title: 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 reference abilities differ across age.
authors.affiliation: Cognitive Neuroscience Division, Department of Neurology, Columbia University, NY, NY 10032, USA. Electronic address: ch629@cumc.columbia.edu.
authors.forename: C
authors.initials: C
authors.lastname: Habeck
authors.affiliation: Cognitive Neuroscience Division, Department of Neurology, Columbia University, NY, NY 10032, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Gazes
authors.affiliation: Cognitive Neuroscience Division, Department of Neurology, Columbia University, NY, NY 10032, USA.
authors.forename: Q
authors.initials: Q
authors.lastname: Razlighi
authors.affiliation: PERFORM Center and Department of Psychology, Concordia University, Montréal, QC H4B 1R6, Canada.
authors.forename: J
authors.initials: J
authors.lastname: Steffener
authors.affiliation: Cognitive Neuroscience Division, Department of Neurology, Columbia University, NY, NY 10032, USA.
authors.forename: A
authors.initials: A
authors.lastname: Brickman
authors.affiliation: Cognitive Neuroscience Division, Department of Neurology, Columbia University, NY, NY 10032, USA.
authors.forename: D
authors.initials: D
authors.lastname: Barulli
authors.affiliation: Department of Psychology, University of Virginia, Charlottesville, VA 22904, USA.
authors.forename: T
authors.initials: T
authors.lastname: Salthouse
authors.affiliation: Cognitive Neuroscience Division, Department of Neurology, Columbia University, NY, NY 10032, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Stern
mesh_terms: D000328:Adult; D000368:Aged; D000375:Aging / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009415:Nerve Net / Q000502:physiology*; D055815:Young Adult