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Macular Anatomy Differs in Dyslexic Subjects.

José Javier García-MedinaNieves Bascuñana-MasPaloma Sobrado-CalvoCelia Gomez-MolinaElena Rubio-VelazquezMaravillas De-Paco-MatallanaVicente C Zanón-MorenoMaria Dolores Pinazo-DuránMonica Del-Rio-Vellosillo
Published in: Journal of clinical medicine (2023)
The macula, as the central part of the retina, plays an important role in the reading process. However, its morphology has not been previously studied in the context of dyslexia. In this research, we compared the thickness of the fovea, parafovea and perifovea between dyslexic subjects and normal controls, in 11 retinal segmentations obtained by optical coherence tomography (OCT). With this aim, we considered the nine sectors of the Early Treatment Diabetic Retinopathy Study (ETDRS) grid and also summarized data from sectors into inner ring subfield (parafovea) and outer ring subfield (perifovea). The thickness in all the four parafoveal sectors was significantly thicker in the complete retina, inner retina and middle retina of both eyes in the dyslexic group, as well as other macular sectors (fovea and perifovea) in the inner nuclear layer (INL), inner plexiform layer (IPL), IPL + INL and outer plexiform layer + outer nuclear layer (OPL + ONL). Additionally, the inner ring subfield (parafovea), but not the outer ring subfield (perifovea), was thicker in the complete retina, inner retina, middle retina (INL + OPL + ONL), OPL + ONL, IPL + INL and INL in the dyslexic group for both eyes. In contrast, no differences were found between the groups in any of the sectors or subfields of the outer retina, retinal nerve fiber layer, ganglion cell layer or ganglion cell complex in any eye. Thus, we conclude from this exploratory research that the macular morphology differs between dyslexic and normal control subjects, as measured by OCT, especially in the parafovea at middle retinal segmentations.
Keyphrases
  • optical coherence tomography
  • diabetic retinopathy
  • optic nerve
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  • working memory
  • deep learning
  • big data