# Cognitive and Molecular Challenges to Statistical Inference Across Healthy Aging.

> **NIH NIH R00** · BROWN UNIVERSITY · 2021 · $247,426

## Abstract

Age-related cognitive decline is a problem of growing importance given the trend toward increased lifespan and the
importance of cognitive function in determining risk for neurodegenerative disease. Despite the importance of this
problem, the cognitive and molecular changes that mediate it are still poorly understood. While there are competing
theories for the mediators of cognitive aging at both psychological and molecular levels, theories on aging and the
research supporting them have typically focused on a single level of analysis. As our understanding of the biological basis
for behavior grows, this divide becomes less sensible. Instead, psychological theory should be constrained according to its
known biology, and biology should in turn inform psychological theory. Here I propose to learn neural network modeling
and magnetic resonance spectroscopy (MRS) techniques in order to bridge human psychological theory that has been the
focus of my recent work to the molecular level theory that was the focus of my post-baccalaureate training at the NIA. I
will build from my recent work that highlights a key statistical problem faced by the brain: how to selectively pool
information across relevant sources but partition information across irrelevant ones. I will closely examine the cognitive
and molecular mechanisms that allow for efficient pooling and partitioning to test the overarching theory that, due to
impaired glutamate and dopamine signaling, older adults develop a selective deficit in pooling relevant sources of
information. I will test this theory in two separate paradigms: visual working memory (K99) and learning and perceptual
inference (R00). During the K99 phase of the award I will examine how pooling information from visual targets with
similar features can 1) improve effective memory capacity, 2) be achieved by a neural network model, and 3) be impaired
by simulated molecular deficiencies (glutamate, dopamine, norepinephrine). This bridge between cognitive and molecular
levels of analysis will be used to test whether age-related memory deficits are due to inefficient pooling and mediated by
molecular deficits in glutamate and dopamine (as measured through MRS and behavioral proxy, respectively). During the
R00 phase of the award I will use the training in neural networks and MRS provided in the K99 phase to examine how 1)
pooling sequential pieces of information affects learning and perceptual bias, 2) efficient pooling and partitioning can be
achieved by a neural network model and 3) these processes are disrupted by specific molecular deficiencies. The
established relationships between statistical properties (sequential pooling and partitioning), psychological measurements
(learning and perceptual bias) and molecular factors (glutamate, dopamine, and norepinephrine signaling levels) will be
used to test whether age-related differences in learning and perceptual bias reflect deficient pooling mediated by local
glutamate deficiency (as m...

## Key facts

- **NIH application ID:** 10171740
- **Project number:** 5R00AG054732-05
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Matthew Nassar
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $247,426
- **Award type:** 5
- **Project period:** 2019-09-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10171740

## Citation

> US National Institutes of Health, RePORTER application 10171740, Cognitive and Molecular Challenges to Statistical Inference Across Healthy Aging. (5R00AG054732-05). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10171740. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*