Comparative analysis of multimodal biomarkers for amyloid-beta positivity detection in Alzheimer's disease cohorts.

Introduction: Efforts to develop cost-effective approaches for detecting amyloid pathology in Alzheimer's disease (AD) have gained significant momentum with a focus on biomarker classification. Recent research has explored non-invasive and readily accessible biomarkers, including magnetic resonance imaging (MRI) biomarkers and some AD risk factors. Methods: In this comprehensive study, we leveraged a diverse dataset, encompassing participants with varying cognitive statuses from multiple sources, including cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and our in-house Dementia Disease Initiation (DDI) cohort. As brain amyloid plaques have been proposed as sufficient for AD diagnosis, our primary aim was to assess the effectiveness of multimodal biomarkers in identifying amyloid plaques, using deep machine learning methodologies. Results: Our findings underscore the robustness of the utilized methods in detecting amyloid beta positivity across multiple cohorts. Additionally, we investigated the potential of demographic data to enhance MRI-based amyloid detection. Notably, the inclusion of demographic risk factors significantly improved our models' ability to detect amyloid-beta positivity, particularly in early-stage cases, exemplified by an average area under the ROC curve of 0.836 in the unimpaired DDI cohort. Discussion: These promising, non-invasive, and cost-effective predictors of MRI biomarkers and demographic variables hold the potential for further refinement through considerations like APOE genotype and plasma markers.

Regression-based normative data for the D-KEFS Color-Word Interference Test in Norwegian adults ages 20-85.

Objective: The Delis-Kaplan Executive Function System (D-KEFS) Color-Word-Interference Test (CWIT; AKA Stroop test) is a widely used measure of processing speed and executive function. While test materials and instructions have been translated to Norwegian, only American age-adjusted norms from D-KEFS are available in Norway. We here develop norms in a sample of 1011 Norwegians between 20 and 85 years. We provide indexes for stability over time and assess demographic adjustments applying the D-KEFS norms. Method: Participants were healthy Norwegian adults from Center for Lifespan Changes in Brain and Cognition (LCBC) (n = 899), the Dementia Disease Initiation (n = 77), and Oslo MCI (n = 35). Using regression-based norming, we estimated linear and non-linear effects of age, education, and sex on the CWIT 1-4 subtests. Stability over time was assessed with intraclass correlation coefficients (ICC). The normative adjustment of the D-KEFS norms was assessed with linear regression models. Results: Increasing age was associated with slower completion on all CWIT subtests in a non-linear fashion (accelerated lowering of performance with older age). Women performed better on CWIT-1&3. Higher education predicted faster completion time on CWIT-3&4. The original age-adjusted norms from D-KEFS did not adjust for sex or education. Furthermore, we observed significant, albeit small effects of age on all CWIT subtests. ICC analyses indicated moderate to good stability over time. Conclusion: We present demographically adjusted regression-based norms and stability indexes for the D-KEFS CWIT subtests. US D-KEFS norms may be inaccurate for Norwegians with high or low educational attainment, especially women.

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.

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).

An overview of graph databases and their applications in the biomedical domain.

Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because of the interconnected nature of its data, the biomedical domain has been one of the early adopters of graph databases, enabling more natural representation models and better data integration workflows, exploration and analysis facilities. In this work, we survey the literature to explore the evolution, performance and how the most recent graph database solutions are applied in the biomedical domain, compiling a great variety of use cases. With this evidence, we conclude that the available graph database management systems are fit to support data-intensive, integrative applications, targeted at both basic research and exploratory tasks closer to the clinic.