- Email: [email protected]
Learning Effect in Perimetry: The Role of Chromatic Discrimination
by our image analysis algorithm, and therefore the statistical comparison of the results is valid. These results cannot be compared to the database of the Spectralis instrument, since a different algorithm was used for generating the data. A review of literature clearly documents differences in thickness measurements between different instruments, stemming from the depth location of the chorioretinal interface.2 For these reasons, in fact, comparison of our measurements to the instrument database as suggested would be flawed. It was also astutely pointed out that central subfield analysis would be useful and clinically meaningful. More extensive work on this topic exploring focal retinal thinning in ETDRS-like macular subfields or more temporal watershed region by SD-OCT, with frequency of focal retinal thinning stratified by stage of retinopathy as well as sickle cell genotype, is currently in submission. QUAN V. HOANG FELIX Y. CHAU MAHNAZ SHAHIDI JENNIFER I. LIM
Chicago, Illinois CONFLICT OF INTEREST DISCLOSURES: SEE THE ORIGINAL article1 for any disclosures of the authors.
REFERENCES
1. Hoang QV, Chau FY, Shahidi M, Lim JI. Central macular splaying and outer retinal thinning in asymptomatic sickle cell patients by spectral domain optical coherence tomography. Am J Ophthalmol 2011;151(6):990 –994. 2. Wolf-Schnurrbusch UE, Ceklic L, Brinkmann CK, et al. Macular thickness measurements in healthy eyes using six different optical coherence tomography instruments. Invest Ophthalmol Vis Sci 2009;50(7):3432–3437.
Learning Effect in Perimetry: The Role of Chromatic Discrimination EDITOR: WE READ WITH INTEREST THE ARTICLE “LEARNING CURVE
and Fatigue Effect of Flicker Defined Form Perimetry,” by Lamparter and associates.1 The authors investigated the learning effect of the relatively new flicker defined form perimetry and found a significant improvement in different perimetric indices between the first 3 tests performed by inexperienced healthy subjects. As they state, the learning effect has been demonstrated in both normal and glaucomatous patients tested with conventional as well as nonconventional perimetric techniques. Reviewing the existing literature about this topic, we found that data about learning effect currently are available for standard automated perimetry, short-wavelength automated perimetry (SWAP), frequency doubling VOL. 152, NO. 6
technology, Rarebit perimetry, and now flicker defined form perimetry. Similar results have been found for standard automated perimetry, frequency doubling technology, Rarebit perimetry, and flicker defined form perimetry, with a learning effect occurring between the first and the second or the third session; conversely, SWAP has shown the occurrence of a learning effect also at the fifth session.2,3 Interestingly, SWAP is the only technique based on colored stimuli (blue dots on a yellow background), whereas the others use differently structured, uncolored stimuli (single white dots on a white screen, black and white striped squares on a white screen, a couple of white dots on a black screen, black and white dots on a mean luminance background). As already suggested by Wild and associates, it is plausible that chromatic discrimination may represent an additional factor influencing the perimetric learning effect.3 The pathway for the interpretation of colors has been reported to be separate from those responsible for object identification, confirmed by evidence indicating that color and orientation selectivity are mutually exclusive and that form vision depends primarily on achromatic information.4,5 Indeed, patients with visual agnosia may maintain the ability to identify the color of objects, whereas patients with cerebral achromatopsia may have normal visual acuity and contrast sensitivity.4 Recent studies in primate primary visual cortex have challenged the view that color and form are represented by distinct neuronal populations. Johnson and associates suggested that S-cones contributes to both chromatic and achromatic visual functions and that the conjoint representation of color and form is a fundamental property of cortical processing.5 Regardless of the exact mechanisms involved in these processes, it therefore is plausible that the detection of stimuli requiring both object and color identification (thus involving 2 separate pathways, different cortical processing, or both) implies higher training than object identification alone. This may be particularly true taking into account the possibly relevant fatigue effect induced by SWAP. Fogagnolo and associates recently reported a mild learning effect in standard automated perimetry– experienced patients tested with a Swedish interactive threshold algorithm program adapted to SWAP and the ability to reduce the examination duration by approximately 70%.6 However, the authors argued that learning effect in patients tested with this strategy who are naïve to perimetry may be higher.6 Further studies should be performed to clarify this issue that, in our opinion, should be taken into account when developing new perimetric techniques.
CORRESPONDENCE
ALESSANDRO BAGNIS GUIDO CORALLO RICCARDO SCOTTO MICHELE IESTER
Genoa, Italy 1075
SAP and SWAP. The patient must report the presence of a luminance increment on a background. However from our understanding, adaptation phenomenons may play an important role in explaining the prolonged learning curve of SWAP. We know that the density of small bistratified cells mediating the blue cone response is much smaller than for ganglion cells mediating the achromatic response and that the blue cone pathway is much more sluggish, and therefore adaptation takes longer.3 Published literature suggests that SWAP is more difficult to undertake, because patients who underwent SAP, frequency doubling technology, and SWAP ranked SWAP as the worst test.4 Given that SWAP may be more difficult to undertake than SAP, as well as acknowledging the critical adaptation requirements for S-cone pathway isolation, it may be that on the initial test, sensitivity readings for SWAP were of a much lower value than the real sensitivity value. Whereas in SAP, where adaptation may be more stable, a sensitivity value closer to the real value may be obtained sooner. Consideration also should be given to the respective dynamic ranges of the instruments with regard to normal sensitivity of each pathway before true comparisons can be made. In conclusion, several factors can be responsible for learning effects in perimetry. We agree that this issue should be clarified and understood properly because this is very important for future development of perimetric techniques. Future studies on this topic should be performed with patients naïve to any perimetric test.
CONFLICT OF INTEREST DISCLOSURES: THE AUTHORS HAVE completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest, and none were reported.
REFERENCES
1. Lamparter J, Schulze A, Schuff A-C, Pfeiffer N, Hoffmann EM. Learning curve and fatigue of flicker defined form perimetry. Am J Ophthalmol 2011;151(6):1057–1064. 2. Rossetti L, Fogagnolo P, Miglior S, Centofanti M, Vetrugno M, Orzalesi N. Learning effect of short-wavelength automated perimetry in patients with ocular hypertension. J Glaucoma 2006:15(5):399 – 404. 3. Wild JM, Kim L, Pacey IE, Cunliffe IA. Evidence for a learning effect in short-wavelength automated perimetry. Ophthalmology 2006;113(2):206 –215. 4. Lawton AW. Retrochiasmal pathways, higher cortical function, and nonorganic visual loss. In. Yanoff MDuker JS, eds. Ophthalmology. 2nd ed. St. Louis: Mosby; 2004:1295–1303. 5. Johnson EN, Van Hooser SD, Fitzpatrick D. The representation of S-cone signal in primary visual cortex. J Neurosci 2010;30(31):10337–10350. 6. Fogagnolo P, Tanga L, Rossetti L, et al. Mild learning effect of short-wavelength automated perimetry using SITA program. J Glaucoma 2010;19(5):319 –323.
REPLY WE ARE GRATEFUL FOR THE COMMENTS ON OUR RECENT
article about learning and fatigue effects in flicker defined form perimetry.1 Bagnis and associates point out that fatigue effects have been shown for several perimetry techniques such as standard automated perimetry (SAP), short-wavelength automated perimetry (SWAP), frequency doubling technology, Rarebit perimetry, and flicker defined form perimetry. They mention that learning effects occur for up to 5 sessions in SWAP, but only between the first and the second or third session in achromatic perimetry, and they therefore ask whether chromatic discrimination may be an additional factor influencing the perimetric learning effect. This is an interesting suggestion. A prolonged learning curve for SWAP also has be shown by Gardiner and associates, who examined the same patient group with both SAP and SWAP to investigate the evidence for continued learning in perimetry over several years.2 To imitate a realistic clinical setting, SAP and SWAP were performed annually over a period of 8 years. Interestingly, mean sensitivity for SAP increased over the first year to stay stable until after year 5, when it started to decline. In contrast to these findings, mean sensitivity continued to improve until year 6 for SWAP. It is somewhat unlikely that object identification and form play a primer role, because although different pathways may be stimulated selectively using each of these tests, the task for the patient remains the same for both 1076
AMERICAN JOURNAL
JULIA LAMPARTER ANDREAS SCHULZE ANN-CHRISTIN SCHUFF ESTHER M. HOFFMANN
Mainz, Germany MANFRED BERRES
Remagen, Germany NORBERT PFEIFFER
CONFLICT OF INTEREST DISCLOSURES: SEE THE ORIGINAL article1 for any disclosures of the authors.
REFERENCES
1. Lamparter J, Schulze A, Schuff A-C, Pfeiffer N, Hoffmann EM. Learning curve and fatigue of flicker defined form perimetry. Am J Ophthalmol 2011;151(6):1057–1064. 2. Gardiner SK, Demirel S, Johnson CA. Is there evidence for continued learning over multiple years in perimetry? Optom Vis Sci 2008;85(11):1043–1048. 3. Dacey DM, Packer OS. Colour coding in the primate retina: diverse cell types and cone-specific circuitry. Curr Opin Neurobiol 2003;13(4):421– 427. 4. Gardiner SK, Demirel S. Assessment of patient opinions of different clinical tests used in the management of glaucoma. Ophthalmology 2008;115(12):2127–2131.
OF
OPHTHALMOLOGY
DECEMBER
2011
Chicago, Illinois CONFLICT OF INTEREST DISCLOSURES: SEE THE ORIGINAL article1 for any disclosures of the authors.
REFERENCES
1. Hoang QV, Chau FY, Shahidi M, Lim JI. Central macular splaying and outer retinal thinning in asymptomatic sickle cell patients by spectral domain optical coherence tomography. Am J Ophthalmol 2011;151(6):990 –994. 2. Wolf-Schnurrbusch UE, Ceklic L, Brinkmann CK, et al. Macular thickness measurements in healthy eyes using six different optical coherence tomography instruments. Invest Ophthalmol Vis Sci 2009;50(7):3432–3437.
Learning Effect in Perimetry: The Role of Chromatic Discrimination EDITOR: WE READ WITH INTEREST THE ARTICLE “LEARNING CURVE
and Fatigue Effect of Flicker Defined Form Perimetry,” by Lamparter and associates.1 The authors investigated the learning effect of the relatively new flicker defined form perimetry and found a significant improvement in different perimetric indices between the first 3 tests performed by inexperienced healthy subjects. As they state, the learning effect has been demonstrated in both normal and glaucomatous patients tested with conventional as well as nonconventional perimetric techniques. Reviewing the existing literature about this topic, we found that data about learning effect currently are available for standard automated perimetry, short-wavelength automated perimetry (SWAP), frequency doubling VOL. 152, NO. 6
technology, Rarebit perimetry, and now flicker defined form perimetry. Similar results have been found for standard automated perimetry, frequency doubling technology, Rarebit perimetry, and flicker defined form perimetry, with a learning effect occurring between the first and the second or the third session; conversely, SWAP has shown the occurrence of a learning effect also at the fifth session.2,3 Interestingly, SWAP is the only technique based on colored stimuli (blue dots on a yellow background), whereas the others use differently structured, uncolored stimuli (single white dots on a white screen, black and white striped squares on a white screen, a couple of white dots on a black screen, black and white dots on a mean luminance background). As already suggested by Wild and associates, it is plausible that chromatic discrimination may represent an additional factor influencing the perimetric learning effect.3 The pathway for the interpretation of colors has been reported to be separate from those responsible for object identification, confirmed by evidence indicating that color and orientation selectivity are mutually exclusive and that form vision depends primarily on achromatic information.4,5 Indeed, patients with visual agnosia may maintain the ability to identify the color of objects, whereas patients with cerebral achromatopsia may have normal visual acuity and contrast sensitivity.4 Recent studies in primate primary visual cortex have challenged the view that color and form are represented by distinct neuronal populations. Johnson and associates suggested that S-cones contributes to both chromatic and achromatic visual functions and that the conjoint representation of color and form is a fundamental property of cortical processing.5 Regardless of the exact mechanisms involved in these processes, it therefore is plausible that the detection of stimuli requiring both object and color identification (thus involving 2 separate pathways, different cortical processing, or both) implies higher training than object identification alone. This may be particularly true taking into account the possibly relevant fatigue effect induced by SWAP. Fogagnolo and associates recently reported a mild learning effect in standard automated perimetry– experienced patients tested with a Swedish interactive threshold algorithm program adapted to SWAP and the ability to reduce the examination duration by approximately 70%.6 However, the authors argued that learning effect in patients tested with this strategy who are naïve to perimetry may be higher.6 Further studies should be performed to clarify this issue that, in our opinion, should be taken into account when developing new perimetric techniques.
CORRESPONDENCE
ALESSANDRO BAGNIS GUIDO CORALLO RICCARDO SCOTTO MICHELE IESTER
Genoa, Italy 1075
SAP and SWAP. The patient must report the presence of a luminance increment on a background. However from our understanding, adaptation phenomenons may play an important role in explaining the prolonged learning curve of SWAP. We know that the density of small bistratified cells mediating the blue cone response is much smaller than for ganglion cells mediating the achromatic response and that the blue cone pathway is much more sluggish, and therefore adaptation takes longer.3 Published literature suggests that SWAP is more difficult to undertake, because patients who underwent SAP, frequency doubling technology, and SWAP ranked SWAP as the worst test.4 Given that SWAP may be more difficult to undertake than SAP, as well as acknowledging the critical adaptation requirements for S-cone pathway isolation, it may be that on the initial test, sensitivity readings for SWAP were of a much lower value than the real sensitivity value. Whereas in SAP, where adaptation may be more stable, a sensitivity value closer to the real value may be obtained sooner. Consideration also should be given to the respective dynamic ranges of the instruments with regard to normal sensitivity of each pathway before true comparisons can be made. In conclusion, several factors can be responsible for learning effects in perimetry. We agree that this issue should be clarified and understood properly because this is very important for future development of perimetric techniques. Future studies on this topic should be performed with patients naïve to any perimetric test.
CONFLICT OF INTEREST DISCLOSURES: THE AUTHORS HAVE completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest, and none were reported.
REFERENCES
1. Lamparter J, Schulze A, Schuff A-C, Pfeiffer N, Hoffmann EM. Learning curve and fatigue of flicker defined form perimetry. Am J Ophthalmol 2011;151(6):1057–1064. 2. Rossetti L, Fogagnolo P, Miglior S, Centofanti M, Vetrugno M, Orzalesi N. Learning effect of short-wavelength automated perimetry in patients with ocular hypertension. J Glaucoma 2006:15(5):399 – 404. 3. Wild JM, Kim L, Pacey IE, Cunliffe IA. Evidence for a learning effect in short-wavelength automated perimetry. Ophthalmology 2006;113(2):206 –215. 4. Lawton AW. Retrochiasmal pathways, higher cortical function, and nonorganic visual loss. In. Yanoff MDuker JS, eds. Ophthalmology. 2nd ed. St. Louis: Mosby; 2004:1295–1303. 5. Johnson EN, Van Hooser SD, Fitzpatrick D. The representation of S-cone signal in primary visual cortex. J Neurosci 2010;30(31):10337–10350. 6. Fogagnolo P, Tanga L, Rossetti L, et al. Mild learning effect of short-wavelength automated perimetry using SITA program. J Glaucoma 2010;19(5):319 –323.
REPLY WE ARE GRATEFUL FOR THE COMMENTS ON OUR RECENT
article about learning and fatigue effects in flicker defined form perimetry.1 Bagnis and associates point out that fatigue effects have been shown for several perimetry techniques such as standard automated perimetry (SAP), short-wavelength automated perimetry (SWAP), frequency doubling technology, Rarebit perimetry, and flicker defined form perimetry. They mention that learning effects occur for up to 5 sessions in SWAP, but only between the first and the second or third session in achromatic perimetry, and they therefore ask whether chromatic discrimination may be an additional factor influencing the perimetric learning effect. This is an interesting suggestion. A prolonged learning curve for SWAP also has be shown by Gardiner and associates, who examined the same patient group with both SAP and SWAP to investigate the evidence for continued learning in perimetry over several years.2 To imitate a realistic clinical setting, SAP and SWAP were performed annually over a period of 8 years. Interestingly, mean sensitivity for SAP increased over the first year to stay stable until after year 5, when it started to decline. In contrast to these findings, mean sensitivity continued to improve until year 6 for SWAP. It is somewhat unlikely that object identification and form play a primer role, because although different pathways may be stimulated selectively using each of these tests, the task for the patient remains the same for both 1076
AMERICAN JOURNAL
JULIA LAMPARTER ANDREAS SCHULZE ANN-CHRISTIN SCHUFF ESTHER M. HOFFMANN
Mainz, Germany MANFRED BERRES
Remagen, Germany NORBERT PFEIFFER
CONFLICT OF INTEREST DISCLOSURES: SEE THE ORIGINAL article1 for any disclosures of the authors.
REFERENCES
1. Lamparter J, Schulze A, Schuff A-C, Pfeiffer N, Hoffmann EM. Learning curve and fatigue of flicker defined form perimetry. Am J Ophthalmol 2011;151(6):1057–1064. 2. Gardiner SK, Demirel S, Johnson CA. Is there evidence for continued learning over multiple years in perimetry? Optom Vis Sci 2008;85(11):1043–1048. 3. Dacey DM, Packer OS. Colour coding in the primate retina: diverse cell types and cone-specific circuitry. Curr Opin Neurobiol 2003;13(4):421– 427. 4. Gardiner SK, Demirel S. Assessment of patient opinions of different clinical tests used in the management of glaucoma. Ophthalmology 2008;115(12):2127–2131.
OF
OPHTHALMOLOGY
DECEMBER
2011