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Updated: Jun 13, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Modeling and Estimating Recall Processing Capacity: Sensitivity and Diagnostic Utility in Application to Mild
Michael K Wenger1, Selamawit Negash, Ronald C Petersen
1The Pennsylvania State University.
None:
We investigate the potential for using latency-based measures of retrieval processing capacity to assess changes in perfomance specific to individuals with mild cognitive impairment (MCI), a reliable precursor state to Alzheimer's Disease. Use of these capacity measures is motivated in part by exploration of the effects of atrophy on a computational model of a basic hippocampal circuit. We use this model to suggest that capacity may be a more sensitive indicator of undelying atrophy than speed of processing, and test this hypothesis by adapting a standard behavioral measure of memory (the free and cued selective reminding test, FCSRT) to allow for the collection of cued recall latencies. Participants were drawn from five groups: college-aged, middle-aged, healthy elderly, those with a diagnosis of MCI, and a sample of MCI control participants. The measure of capacity is shown to offer increased classificatory sensitivity relative to the standard behavioral measures, and is also shown to be the behavioral measure that correlated most strongly with hippocampal volume.
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