Longitudinal normative data obtained from a robust elderly sample (i.e., believed to be free from neurodegenerative disease) are sparse. The purpose of the present study was to develop reliable change indices (RCIs) that can assist with interpretation of test score changes relative to a healthy sample of older adults (ages 50+). Participants were 4217 individuals who completed at least three annual evaluations at one of 34 past and present Alzheimer's Disease Centers throughout the United States. All participants were diagnosed as cognitively normal at every study visit, which ranged from three to nine approximately annual evaluations. One-year RCIs were calculated for 11 neuropsychological variables in the Uniform Data Set by regressing follow-up test scores onto baseline test scores, age, education, visit number, post-baseline assessment interval, race, and sex in a linear mixed effects regression framework. In addition, the cumulative frequency distributions of raw score changes were examined to describe the base rates of test score changes. Baseline test score, age, education, and race were robust predictors of follow-up test scores across most tests. The effects of maturation (aging) were more pronounced on tests related to attention and executive functioning, whereas practice effects were more pronounced on tests of episodic and semantic memory. Interpretation of longitudinal changes on 11 cognitive test variables can be facilitated through the use of reliable change intervals and base rates of score changes in this robust sample of older adults. A Web-based calculator is provided to assist neuropsychologists with interpretation of longitudinal change.

Introduction

The present study sought to investigate the measurement invariance of commonly used neuropsychological tests in an ethnically (Hispanic vs. non-Hispanic) and linguistically (Spanish vs. English) diverse sample.

Methods

Participants were 736 middle-aged and older adults (M _Age = 62.1, SD = 9.1) assessed at baseline. Measurement invariance testing was performed using multiple-group confirmatory factor analysis.

Results

A five-factor model (memory, attention/executive functioning/processing speed, language, visuospatial, and motor) fit the data well (CFI = 0.979, RMSEA = 0.047) and the composite reliability of the factors ranged from .76 (visuospatial) to .97 (motor). The five-factor model was found to possess strict measurement invariance for ethnicity and language without a decrement in fit compared to a strong (scalar) invariance model (ΔCFI = .000, ΔRMSEA = .002).

Discussion

These results indicate that a five-factor model is suitable for estimating cognitive functioning in Mexican Americans and non-Hispanic whites without bias by ethnicity or language.

Objective

Late-life changes in cognition and brain integrity are both highly multivariate, time-dependent processes that are essential for understanding cognitive aging and neurodegenerative disease outcomes. The present study seeks to identify a latent variable model capable of efficiently reducing a multitude of structural brain change magnetic resonance imaging (MRI) measurements into a smaller number of dimensions. We further seek to demonstrate the validity of this model by evaluating its ability to reproduce patterns of coordinated brain volume change and to explain the rate of cognitive decline over time.

Method

We used longitudinal cognitive data and structural MRI scans, obtained from a diverse sample of 358 participants (M_age = 74.81, SD = 7.17), to implement latent variable models for measuring brain change and to estimate the effects of these brain change factors on cognitive decline.

Results

Results supported a bifactor model for brain change with four group factors (prefrontal, temporolimbic, medial temporal, and posterior association) and one general change factor (global atrophy). Atrophy in the global (β = 0.434, SE = 0.070), temporolimbic (β = 0.275, SE = 0.085), and medial temporal (β = 0.240, SE = 0.085) factors were the strongest predictors of global cognitive decline. Overall, the brain change model explained 59% of the variance in global cognitive slope.

Conclusions

The current results suggest that brain change across 27 bilateral regions of interest can be grouped into five change factors, three of which (global gray matter, temporolimbic, and medial temporal lobe atrophy) are strongly associated with cognitive decline. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Background

The residual approach to measuring cognitive reserve (using the residual reserve index) aims to capture cognitive resilience conferred by cognitive reserve, but may be confounded by factors representing brain resilience. We sought to distinguish between brain and cognitive resilience by comparing interactions between the residual reserve index and amyloid, tau, and neurodegeneration ["AT(N)"] biomarkers when predicting executive function. We hypothesized that the residual reserve index would moderate at least one path from an AT(N) biomarker to executive function (consistent with cognitive resilience), as opposed to moderating a path between two AT(N) biomarkers (suggestive of brain resilience).

Methods

Participants (N = 332) were from the Alzheimer's Disease Neuroimaging Initiative. The residual reserve index represented the difference between observed and predicted memory performance (a positive residual reserve index suggests higher cognitive reserve). AT(N) biomarkers were: CSF β-amyloid_1-42/β-amyloid_1-40 (A), plasma phosphorylated tau-181 (T), and FDG metabolism in AD-specific regions ([N]). AT(N) biomarkers (measured at consecutive time points) were entered in a sequential mediation model testing the indirect effects from baseline amyloid to executive function intercept (third annual follow-up) and slope (baseline to seventh follow-up), via tau and/or FDG metabolism. The baseline residual reserve index was entered as a moderator of paths between AT(N) biomarkers (e.g., amyloid-tau), and paths between AT(N) biomarkers and executive function.

Results

The residual reserve index interacted with amyloid pathology when predicting FDG metabolism: the indirect effect of amyloid → FDG metabolism → executive function intercept and slope varied as a function of the residual reserve index. With lower amyloid pathology, executive function performance was comparable at different levels of the residual reserve index, but a higher residual reserve index was associated with lower FDG metabolism. With higher amyloid pathology, a higher residual reserve index predicted better executive function via higher FDG metabolism.

Conclusion

The effect of the residual reserve index on executive function performance via FDG metabolism was consistent with cognitive resilience. This suggests the residual reserve index captures variation in cognitive reserve; specifically, neural efficiency, and neural capacity to upregulate metabolism to enhance cognitive resilience in the face of greater amyloid pathology. Implications for future research include the potential bidirectionality between neural efficiency and amyloid accumulation.

Age-related deficits in prospective memory (PM) are well established, but it is not known whether PM is stable over time among older adults. In this study, 271 community-dwelling older adults underwent abaseline neuropsychological evaluation and up to three follow-up visits, approximately 2.4 years apart. Mixed effects linear longitudinal models revealed small, but significant linear declines and between-subjects variability in event-based PM performance. There were no changes in performance on measures of time-based PM, retrospective memory, or executive functions. Changes in event-based PM were not associated with age, retrospective memory, executive functions, or everyday functioning. Among older adults, event-based PM appears to be more susceptible to linear declines than does time-based PM, which future research might examine with regard to the possible underlying cognitive mechanisms of cue encoding, monitoring, detection, and retrieval processes.

Introduction

The Executive Interview (EXIT25) is an effective measure of executive dysfunction, but may be inefficient due to the time it takes to complete 25 interview-based items. The current study aimed to examine psychometric properties of the EXIT25, with a specific focus on determining whether a briefer version of the measure could comprehensively assess executive dysfunction.

Method

The current study applied a graded response model (a type of item response theory model for polytomous categorical data) to identify items that were most closely related to the underlying construct of executive functioning and best discriminated between varying levels of executive functioning. Participants were 660 adults ages 40 to 96 years living in West Texas, who were recruited through an ongoing epidemiological study of rural health and aging, called Project FRONTIER. The EXIT25 was the primary measure examined. Participants also completed the Trail Making Test and Controlled Oral Word Association Test, among other measures, to examine the convergent validity of a brief form of the EXIT25.

Results

Eight items were identified that provided the majority of the information about the underlying construct of executive functioning; total scores on these items were associated with total scores on other measures of executive functioning and were able to differentiate between cognitively healthy, mildly cognitively impaired, and demented participants. In addition, cutoff scores were recommended based on sensitivity and specificity of scores.

Conclusion

A brief, eight-item version of the EXIT25 may be an effective and efficient screening for executive dysfunction among older adults.

Objective

Royall and colleagues identified a latent dementia phenotype, "δ", reflecting the "cognitive correlates of functional status." We sought to cross-validate and extend these findings in a large clinical case series of adults with and without dementia.

Method

A confirmatory factor analysis (CFA) model for δ was fit to National Alzheimer's Coordinating Center data (n = 26,068). Factor scores derived from δ were compared with the Clinical Dementia Rating Sum of Boxes (CDR-SB) and to clinically diagnosed dementia. A longitudinal parallel-process growth model compared changes in δ with changes in CDR-SB over 6 annual evaluations.

Results

The CFA model fit well; CFI = 0.971, RMSEA = 0.070. Factor scores derived from δ discriminated between demented and nondemented participants with an area under the curve of .96. The growth model also fit well, CFI = 0.969, RMSEA = 0.032.

Conclusions

The δ construct represents a novel approach to measuring dementia-related changes and has potential to improve cognitive assessment of neurodegenerative diseases.

Background

Dementia severity can be modeled as the construct δ, representing the "cognitive correlates of functional status."

Objective

We recently validated a model for estimating δ in the National Alzheimer's Coordinating Center's Uniform Data Set; however, the association of δ with neuropathology remains untested.

Methods

We used data from 727 decedents evaluated at Alzheimer's Disease (AD) Centers nationwide. Participants spoke English, had no genetic abnormalities, and were pathologically diagnosed with AD as a primary or contributing etiology. Clinical data from participants' last visit prior to death were used to estimate dementia severity (δ).

Results

A structural equation model using age, education, race, and apolipoprotein E (APOE) genotype (number of ɛ2 and ɛ4 alleles) as predictors and latent AD pathology and cerebrovascular disease (CVD) pathology as mediators fit the data well (RMSEA = 0.031; CFI = 0.957). AD pathology mediated the effects of age and APOE genotype on dementia severity. An older age at death and more ɛ2 alleles were associated with less AD pathology and, in turn, with less severe dementia. In contrast, more ɛ4 alleles were associated with more pathology and more severe dementia. Although age and race contributed to differences in CVD pathology, CVD pathology was not related to dementia severity in this sample of decedents with pathologically-confirmed AD.

Conclusions

Using δ as an estimate of dementia severity fits well within a structural model in which AD pathology directly affects dementia severity and mediates the relationship between age and APOE genotype on dementia severity.

The "brain signature of cognition" concept has garnered interest as a data-driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a method for computing signatures of episodic memory. However, to be a robust brain measure, the signature approach requires a rigorous validation of model performance across a variety of cohorts. Here we report validation results and provide an example of extending it to a second behavioral domain. In each of two discovery data cohorts, we derived regional brain gray matter thickness associations for two domains: neuropsychological and everyday cognition memory. We computed regional association to outcome in 40 randomly selected discovery subsets of size 400 in each cohort. We generated spatial overlap frequency maps and defined high-frequency regions as "consensus" signature masks. Using separate validation datasets, we evaluated replicability of cohort-based consensus model fits and explanatory power by comparing signature model fits with each other and with competing theory-based models. Spatial replications produced convergent consensus signature regions. Consensus signature model fits were highly correlated in 50 random subsets of each validation cohort, indicating high replicability. In comparisons over each full cohort, signature models outperformed other models. In this validation study, we produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Signatures in two memory domains suggested strongly shared brain substrates. Robust brain signatures may therefore be achievable, yielding reliable and useful measures for modeling substrates of behavioral domains.

Level of education is often regarded as a proxy for cognitive reserve in older adults. This implies that brain degeneration has a smaller effect on cognitive decline in those with more education, but this has not been directly tested in previous research. We examined how education, quantitative magnetic resonance imaging-based measurement of brain degeneration, and their interaction affect cognitive decline in diverse older adults spanning the spectrum from normal cognition to dementia. Gray matter atrophy was strongly related to cognitive decline. While education was not related to cognitive decline, brain atrophy had a stronger effect on cognitive decline in those with more education. Importantly, high education was associated with slower decline in individuals with lesser atrophy but with faster decline in those with greater atrophy. This moderation effect was observed in Hispanics (who had high heterogeneity of education) but not in African-Americans or Caucasians. These results suggest that education is an indicator of cognitive reserve in individuals with low levels of brain degeneration, but the protective effect of higher education is rapidly depleted as brain degeneration progresses.