Tag Archives: Nayuta Yoshioka

Development Of A Spatial Model Of Age-Related Change In The Macular Ganglion Cell Layer To Predict Function From Structural Changes

We have a new paper out in the Americal Journal of Ophthalmology, Development of a spatial model of age-related change in the macular ganglion cell layer to predict function from structural changes.

Authors: Janelle Tong, Jack Phu, Sieu K. Khuu, Nayuta Yoshioka, Agnes Y. Choi, Lisa Nivison-Smith, Robert E. Marc, Bryan W. Jones, Rebecca L. Pfeiffer, Michael Kalloniatis, and Barbara Zangerl.

Purpose: To develop location specific models of normal, age-related changes in the macular ganglion cell layer (GCL) from optical coherence tomography (OCT). Using these OCT-derived models, we predicted visual field (VF) sensitivity and compared these results to actual VF sensitivities.

Design: Retrospective cohort study

Methods: Single eyes of 254 normal participants were retrospectively enrolled from the Centre for Eye Health (Sydney, Australia). Macular GCL measurements were obtained using Spectralis OCT. Cluster algorithms were performed to identify spatial patterns demonstrating similar age- related change. Quadratic and linear regression models were subsequently utilized to characterize age-related GCL decline. 40 participants underwent additional testing with Humphrey VFs, and 95% prediction intervals were calculated to measure the predictive ability of structure-function models incorporating cluster-based pooling, age-correction and consideration of spatial summation.

Results: Quadratic GCL regression models provided a superior fit (p = <0.0001-0.0066), establishing that GCL decline commences in the late 30’s across the macula. The equivalent linear rates of GCL decline showed eccentricity-dependent variation (0.13μm/year centrally versus 0.06μm/year peripherally), however average, normalized GCL loss per year was consistent across the 64 macular measurement locations at 0.26%. The 95% prediction intervals describing predicted VF sensitivities were significantly narrower across all cluster- based structure-function models (3.79-4.99dB) compared with models without clustering applied (5.66-6.73dB, p <0.0001).

Conclusions: Combining spatial clustering with age-dependent regression allowed the development of robust models describing GCL changes with age. The resultant superior predictive ability of VF sensitivity from ganglion cell measurements may be applied to future models of disease development to improve detection of early macular GCL pathology.

Pattern Recognition Analysis of Age-Related Retinal Ganglion Cell Signatures In The Human Eye

We have a new publication in IOVS, Pattern Recognition Analysis of Age-Related Retinal Ganglion Cell Signatures In The Human Eye (Direct link here).  Authors are:  Nayuta Yoshioka, Barbara Zangerl, Lisa Nivison-Smith, Sieu Khuu, Bryan W. Jones, Rebecca Pfeiffer, Robert Marc, and Michael Kalloniatis.

Purpose: We recently used pattern recognition analysis to show macula areas can be classified into statistically distinct clusters in accordance to their age-related retinal ganglion cell layer (RGCL) thickness change in a normal population. The aim of this study was to perform a retrospective cross-sectional analysis utilizing a large cohort of patients to establish accuracy of this model and to develop a normative dataset using a 50-year-old equivalent cohort.

Methods: Data was collected from patients seen at the Centre for Eye Health for optic nerve assessment without posterior pole disease. The grid-wise RGCL thickness was obtained from a single eye of each patient via Spectralis OCT macular scan over an 8×8 measurement grid. Measurements for patients outside the 45-54 age range (training cohort) were converted to 50-year-old equivalent value utilizing pattern recognition derived regression model which, in brief, consists of 8×8 grid clustered into 8 distinct classes according to the pattern of RGCL thickness change with age. Accuracy of the predictions was assessed by comparing the training cohort’s measurements to the 45-54 year reference cohort using t-test and one-way ANOVA.

Results: Data were collected from a total 248 patients aged 20 to 78.1 years. 80 patients within this group were aged 45 – 54 and formed the reference cohort (average±SD 49.6±2.83) and the remaining 168 eyes formed the training cohort (average age±SD 50.7±17.34). Converted values for the training set matched those of the reference cohort (average disparity±SD 0.10±0.42µm, range -0.74-1.34µm) and were not significantly different (p > 0.9). Most variability was observed with patients above 70 years of age (average disparity±SD -0.09±1.73µm, range -3.67 to 6.16µm) and central grids corresponding to the fovea (average disparity±SD 0.47±0.72µm, range -0.22 to 1.34µm).

Conclusions: Our regression model for normal age-related RGCL change can accurately convert and/or predict RGCL thickness for individuals in comparison to 50-year-equivalent reference cohort and could allow for more accurate assessment of RGCL thickness and earlier detection of significant loss in the future. Caution may be needed when applying the model in the foveal area or for patients older than 70 years.

Predicting Age-related Changes with High Accuracy using a Pattern Recognition Derived Retinal Ganglion Cell Regression Model

This abstract was presented yesterday, May 7th at the 2017 Association for Research in Vision and Opthalmology (ARVO) meetings in Baltimore, Maryland by Nayuta Yoshioka, Barbara Zangerl, Lisa Nivison-Smith, Sieu Khuu, Bryan W. Jones, Rebecca Pfeiffer, Robert Marc, and Michael Kalloniatis.

Purpose: We recently used pattern recognition analysis to show macula areas can be classified into statistically distinct clusters in accordance to their age-related retinal ganglion cell layer (RGCL) thickness change in a normal population. The aim of this study was to perform a retrospective cross-sectional analysis utilizing a large cohort of patients to establish accuracy of this model and to develop a normative dataset using a 50-year-old equivalent cohort.

Methods: Data was collected from patients seen at the Centre for Eye Health for optic nerve assessment without posterior pole disease. The grid-wise RGCL thickness was obtained from a single eye of each patient via Spectralis OCT macular scan over an 8×8 measurement grid. Measurements for patients outside the 45-54 age range (training cohort) were converted to 50-year-old equivalent value utilizing pattern recognition derived regression model which, in brief, consists of 8×8 grid clustered into 8 distinct classes according to the pattern of RGCL thickness change with age. Accuracy of the predictions was assessed by comparing the training cohort’s measurements to the 45-54 year reference cohort using t-test and one-way ANOVA.

Results: Data were collected from a total 248 patients aged 20 to 78.1 years. 80 patients within this group were aged 45 – 54 and formed the reference cohort (average±SD 49.6±2.83) and the remaining 168 eyes formed the training cohort (average age±SD 50.7±17.34). Converted values for the training set matched those of the reference cohort (average disparity±SD 0.10±0.42µm, range -0.74-1.34µm) and were not significantly different (p > 0.9). Most variability was observed with patients above 70 years of age (average disparity±SD -0.09±1.73µm, range -3.67 to 6.16µm) and central grids corresponding to the fovea (average disparity±SD 0.47±0.72µm, range -0.22 to 1.34µm).

Conclusions: Our regression model for normal age-related RGCL change can accurately convert and/or predict RGCL thickness for individuals in comparison to 50-year-equivalent reference cohort and could allow for more accurate assessment of RGCL thickness and earlier detection of significant loss in the future. Caution may be needed when applying the model in the foveal area or for patients older than 70 years.