# Neuroimaging Core

> **NIH NIH P30** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2021 · $314,068

## Abstract

ABSTRACT- NEUROIMAGING CORE
Neuroimaging Core continues its support of NYU ADRC with standardized image acquisitions and the
development of new imaging protocols. The Core has evolved as a significant world-wide resource of imaging
tools and metrics to study brain aging and to translates image-based animal models of AD to human studies.
The Core includes faculty experienced in structural and functional neuroimaging whose work led to multiple
NIH-funded awards designed to both identify early risk factors and elucidate mechanisms of AD progression.
The three specific aims are: 1) to assure standard, quality-controlled protocols and provide key brain measures
for all subjects; 2) to develop and validate new imaging modalities/software to support the scientific themes of
NYU ADRC; and 3) to provide imaging guidance and training. To achieve these goals we will introduce amyloid
and tau PET imaging to augment existing MRI sequences. We will provide stringent quality control, compile
normative reference values for all image-derived metrics, and provide the Neuropathology Core with in vivo
and post mortem MRI to validate anatomical findings. The core will also devote significant effort to add
machine learning models to diagnostic and processing workflows.

## Key facts

- **NIH application ID:** 10148614
- **Project number:** 5P30AG066512-02
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** HENRY RUSINEK
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $314,068
- **Award type:** 5
- **Project period:** 2020-05-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10148614

## Citation

> US National Institutes of Health, RePORTER application 10148614, Neuroimaging Core (5P30AG066512-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10148614. Licensed CC0.

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