ERG rod a-wave in Oguchi disease

A compound heterozygous mutation in the S-Antigen Visual Arrestin SAG gene in a Chinese patient with Oguchi type one: a case report

Background

Oguchi disease is a rare autosomal recessive form of congenital quiescent night blindness. Oguchi disease has been found to be associated with gene mutations in SAG and GRK1, which are vital factors in the recovery phase of phototransduction after light stimuli. We report a case of Oguchi disease with novel heterozygous mutations in SAG.

Case presentation

A 7-year-old girl with a history of night blindness since childhood, was referred to our hospital. Ophthalmologic examinations included visual acuity, fundus examinations, fundus photography, spectral-domain optical coherence tomography, electroretinographic (ERG). Mutation screening of the SAG and GRK1 genes was performed. This patient exhibited typical clinical characteristics of Oguchi disease, including night blindness, golden fundus with the Mizuo–Nakamura phenomenon, packed structure of the parafovea in optical coherence tomography and reduced a-waves and b-waves in scotopic 3.0 ERG. Genetic testing revealed a heterozygous change in nucleotide c.72_75+15delATCGGTGAGTGGTGCACAA in exon 2 of the SAG gene in this patient, her unaffected mother and younger brother. A splicing alteration of nucleotide c.376-2A>C was identified in exon 6 of the SAG gene with heterozygous status in this patient and her unaffected father.

Conclusions

Compound heterozygosity of a nonsense p.S25X mutation in exon 2 and a splicing alteration in exon 6 of the SAG gene is the cause of this patient with Oguchi type 1 disease in China.

Time-Frequency Analysis of Photopic Negative Response in CRVO Patients

PURPOSE

The PhNR is driven by retinal ganglion cells (RGCs). Therefore, the function of RGCs could be objectively evaluated by analyzing the PhNR. The aim of this article is to determine the effect of central retinal vein occlusion (CRVO) on PhNR and RGCs performances.

METHODS

Seventeen patients with CRVO were included. Full-field photopic ERGs, including PhNR, were recorded and compared with the fellow normal eyes. ERG signals were analyzed based on the standard time-domain analyses of the PhNR as well as a continuous wavelet transform (CWT) to extract time-frequency components that correspond to the PhNR using MATLAB. We obtained the main frequencies and their occurrence time from CWT.

RESULTS

All a-wave, b-wave, and PhNR amplitudes of CRVO eyes showed a significant reduction compared to those of the fellow eyes (P < .01, P < .001, and P < .001, respectively). The peak times of a-wave, b-wave, and PhNR were increased significantly in the CRVO eyes (P = .04, P = .04, and P = .003, respectively). The dominant f₃ frequency, which corresponds to the PhNR in CRVO patients, showed a more significant decrease (P < .001) compared to other dominant frequencies (f₀, f₁, and f₂). The occurrence time of f₃ (t₃) was significantly higher in the CRVO eyes (P < .001). Time-domain of the PhNR was also affected in CRVO patients (P < .001).

CONCLUSION

CWT allows quantifications of ERG responses, especially for PhNR. The PhNR was severely affected in CRVO eyes implicating loss of RGCs. CWT might demonstrate the severity of CRVO more precisely and identify diagnostically significant changes of ERG waveforms that are not resolved when the analysis is only limited to the time-domain measurements.

Congenital Stationary Night Blindness

In congenital stationary night blindness (CSNB), there is a defect in rod photoreceptor signal transmission. This disorder of night vision is non-progressive. The most common inheritance pattern is X-linked, though autosomal recessive and autosomal dominant patterns have been described (Fig. 13.1). There is genetic heterogeneity within these types.

Empirical mode decomposition and neural network for the classification of electroretinographic data

The processing of biosignals is increasingly being utilized in ambulatory situations in order to extract significant signals' features that can help in clinical diagnosis. However, this task is hampered by the fact that biomedical signals exhibit a complex behavior characterized by strong nonlinear and non-stationary properties that cannot always be perceived by simple visual examination. New processing methods need be considered. In this context, we propose a signal processing method, based on empirical mode decomposition and artificial neural networks, to analyze electroretinograms, i.e., the retinal response to a light flash, with the aim to detect and classify retinal diseases. The present application focuses on two retinal pathologies: achromatopsia, which is a cone disease, and congenital stationary night blindness, which affects the photoreceptoral signal transmission. The results indicate that, under suitable conditions, the method proposed here has the potential to provide a powerful tool for routine clinical examinations, since it is able to recognize with high level of confidence the eventual presence of one of the two pathologies.

A comparison among different techniques for human ERG signals processing and classification

Feature detection in biomedical signals is crucial for deepening our knowledge about the involved physiological processes. To achieve this aim, many analytic approaches can be applied but only few are able to deal with signals whose time dependent features provide useful clinical information. Among the biomedical signals, the electroretinogram (ERG), that records the retinal response to a light flash, can improve our comprehension of the complex photoreceptoral activities. The present study is focused on the analysis of the early response of the photoreceptoral human system, known as a-wave ERG-component. This wave reflects the functional integrity of the photoreceptors, rods and cones, whose activation dynamics are not yet completely understood. Moreover, since in incipient photoreceptoral pathologies eventual anomalies in a-wave are not always detectable with a "naked eye" analysis of the traces, the possibility to discriminate pathologic from healthy traces, by means of appropriate analytical techniques, could help in clinical diagnosis. In the present paper, we discuss and compare the efficiency of various techniques of signal processing, such as Fourier analysis (FA), Principal Component Analysis (PCA), Wavelet Analysis (WA) in recognising pathological traces from the healthy ones. The investigated retinal pathologies are Achromatopsia, a cone disease and Congenital Stationary Night Blindness, affecting the photoreceptoral signal transmission. Our findings prove that both PCA and FA of conventional ERGs, don't add clinical information useful for the diagnosis of ocular pathologies, whereas the use of a more sophisticated analysis, based on the wavelet transform, provides a powerful tool for routine clinical examinations of patients.