June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Predicting face recognition ability using macular focal cone electroretinography in patients with macular degeneration
Author Affiliations & Notes
  • Jorge Aurelio Menendez
    Department of Psychological and Brain Sciences, Johns Hopkins University, Bethesda, MD
    Institute of Ophthalmology, Catholic University, Policlinico Gemelli, Rome, Italy
  • Benedetto Falsini
    Institute of Ophthalmology, Catholic University, Policlinico Gemelli, Rome, Italy
  • Lucia Ambrosio
    Institute of Ophthalmology, Catholic University, Policlinico Gemelli, Rome, Italy
  • Giovanni Corbo
    Institute of Ophthalmology, Catholic University, Policlinico Gemelli, Rome, Italy
  • Footnotes
    Commercial Relationships Jorge Menendez, None; Benedetto Falsini, None; Lucia Ambrosio, None; Giovanni Corbo, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 4786. doi:
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      Jorge Aurelio Menendez, Benedetto Falsini, Lucia Ambrosio, Giovanni Corbo; Predicting face recognition ability using macular focal cone electroretinography in patients with macular degeneration. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):4786.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: An important part of clinical research in vision is understanding how visual deficits affect a patient’s everyday life. Much research has been done on investigating the effect of macular degeneration on face recognition, but with contradictory results. We hypothesize that part of the problem may lie in the imprecision of behavioral measures of visual function, and that a more precise predictor may be found in electrophysiological measures. To test this, we investigated the relationship between macular focal cone electroretinogram (fERG) signal amplitude and face recognition ability in patients with macular degeneration.

Methods: Face recognition ability was assessed monocularly using a face-matching task in which patients had to decide which of 8 faces corresponded to a target face. Visual acuity was measured using standard Snellen chart. fERG signal was recorded monocularly from the central 18° region using a flickering uniform red field superimposed on a constant equiluminant steady adapting background minimizing stray-light modulation, after dilating the pupil pharmacologically to a diameter ≥ 8 mm. All eyes (n = 23) suffered from inherited macular degeneration (14 eyes Stargardt disease, 9 eyes cone-rod dystrophy). During face recognition ability assessment, patients were allowed to wear their prescription eyewear and to freely move their heads, to resemble realistic daily face recognition as closely as possible. Pearson correlation was used for statistical analysis.

Results: Percent correct on the face-matching task was significantly correlated with visual acuity (r2 = 0.37, p < .005) and with fERG signal amplitude (r2 = 0.64, p < .005). While fERG signal and visual acuity were significantly correlated (r2 = 0.43, p < .005), fERG was a stronger predictor of performance on the face-matching task.

Conclusions: Our results suggest that fERG signal amplitude is a reliable predictor of face recognition ability, perhaps more accurate than visual acuity. These results support the hypothesis that electrophysiological measures of visual function may be more sensitive and precise than behavioral ones for examining the effect of visual deficits on performance of complex everyday tasks such as recognizing faces. The robustness of our results despite allowing patients considerable freedom in the face recognition task is evidence that fERG signal is an especially reliable predictor.

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