Regression-Based Norms and Validation of the Cambridge Neuropsychological Test Automated Battery among Community-Living Older Adults in Singapore

Objective The Cambridge Neuropsychological Test Automated Battery (CANTAB) is widely used in research and clinical settings. However, little is known about the use of the CANTAB in the local aging context. This study aimed to develop normative data for various CANTAB measures in community-living older adults in Singapore. Normative data were built using the regression-based approach. A secondary aim was to examine the concurrent validity of CANTAB measures with their traditional neurocognitive test counterparts. Method A total of 210 older adults (Mage = 67.27 years, SD = 5.45) from an existing cohort study completed standard neurocognitive tests and a CANTAB battery. A total of 160 were normal aging, 46 diagnosed with Mild Cognitive Impairment (MCI), and one diagnosed with Dementia. Older adults with MCI and Dementia were not included in the calculation of norms but were included in other analyses. For the CANTAB battery, regression-based norms were generated for each CANTAB measure, with age, sex, and education included as covariates. Concurrent validity was examined by correlating the CANTAB measures with their traditional neurocognitive counterparts. Results As expected, performance across most CANTAB measures declined significantly with increasing age and decreasing education levels. There were no significant effects of sex on most CANTAB measures. Our study also showed that some CANTAB measures demonstrated good concurrent validity as they significantly correlated with their traditional neurocognitive test counterparts. Conclusions We have developed age, sex, and education-specific CANTAB norms for use in the local aging context. The advantages and challenges of using the CANTAB in the local aging context are discussed.

Tau deposition and structural connectivity demonstrate differential association patterns with neurocognitive tests

Tau neurofibrillary tangles have a central role in the pathogenesis of Alzheimer’s Disease (AD). Mounting evidence indicates that the propagation of tau is assisted by brain connectivity with weakened white-matter integrity along the propagation pathways. Recent advances in tau positron emission tomography tracers and diffusion magnetic resonance imaging allow the visualization of tau pathology and white-matter connectivity of the brain in vivo. The current study aims to investigate how tau deposition and structural connectivity are associated with memory function in prodromal AD. In this study, tau accumulation and structural connectivity data from 83 individuals (57 cognitively normal participants and 26 participants with mild cognitive impairment) were associated with neurocognitive test scores. Statistical analyses were performed in 70 cortical/subcortical brain regions to determine: 1. the level of association between tau and network metrics extracted from structural connectivity and 2. the association patterns of brain memory function with tau accumulation and network metrics. The results showed that tau accumulation and network metrics were correlated in early tau deposition regions. Furthermore, tau accumulation was associated with worse performance in almost all neurocognitive tests performance evaluated in the study. In comparison, decreased network connectivity was associated with declines in the delayed memory recall in Craft Stories and Benson Figure Copy. Interaction analysis indicates that tau deposition and dysconnectivity have a synergistic effect on the delayed Benson Figure Recall. Overall, our findings indicate that both tau deposition and structural dysconnectivity are associated with neurocognitive dysfunction. They also suggest that tau-PET may have better sensitivity to neurocognitive performance than diffusion MRI-derived measures of white-matter connectivity.

Benchmarks of meaningful improvement on neurocognitive tests in multiple sclerosis

Background: Previous studies have established benchmarks of clinically meaningful decline on neuropsychological tests. However, little is known about meaningful testing benchmarks based on gains in function. Objective: Investigate neuropsychological changes in multiple sclerosis (MS) patients with work gains and calculate benchmarks of meaningful improvement on neuropsychological tests. Methods: A total of 323 people with MS were monitored longitudinally with neuropsychological testing and the Buffalo Vocational Monitoring Survey. Results/Conclusions: Those with work gains showed significant improvement (~3 points) on the Symbol Digit Modalities Test (SDMT) over time, p = 0.01. Benchmarks for clinically meaningful improvement on the SDMT are similar to those previously established for clinically meaningful decline.

A novel olfactory threshold test for screening cognitive decline among elderly people

Olfactory impairment is associated with dementia and is a potential early biomarker of cognitive decline. We developed a novel olfactory threshold test called Sniff Bubble using rose odor-containing beads made with 2-phenylethyl alcohol. We aimed to define cut-off scores for this tool to help identify cognitive decline among elderly people. In total, 162 elderly people (mean age ± SD: 73.04 ± 8.73 years) were administered olfactory threshold and neurocognitive tests. For analyses, we divided the participants into two groups based on cognitive functioning, namely cognitive decline (n = 44) and normal cognition (n = 118) groups. The Sniff Bubble and YSK olfactory function test for olfactory threshold and the Structured Clinical Interview for DSM-5 Disorders-Clinician Version and Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease assessment packet for neurocognitive functioning were used. We used K-means cluster analyses and receiver operating characteristic (ROC) analyses to identify the most appropriate cut-off value. We established a positive correlation between the Sniff Bubble and neurocognitive function test scores (r = 0.431, p < 0.001). We defined the cut-off score, using the ROC curve analyses for Sniff Bubble scores, at 3 and higher with an area under the curve of 0.759 (p < 0.001). The Sniff Bubble test can adequately detect cognitive decline in elderly people and may be used clinically as the first step in the screening process.

Tau Deposition and Structural Connectivity Demonstrate Differential Association Patterns with Neurocognitive Tests

Tau neurofibrillary tangles have a central role in the pathogenesis of Alzheimer’s Disease (AD). Mounting evidence indicates that the propagation of tau is assisted by brain connectivity with weakened white-matter integrity along the propagation pathways. Recent advances in tau positron emission tomography tracers and diffusion magnetic resonance imaging allow the visualization of tau pathology and white-matter connectivity of the brain in vivo. The current study aims to investigate how tau deposition and structural connectivity are associated with memory function in prodromal AD. In this study, tau accumulation and structural connectivity data from 83 individuals (57 cognitively normal participants and 26 participants with mild cognitive impairment) were associated with neurocognitive test scores. Statistical analyses were performed in 70 cortical/subcortical brain regions to determine: 1. the level of association between tau and network metrics extracted from structural connectivity and 2. the association patterns of brain memory function with tau accumulation and network metrics. The results showed that tau accumulation and network metrics were correlated in early tau deposition regions. Furthermore, tau accumulation was associated with worse performance in almost all neurocognitive tests performance evaluated in the study. In comparison, decreased network connectivity was associated with declines in the delayed memory recall in Craft Stories and Benson Figure Copy. Interaction analysis indicates that tau deposition and dysconnectivity have a synergistic effect on the delayed Benson Figure Recall. Overall, our findings indicate that both tau deposition and structural dysconnectivity are associated with neurocognitive dysfunction. They also suggest that tau-PET may have better sensitivity to neurocognitive performance than diffusion MRI-derived measures of white-matter connectivity

Development and Application of Novel Performance Validity Metrics for Computerized Neurocognitive Batteries

Objective: Data from neurocognitive assessments may not be accurate in the context of factors impacting validity, such as disengagement, unmotivated responding, or intentional underperformance. Performance validity tests (PVTs) were developed to address these phenomena and assess underperformance on neurocognitive tests. However, PVTs can be burdensome, rely on cutoff scores that reduce information, do not examine potential variations in task engagement across a battery, and are typically not well-suited to acquisition of large cognitive datasets. Here we describe the development of novel performance validity measures that could address some of these limitations by leveraging psychometric modeling from data embedded within the Penn Computerized Neurocognitive Battery (PennCNB). Method: We first developed these validity measures using simulations of invalid response patterns with parameters drawn from real data. Next, we examined their application in two large, independent samples: 1) children and adolescents from the Philadelphia Neurodevelopmental Cohort (n=9,498); and 2) adult servicemembers from the Marine Resiliency Study-II (n=1,444). Results: Our performance validity metrics detected patterns of invalid responding in simulated data, even at subtle levels. Furthermore, a combination of these metrics significantly predicted previously established validity rules for these tests in both developmental and adult datasets. Moreover, most clinical diagnostic groups did not show reduced validity estimates. Conclusion: These results provide proof-of-concept evidence for multivariate, data-driven performance validity metrics. These metrics offer a novel method for determining the performance validity for individual neurocognitive tests that is scalable, applicable across different tests, less burdensome, and dimensional. However, more research is needed into their application.

Recovery of consciousness and cognition after general anesthesia in humans

Understanding how the brain recovers from unconsciousness can inform neurobiological theories of consciousness and guide clinical investigation. To address this question, we conducted a multicenter study of 60 healthy humans, half of whom received general anesthesia for three hours and half of whom served as awake controls. We administered a battery of neurocognitive tests and recorded electroencephalography to assess cortical dynamics. We hypothesized that recovery of consciousness and cognition is an extended process, with differential recovery of cognitive functions that would commence with return of responsiveness and end with return of executive function, mediated by prefrontal cortex. We found that, just prior to the recovery of consciousness, frontal-parietal dynamics returned to baseline. Consistent with our hypothesis, cognitive reconstitution after anesthesia evolved over time. Contrary to our hypothesis, executive function returned first. Early engagement of prefrontal cortex in recovery of consciousness and cognition is consistent with global neuronal workspace theory.

Breastfeeding Duration Is Associated With Domain-Specific Improvements in Cognitive Performance in 9–10-Year-Old Children

Significant immunological, physical and neurological benefits of breastfeeding in infancy are well-established, but to what extent these gains persist into later childhood remain uncertain. This study examines the association between breastfeeding duration and subsequent domain-specific cognitive performance in a diverse sample of 9–10-year-olds enrolled in the Adolescent Brain Cognitive Development (ABCD) Study®. The analyses included 9,116 children that attended baseline with their biological mother and had complete neurocognitive and breastfeeding data. Principal component analysis was conducted on data from an extensive battery of neurocognitive tests using varimax-rotation to extract a three-component model encompassing General Ability, Executive Functioning, and Memory. Propensity score weighting using generalized boosted modeling was applied to balance the distribution of observed covariates for children breastfed for 0, 1–6, 7–12, and more than 12 months. Propensity score-adjusted linear regression models revealed significant association between breastfeeding duration and performance on neurocognitive tests representing General Ability, but no evidence of a strong association with Executive Function or Memory. Benefits on General Ability ranged from a 0.109 (1–6 months) to 0.301 (&gt;12 months) standardized beta coefficient difference compared to those not breastfed. Results indicate clear cognitive benefits of breastfeeding but that these do not generalize to all measured domains, with implications for public health policy as it pertains to nutrition during infancy.

Deep Learning and Risk Score Classification of Mild Cognitive Impairment and Alzheimer’s Disease

Background: Many neurocognitive and neuropsychological tests are used to classify early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s disease (AD) from cognitive normal (CN). This can make it challenging for clinicians to make efficient and objective clinical diagnoses. It is possible to reduce the number of variables needed to make a reasonably accurate classification using machine learning. Objective: The goal of this study was to develop a deep learning algorithm to identify a few significant neurocognitive tests that can accurately classify these four groups. We also derived a simplified risk-stratification score model for diagnosis. Methods: Over 100 variables that included neuropsychological/neurocognitive tests, demographics, genetic factors, and blood biomarkers were collected from 383 EMCI, 644 LMCI, 394 AD patients, and 516 cognitive normal from the Alzheimer’s Disease Neuroimaging Initiative database. A neural network algorithm was trained on data split 90% for training and 10% testing using 10-fold cross-validation. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis. We also evaluated five different feature selection methods. Results: The five feature selection methods consistently yielded the top classifiers to be the Clinical Dementia Rating Scale - Sum of Boxes, Delayed total recall, Modified Preclinical Alzheimer Cognitive Composite with Trails test, Modified Preclinical Alzheimer Cognitive Composite with Digit test, and Mini-Mental State Examination. The best classification model yielded an AUC of 0.984, and the simplified risk-stratification score yielded an AUC of 0.963 on the test dataset. Conclusion: The deep-learning algorithm and simplified risk score accurately classifies EMCI, LMCI, AD and CN patients using a few common neurocognitive tests.

Evidence-Based Use of Cognitive Testing for Academic Interventions: A Critical Appraisal of Meta-Analytic Methodologies

Research suggests Specific Learning Disabilities (SLD) are directly linked to specific neurocognitive deficits that result in unexpected learning delays in academic domains for children in schools. However, meta-analytic studies have failed to find supporting evidence for using neurocognitive tests and, consequently, have discouraged their inclusion in SLD identification policies. The current study critically reviews meta-analytic findings and the methodological validity of over 200 research studies used in previous meta-analytic studies to estimate the causal effect of neurocognitive tests on intervention outcomes. Results suggest that only a very small percentage (6–12%) of studies used in previous meta-analytic studies were methodologically valid to estimate a direct effect of cognitive tests on academic intervention outcomes, with the majority of studies having no causal link between neurocognitive tests and intervention outcomes. Additionally, significant reporting discrepancies and inaccurate effect size estimates were found that warranted legitimate concerns for conclusions and policy recommendations provided in several meta-analytic studies. Given the lack of methodological rigor linking cognitive testing to academic interventions in current research, removing neurocognitive testing from learning disability evaluations may be premature. Suggestions for future studies evaluating the impact of neurocognitive tests on intervention outcomes as well as guidelines for synthesizing meta-analytic findings are discussed.