Neural regions associated with retrieval success were identified using event-related fMRI procedures and randomly ordered trials on a recognition memory test. Differences between hits and correct rejections (CRs) occurred in multiple regions, including bilateral anterior and right dorsolateral prefrontal cortex, bilateral inferior parietal cortex, and right superior parietal cortex (all hits > CRs), and right occipital cortex (CRs > hits). The hit > CR pattern is not compromised by time-on-task explanations because response latencies for correctly rejected words exceeded those for hits. Converging evidence for the claim that the hit > CR pattern identified neural correlates of retrieval success was obtained by unconfounding item history and retrieval success. That is, we implemented a third condition in which nonstudied words were presented, yet retrieval success was hypothesized to facilitate CRs of these lures. Specifically, when confronted with a familiar, yet nonstudied word, (e.g., nosedive after studying nosebleed and skydive), subjects might adopt a strategy whereby they recall the studied word(s) that gave rise to the familiarity (nosebleed, skydive) and thereby reject the lure. This method of instantiating retrieval success under conditions in which the target word had not been studied offers converging evidence for the claim that anterior-prefrontal cortex (among other regions) demonstrates enhanced activation during retrieval success.

(2009). Challenges in the Neuropsychological Assessment of Ethnic Minorities: Summit Proceedings. The Clinical Neuropsychologist: Vol. 23, No. 5, pp. 761-779.

Background: The aims of this study were to evaluate the relationship between anticholinergic drug burden (ACB) cognitive impairment, physical frailty, and cognitive frailty, and to determine if ACB is predictive of these phenotypes when modeled with biological and genomic biomarkers. Methods: In a retrospective cohort study, a total of 1,453 adults aged 20–102 years were used to examine ACB as a predictor for cognitive impairment, physical frailty, and cognitive frailty. Anticholinergic burden is examined as a predictor for all phenotypes in a cross-sectional analysis using logistic, ordinal regression models, and Extreme Gradient Boosting for population predictive modeling. Results: A significant association was found between ACB and cognitive decline (p = .02), frailty (p < .001), and cognitive frailty (p < .001). The odds of cognitive impairment increased by 1.21 (95% confidence interval [CI] = 1.06–1.37, p < .001), odds of being frail increased by 1.33 (95% CI = 1.18–1.50, p < .001), and odds of having cognitive frailty increased by 1.36 (95% CI = 1.21–1.54, p < .001). Population modeling results indicated ACB score as one of the stronger predictors for cognitive impairment, physical frailty, and cognitive frailty with area under the curves ranging from 0.81 to 0.88. Conclusions: Anticholinergic medications are a potentially modifiable risk factor for the prevention of cognitive and physical decline. Identification of reversible causes for cognitive and physical impairment is critical for the aging population. These findings encourage new research that may lead to effective interventions for deprescribing programs for the prevention of cognitive and physical decline in older adults.