# Exploring Cognitive Aging Using Reference Ability Neural Networks

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2024 · $2,460,208

## Abstract

PROJECT SUMMARY:
This study focus on the optimal functional and structural imaging characterization of the cognitive aging and
preclinical Alzheimer's disease (AD). It has been repeatedly demonstrated that performance across the age
span on large batteries of diverse cognitive tests can be parsimoniously represented by a set of four reference
abilities: episodic memory, perceptual speed, fluid ability, and vocabulary. In contrast, neuroimaging
researchers typically evaluate age differences in neural activation associated with the performance of a single
specific task that may or may not be fully representative of these reference abilities. Successful identification of
these "reference ability neural networks" may lead to a paradigm shift in research on the neural bases of age
differences in cognition by focusing on the broad and replicable aspects common to several tasks rather than
the possibly idiosyncratic features of individual tasks. We have identified the latent brain networks associated
with each of the four reference abilities across adulthood. While undergoing functional imaging, we tested large
group of healthy adults aged 20 to 80 with a series of 12 cognitive tasks that represent the four reference
abilities (3 per construct). Using unique expertise in spatial covariance and other analyses of the fMRI imaging
data, we have derived 4 latent spatial, brain-wide fMRI networks that are associated with the latent cognitive
structure of the reference abilities across adulthood. We are presently following up this group at 5 years and
beginning to delineate how expression of these networks changes with aging and with the onset of mild
cognitive impairment and AD. We use multimodal imaging to evaluate potential mediators of age and
dementia-related differences in the utilization of the networks. These include change in brain volume, cortical
thickness, white matter hyperintensity burden; integrity of white matter tracts; resting CBF; and resting BOLD
networks. Importantly, we use PET to assess amyloid and tau burden. We now propose to extend the follow up
of this important cohort to 10 years. The proposed study develops a completely new imaging approach to the
study of cognitive aging and preclinical AD and is also unique in its age span. It has the potential to provide key
insights into the nature and causes of the neural changes that underlie cognitive aging and to more accurately
describe the preclinical phase of AD.

## Key facts

- **NIH application ID:** 10891748
- **Project number:** 5R01AG038465-10
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Christian Georg Habeck
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $2,460,208
- **Award type:** 5
- **Project period:** 2011-09-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10891748

## Citation

> US National Institutes of Health, RePORTER application 10891748, Exploring Cognitive Aging Using Reference Ability Neural Networks (5R01AG038465-10). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10891748. Licensed CC0.

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