Older adults with lower education are at greater risk for dementia. It is unclear which brain changes lead to these outcomes. Longitudinal imaging-based measures of brain structure and function were examined in adult individuals (baseline age, 45–86 years; two to five visits per participant over 1–9 years). College degree completion differentiates individual-based and neighborhood-based measures of socioeconomic status and disadvantage. Older adults (~65 years and over) without a college degree exhibit a pattern of declining large-scale functional brain network organization (resting-state system segregation) that is less evident in their college-educated peers. Declining brain system segregation predicts impending changes in dementia severity, measured up to 10 years past the last scan date. The prognostic value of brain network change is independent of Alzheimer’s disease (AD)-related genetic risk (APOE status), the presence of AD-associated pathology (cerebrospinal fluid phosphorylated tau, cortical amyloid) and cortical thinning. These results demonstrate that the trajectory of an individual’s brain network organization varies in relation to their educational attainment and, more broadly, is a unique indicator of individual brain health during older age.

Background Comprehensive testing of cognitive functioning is standard practice in studies of Alzheimer disease (AD). Short-form tests like the Montreal Cognitive Assessment (MoCA) use a “sampling” of measures, administering key items in a shortened format to efficiently assess cognition while reducing time requirements, participant burden, and administrative costs. We compared the MoCA to a commonly used long-form cognitive battery in predicting AD symptom onset and sensitivity to AD neuroimaging biomarkers. Methods Survival, area under the receiver operating characteristic (ROC) curve (AUC), and multiple regression analyses compared the MoCA and long-form measures in predicting time to symptom onset in cognitively normal older adults (n = 6230) from the National Alzheimer’s Coordinating Center (NACC) cohort who had, on average, 2.3 ± 1.2 annual assessments. Multiple regression models in a separate sample (n = 416) from the Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC) compared the sensitivity of the MoCA and long-form measures to neuroimaging biomarkers including amyloid PET, tau PET, and cortical thickness. Results Hazard ratios suggested that both the MoCA and the long-form measures are similarly and modestly efficacious in predicting symptomatic conversion, although model comparison analyses indicated that the long-form measures slightly outperformed the MoCA (HRs > 1.57). AUC analyses indicated no difference between the measures in predicting conversion (DeLong’s test, Z = 1.48, p = 0.13). Sensitivity to AD neuroimaging biomarkers was similar for the two measures though there were only modest associations with tau PET (rs = − 0.13, ps < 0.02) and cortical thickness in cognitively normal participants (rs = 0.15–0.16, ps < 0.007). Conclusions Both test formats showed weak associations with symptom onset, AUC analyses indicated low diagnostic accuracy, and biomarker correlations were modest in cognitively normal participants. Alternative assessment approaches are needed to improve how clinicians and researchers monitor cognitive changes and disease progression prior to symptom onset.

Background The Montreal Cognitive Assessment (MoCA) could be described as a “sampling” of more comprehensive cognitive measures. We investigated the utility of the MoCA in predicting disease progression in the National Alzheimer’s Coordinating Center (NACC) sample in comparison to standard comprehensive cognitive measures collected in the NACC Uniform Dataset 3 (UDS 3). In addition, using the imaging biomarker‐rich cohorts at the Knight Alzheimer Disease Research Center (ADRC), we compared the sensitivity of the MoCA and UDS 3 measures to APOE 4 status, amyloid PET, tau PET, and structural MRI. Method Analysis One: In cognitively normal older adults from the NACC cohort (n=6,273, 2.3 +/‐ 0.9 years follow‐up), survival and receiver operating characteristic (ROC) analyses compared the MoCA and UDS 3 measures on their ability to classify disease progression, defined as change in Clinical Dementia Rating (CDR) status from 0 to >0. Analysis Two: In Knight ADRC participants (n = 445), regression models compared the sensitivity of the MoCA and standard cognitive measures to AD biomarkers in all participants and separately in CDR 0s. Result Disease progression analyses in the NACC sample resulted in an area under the curve (AUC) estimate for the MoCA of 0.69 for the total score and 0.55‐0.73 for domain scores. For the UDS 3 measures, AUCs ranged from 0.57‐0.73. A cognitive composite similar to a “PACC” yielded an AUC of 0.71. Sensitivity to AD biomarkers including amyloid PET, tau PET, cortical thickness, and APOE 4 status was similar for both the MoCA and UDS 3 measures (all p’s < 0.001). Analyses of CDR 0 participants produced small but significant relationships only with tau PET and cortical thickness (p’s 0.01 – 0.02) for both MoCA and UDS 3 tests. Conclusion Neither the MoCA nor UDS 3 cognitive measures demonstrated adequate classification of disease progression. Correlations with biomarkers suggests that the MoCA is capable of tracking pathological indicators of AD in individuals with symptomatic disease. However, in cognitively normal participants, both the MoCA and UDS 3 measures were weakly correlated with indicators of neurodegeneration. More sensitive measures or improved assessment methodology is required to reliably detect AD pathology prior to clinical diagnosis.