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Autosomal recessive cerebellar ataxias: a diagnostic classification approach according to ocular features.

Author information

Affiliations

  • 1. Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy.
      Authors
    • Lopergolo D 1
    • Rufa A 1, 4
    (2 authors)
  • 2. UOC Stroke Unit, Department of Emergenza-Urgenza, Azienda Ospedaliero-Universitaria Senese, Siena, Italy.
      Authors
    • Rosini F 2
    (1 author)
  • 3. Unit of Neurology, Centre Hospitalier Universitaire Vaudoise Lausanne, Unit of Neurology and Cognitive Neurorehabilitation, Universitary Hospital of Fribourg, Fribourg, Switzerland.
      Authors
    • Pretegiani E 3
    (1 author)
  • 4. Evalab-Neurosense, Department of Medicine Surgery and Neuroscience, University of Siena, Siena, Italy.
      Authors
    • Bargagli A 4
    • Serchi V 4
    • Rufa A 1, 4
    (3 authors)

ORCIDs linked to this article

Frontiers in Integrative Neuroscience, 07 Feb 2024, 17:1275794
https://doi.org/10.3389/fnint.2023.1275794 PMID: 38390227 PMCID: PMC10883068

This article is in the Europe PMC Open access subset. Refer to the copyright information in the article for licensing details.
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Abstract 

Autosomal recessive cerebellar ataxias (ARCAs) are a heterogeneous group of neurodegenerative disorders affecting primarily the cerebellum and/or its afferent tracts, often accompanied by damage of other neurological or extra-neurological systems. Due to the overlap of clinical presentation among ARCAs and the variety of hereditary, acquired, and reversible etiologies that can determine cerebellar dysfunction, the differential diagnosis is challenging, but also urgent considering the ongoing development of promising target therapies. The examination of afferent and efferent visual system may provide neurophysiological and structural information related to cerebellar dysfunction and neurodegeneration thus allowing a possible diagnostic classification approach according to ocular features. While optic coherence tomography (OCT) is applied for the parametrization of the optic nerve and macular area, the eye movements analysis relies on a wide range of eye-tracker devices and the application of machine-learning techniques. We discuss the results of clinical and eye-tracking oculomotor examination, the OCT findings and some advancing of computer science in ARCAs thus providing evidence sustaining the identification of robust eye parameters as possible markers of ARCAs.

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Time: 2024/11/20 08:44:48

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Read article at publisher's site: https://doi.org/10.3389/fnint.2023.1275794

Read article for free, from open access legal sources, via Unpaywall: https://www.frontiersin.org/articles/10.3389/fnint.2023.1275794/pdf?isPublishedV2=FalseUnpaywall

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