An Initial Empirical Operationalization of the Earliest Stages of the Alzheimer's Continuum

Recent Advances in Neuropsychological Test Interpretation for Clinical Practice

Much attention in the field of clinical neuropsychology has focused on adapting to the modern healthcare environment by advancing telehealth and promoting technological innovation in assessment. Perhaps as important (but less discussed) are advances in the development and interpretation of normative neuropsychological test data. These techniques can yield improvement in diagnostic decision-making and treatment planning with little additional cost. Brooks and colleagues (Can Psychol 50: 196-209, 2009) eloquently summarized best practices in normative data creation and interpretation, providing a practical overview of norm development, measurement error, the base rates of low scores, and methods for assessing change. Since the publication of this seminal work, there have been several important advances in research on development and interpretation of normative neuropsychological test data, which may be less familiar to the practicing clinician. Specifically, we provide a review of the literature on regression-based normed scores, item response theory, multivariate base rates, summary/factor scores, cognitive intraindividual variability, and measuring change over time. For each topic, we include (1) an overview of the method, (2) a rapid review of the recent literature, (3) a relevant case example, and (4) a discussion of limitations and controversies. Our goal was to provide a primer for use of normative neuropsychological test data in neuropsychological practice.

Clinical importance in Alzheimer's disease: effects of anchor agreement and disease severity

OBJECTIVES

Methods of evaluating clinically meaningful decline are critical in research on Alzheimer's disease. A common method of quantifying clinically meaningful change is to calculate an anchor-based minimal clinically important difference (MCID) score. In this approach, individuals who report a meaningful change serve as the "anchors", and the mean level of change for this group serves as the MCID. In research on Alzheimer's disease, there are several possible anchors, including patients, knowledgeable observers (e.g., a family member), and clinicians. The goal of this study was to examine the extent to which agreement among anchors impacts MCID estimation and whether this relationship is moderated by cognitive severity status.

METHODS

Analyses were completed on a longitudinal sample of 2247 adults, aged 50-103, from the Uniform Data Set. Outcome measures included the Montreal Cognitive Assessment, Clinical Dementia Rating-Sum of Boxes, and Functional Activities Questionnaire.

RESULTS

For all of the outcomes, the MCID estimate was significantly higher when meaningful decline was endorsed by all of the anchors compared to when there was disagreement among the anchors. In addition, the MCID estimate was higher with increasing severity of cognitive impairment. Finally, cognitive severity status moderated the influence of agreement among anchors on MCID estimation; as disease severity increased, anchor agreement demonstrated less influence on the MCID.

CONCLUSIONS

MCID estimates based on one anchor may underestimate meaningful change, and researchers should consider the viewpoints of multiple anchors in constructing MCIDs, particularly in the early stages of cognitive decline.

Ecological validity of cognitive fluctuations in dementia with Lewy bodies

OBJECTIVES

Cognitive fluctuations are a core clinical feature of dementia with Lewy bodies (DLB), but their contribution to the everyday functioning difficulties evident DLB are not well understood. The current study evaluated whether intraindividual variability across a battery of neurocognitive tests (intraindividual variability-dispersion) and daily cognitive fluctuations as measured by informant report are associated with worse daily functioning in DLB.

METHODS

The study sample included 97 participants with consensus-defined DLB from the National Alzheimer's Coordinating Center (NACC). Intraindividual variability-dispersion was measured using the coefficient of variation, which divides the standard deviation of an individual's performance scores across 12 normed neurocognitive indices from the NACC neuropsychological battery by that individual's performance mean. Informants reported on daily cognitive fluctuations using the Mayo Fluctuations Scale (MFS) and on daily functioning using the functional activities questionnaire (FAQ).

RESULTS

Logistic regression identified a large univariate association of intraindividual variability-dispersion and presence of daily cognitive fluctuations on the MFS (Odds Ratio = 73.27, 95% Confidence Interval = 1.38, 3,895.05). Multiple linear regression demonstrated that higher intraindividual variability-dispersion and presence of daily cognitive fluctuations as assessed by the MFS were significantly and independently related to worse daily functioning (FAQ scores).

CONCLUSIONS

Among those with DLB, informant-rated daily cognitive fluctuations and cognitive fluctuations measured in the clinic (as indexed by intraindividual variability-dispersion across a battery of tests) were independently associated with poorer everyday functioning. These data demonstrate ecological validity in measures of cognitive fluctuations in DLB.

The relationship between learning slopes and Alzheimer's Disease biomarkers in cognitively unimpaired participants with and without subjective memory concerns

OBJECTIVE

Learning slopes represent serial acquisition of information during list-learning tasks. Although several calculations for learning slopes exist, the Learning Ratio (LR) has recently demonstrated the highest sensitivity toward changes in cognition and Alzheimer's disease (AD) biomarkers. However, investigation of learning slopes in cognitively unimpaired individuals with subjective memory concerns (SMC) has been limited. The current study examines the association of learning slopes to SMC, and the role of SMC in the relationship between learning slopes and AD biomarkers in cognitively unimpaired individuals.

METHOD

Data from 950 cognitively unimpaired participants from the Alzheimer's Disease Neuroimaging Initiative (aged 55 to 89) were used to calculate learning slope metrics. Learning slopes among those with and without SMC were compared with demographic correction, and the relationships of learning slopes with AD biomarkers of bilateral hippocampal volume and β-amyloid pathology were determined.

RESULTS

Learning slopes were consistently predictive of hippocampal atrophy and β-amyloid deposition. Results were heightened for LR relative to the other learning slopes. Additionally, interaction analyses revealed different associations between learning slopes and hippocampal volume as a function of SMC status.

CONCLUSIONS

Learning slopes appear to be sensitive to SMC and AD biomarkers, with SMC status influencing the relationship in cognitively unimpaired participants. These findings advance our knowledge of SMC, and suggest that LR - in particular - can be an important tool for the detection of AD pathology in both SMC and in AD clinical trials.

Dispersion-based cognitive intra-individual variability in dementia with Lewy bodies

OBJECTIVE

Cognitive fluctuations are characteristic of dementia with Lewy bodies (DLB) but challenging to measure. Dispersion-based intra-individual variability (IIV-d) captures neurocognitive performance fluctuations across a test battery and may be sensitive to cognitive fluctuations but has not been studied in DLB.

METHOD

We report on 5,976 participants that completed the uniform data set 3.0 neuropsychological battery (UDS3NB). IIV-d was calculated via the intra-individual standard deviation across 12 primary UDS3NB indicators. Separate models using mean USD3NB score and the Montreal cognitive assessment (MoCA) total score tested the reproducibility of the incremental value of IIV-d over-and-above global cognition. Binary logistic regressions tested whether IIV-d could classify individuals with and without clinician-rated cognitive fluctuations. Multinomial logistic regressions tested whether IIV-d could differentiate participants with DLB, participants with Alzheimer's disease (AD), and participants with healthy cognition (CH), as well as the incremental diagnostic utility of IIV-d over-and-above clinician-rated cognitive fluctuations.

RESULTS

IIV-d exhibited large univariate associations with clinician-rated and non-clinician-informant reported cognitive fluctuations, which persisted when adjusting for MoCA but not the full battery mean. Of diagnostic relevance, greater IIV-d was consistently associated with DLB and AD relative to CH over-and-above global cognition and clinician-rated cognitive fluctuations. Greater IIV-d was less consistently associated with an increased probability of DLB relative to AD when controlling for global cognition.

CONCLUSIONS

IIV-d accurately differentiates DLB from CH over-and-above global cognition and clinician-rated cognitive fluctuations. IIV-d may supplement a thorough clinical interview of cognitive fluctuations and serve as a standardized performance-based indicator of this transdiagnostic phenomenon. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Comparison of Operational Definitions for Mild Cognitive Impairment

Background: Consideration of many tests from different cognitive domains in defining mild cognitive impairment (MCI) is clinical routine, but guidelines for a neuropsychological operationalization of MCI are lacking. Objective: Among different operational MCI criteria, to identify those which are best in predicting either conversion to dementia, or a biomarker profile indicative for Alzheimer’s disease (AD). Methods: Memory-clinic patients without dementia (N = 558; mean age = 66; up to 3 years of follow-up; n = 360 with baseline CSF biomarkers) were included in an observational study using most liberal criteria of cognitive impairment. Four operational definitions of MCI were retrospectively applied: 1) amnestic MCI (word list delayed recall), 2) CERAD total score, 3) comprehensive criteria and 4) base rate corrected CERAD. We compared their accuracy in predicting incident all-cause dementia or AD dementia within three years, or a concurrent CSF Aβ42/tau-ratio indicative of AD. Results: The four definitions overlapped considerably, classified 35–58% of the original sample as impaired and were associated with markedly increased PPVs regarding incident all-cause dementia (39–46% versus 26% of the original sample), AD dementia and AD biomarker positivity. The base-rate corrected MCI definition had the highest prognostic accuracy. Conclusion: he operational criteria examined seem suitable to specify MCI in memory clinic settings, as they identify subjects at high risk of clinical progression. Depending on the neuropsychological battery in use, one or several of these criteria could help to calibrate the clinical judgment of test results, reduce false-positive decisions, and define risk-enriched groups for clinical trials.

Empirically defining the preclinical stages of the Alzheimer's continuum in the Alzheimer's Disease Neuroimaging Initiative

AIM

The National Institute on Aging and the Alzheimer's Association published new research criteria defining the Alzheimer's continuum (AC) by the presence of positive amyloid-β biomarkers. Symptom severity of those on the AC is staged across six levels, including two preclinical stages (stages 1 and 2). AC stage 2 is defined by the presence of at least one of the following: (i) transitional cognitive decline; (ii) subjective cognitive decline; or (iii) neurobehavioural symptoms. In contrast, AC stage 1 is defined by the absence of symptoms.

METHODS

Initial empirical definitions for each symptom class were developed. These empirical criteria were then applied in a sample of 285 cognitively normal, amyloid-positive individuals from the Alzheimer's Disease Neuroimaging Initiative for purposes of AC stage 1 and 2 classification.

RESULTS

In this sample, 56.10% of participants were asymptomatic and classified as AC stage 1. In contrast, 42.46% of individuals were positive for at least one symptom class: 22.11% for transitional cognitive decline, 20.35% for subjective cognitive decline, and 14.74% for neurobehavioural symptoms. AC stage was a predictor of cognitive/functional decline over 4 years of follow up in a longitudinal growth model (B = 0.33, P < 0.001).

CONCLUSIONS

Results provide a methodology to operationalize the National Institute on Aging and the Alzheimer's Association AC stage 1 and 2 criteria and include preliminary evidence of the validity of this approach. The methods outlined in this manuscript can be used to test hypotheses regarding prodromal Alzheimer's disease, as well as implemented in clinical trial selection procedures.