Combining Ganglion Cell Topology and Glaucomatous Patient Data to Determine a Structure-Function Map
Andrew Turpin 1*,
Geoff Sampson 2,
and
Allison M. McKendrick 3
1 Computer Science & IT, RMIT University, Melbourne, Victoria, Australia
2 Victorian College of Optometry, Melbourne, Victoria, Australia
3 University of Melbourne, Melbourne, Victoria, Australia
* To whom correspondence should be addressed. E-mail: andrew.turpin{at}rmit.edu.au.
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Abstract |
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Purpose: To introduce techniques for deriving a map that
relates visual field locations to optic nerve head (ONH) sectors,
and to use the techniques to derive a map relating Medmont perimetric
data to data from the Heidelberg Retinal Tomograph. Methods:
Spearman correlation coefficients were calculated relating each
visual field location (Medmont M700) to rim area and volume measures
for 10 degree ONH sectors (HRT III software) for 57 participants:
34 glaucoma, 18 glaucoma suspects, and 5 ocular hypertensives.
Correlations were constrained to be anatomically plausible using a
computational model of the axon growth of retinal ganglion cells
(Algorithm GROW). GROW generated a map relating field locations
to sectors of the ONH. The sector with the maximum, statistically
significant (p<0.05) correlation coefficient within 40 degrees
of the angle predicted by GROW for each location was computed.
Prior to correlation, both functional and structural data were
normalized by either normative data or the fellow eye in each
participant. Results: The model of axon growth produces a
24-2 map that is qualitatively similar to maps derived previously
from empirical data (for example, Garway-Heath et al., 2000). Using
GROW in conjunction with normative data, 31% of field locations
exhibit a statistically significant relationship. This is increased
to 67% (Z-test, Z=4.84, p<0.001) when normalizing both fields
and rim area data with the fellow eye. Conclusions: A
computational model of axon growth and normalizing data by the
fellow eye can assist in constructing an anatomically plausible map
connecting visual field data and sectoral ONH data.
Key Words:
glaucoma, visual field, image analysis
