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P2‐205: Neuropsychological characteristics of clinical dementia rating 0.5 with uncertain impairment
Author(s) -
Seo Eun Hyun,
Park Woon Yeong,
Kim Hoowon,
Kim Hyun Hee,
Lee Kun Ho,
Choo I.L. Han
Publication year - 2015
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.2015.06.745
Subject(s) - clinical dementia rating , neuropsychology , dementia , geriatric depression scale , psychology , neuropsychological test , boston naming test , stroop effect , clinical psychology , depression (economics) , medicine , psychiatry , cognition , disease , depressive symptoms , economics , macroeconomics
(MCI) and subsequent conversion to Alzheimer’s disease (AD) remains an important goal and critical bottleneck for treatment development to prevent this debilitating disease. Beyond the identification of behavioral and neural impairment, it is also critical that we understand how the underlying cognitive processes are impaired. While neuroimaging and other biomarkers have proven useful in detection of MCI and AD, behavioral-based testing would permit more widespread use and potentially earlier diagnosis at a much lower cost. Here we apply a trial-level Bayesian analysis with a mechanistic cognitive model to uncover process-specific signatures of declining memory performance to better understand changes in memory function in MCI, including use of alternative processing strategies by individuals with declining memory. Methods:Sixty-seven participants including 30 MCI and 37 cognitively normal age-matched controls (CN) performed a continuous recognition memory task for words with items repeated at short (<3 intervening items) or long (>8 intervening items) lags. We developed a computational model of the continuous recognition task that can track memory strength and simultaneously account for trial-level choice and response time (RT) data. We fit both individual and group data within a Bayesian framework, allowing us to estimate full posterior distributions of the parameters that govern the underlying mechanisms in the model. Results:Overall behavioral performance, as measured with d prime, was higher for the short than for the long lags. RTs followed this pattern and were slower for the longer lags. MCI participants performed worse than CN in all conditions. As the diagnoses moved from normal to mild and then more severe memory impairments, participants showed lower learning rate parameters, higher reliance on working memory processes relative to episodic memory processes, and lower decision thresholds. Conclusions: Our computational model quantitatively characterized cross-sectional memory performance differences and provided mechanistic information regarding declining performance. Model-based, trial-level analysis of continuous recognition memory data holds promise for earlier diagnosis and an increased understanding of the underlying mechanisms contributing to memory decline in MCI.