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TD‐P‐11: PROGNOSIS OF CONVERSION TO MILD COGNITIVE IMPAIRMENT AND TIME TO RELIABLE CHANGES VIA MEMORY‐RELATED ELECTROPHYSIOLOGICAL BRAIN SIGNATURES
Author(s) -
Jiang Yang,
Li Juan,
Jicha Gregory A.,
Smith Charles D.,
Munro Nancy,
Zhao Xiaopeng,
Schmitt Frederick A.,
Kryscio Richard J.,
Abner Erin L.
Publication year - 2019
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2019.06.4322
Subject(s) - audiology , psychology , neuropsychology , neuropathology , electroencephalography , cognition , dementia , memory impairment , electrophysiology , neuroscience , medicine , disease
Methods: Only manuscripts investigating AD’s biomarkers through the eye using retinal imaging technology published in peer-reviewed publications between the years 2001-December 2018 were considered for this review. As part of a systematic search, the online citation index service PubMed was searched using the keywords retinal imaging, eye, AND Alzheimer’s disease. The manuscripts were reviewed to extract data on patient selection criteria and sample size, the retinal imaging technology used, study limitations and findings to document relevant information. Results: After content analysis, it was found that less than a third of the studies used MRI or CT screening for healthy controls, and the minority employed MRI or PET for the diagnosis in the AD group. Besides, most cross-sectional and longitudinal studies reported cognitive impairment consistently to be associated with retinal abnormalities. Typical retinal abnormalities that have been correlated with worse cognitive functioning inmild cognitive impairment subjects and AD patients include arteriolar narrowing and venular widening, scarcer vasculature and suboptimal bifurcation angles as well as suboptimal junctional branching coefficients, thinning of the nerve fiber layer, changes in macular volume, degeneration of both the retinal ganglion cell and optic nerve, and retinal angiopathy involving reduced blood flow and vascular structural alterations. Most of the biomarker identification methods and AI approaches are still under development and unresolved due to the need for big data and training experience to facilitate the transitioning into the clinical diagnostic arena. Conclusions: This short review offers an insight into the methodologies available for investigating the AD’s detection using the eye as well as showcase big data analysis and potential AI applications. Together the ophthalmic technologies provide us with the capability to move AD detection research forwards by using novel peripheral biomarkers.