R.; Duning, T.; et al. Retinal pathology in Susac syndrome detected by spectral-domain optical coherence tomography. Neurology 2015, 85, 610–618.
  31. Toledo, J.; Sepulcre, J.; Salinas-Alaman, A.; García-Layana, A.; Murie-Fernandez, M.; Bejarano, B.; Villoslada, P. Retinal nerve fiber layer atrophy is associated with physical and cognitive disability in multiple sclerosis. Mult. Scler. J. 2008, 14, 906–912.
  32. Sedighi, B.; Shafa, M.A.; Abna, Z.; Ghaseminejad, A.K.; Farahat, R.; Nakhaee, N.; Hassani, B. Association of Cognitive deficits with Optical Coherence Tomography changes in Multiple Sclerosis Patients. J. Mult. Scler. 2014, 1, 117.
  33. Coric, D.; Balk, L.J.; Verrijp, M.; Eijlers, A.; Schoonheim, M.M.; Killestein, J.; Uitdehaag, B.M.; Petzold, A. Cognitive impairment in patients with multiple sclerosis is associated with atrophy of the inner retinal layers. Mult. Scler. J. 2018, 24, 158–166.
  34. Pul, R.; Saadat, M.; Morbiducci, F.; Skripuletz, T.; Pul, U.; Brockmann, D.; Sühs, K.-W.; Schwenkenbecher, P.; Kahl, K.G.; Pars, K.; et al. Longitudinal time-domain optic coherence study of retinal nerve fiber layer in IFNβ-treated and untreated multiple sclerosis patients. Exp. Ther. Med. 2016, 12, 190–200.
  35. Button, J.; Al-Louzi, O.; Lang, A.; Bhargava, P.; Newsome, S.D.; Frohman, T.; Balcer, L.J.; Frohman, E.M.; Prince, J.; Calabresi, P.A.; et al. Disease-modifying therapies modulate retinal atrophy in multiple sclerosis: A retrospective study. Neurology 2017, 88, 525–532.
  36. Klistorner, A.; Arvind, H.; Nguyen, T.; Garrick, R.; Paine, M.; Graham, S.; O’Day, J.; Yiannikas, C. Multifocal VEP and OCT in optic neuritis: A topographical study of the structure–function relationship. Doc. Ophthalmol. 2009, 118, 129–137.
  37. Trip, S.A.; Schlottmann, P.G.; Jones, S.J.; Altmann, D.R.; Garway-Heath, D.F.; Thompson, A.J.; Plant, G.T.; Miller, D.H. Retinal nerve fiber layer axonal loss and visual dysfunction in optic neuritis. Ann. Neurol. 2005, 58, 383–391.
  38. Meltzer, E.; Sguigna, P.V.; Subei, A.; Beh, S.; Kildebeck, E.; Conger, D.; Conger, A.; Lucero, M.; Frohman, B.S.; Frohman, A.N.; et al. Retinal Architecture and Melanopsin-Mediated Pupillary Response Characteristics: A Putative Pathophysiologic Signature for the Retino-Hypothalamic Tract in Multiple Sclerosis. JAMA Neurol. 2017, 74, 574–582.
  39. Wicklein, R.; Wauschkuhn, J.; Giglhuber, K.; Kümpfel, T.; Hemmer, B.; Havla, J.; Knier, B. Association of peripapillary hyper-reflective ovoid masslike structures and disease duration in primary progressive multiple sclerosis. Eur. J. Neurol. 2021, 28, 15056.
  40. López-Dorado, A.; Ortiz, M.; Satue, M.; Rodrigo, M.J.; Barea, R.; Sánchez-Morla, E.M.; Cavaliere, C.; Rodríguez-Ascariz, J.M.; Orduna-Hospital, E.; Boquete, L.; et al. Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation. Sensors 2021, 22, 167.
  41. Montolío, A.; Cegoñino, J.; Garcia-Martin, E.; del Palomar, A.P. Comparison of Machine Learning Methods Using Spectralis OCT for Diagnosis and Disability Progression Prognosis in Multiple Sclerosis. Ann. Biomed. Eng. 2022, 50, 507–528.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
Video Production Service
Feedback