Perimetric defects and ganglion cell damage: interpreting linear relations using a two-stage neural model.
OBJECTIVE
To better understand the relations between glaucomatous perimetric defects and ganglion cell damage, a neural model was developed to interpret empiric findings on linear relations between perimetric defects and measures of ganglion cell loss.


METHODS
A two-stage model computed responses of ganglion cell mosaics (first stage), then computed perimetric sensitivity in terms of processing by spatial filters (second stage) that pool the ganglion cell responses. Cell death and dysfunction were introduced in a local patch of the first-stage ganglion cell mosaic, and perimetric defect depth was computed for the corresponding region of the visual field. Calculations were performed for both sparse and dense ganglion cell mosaics and for spatial filters with peak frequencies from 0.5 to 4.0 cyc/deg.


RESULTS
The model yielded nonlinear functions for perimetric defect depth in decibel versus the percentage of ganglion cell damage, but functions for lower spatial frequencies became linear when perimetric defect was expressed as a percentage of normal. The relations between perimetric defects and percentage of ganglion cell loss were determined primarily by spatial tuning of the second-stage spatial filters. For averaging sensitivities across different visual field locations, linear units (arithmetic mean) can more closely approximate mean ganglion cell loss than decibel units (geometric mean). Fits to data from experimental glaucoma required ganglion cell dysfunction in addition to ganglion cell loss.


CONCLUSIONS
Pooling by second-stage spatial filters can account for empiric findings of linear relations between perimetric defects and measures of ganglion cell loss.
Swanson
William H
WH
Departmentof Clinical Sciences, Glaucoma Institute, SUNY State College of Optometry, New York, New York 10036, USA. bswanson@sunyopt.edu
Felius
Joost
J
Pan
Fei
F
D005901:Glaucoma / Q000175:diagnosis*; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D012165:Retinal Ganglion Cells / Q000473:pathology*; D014786:Vision Disorders / Q000175:diagnosis*; D058609:Visual Field Tests; D014794:Visual Fields