Visuospatial memory paradigm that passively assesses memory using eye movements, rather than explicit memory judgements, to detect objective memory impairment and to predict Alzheimer’s disease and cognitive status.
- Does not require verbal or written response for assessment, reducing social and cultural bias.
- Requires minimal supervision and can be adapted and administered remotely for varied degrees of severity of impairment.
- Less than 4-5 minutes of test duration makes it applicable in both clinical and research settings.
Alzheimer’s Disease (AD) is the sixth leading cause of death and affects nearly 5.7 million Americans, with increasing prevalence. Mild cognitive impairment (MCI) is a symptom of various neurological conditions, including AD and dementia. MCI is associated with physiological biomarkers, such as amyloid beta accumulation, an early sign of AD. There is evidence that there are more subtle changes in cognitive function that occur before MCI is evident. However, the current methods for diagnosis and assessment of cognitive and memory impairment lack sensitivity in detecting preclinical symptom onset. There is a need for assessments that screen for more subtle forms of cognitive impairment and can track memory impairment through different stages of a neurological condition. Such assessment tool would allow clinicians to provide necessary and appropriate treatments to the affected patients, in a timely manner.
Emory researchers have developed a computer-based assessment model aiming to test visuospatial memory passively by using eye movements rather than declarative responses. Unlike conventional memory tests, this memory task, which is mediated by the entorhinal-hippocampal circuit, does not require clinical professionals for administration, allows adjustable difficulty levels, and decreases the influence of confounding factors like literacy and cultural variation. The technology offers a sensitive and efficient memory paradigm that can be used as an excellent screening tool for measuring memory loss and therefore, may serve as an early indicator of mild cognitive impairment, Alzheimer’s disease, and other related conditions.
The technology has been successfully implemented and reliably tested on data from around 300 control or memory-impaired subjects yielding results with high sensitivity and specificity.
Publication: Haque, R. U. et al. (2019). Learning & Memory, 26(3), 93-100.