Visual short-term memory and neuropsychological test results in patients with amnestic mild cognitive impairment
Tue-B16-Talk V-04
Presented by: Annie Srowig
Patients with amnestic Mild Cognitive Impairment (aMCI) are at increased risk for further cognitive decline and development of Alzheimer’s dementia. Recent studies using psychophysical paradigms of whole report based on the theory of visual attention (TVA; Bundesen, 1990) suggest that, in addition to episodic longterm memory deficits, aMCI patients also show a reduced visual short-term memory (VSTM) capacity. That is, the maximum number of elements they can represented in VSTM in a given instant is reduced compared to healthy older adults.
The present study aims to evaluate whether and which of the early neurocognitive symptoms shown by patients with aMCI in the established neuropsychological CERAD+ (Consortium to Establish a Registry for Alzheimer’s Disease) can be explained by such VSTM capacity reduction.
Patients with aMCI diagnosis were recruited at the Jena University Hospital Memory Center. They underwent a TVA-based whole report assessment delivering quantitative estimates of VSTM capacity. Partial correlation and regression analyses revealed that VSTM capacity is related to and can predict neuropsychological symptoms in aMCI patients.
The present study aims to evaluate whether and which of the early neurocognitive symptoms shown by patients with aMCI in the established neuropsychological CERAD+ (Consortium to Establish a Registry for Alzheimer’s Disease) can be explained by such VSTM capacity reduction.
Patients with aMCI diagnosis were recruited at the Jena University Hospital Memory Center. They underwent a TVA-based whole report assessment delivering quantitative estimates of VSTM capacity. Partial correlation and regression analyses revealed that VSTM capacity is related to and can predict neuropsychological symptoms in aMCI patients.
Keywords: Visual Short-Term Memory Capacity, Visual Attention, aMCI, Neuro-cognitive biomarkers, Theory of Visual Attention, Neuropsychology, Computational Modelling