Autosomal recessive cerebellar ataxias: a diagnostic classification approach according to ocular features.
Details
Serval ID
serval:BIB_E65C9B265AB7
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Autosomal recessive cerebellar ataxias: a diagnostic classification approach according to ocular features.
Journal
Frontiers in integrative neuroscience
ISSN
1662-5145 (Print)
ISSN-L
1662-5145
Publication state
Published
Issued date
2023
Peer-reviewed
Oui
Volume
17
Pages
1275794
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Publication Status: epublish
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.
Keywords
artificial intelligence, autosomal recessive cerebellar ataxias, eye movements, eye-tracking, optical coherence tomography
Pubmed
Web of science
Open Access
Yes
Create date
01/03/2024 11:21
Last modification date
09/08/2024 15:07