Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer's disease

The Mnemonic Similarity Task (MST) is a popular memory task designed to assess hippocampal integrity. We assessed whether analyzing MST performance using a multinomial processing tree (MPT) cognitive model could detect individuals with elevated Alzheimer's disease (AD) biomarker status prior to cognitive decline.

We analyzed MST data from >200 individuals (young, cognitively healthy older adults and individuals with mild cognitive impairment [MCI]), a subset of which also had existing cerebrospinal fluid (CSF) amyloid beta (Aβ) and phosphorylated tau (pTau) data using both traditional and model-derived approaches. We assessed how well each could predict age group, memory ability, MCI status, Aβ, and pTau status using receiver operating characteristic analyses.

Both approaches predicted age group membership equally, but MPT-derived metrics exceeded traditional metrics in all other comparisons.

A MPT model of the MST can detect individuals with AD prior to cognitive decline, making it a potentially useful tool for screening and monitoring older adults during the asymptomatic phase of AD.

The MST, along with cognitive modeling, identifies individuals with memory deficits and cognitive impairment. Cognitive modeling of the MST identifies individuals with increased AD biomarkers prior to changes in cognitive function. The MST is a digital biomarker that identifies individuals at high risk of AD.