The Reference Ability Neural Network Study: Motivation, design, and initial feasibility analyses

Abstract

We introduce and describe the Reference Ability Neural Network Study and provide initial feasibility data. Based on analyses of large test batteries administered to individuals ranging from young to old, four latent variables, or reference abilities (RAs) that capture the majority of the variance in age-related cognitive change have been identified: episodic memory, fluid reasoning, perceptual speed, and vocabulary. We aim to determine whether spatial fMRI networks can be derived that are uniquely associated with the performance of each reference ability. We plan to image 375 healthy adults (50 per decade from age 20 to 50; 75 per decade from age 50 to 80) while performing a set of 12 cognitive tasks. Data on 174 participants are reported here. Three tasks were grouped a priori into each of the four reference ability domains. We first assessed to what extent both cognitive task scores and activation patterns readily show convergent and discriminant validity, i.e. increased similarity between tasks within the same domain and decreased similarity between tasks between domains, respectively. Block-based time-series analysis of each individual task was conducted for each participant via general linear modeling. We partialled activation common to all tasks out of the imaging data. For both test scores and activation topographies, we then calculated correlations for each of 66 possible pairings of tasks, and compared the magnitude of correlation of tasks within reference ability domains to that of tasks between domains. For the behavioral data, globally there were significantly stronger inter-task correlations within than between domains. When examining individual abilities, 3 of the domains also met these criteria but memory reached only borderline significance. Overall there was greater topographic similarity within reference abilities than between them (p<0.0001), but when examined individually, statistical significance was reached only for episodic memory and perceptual speed. We then turned to a multivariate technique, linear indicator regression analysis, to derive four unique linear combinations of Principal Components (PC) of imaging data that were associated with each RA. We investigated the ability of the identified PCs to predict the reference domain associated with the activation of individual subjects for individual tasks. Median accuracy rates for associating component task activation with a particular reference ability were quite good: memory: 82%; reasoning: 87%; speed: 84%; vocabulary: 77%. These results demonstrate that even using basic GLM analysis, the topography of activation of tasks within a domain is more similar than tasks between domains. The follow-up regression analyses suggest that all tasks with each RA rely on a common network, unique to that RA. Our ultimate goal is to better characterize these RA neural networks and then study how their expression changes across the age span. Our hope is that by focusing on these networks associated with key features of cognitive aging, as opposed to t...

Selective association between cortical thickness and reference abilities in normal aging

“…In addition, any performance on tests in the cognitive test battery that was indicative of mild cognitive impairment was grounds for exclusion. More detailed description about this study can be found in (Salthouse et al, 2015; Stern et al, 2014). …”

Perceptual and memory inhibition deficits in clinically healthy older adults are associated with region-specific, doubly dissociable patterns of cortical thinning.

“…Age-related changes to mental processes occur across a host of different cognitive abilities ranging from fluid reasoning to perceptual speed (Braver & West, 2008; Craik & Byrd, 1982; Stern et al, 2014). Though many studies have investigated changes to brain structure across the life span (De Leon et al, 1997; Fischl & Dale, 2000; Fjell et al, 2006; Jack et al, 1997; Salat et al, 2004), and a number of studies have investigated the relationship between volumetric measures of brain integrity and cognition (see Kaup, Mirzakhanian, Jeste, & Eyler, 2011, for a recent review), relatively few studies have explored the relationship between cortical thickness and cognitive decline (c.f.…”

The Reference Ability Neural Network Study: Life-time stability of reference-ability neural networks derived from task maps of young adults

“…In a previous paper (Stern et al, 2014), we introduced the Reference Ability Neural Network Study, which administers 12 cognitive neuroimaging tasks (3 for each RA) to healthy adults age 20–80 in order to derive unique neural networks underlying these 4 RAs and investigate how these networks may be affected by aging. We used a multivariate approach, linear indicator regression, to derive a unique covariance pattern or Reference Ability Neural Network (RANN) for each of the 4 RAs. The RANNs were derived from the neural task data of 64 younger adults of age 30 and below.…”

“…In a previous paper (Stern et al, 2014), we introduced the Reference Ability Neural Network Study, which administers 12 cognitive neuroimaging tasks (3 for each RA) to healthy adults age 20–80 in order to derive unique neural networks underlying these 4 RAs and investigate how these networks may be affected by aging.…”