Identifying a task-invariant cognitive reserve network using task potency

Cognitive reserve (CR) is thought to protect against the consequence of age- or disease-related structural brain changes across multiple cognitive domains. The neural basis of CR may therefore comprise a functional network that is actively involved in many different cognitive processes. To investigate the existence of such a “task-invariant” CR network, we measured functional connectivity in a cognitively normal sample between 20 and 80 years old (N ​= ​265), both at rest and during the performance of 11 separate tasks that aim to capture four latent cognitive abilities (i.e. vocabulary, episodic memory, processing speed, and fluid reasoning). For each individual, we determined the change in functional connectivity from the resting state to each task state, which is referred to as “task potency” (Chauvin et al., 2018, 2019). Task potency was calculated for each pair among 264 nodes (Power et al., 2012) and then summarized across tasks reflecting the same cognitive ability. Subsequently, we established the correlation between task potency and IQ or education (i.e. CR factors). We identified a set of 57 pairs in which task potency showed significant correlations with IQ, but not education, across all four cognitive abilities. These pairs were included in a principal component analysis, from which we extracted the first component to obtain a latent variable reflecting task potency in this task-invariant CR network. This task potency variable was associated with better episodic memory (β ​= ​0.19, p ​< ​.01) and fluid reasoning performance (β ​= ​0.17, p ​< ​.01) above and beyond the effects of cortical thickness (range [absolute] β ​= ​0.28-0.32, p ​< ​.001). Our identification of this task-invariant network contributes to a better understanding of the mechanism underlying CR, which may facilitate the development of CR-enhancing treatments. Our work also offers a useful alternative operational measure of CR for future studies

High occurrence of transportation and logistics occupations among vascular dementia patients : An observational study

Background: Growing evidence suggests a role of occupation in the emergence and manifestation of dementia. Occupations are often defined by complexity level, although working environments and activities differ in several other important ways. We aimed to capture the multi-faceted nature of occupation through its measurement as a qualitative (instead of a quantitative) variable and explored its relationship with different types of dementia. Methods: We collected occupational information of 2121 dementia patients with various suspected etiologies from the Amsterdam Dementia Cohort (age 67 ± 8, 57% male; MMSE 21 ± 5). Our final sample included individuals with Alzheimer's disease (AD) dementia (n = 1467), frontotemporal dementia (n = 281), vascular dementia (n = 98), Lewy body disease (n = 174), and progressive supranuclear palsy/corticobasal degeneration (n = 101). Within the AD group, we used neuropsychological data to further characterize patients by clinical phenotypes. All participants were categorized into 1 of 11 occupational classes, across which we evaluated the distribution of dementia (sub)types with χ 2 analyses. We gained further insight into occupation-dementia relationships through post hoc logistic regressions that included various demographic and health characteristics as explanatory variables. Results: There were significant differences in the distribution of dementia types across occupation groups (χ 2 = 85.87, p <.001). Vascular dementia was relatively common in the Transportation/Logistics sector, and higher vascular risk factors partly explained this relationship. AD occurred less in Transportation/Logistics and more in Health Care/Welfare occupations, which related to a higher/lower percentage of males. We found no relationships between occupational classes and clinical phenotypes of AD (χ 2 = 53.65, n.s.). Conclusions: Relationships between occupation and dementia seem to exist beyond the complexity level, which offers new opportunities for disease prevention and improvement of occupational health policy

Issues and recommendations for the residual approach to quantifying cognitive resilience and reserve.

BackgroundCognitive reserve and resilience are terms used to explain interindividual variability in maintenance of cognitive health in response to adverse factors, such as brain pathology in the context of aging or neurodegenerative disorders. There is substantial interest in identifying tractable substrates of resilience to potentially leverage this phenomenon into intervention strategies. One way of operationalizing cognitive resilience that has gained popularity is the residual method: regressing cognition on an adverse factor and using the residual as a measure of resilience. This method is attractive because it provides a statistical approach that is an intuitive match to the reserve/resilience conceptual framework. However, due to statistical properties of the regression equation, the residual approach has qualities that complicate its interpretation as an index of resilience and make it statistically inappropriate in certain circumstances.Methods and resultsWe describe statistical properties of the regression equation to illustrate why the residual is highly correlated with the cognitive score from which it was derived. Using both simulations and real data, we model common applications of the approach by creating a residual score (global cognition residualized for hippocampal volume) in individuals along the AD spectrum. We demonstrate that in most real-life scenarios, the residual measure of cognitive resilience is highly correlated with cognition, and the degree of this correlation depends on the initial relationship between the adverse factor and cognition. Subsequently, any association between this resilience metric and an external variable may actually be driven by cognition, rather than by an operationalized measure of resilience. We then assess several strategies proposed as potential solutions to this problem, such as including both the residual and original cognitive measure in a model. However, we conclude these solutions may be insufficient, and we instead recommend against "pre-regression" strategies altogether in favor of using statistical moderation (e.g., interactions) to quantify resilience.ConclusionsCaution should be taken in the use and interpretation of the residual-based method of cognitive resilience. Rather than identifying resilient individuals, we encourage building more complete models of cognition to better identify the specific adverse and protective factors that influence cognitive decline

Issues and recommendations for the residual approach to quantifying cognitive resilience and reserve

Background: Cognitive reserve and resilience are terms used to explain interindividual variability in maintenance of cognitive health in response to adverse factors, such as brain pathology in the context of aging or neurodegenerative disorders. There is substantial interest in identifying tractable substrates of resilience to potentially leverage this phenomenon into intervention strategies. One way of operationalizing cognitive resilience that has gained popularity is the residual method: regressing cognition on an adverse factor and using the residual as a measure of resilience. This method is attractive because it provides a statistical approach that is an intuitive match to the reserve/resilience conceptual framework. However, due to statistical properties of the regression equation, the residual approach has qualities that complicate its interpretation as an index of resilience and make it statistically inappropriate in certain circumstances. Methods and results: We describe statistical properties of the regression equation to illustrate why the residual is highly correlated with the cognitive score from which it was derived. Using both simulations and real data, we model common applications of the approach by creating a residual score (global cognition residualized for hippocampal volume) in individuals along the AD spectrum. We demonstrate that in most real-life scenarios, the residual measure of cognitive resilience is highly correlated with cognition, and the degree of this correlation depends on the initial relationship between the adverse factor and cognition. Subsequently, any association between this resilience metric and an external variable may actually be driven by cognition, rather than by an operationalized measure of resilience. We then assess several strategies proposed as potential solutions to this problem, such as including both the residual and original cognitive measure in a model. However, we conclude these solutions may be insufficient, and we instead recommend against “pre-regression” strategies altogether in favor of using statistical moderation (e.g., interactions) to quantify resilience. Conclusions: Caution should be taken in the use and interpretation of the residual-based method of cognitive resilience. Rather than identifying resilient individuals, we encourage building more complete models of cognition to better identify the specific adverse and protective factors that influence cognitive decline

Cortical Thickness, Surface Area and Subcortical Volume Differentially Contribute to Cognitive Heterogeneity in Parkinson's Disease.

Parkinson's disease (PD) is often associated with cognitive deficits, although their severity varies considerably between patients. Recently, we used voxel-based morphometry (VBM) to show that individual differences in gray matter (GM) volume relate to cognitive heterogeneity in PD. VBM does, however, not differentiate between cortical thickness (CTh) and surface area (SA), which might be independently affected in PD. We therefore re-analyzed our cohort using the surface-based method FreeSurfer, and investigated (i) CTh, SA, and (sub)cortical GM volume differences between 93 PD patients and 45 matched controls, and (ii) the relation between these structural measures and cognitive performance on six neuropsychological tasks within the PD group. We found cortical thinning in PD patients in the left pericalcarine gyrus, extending to cuneus, precuneus and lingual areas and left inferior parietal cortex, bilateral rostral middle frontal cortex, and right cuneus, and increased cortical surface area in the left pars triangularis. Within the PD group, we found negative correlations between (i) CTh of occipital areas and performance on a verbal memory task, (ii) SA and volume of the frontal cortex and visuospatial memory performance, and, (iii) volume of the right thalamus and scores on two verbal fluency tasks. Our primary findings illustrate that i) CTh and SA are differentially affected in PD, and ii) VBM and FreeSurfer yield non-overlapping results in an identical dataset. We argue that this discrepancy is due to technical differences and the subtlety of the PD-related structural changes

Mechanisms underlying resilience in ageing

Non peer reviewe

(Sub)cortical volume + cortical pial surface area group analysis.

<p>(Sub)cortical volume + cortical pial surface area group analysis.</p

Between-group differences in cortical thickness and thickness and correlation with task performance.

<p>HC had increased cortical thickness in the left pericalcarine gyrus, extending to cuneus, precuneus and lingual areas left inferior parietal cortex, bilateral rostral middle frontal cortex, and right cuneus, when compared with PD patients (a-d). Within the PD sample, we found a negative correlation between the left lateral occipital and lingual gyrus and performance on the RAVLT (e-f). Clusters were significant after multiple comparison correction with Monte Carlo simulations.</p

Partial correlations of (sub)cortical volume and surface area with neuropsychological task performance, corrected for age, sex, education, and tGM volume.

<p>Partial correlations of (sub)cortical volume and surface area with neuropsychological task performance, corrected for age, sex, education, and tGM volume.</p

Vertex-wise cortical thickness group analysis.

<p>Vertex-wise cortical thickness group analysis.</p