Tag Archives: Michael Kalloniatis

Pattern Recognition Analysis Reveals Unique Contrast Sensitivity Isocontours Using Static Perimetry Thresholds Across The Visual Field

We have a new publication in IOVS, Pattern Recognition Analysis Reveals Unique Contrast Sensitivity Isocontours Using Static Perimetry Thresholds Across The Visual Field (Direct link here).  Authors are:  Jack Phu, Sieu Khuu, Lisa Nivison-Smith, Barbara Zangerl, Agnes Yiu, Jeung Choi, Bryan W. JonesRebecca Pfeiffer, Robert Marc, and Michael Kalloniatis.

Purpose
To determine the locus of test locations that exhibit statistically similar age-related decline in sensitivity to light increments and age-corrected contrast sensitivity isocontours (CSIs) across the central visual field (VF). We compared these CSIs with test point clusters used by the Glaucoma Hemifield Test (GHT).

Methods
Sixty healthy observers underwent testing on the Humphrey Field Analyzer 30-2 test grid using Goldmann (G) stimulus sizes I-V. Age-correction factors for GI-V were determined using linear regression analysis. Pattern recognition analysis was used to cluster test locations across the VF exhibiting equal age-related sensitivity decline (age-related CSIs), and points of equal age-corrected sensitivity (age-corrected CSIs) for GI-V.

Results
There was a small but significant test size–dependent sensitivity decline with age, with smaller stimuli declining more rapidly. Age-related decline in sensitivity was more rapid in the periphery. A greater number of unique age-related CSIs was revealed when using smaller stimuli, particularly in the mid-periphery. Cluster analysis of age-corrected sensitivity thresholds revealed unique CSIs for GI-V, with smaller stimuli having a greater number of unique clusters. Zones examined by the GHT consisted of test locations that did not necessarily belong to the same CSI, particularly in the periphery.

Conclusions
Cluster analysis reveals statistically significant groups of test locations within the 30-2 test grid exhibiting the same age-related decline. CSIs facilitate pooling of sensitivities to reduce the variability of individual test locations. These CSIs could guide future structure-function and alternate hemifield asymmetry analyses by comparing matched areas of similar sensitivity signatures.

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.

The Rod-Cone Crossover Connectome of Mammalian Bipolar Cells

We have a new publication out (direct link), The rod-cone crossover connectome of mammalian bipolar cells authored by Scott Lauritzen, Crystal Sigulinsky, James Anderson, Michael Kalloniatis, Noah Nelson, Danny Emrich, Chris Rapp, Nicolas McCarthy, Ethan Kerzner, Mariah Meyer, Bryan W. Jones, and Robert Marc.

Abstract: The basis of cross-suppression between rod and cone channels has long been an enigma. Using rabbit retinal connectome RC1, we show that all cone bipolar cell (BC) classes inhibit rod BCs via amacrine cell (AC) motifs (C1-6); that all cone BC classes are themselves inhibited by AC motifs (R1-5, R25) driven by rod BCs. A sparse symmetric AC motif (CR) is presynaptic and postsynaptic to both rod and cone BCs. ON cone BCs of all classes drive inhibition of rod BCs via motif C1 wide-field GABAergic ACs (γACs) and motif C2 narrow field glycinergic ON ACs (GACs). Each rod BC receives ≈ 10 crossover AC synapses and each ON cone BC can target ≈ 10 or more rod BCs via separate AC processes. OFF cone BCs mediate monosynaptic inhibition of rod BCs via motif C3 driven by OFF γACs and GACs and disynaptic inhibition via motifs C4 and C5 driven by OFF wide-field γACs and narrow-field GACs, respectively. Motifs C4 and C5 form halos of 60-100 inhibitory synapses on proximal dendrites of AI γACs. Rod BCs inhibit surrounding arrays of cone BCs through AII GAC networks that access ON and OFF cone BC patches via motifs R1, R2, R4 R5 and a unique ON AC motif R3 that collects rod BC inputs and targets ON cone BCs. Crossover synapses for motifs C1, C4, C5 and R3 are 3-4x larger than typical feedback synapses, which may be a signature for synaptic winner-take-all switches.

Pattern Recognition Reveals Different Visual Field Signature Patterns When Using Spatially Equated Test Sizes Compared To Standard Goldmann III Alone

This abstract was presented today, May 2th at the 2016 Association for Research in Vision and Opthalmology (ARVO) meetings in Seattle, Washington by Michael Kalloniatis, Robert E. Marc, Sieu K. Khuu, Jack Phu, Barbara Zangerl, Lisa Nivison-Smith, Bryan W. Jones, and Rebecca L. Pfeiffer. 

Abstract Number: 4745

Author Block: Michael Kalloniatis, Robert E. Marc, Sieu K. Khuu, Jack Phu, Barbara Zangerl, Lisa Nivison-Smith, Bryan W. Jones, Rebecca L. Pfeiffer
1 Centre for Eye Health, SOVS, University of New South Wales, Kensington, New South Wales, Australia; 2 SOVS, UNSW, Sydney, New South Wales, Australia; 3 Univ of Utah/Moran Eye Center, Salt Lake City, Utah, United States

Disclosure Block:Michael Kalloniatis, 2014/094035 A1 (USA) and 13865419.9 (EU) (Code P (Patent) ); Robert E. Marc, None; Sieu K. Khuu, 2014/094035 A1 (USA) and 13865419.9 (EU) (Code P (Patent) ); Jack Phu, None; Barbara Zangerl, None; Lisa Nivison-Smith, None; Bryan W. Jones, None; Rebecca L. Pfeiffer, None

Purpose:To identify areas within the visual field with matching contrast sensitivity (CS) signature patterns as a function of age using pattern recognition and determine the discrimination of CS data when using spatially equated test stimuli compared to the single size Goldmann (G)III alone.

Methods:52 subjects (classified in decade age groups from 20-60+ years) were tested using the Humphrey Visual Field Analyser 30-2 paradigm in full threshold mode for GI to GV. At least two thresholds were obtained per size. Two visual field maps were analyzed: a spatially equated visual field where GI was used centrally, GII mid-peripherally and GIII in the outer rings to place the test size at or close to complete spatial summation and a second where a single GIII was used at all locations. Thresholds were expressed as dB* (Khuu & Kalloniatis, IOVS 2015), converted to pixel values and analyzed using an unsupervised classification using isodata clustering (PCI, Geomatica, Canada). Class separation was extracted across the ages to develop dot plots of decade measures of CS.

Results:The 77 data points across the central 60° visual field can be distilled into 6 functional classes using the spatially equated visual field (Class separation 1). The 6 classes reflect areas in visual space that change in a similar manner across the ages. The use of the single GIII target resulted in only 4 classes displaying a poorer discrimination over the central visual field (Class separation 2). Extracted dot plots from class separation illustrated average CS within each class could be assessed across the decades.

Conclusions:When using spatially equated visual field testing, concentric areas were separated into distinct CS signatures consistent with known visual field sensitivity. We confirmed these areas change systematically with age. GIII failed to discriminate central areas of the 30-2 that likely reflects the fact that this size operates outside complete spatial summation and thus may not be the optimal test size for assessing visual function in the central visual field. More importantly, we showed pattern recognition can be applied to complex visual field data sets to identify common features and age-related visual function changes. This analysis allows regions to be averaged as they are statistically identical: this approach will likely assist structure-function studies.