# Dysregulated default mode network in individuals at risk of Alzheimer's disease: From vascular support and functional synaptic connectivity to rapid activity patterns

> **NIH NIH R56** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $515,459

## Abstract

Abstract. Three major principles are at the forefront of current understanding about the pathology and
potential therapeutic approach to addressing the massive public health burden of Alzheimer’s disease (AD).
First, the clinical symptoms and functional dependence resulting from this disease are known to occur after
potentially decades of degenerative brain changes linked to amyloid plaque and neurofibrillary tangle cortical
pathologies. Second, prevalent comorbid pathologies, particularly metabolic and cerebrovascular dysfunction,
contribute to a hastening of disease processes and clinical decline. Third, any therapeutic intervention
targeting either of these pathologic domains would need to be implemented at the earliest time possible, prior
any substantial irrecoverable neurodegeneration has transpired.
Functional magnetic resonance imaging (fMRI) is used to measure brain activity and previously contributed
extensively to the characterization of AD progression. Individuals at genetic risk of AD show altered fMRI
indicators even prior to expression of cognitive impairment, and thus, fMRI has provided critical insights into
pathophysiology of preclinical AD. The fMRI signal is an indirect correlate of neural activity that reflects
coupling between metabolic demand of active brain cells and a nutritive increase in cerebral blood flow. This
hemodynamic response (HDR) is measured through the blood oxygenation level dependent (BOLD) contrast
mechanism. A critical barrier in the application of fMRI to the study of AD is the intricate entanglement of neural
and vascular physiology at the basis of the BOLD signal resulting in an inability to differentiate between the
effects of neural dysfunction and comorbid vascular pathology.
The goal of this R01 research proposal is to distinguish neurophysiological from hemodynamic components of
the fMRI BOLD signal and to apply this new technology to the study of the functional synaptic connectome –
the substrate for human thought and memory – in older adults at genetic risk of AD, before any evidence of
cognitive and functional decline. We will implement a cutting-edge scanning and analysis paradigm by [1]
simultaneously recording electroencephalographic (EEG) and fMRI data, [2] quantifying transient events of
intrinsic neurophysiological activity in brain networks, [3] using these events to anchor measurement of the
neurally induced HDR, and [4] isolating changes in neurophysiological events and HDR between stable and
learning regimes of the dynamic functional connectome. Successful implementation of this approach would
provide novel insight into how genetic vulnerabilities are linked to distinct neural and vascular mechanisms
dysregulating balance between stability and learning in the functional connectome. Disruption in stable
patterns of spontaneous neural activity has been suggested to influence the plaque pathology in AD. Targeting
specific neural and vascular pathophysiology by novel, alternative therapie...

## Key facts

- **NIH application ID:** 10231993
- **Project number:** 1R56AG066164-01A1
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Tatiana Sitnikova
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $515,459
- **Award type:** 1
- **Project period:** 2020-09-15 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10231993

## Citation

> US National Institutes of Health, RePORTER application 10231993, Dysregulated default mode network in individuals at risk of Alzheimer's disease: From vascular support and functional synaptic connectivity to rapid activity patterns (1R56AG066164-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10231993. Licensed CC0.

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