# CRCNS: Investigating Brain Dynamics through the Lens of Statistical Mechanics

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $290,544

## Abstract

Synaptic dysfunction has been hypothesized to be one of the earliest brain changes in Alzheimer’s
 disease (AD), leading to hyper-excitation in neuronal circuits. However, network changes related to age
 and sex tend to overlap with disease neuropathology, increasing the difficulty of separating
 disease-specific alterations from those related to normal aging trajectories in males and females. Indeed,
 AD disproportionately affects women, who comprise two thirds of all persons diagnosed with AD
dementia.
 Leveraging resting state fMRI connectome and diffusion MRI-derived structural connectome, we will use a
 novel hybrid resting-state structural connectome (rs-SC) to study excitation-inhibition balance. Recently,
 using a group of cognitively normal APOE-ε4 carriers and age/gender matched non-carriers we
 demonstrated a sex-by-age-by-phenotype interaction, with significant hyperexcitation with increasing age
 only observable in women, but not in men. Further, hyperexcitation in female carriers began to exhibit at
 age 50 in the anterior cingulate, parahippocampal gyrus and temporal lobe regions, and the degree of
 hyperexcitation is linked to compensatory recruitment of neuronal resources during a spatial learning
 memory task.
 In this proposal, we will characterize 1) sex-specific normative trajectories of excitation-inhibition balance
 using the Human Connectome Project (HCP) data, and 2) altered excitation-inhibition balance in
 abnormal aging using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, as well as 3) further
 test and validate our hyperexcitation framework in longitudinal mouse models of AD.
RELEVANCE (See instructions):
 In this proposal, we will develop novel computational tools to characterize hyper-excitation patterns in
 aging and Alzheimer's Disease and validate our hyperexcitation framework on human data (ADNI and
 HCP) as well as longitudinal mouse models of AD. This will significantly improve our understanding of AD
 and potentially accelerate the discovery of more robust non-invasive imaging biomarkers of AD.

## Key facts

- **NIH application ID:** 10401891
- **Project number:** 5R01AG071243-03
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Alex Leow
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $290,544
- **Award type:** 5
- **Project period:** 2020-08-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10401891

## Citation

> US National Institutes of Health, RePORTER application 10401891, CRCNS: Investigating Brain Dynamics through the Lens of Statistical Mechanics (5R01AG071243-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10401891. Licensed CC0.

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