Linear and nonlinear relationships between cognitive subdomains of ability discrepancy and Alzheimer's disease biomarkers

Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer's disease

Cognitive resilience (CR) describes the phenomenon of individuals evading cognitive decline despite prominent Alzheimer's disease neuropathology. Operationalization and measurement of this latent construct is non-trivial as it cannot be directly observed. The residual approach has been widely applied to estimate CR, where the degree of resilience is estimated through a linear model's residuals. We demonstrate that this approach makes specific, uncontrollable assumptions and likely leads to biased and erroneous resilience estimates. This is especially true when information about CR is contained in the data the linear model was fitted to, either through inclusion of CR-associated variables or due to correlation. We propose an alternative strategy which overcomes the standard approach's limitations using machine learning principles. Our proposed approach makes fewer assumptions about the data and CR and achieves better estimation accuracy on simulated ground-truth data.

Rethinking the residual approach: Leveraging machine learning to operationalize cognitive resilience in Alzheimer's disease

Cognitive resilience describes the phenomenon of individuals evading cognitive decline despite prominent Alzheimer's disease neuropathology. Operationalization and measurement of this latent construct is non-trivial as it cannot be directly observed. The residual approach has been widely applied to estimate CR, where the degree of resilience is estimated through a linear model's residuals. We demonstrate that this approach makes specific, uncontrollable assumptions and likely leads to biased and erroneous resilience estimates. We propose an alternative strategy which overcomes the standard approach's limitations using machine learning principles. Our proposed approach makes fewer assumptions about the data and construct to be measured and achieves better estimation accuracy on simulated ground-truth data.

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach

Background

The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points.

Objective

To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach.

Methods

We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline.

Results

We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways.

Conclusions

Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.

Cognitive differences across ethnoracial category, socioeconomic status across the Alzheimer's disease spectrum: Can an ability discrepancy score level the playing field?

An ability discrepancy (crystallized minus fluid abilities) might be a personally relevant cognitive marker of risk for Alzheimer's disease (AD) and might help reduce measurement bias often present in traditional measures of cognition. In a large national sample of adults aged 60-104 years (N = 14,257), we investigated whether the intersectionality of group characteristics previously shown to pose a risk for AD including ethnoracial category, socioeconomic status, and sex (a) differed in ability discrepancy compared to traditional neuropsychological tests and (b) moderated the relationship between an ability discrepancy and AD symptom severity. In cognitively normal older adults, results indicated that across each decade, fluid and memory composite scores generally exhibited large group differences with sex, education, and ethnoracial category. In contrast, the ability discrepancy score showed much smaller group differences, thus removing much of the biases inherent in the tests. Women with higher education differed in discrepancy performance from other groups, suggesting a subgroup in which this score might reduce bias to a lesser extent. Importantly, a greater ability discrepancy was associated with greater AD symptom severity across the AD continuum. Subgroup analyses suggest that this relationship holds for all groups except for some subgroups of Hispanic Americans. These findings suggest that an ability discrepancy measure might be a better indicator of baseline cognition than traditional measures that show more egregious measurement bias across diverse groups of people.

Distinct components of cardiovascular health are linked with age-related differences in cognitive abilities

Cardiovascular ageing contributes to cognitive impairment. However, the unique and synergistic contributions of multiple cardiovascular factors to cognitive function remain unclear because they are often condensed into a single composite score or examined in isolation. We hypothesized that vascular risk factors, electrocardiographic features and blood pressure indices reveal multiple latent vascular factors, with independent contributions to cognition. In a population-based deep-phenotyping study (n = 708, age 18-88), path analysis revealed three latent vascular factors dissociating the autonomic nervous system response from two components of blood pressure. These three factors made unique and additive contributions to the variability in crystallized and fluid intelligence. The discrepancy in fluid relative to crystallized intelligence, indicative of cognitive decline, was associated with a latent vascular factor predominantly expressing pulse pressure. This suggests that higher pulse pressure is associated with cognitive decline from expected performance. The effect was stronger in older adults. Controlling pulse pressure may help to preserve cognition, particularly in older adults. Our findings highlight the need to better understand the multifactorial nature of vascular aging.

Intra-individual cognitive variability in neuropsychological assessment: a sign of neural network dysfunction

Intra-Individual Cognitive Variability (IICV) predicts progression in neurocognitive disorders . Given important clinical applications, we investigated the association between IICV and multiple brain metrics across 17 networks to better understand the brain mechanisms underlying this performance measure. Sixty-three middle-aged and older adults without dementia underwent a neuropsychological battery, resting-state fMRI, and structural MRI scans. In a linear mixed effect model, higher IICV was associated with lower functional connectivity in control C network relative to medial occipital network (the reference). A multivariate partial least squares analysis revealed that lower mean and higher variability were both associated with lower connectivity in sensorimotor and default mode networks, while higher mean and higher variability were associated with lower volume in default mode and limbic networks. This study suggests that IICV signals widespread network dysfunction across multiple brain networks. These brain abnormalities offer new insights into mechanisms of early cognitive dysfunction. Clinical implications are discussed.

Biomarkers and Tools for Predicting Alzheimer's Disease in the Preclinical Stage

Alzheimer's disease (AD) is the only leading cause of death for which no disease-modifying therapy is currently available. Over the past decade, a string of disappointing clinical trial results has forced us to shift our focus to the preclinical stage of AD, which represents the most promising therapeutic window. However, the accurate diagnosis of preclinical AD requires the presence of brain β- amyloid deposition determined by cerebrospinal fluid or amyloid-positron emission tomography, significantly limiting routine screening and diagnosis in non-tertiary hospital settings. Thus, an easily accessible marker or tool with high sensitivity and specificity is highly needed. Recently, it has been discovered that individuals in the late stage of preclinical AD may not be truly "asymptomatic" in that they may have already developed subtle or subjective cognitive decline. In addition, advances in bloodderived biomarker studies have also allowed the detection of pathologic changes in preclinical AD. Exosomes, as cell-to-cell communication messengers, can reflect the functional changes of their source cell. Methodological advances have made it possible to extract brain-derived exosomes from peripheral blood, making exosomes an emerging biomarker carrier and liquid biopsy tool for preclinical AD. The eye and its associated structures have rich sensory-motor innervation. In this regard, studies have indicated that they may also provide reliable markers. Here, our report covers the current state of knowledge of neuropsychological and eye tests as screening tools for preclinical AD and assesses the value of blood and brain-derived exosomes as carriers of biomarkers in conjunction with the current diagnostic paradigm.

Discrepancy in Fluid and Crystallized Intelligence: An Early Cognitive Marker of Dementia from the LASI-DAD Cohort

Background

Cognitive aging is a complex phenomenon, which comprises various cognitive skills, broadly categorized into fluid and crystallized intelligence. Crystallized intelligence (gc) tends to be maintained, as opposed to fluid intelligence (gf), which tends to decline rapidly with age. The association of the two with cognitive decline remains a matter of conjecture requiring further research.

Aim

The aim of the study was to identify the variables of gc and gf from a population data of Longitudinal Aging Study in India-Diagnostic Assessment of Dementia (LASI-DAD) study and investigate its relationship with the onset of cognitive impairment using discrepancy analysis against neuropsychological tests.

Methods

This analysis of data from LASI-DAD study was carried out on a sample of 3,223 participants. They were assessed on extensive thirteen cognitive tests and one subjective test of cognition. Standardized score was used for discrepancy analysis. Fluid ability minus crystallized ability was used to assess the cognitive impairment. Any statistical significance with the score difference >0.99 SD was defined as a presence of cognitive decline. Hindi Mental Status Examination (HMSE) and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) were used as gold standard.

Results

With increased discrepancy score, each cognitive parameter score declined which was found to be statistically significant. In HMSE (Normal = 25.81 ± 3.39; Impaired = 23.17 ± 3.54; p = <0.001), there was a drop of 2 point scores in identifying cognitive impairment in the population sample as per the gold standard. A similar trend was evident in other neurocognitive domains as well.

Conclusion

Crystallized-fluid intelligence discrepancy analysis has a strong potential in predicting the onset of cognitive decline ahead of time, facilitating early intervention.