Accuracy of the Montreal Cognitive Assessment tool for detecting mild cognitive impairment: A systematic review and meta-analysis.

INTRODUCTION: This systematic review evaluates the accuracy of the Montreal Cognitive Assessment (MoCA) for detecting mild cognitive impairment (MCI). METHODS: We searched MEDLINE, PSYCInfo, EMBASE, and Cochrane CENTRAL (1995-2021) for studies comparing the MoCA with validated diagnostic criteria to identify MCI in general practice. Screening, data extraction, and risk of bias assessment were performed independently, in duplicate. Pooled sensitivity and specificity for MoCA cutoffs were estimated using bivariate meta-analysis. RESULTS: Thirteen studies [2158 participants, 948(44%) with MCI] were included; 10 used Petersen criteria as the reference standard. Risk of bias of studies were high or unclear for all domains except reference standard. Sensitivity and specificity were 73.5%(95% confidence interval: 56.7-85.5) and 91.3%(84.6-95.3) at cutoff <23; 79.5%(67.1-88.0) and 83.7%(75.4-89.6) at cutoff <24; and 83.8%(75.6-89.6) and 70.8(62.1-78.3) at cutoff <25. DISCUSSION: MoCA cutoffs <23 to <25 maximized the sum of sensitivity and specificity for detecting MCI. The risk of bias of included studies limits confidence in these findings.

Subjective difficulty in a verbal recognition-based memory task: Exploring brain-behaviour relationships at the individual level in healthy young adults.

The vast majority of fMRI studies of task-related brain activity utilize common levels of task demands and analyses that rely on the central tendencies of the data. This approach does not take into account perceived difficulty nor regional variations in brain activity between people. The results are findings of brain-behavior relationships that weaken as sample sizes increase. Participants of the current study included twenty-six healthy young adults evenly split between the sexes. The current work utilizes five parametrically modulated levels of memory load centered around each individual's predetermined working memory cognitive capacity. Principal components analyses (PCA) identified the group-level central tendency of the data. After removing the group effect from the data, PCA identified individual-level patterns of brain activity across the five levels of task demands. Expression of the group effect significantly differed between the sexes across all load levels. Expression of the individual level patterns demonstrated a significant load by sex interaction. Furthermore, expressions of the individual maps make better predictors of response time behavior than group-derived maps. We demonstrated that utilization of an individual's unique pattern of brain activity in response to increasing a task's perceived difficulty is a better predictor of brain-behavior relationships than study designs and analyses focused on identification of group effects. Furthermore, these methods facilitate exploration into how individual differences in patterns of brain activity relate to individual differences in behavior and cognition.