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

Abstract

Analyses of large test batteries administered to individuals ranging from young to old have consistently yielded a set of latent variables representing reference abilities (RAs) that capture the majority of the variance in age-related cognitive change: Episodic Memory, Fluid Reasoning, Perceptual Processing Speed, and Vocabulary. 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. We then prospectively applied the RANNs to fMRI data from the remaining sample of 227 adults of age 31 and above in order to classify each subject-task map into one of the 4 possible reference domains. Overall classification accuracy across subjects in the sample age 31 and above was 0.80 ± 0.18. Classification accuracy by RA domain was also good, but variable; memory: 0.72 ± 0.32; reasoning: 0.75 ± 0.35; speed: 0.79 ± 0.31; vocabulary: 0.94 ± 0.16. Classification accuracy was not associated with cross-sectional age, suggesting that these networks, and their specificity to the respective reference domain, might remain intact throughout the age range. Higher mean brain volume was correlated with increased overall classification accuracy; better overall performance on the tasks in the scanner was also associated with classification accuracy. For the RANN network scores, we observed for each RANN that a higher score was associated with a higher corresponding classification accuracy for that reference ability. Despite the absence of behavioral performance information in the derivation of these networks, we also observed some brain-behavioral correlations, notably for the fluid-reasoning network whose network score correlated with performance on the memory and fluid-reasoning tasks. While age did not influence the expression of this RANN, the slope of the association between network score and fluid-reasoning performance was negatively associated with higher ages. These results provide support for the hypothesis that a set of specific, age-invariant neural networks underlies these four RAs, and that these networks maintain their cognitive specificity and level of intensity across age. Activation common to all 12 tasks was identified as another activation pattern resulting from a mean-contrast Partial-Least-Squares technique. This common pattern did show associations with age and some subject demographics for some of the reference domains, lending support to the overall conclusion that aspects of neural processing that are specific to any cognitive reference ability stay constant across age, while aspects that are common to all...

Identifying a task-invariant cognitive reserve network using task potency

“…Positive loadings to this pattern were found within regions associated with the dorsal attention system, and negative loadings were within areas that were reminiscent of the default mode system. 12 In a different study, Stern et al used the same RANN data and determined the number of principal components that optimally predicted premorbid IQ in a linear regression. 11 Regression weights from this analysis were used to create an IQ-related task-invariant BOLD pattern, and expression of this pattern modulated between cortical thickness and fluid reasoning performance among healthy individuals across the age span.…”

“…Based on the comparison of BOLD activation patterns from multiple cognitive tasks, several studies identified common regions of activity across conditions. 12–14 Recruitment of these regions showed direct associations with CR factors, such as education 12 and premorbid IQ. 13,14 In addition, resting state fMRI, which has the advantage of providing information about the brain’s functional organization rather than regional activity, has also been used in the context of CR.…”

Brain biomarkers and cognition across adulthood

“…The data set consisted of participants from two studies: The reference ability neural network (RANN), and the cognitive reserve (CR) studies. The RANN study was designed to identify networks of brain activity uniquely associated with performance across adulthood of each of the four reference abilities: memory, reasoning, speed of processing, and vocabulary (Habeck et al, ). The CR study was designed to elucidate the neural underpinnings of CR and the concept of brain reserve (Stern, ).…”

“…Both RANN and CR have been approved by the Institutional Review Board of Columbia University. More detailed information about the two studies can be found in previous publications (Habeck et al, ; Habeck et al, ; Razlighi, Habeck, Barulli, & Stern, ; Stern, ; Stern, ; Stern et al, ).…”

White matter regions with low microstructure in young adults are associated with white matter hyperintensities in late life

“…The use of a young, healthy sample mimics the principle of normative modeling in which normative metrics are defined in a cohort of interest (e.g., young and healthy) to investigate deviations due to pathological conditions (e.g., aging and ischemic injury) [26, 27]. The sample of young adults was a subset of a larger sample enrolled in an ongoing imaging study called the Reference Ability Neural Network study (RANN [28, 29]). The inclusion criteria included native English speaking, strongly right-handed, and at least a fourth grade reading level.…”