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Objectives: Identify which NIH Toolbox Cognition Battery (NIHTB-CB) subtest(s) best differentiate healthy controls (HC) from those with amnestic mild cognitive impairment (aMCI) and compare the discriminant accuracy between a model using a priori "Norm Adjusted" scores versus "Unadjusted" standard scores with age, sex, race/ethnicity, and education controlled for within the model. Racial differences were also examined., Methods: Participants were Black/African American (B/AA) and White consensus-confirmed (HC = 96; aMCI = 62) adults 60-85 years old that completed the NIHTB-CB for tablet. Discriminant function analysis (DFA) was used in the Total Sample and separately for B/AA ( n = 80) and White participants ( n = 78)., Results: Picture Sequence Memory (an episodic memory task) was the highest loading coefficient across all DFA models. When stratified by race, differences were noted in the pattern of the highest loading coefficients within the DFAs. However, the overall discriminant accuracy of the DFA models in identifying HCs and those with aMCI did not differ significantly by race (B/AA, White) or model/score type (Norm Adjusted versus Unadjusted)., Conclusions: Racial differences were noted despite the use of normalized scores or demographic covariates-highlighting the importance of including underrepresented groups in research. While the models were fairly accurate at identifying consensus-confirmed HCs, the models proved less accurate at identifying White participants with an aMCI diagnosis. In clinical settings, further work is needed to optimize computerized batteries and the use of NIHTB-CB norm adjusted scores is recommended. In research settings, demographically corrected scores or within model correction is suggested.

Objective: The U.S. population is aging and increasing numbers of older adults are using cannabis. Cognitive decline is common in older age and subjective memory complaints (SMC) have been associated with increased risk for dementia. While residual cognitive effects of cannabis use at younger ages are well understood, the links between cannabis use and cognition in older adults is less clear. The present study represents the first population-level analysis of cannabis use and SMC in older adults in the U.S., Method: We used the National Survey of Drug Use and Health (NSDUH) dataset to evaluate SMC in respondents over age 50 (N = 26,399) according to past-year cannabis use., Results: Results revealed that 13.2% (95%CI: 11.5%-15.0%) of those who reported cannabis use also reported SMC, compared to 6.4% (95%CI: 6.1%-6.8%) among individuals with no cannabis use. Logistic regression revealed a two-fold increase (OR = 2.21, 95%CI: 1.88-2.60) of reporting SMC in respondents who had used cannabis in the past year, which was attenuated (OR = 1.38, 95%CI: 1.10-1.72) when controlling for additional factors. Other covariates, including physical health conditions, misuse of other substances, and mental illness also significantly contributed to SMC outcomes., Conclusions: Cannabis use represents a modifiable lifestyle factor that has potential for both risk and protective properties that may impact the trajectory of cognitive decline in older age. These hypothesis generating results are important for characterizing and contextualizing population-level trends related to cannabis use and SMC in older adults.