# Evaluating Longitudinal Changes in the Human Structural Connectome in Relation to Cognitive Aging

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2021 · $470,535

## Abstract

PROJECT SUMMARY
Progressive aging-related cognitive declines are associated with limitations in self-care and functional
independence, deteriorating physical health, and impending dementia and mortality, even among the otherwise
healthy. Identifying and understanding the neurodegenerative processes that underlie cognitive aging is key to
developing interventions to prevent or ameliorate cognitive decline. Disconnection theories of aging specifically
implicate weakening of structural brain connectivity as a key mechanism of cognitive decline, but until recently,
diffusion MRI data and connectomic methods needed to rigorously test such theories have been lacking. To
expedite understanding how aging-related changes in the human structural connectome relate to aging-related
cognitive declines, we will apply the latest connectomic and multivariate data analysis methods to
existing data from two highly unique datasets: (1) The UK Biobank, a cross-sectional sample of
~10,000 40-75 year old adults, who have undergone diffusion MRI scanning, have been measured with
multiple cognitive tests, and have provided extensive sociodemographic and medical information; and
(2) The Lothian Birth Cohort of 1936, a narrow-age cohort of older adults (baseline age = 73 years; N =
731) who have undergone diffusion MRI scanning, have been measured with multiple cognitive tests,
and have provided extensive sociodemographic and medical information on each of three separate
occasions, each separated by three years. Using recently developed graph-theoretic models, we will
construct structural brain connectome networks for each participant's diffusion MRI data at each wave and
extract indices reflective of network topology within several specific networks of interest (NOIs) identified ex
ante. We will also identify topologically central hub regions that disproportionately govern efficiency within each
individual's connectome network. We will apply cross-sectional and longitudinal structural equation models to
examine aging-related transformations in network indices, examine concurrent and longitudinal coupling
between network indices and cognitive abilities, and test predictors of levels and changes in network indices
and cognitive abilities. This will allow us to contrast the predictive utility of the selected NOIs for cognitive aging
and to identify specific features of network architecture involved in cognitive aging and mediate the effects of
demographic, medical, and lifestyle risk factors for cognitive aging. We additionally implement machine-
learning methods to estimate an upper bound of prediction of cognitive aging from network indices, and identify
novel features of network topology as candidate mechanisms of cognitive decline. The availability of two
uniquely large and well-characterized datasets will allow us to ensure that findings are rigorous and
reproducible using within sample (holdout) and between sample cross-validation. For all aims, we will place
consider...

## Key facts

- **NIH application ID:** 10163115
- **Project number:** 5R01AG054628-05
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Elliot Max Tucker-Drob
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $470,535
- **Award type:** 5
- **Project period:** 2017-09-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10163115

## Citation

> US National Institutes of Health, RePORTER application 10163115, Evaluating Longitudinal Changes in the Human Structural Connectome in Relation to Cognitive Aging (5R01AG054628-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10163115. Licensed CC0.

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