Demyelinating optic neuritis: Optical coherence tomography and multifocal electroretinography data correlation

Нероев, В. В.; Eliseeva, E. K.; Mv, Zueva; Lysenko, V. S.; Zakharova, M. N.; Цапенко, И. В.; Semenova, N. A.; Simaniv, T. O.

doi:10.1134/s0362119716080090

Cited by 8 publications

(10 citation statements)

References 13 publications

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“…Our hypothesis is that the glutamate imbalance would be transported anterogradely from the brain by the axons, reaching the optic nerve. It would be possible to detect topographical retinal dysfunction in the outer rings and the nasal quadrant in the form of an increase in mfERG amplitude in early-stage MS. With time, in later or more severe stages [7,15,[17][18][19] there would be a decline, as was detected in [17], in which 5 out of 7 patients showed decreased amplitude with predominant macular thinning when examined using optical coherence tomography.…”

Section: Discussionmentioning

confidence: 79%

“…It should likewise be noted that the studies conducted include patients with different subtypes of the disease and with or without optic neuritis (see Table 3). In MS diagnosis based on the amplitude and latency parameters of first-order kernel waves in previous studies, prolonged latency is the altered parameter found most frequently [7,15,16,18,19], and even Neroev et al [19] suggested using the latency of wave P1 in the parafovea as a marker of MS progression. The studies that detected the classical parameters with greatest alteration (lower amplitudes or higher latencies) in MS patients generally employed a 61-hexagon test [7,16,18,19].…”

Section: Discussionmentioning

confidence: 99%

“…However, the few papers that have investigated the potential of mfERG analysis for diagnosing MS are based on traditional analysis of amplitudes and latencies and have produced conflicting findings [7,[15][16][17][18][19][20], perhaps because of the heterogeneity of the subjects analyzed (different types of MS and presence/absence of ON) and the use of different mfERG technologies.…”

Section: Introductionmentioning

confidence: 99%

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Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis

Santiago

Castillo

García‐Martín

et al. 2019

Sensors

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As multiple sclerosis (MS) usually affects the visual pathway, visual electrophysiological tests can be used to diagnose it. The objective of this paper is to research methods for processing multifocal electroretinogram (mfERG) recordings to improve the capacity to diagnose MS. MfERG recordings from 15 early-stage MS patients without a history of optic neuritis and from 6 control subjects were examined. A normative database was built from the control subject signals. The mfERG recordings were filtered using empirical mode decomposition (EMD). The correlation with the signals in a normative database was used as the classification feature. Using EMD-based filtering and performance correlation, the mean area under the curve (AUC) value was 0.90. The greatest discriminant capacity was obtained in ring 4 and in the inferior nasal quadrant (AUC values of 0.96 and 0.94, respectively). Our results suggest that the combination of filtering mfERG recordings using EMD and calculating the correlation with a normative database would make mfERG waveform analysis applicable to assessment of multiple sclerosis in early-stage patients. Author Contributions: Conceptualization, E.G.-M., M.J.R., E.M.S.M., and L.B.; methodology, L.d.S., M.O.d.C., C.C., J.M.M., A.L., and B.C.; software, L.d.S., M.O.d.C., C.C., J.M.M., and A.L.; validation, L.d.S., M.O.d.C., C.C., J.M.M., and A.L.; formal Analysis, M.O.d.C. and J.M.M.; investigation, E.G.-M., M.J.R., E.M.S.M., and L.B.; resources, L.B. and E.G.-M.; data Curation, L.d.S., M.O.d.C., C.C., J.M.M., A.L., and B.C.; writing-original draft preparation, L.B., E.G.-M., and M.J.R.; writing-review and editing, L.B., E.G.-M., and M.J.R.; visualization, L.B. and E.G.-M.; supervision, L.B. and E.G.-M.; project administration, L.B. and E.G.-M.; funding acquisition, L.B. and E.G.-M. All authors have read and agreed to the published version of the manuscript.

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Section: Discussionmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis

Santiago

Castillo

García‐Martín

et al. 2019

Sensors

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“…• With multifocal visual-evoked potentials, it can be used to analyse mfERG, mfVEP or mfPERG 8,9,10,13,17,20,25,28,34,37,42,45,49,52,53,54,55 0:5125 0:5328 0:5438…”

Section: Discussionmentioning

confidence: 99%

“…Analysis by quadrant makes it possible to carry out comparisons between mfERG studies and optical coherence tomography (OCT) [7,8]. It is also common to find the results of clustering by ring and quadrant in publications that analyse the technical aspects of mfERG [9,10]. More recently, [11] detected altered responses in clustering by ring and quadrant in patients with nasopharyngeal carcinoma after radiotherapy.…”

Section: Introductionmentioning

confidence: 99%

Identification of clusters in multifocal electrophysiology recordings to maximize discriminant capacity (patients vs. control subjects)

et al. 2019

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Purpose To propose a new method of identifying clusters in multifocal electrophysiology (multifocal electroretinogram: mfERG; multifocal visual-evoked potential: mfVEP) that conserve the maximum capacity to discriminate between patients and control subjects. Methods The theoretical framework proposed creates arbitrary N-size clusters of sectors. The capacity to discriminate between patients and control subjects is assessed by analysing the area under the receiver operator characteristic curve (AUC). As proof of concept, the method is validated using mfERG recordings taken from both eyes of control subjects (n = 6) and from patients with multiple sclerosis (n = 15). ResultsConsidering the amplitude of wave P1 as the analysis parameter, the maximum value of AUC = 0.7042 is obtained with N = 9 sectors. Taking into account the AUC of the amplitudes and latencies of waves N1 and P1, the maximum value of the AUC = 0.6917 with N = 8 clustered sectors. The greatest discriminant capacity is obtained by analysing the latency of wave P1: AUC = 0.8854 with a cluster of N = 12 sectors. Conclusion This paper demonstrates the effectiveness of a method able to determine the arbitrary clustering of multifocal responses that possesses the greatest capacity to discriminate between control subjects and patients when applied to the visual field of mfERG or mfVEP recordings. The method may prove helpful in diagnosing any disease that is

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OCT and Multiple Sclerosis

Hanson

Wicki

Manogaran

et al. 2020

OCT and Imaging in Central Nervous System Diseases

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Demyelinating optic neuritis: Optical coherence tomography and multifocal electroretinography data correlation

Cited by 8 publications

References 13 publications

Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis

Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis

Identification of clusters in multifocal electrophysiology recordings to maximize discriminant capacity (patients vs. control subjects)

OCT and Multiple Sclerosis

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