Demographically adjusted Rey-Osterrieth Complex Figure Test norms in a Swedish and Norwegian cohort aged 49-77 years and comparison with North American norms.

INTRODUCTION: The Rey-Osterrieth Complex Figure Test (RCFT) is one of the most commonly used neuropsychological tests in Sweden and Norway. However, no publications provide normative data for this population. The objective of this study was to present demographically adjusted norms for a Swedish and Norwegian population and to evaluate these in an independent comparison group. METHODS: The RCFT was administrated to 344 healthy controls recruited from the Swedish Gothenburg MCI study, the Norwegian Dementia Disease Initiation study, and the Swedish Cardiopulmonary Bioimage Study. Age ranged from 49 to 77 years (mean = 62.4 years, SD = 5.0 years), and education ranged from 6 to 24 years (mean = 13.3 years, SD = 3.0 years). Using a regression-based procedure, we investigated the effects of age, sex, and years of education on test performance. We compared and evaluated our Swedish and Norwegian norms with North American norms in an independent comparison group of 145 individuals. RESULTS: In healthy controls, age and education were associated with performance on the RCFT. When comparing normative RCFT performance in an independent comparison group, North American norms generally overestimated immediate and delayed recall performance. In contrast, our Swedish and Norwegian norms appear to better take into account factors of age and education. CONCLUSIONS: We presented demographically adjusted norms for the RCFT in a Swedish and Norwegian sample. This is the first normative study of the RCFT that presents normative data for this population. In addition, we showed that North American norms might produce inaccurate normative estimations in an independent comparison group.

Dissociating memory and executive function impairment through temporal features in a word list verbal learning task.

The Rey Auditory Verbal Learning Test (RAVLT) is an established verbal learning test commonly used to quantify memory impairments due to Alzheimer's Disease (AD) both at a clinical dementia stage or prodromal stage of mild cognitive impairment (MCI). Focal memory impairment-as quantified e.g. by the RAVLT-at an MCI stage is referred to as amnestic MCI (aMCI) and is often regarded as the cognitive phenotype of prodromal AD. However, recent findings suggest that not only learning and memory but also other cognitive domains, especially executive functions (EF) and processing speed (PS), influence verbal learning performance. This research investigates whether additional temporal features extracted from audio recordings from a participant's RAVLT response can better dissociate memory and EF in such tasks and eventually help to better describe MCI subtypes. 675 age-matched participants from the H70 Swedish birth cohort were included in this analysis; 68 participants were classified as MCI (33 aMCI and 35 due to executive impairment). RAVLT performances were recorded and temporal features extracted. Novel temporal features were correlated with established neuropsychological tests measuring EF and PS. Lastly, the downstream diagnostic potential of temporal features was estimated using group differences and a machine learning (ML) classification scenario. Temporal features correlated moderately with measures of EF and PS. Performance of an ML classifier could be improved by adding temporal features to traditional counts. We conclude that RAVLT temporal features are in general related to EF and that they might be capable of dissociating memory and EF in a word list learning task.

Regression-based cognitive change norms applied in biochemically defined predementia Alzheimer's disease.

OBJECTIVE: We aim to develop 2-year cognitive change norms for adults ages 41-84 for six cognitive tests, and to evaluate these norms in groups with AD biomarkers. BACKGROUND: Practice effects are common in repeated neuropsychological testing. Not accounting for practice effects may obscure cognitive decline in early Alzheimer's disease (AD). METHOD: We developed standardized regression-based change norms from normative samples consisting of healthy controls from the Dementia Disease Initiation study (n = 125), the Trønderbrain study (n = 57), and the Gothenburg mild cognitive impairment (MCI) study (n = 65). Norms were applied in a sample with cognitive symptoms (subjective cognitive decline or MCI) and AD cerebrospinal fluid (CSF) biomarkers (n = 246), classified according to the A/T/N system. RESULTS: The change norms adjusted for pertinent demographics and practice effects. The group with cognitive complaints displayed a trend toward cognitive decline compared to the normative group, with the A +T/N + subgroup showing the most marked decline. This was observed in tests of episodic memory and cognitive flexibility/divided attention. CONCLUSIONS: We present 2-year cognitive change norms for adults between 41 and 84 years, adjusted for practice and demographics. A web-based change norm calculator is provided. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Regression-based normative data for the Rey Auditory Verbal Learning Test in Norwegian and Swedish adults aged 49-79 and comparison with published norms.

Objective: The Rey Auditory Verbal Learning Test (RAVLT) is a widely used measure of episodic verbal memory. To our knowledge, culturally adapted and demographically adjusted norms for the RAVLT are currently not available for Norwegian and Swedish adults, and imported North American norms are often used. We here develop regression-based norms for Norwegian and Swedish adults and compare our norms to North American norms in an independent sample of cognitively healthy adults. Method: Participants were 244 healthy adults from Norway and Sweden between the aged 49 and 79 years, with between 6 and 24 years of education. Using a multiple multivariate regression-based norming procedure, we estimated effects of age, sex, and years of education on basic and derived RAVLT test scores. The newly developed norms were assessed in an independent comparison group of cognitively healthy adults (n = 145) and compared to recently published North American regression-based norms. Results: Lower age, female sex and more years of education predicted higher performance on the RAVLT. The new norms adequately adjusted for age, education, and sex in the independent comparison group. The American norms corrected for demographics on all RAVLT trials except trials 4, 7, list B, and trials 1-5 total. Test-retest (M = 2.55 years) reliability varied from poor to good. Conclusion: We propose regression-based norms for the RAVLT adjusting for pertinent demographics. The norms may be used for assessment of Norwegian and Swedish adults between the aged of 49 and 79 years, with between 6 and 24 years of education.

Unsupervised mobile app-based cognitive testing in a population-based study of older adults born 1944.

Background: Mobile app-based tools have the potential to yield rapid, cost-effective, and sensitive measures for detecting dementia-related cognitive impairment in clinical and research settings. At the same time, there is a substantial need to validate these tools in real-life settings. The primary aim of this study was thus to evaluate the feasibility, validity, and reliability of mobile app-based tasks for assessing cognitive function in a population-based sample of older adults. Method: A total of 172 non-demented (Clinical Dementia Rating 0 and 0.5) older participants (aged 76-77) completed two mobile app-based memory tasks-the Mnemonic Discrimination Task for Objects and Scenes (MDT-OS) and the long-term (24 h) delayed Object-In-Room Recall Task (ORR-LDR). To determine the validity of the tasks for measuring relevant cognitive functions in this population, we assessed relationships with conventional cognitive tests. In addition, psychometric properties, including test-retest reliability, and the participants' self-rated experience with mobile app-based cognitive tasks were assessed. Result: MDT-OS and ORR-LDR were weakly-to-moderately correlated with the Preclinical Alzheimer's Cognitive Composite (PACC5) (r = 0.3-0.44, p < .001) and with several other measures of episodic memory, processing speed, and executive function. Test-retest reliability was poor-to-moderate for one single session but improved to moderate-to-good when using the average of two sessions. We observed no significant floor or ceiling effects nor effects of education or gender on task performance. Contextual factors such as distractions and screen size did not significantly affect task performance. Most participants deemed the tasks interesting, but many rated them as highly challenging. While several participants reported distractions during tasks, most could concentrate well. However, there were difficulties in completing delayed recall tasks on time in this unsupervised and remote setting. Conclusion: Our study proves the feasibility of mobile app-based cognitive assessments in a community sample of older adults, demonstrating its validity in relation to conventional cognitive measures and its reliability for repeated measurements over time. To further strengthen study adherence, future studies should implement additional measures to improve task completion on time.

Current advances in digital cognitive assessment for preclinical Alzheimer's disease.

There is a pressing need to capture and track subtle cognitive change at the preclinical stage of Alzheimer's disease (AD) rapidly, cost-effectively, and with high sensitivity. Concurrently, the landscape of digital cognitive assessment is rapidly evolving as technology advances, older adult tech-adoption increases, and external events (i.e., COVID-19) necessitate remote digital assessment. Here, we provide a snapshot review of the current state of digital cognitive assessment for preclinical AD including different device platforms/assessment approaches, levels of validation, and implementation challenges. We focus on articles, grants, and recent conference proceedings specifically querying the relationship between digital cognitive assessments and established biomarkers for preclinical AD (e.g., amyloid beta and tau) in clinically normal (CN) individuals. Several digital assessments were identified across platforms (e.g., digital pens, smartphones). Digital assessments varied by intended setting (e.g., remote vs. in-clinic), level of supervision (e.g., self vs. supervised), and device origin (personal vs. study-provided). At least 11 publications characterize digital cognitive assessment against AD biomarkers among CN. First available data demonstrate promising validity of this approach against both conventional assessment methods (moderate to large effect sizes) and relevant biomarkers (predominantly weak to moderate effect sizes). We discuss levels of validation and issues relating to usability, data quality, data protection, and attrition. While still in its infancy, digital cognitive assessment, especially when administered remotely, will undoubtedly play a major future role in screening for and tracking preclinical AD.

Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers.

Recent work has indicated the potential utility of automated language analysis for the detection of mild cognitive impairment (MCI). Most studies combining language processing and machine learning for the prediction of MCI focus on a single language task; here, we consider a cascaded approach to combine data from multiple language tasks. A cohort of 26 MCI participants and 29 healthy controls completed three language tasks: picture description, reading silently, and reading aloud. Information from each task is captured through different modes (audio, text, eye-tracking, and comprehension questions). Features are extracted from each mode, and used to train a series of cascaded classifiers which output predictions at the level of features, modes, tasks, and finally at the overall session level. The best classification result is achieved through combining the data at the task level (AUC = 0.88, accuracy = 0.83). This outperforms a classifier trained on neuropsychological test scores (AUC = 0.75, accuracy = 0.65) as well as the "early fusion" approach to multimodal classification (AUC = 0.79, accuracy = 0.70). By combining the predictions from the multimodal language classifier and the neuropsychological classifier, this result can be further improved to AUC = 0.90 and accuracy = 0.84. In a correlation analysis, language classifier predictions are found to be moderately correlated (ρ = 0.42) with participant scores on the Rey Auditory Verbal Learning Test (RAVLT). The cascaded approach for multimodal classification improves both system performance and interpretability. This modular architecture can be easily generalized to incorporate different types of classifiers as well as other heterogeneous sources of data (imaging, metabolic, etc.).