# Model-based cerebrovascular markers extracted from hemodynamic data for diagnosing MCI or AD and predicting disease progression.

> **NIH NIH R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2022 · $1,793,565

## Abstract

"Model-based cerebrovascular markers extracted from hemodynamic data for
non-invasive, portable and inexpensive diagnosis of MCI or mild AD and prediction of
disease progression"
PROJECT SUMMARY
The goal of the proposed multi-PI project is to establish proof of concept for the utility of a new
class of cerebrovascular markers that may aid in the improved diagnosis and prediction of
disease progression in Mild Cognitive Impairment (MCI) and mild Alzheimer's disease (AD).
The means for obtaining these markers are non-invasive, inexpensive and portable, so that they
can be used for screening in a primary-care setting. The scientific rationale for this new class of
cerebrovascular markers is provided by the recent promising results of our group and the
mounting evidence of a strong correlation between MCI/AD and cerebrovascular dysregulation.
A recently published retrospective study on a large cohort of 1,171 subjects from the ADNI
database utilized multi-factorial data-driven analysis to assess the relation between MCI/AD
disease progression and commonly used biomarkers (obtained from MRI/PET and plasma/CSF)
and concluded that cerebrovascular dysregulation is the earliest and strongest pathologic
factor associated with AD progression, corroborating the hypothesis of cerebrovascular
dysregulation.
Quantification of cerebrovascular dysregulation in that large-cohort study was achieved through
analysis of ASL-MRI data of cerebral perfusion. We propose instead to explore a novel
integrative dynamic modeling approach that analyzes the cerebral hemodynamics of
persons with no cognitive impairment and MCI/AD patients with a methodology that yields input-
output predictive models of the dynamic relationships between changes in beat-to-beat cerebral
blood flow velocity (via Transcranial Doppler) or cerebral tissue oxygenation (via Near Infrared
Spectroscopy) in response to changes in arterial blood pressure and end-tidal CO2 data. The
obtained data-based models are subsequently used to compute markers of the dynamics of
cerebrovascular regulation. Initial results of the advocated approach have achieved
statistically significant delineation between 46 MCI patients and 20 age-matched controls on the
basis of a model-based marker of dynamic vasomotor reactivity (DVR). Evaluation of the
DVR marker against established MRI-based and PET-based biomarkers, as well as
neuropsychological test data, from the larger cohort of the proposed project offers the promise
of portable, non-invasive, inexpensive and sensitive means for detecting cerebrovascular
dysregulation at the early stages of MCI or mild AD, and monitoring disease progression.
Important co-variates of this study include age, gender, education, ApoE genotype, site and
amyloid burden.

## Key facts

- **NIH application ID:** 10404604
- **Project number:** 5R01AG058162-05
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Sandra A Billinger
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,793,565
- **Award type:** 5
- **Project period:** 2018-09-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10404604

## Citation

> US National Institutes of Health, RePORTER application 10404604, Model-based cerebrovascular markers extracted from hemodynamic data for diagnosing MCI or AD and predicting disease progression. (5R01AG058162-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10404604. Licensed CC0.

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