# Leveraging simultaneous BOLD-fMRI and wide-field Ca2+ imaging to reveal measures of neurovascular health that cross scales and species which have AD symptom and treatment response prognostic utility

> **NIH NIH R21** · YALE UNIVERSITY · 2022 · $460,625

## Abstract

PROJECT SUMMARY
There is still little we can do to effectively treat patients with Alzheimer’s disease (AD). In part, this is due to the
lack of a comprehensive disease model, clinically actionable biomarkers, and the challenge of translating findings
between basic science and patients. To interrogate disease processes, researchers use a variety of methods –
each with relative strengths and weaknesses – that specialize in capturing one or a few aspects of AD pathology
within an optimized, but confined, spatiotemporal milieu. To close knowledge gaps across scales, and species,
we can develop simultaneous multi-modal imaging approaches that leverage the strengths of complementary
modalities. These approaches will help to deepen our understanding of how the pathophysiological mechanism
that influence AD progression and treatment response, evident in animal models at the micro- or meso-scale,
manifest clinically. Further, the use of multi-modal imaging methods allows us to interrogate changes in
spontaneous activity (brain function in the absence of presented stimuli) which constitutes the vast majority of
all brain activity, is boundless in that it emerges across the whole-brain and has been shown to provide insight
into brain health that is independent from stimulus-evoked activity.
Fully aligned with NOT-AG-19-033, our objective is to use our recently published method of simultaneous blood-
oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) and wide-field calcium (WF-
Ca2+) imaging, to study multi-modal predictive modeling of AD progression and treatment response in a murine
disease model. We propose to conduct a longitudinal study which will include both an awake and anesthetized
multi-modal imaging protocol. WF-Ca2+ imaging provides a cell-type specific high spatiotemporal resolution
measurement of cortex-wide activity, while BOLD-fMRI is a whole-brain cell-type agnostic measure with limited
specificity but high sensitivity. Where WF-Ca2+ imaging is limited to a preclinical setting, BOLD-fMRI is a routine
clinical measure. Together, BOLD-fMRI and WF-Ca2+ imaging can advance our understanding of the cellular
origins of AD-related BOLD-fMRI signal changes and provide a direct link between animal and human studies.
Our overarching hypothesis is that our multi-modal approach can provide direct evidence of regional and circuit-
level neurovascular coupling health from measures of spontaneous activity that can be used for AD-symptom
severity (Aim 1) and treatment-response (Aim 2) prediction.
The significance of this proposal lies in advancing our understanding of the mechanisms which drive BOLD-
fMRI signal changes, which helps to validate clinically accessible imaging biomarkers of AD and contributes to
a more comprehensive understanding of AD pathology. The innovation of this proposal lies in establishing an
experimental and analysis framework which enables the study of AD-related changes in spontaneous activity
that are pred...

## Key facts

- **NIH application ID:** 10370675
- **Project number:** 1R21AG075778-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Evelyn MR Lake
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $460,625
- **Award type:** 1
- **Project period:** 2022-02-15 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10370675

## Citation

> US National Institutes of Health, RePORTER application 10370675, Leveraging simultaneous BOLD-fMRI and wide-field Ca2+ imaging to reveal measures of neurovascular health that cross scales and species which have AD symptom and treatment response prognostic utility (1R21AG075778-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10370675. Licensed CC0.

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