# Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortium

> **NIH NIH RF1** · UNIVERSITY OF PENNSYLVANIA · 2022 · $2,290,964

## Abstract

Abstract
Brain aging is commonly accompanied by a number of neuropathologic processes, often co-occurring, that
may lead to cognitive decline and dementia. Vascular contributions to cognitive impairment and dementia
(VCID) are also extremely common and, due to associations with cardiovascular risk factors, may be
mitigated with current therapies. It is clear that effective treatments for AD-related dementias (ADRD) will
require early detection of pathologic brain change at prodromal and cognitively normal stages. Imaging
methods offer the opportunity to study diverse brain changes present in aging and prodromal AD in ways
that were previously impossible. Characterizing these multi-faceted aspects of brain structure, function and
pathology not only provides insights into the underlying pathophysiological processes, but also novel
predictive in vivo biomarkers. Various studies have shown that relatively early signs of neurodegenerative
processes can be detected via AI-based pattern analysis and machine learning (PAML) methods, and that
these tools can provide powerful predictive individualized panels of predictors. Our group has been on the
frontier of developing PAML methods, and applying them to the new “Imaging-based coordinate SysTem
for AGing and NeurodeGenerative diseases” (iSTAGING) consortium, a large-scale effort pursued in the
current phase of our grant, which successfully brought together and harmonized over 51,000 MRIs and
clinical data from 11 studies and ~34,000 individuals. We aim to capture the heterogeneity of brain
change with aging and prodromal AD, by applying our heterogeneity analysis PAML deep learning (DL)
methods, which help structuring imaging patterns associated with different brain aging trajectories. Our
goal is to enrich the different dimensions of iSTAGING which will reflect various patterns of brain change,
hence capturing the underlying heterogeneity in quantifiable and replicable metrics. Although we will
include our previously derived measures of rsfMRI networks and of amyloid burden, in the proposed work
we will focus on further dissecting neuroanatomical heterogeneity, i.e. on refining the `N' in the AT(N)
framework to measure variability in AD neurodegeneration and the contributions of copathologies, and on
using these intermediate neuroimaging phenotypes to predict cognitive decline and clinical progression.
This will allow us to place each individual into the iSTAGING brain chart and map his/her trajectory, as well
to determine predictive indices of brain change and cognitive decline. The current project builds on the
foundational work of the previous funding phase, and expands this unique resource to include several
studies focusing on longitudinal data, on groups of under-represented socio-economic status, as well as on
various co-morbidities including hypertension, diabetes, obesity, smoking and sleep disturbances. The
proposed work will also leverage recent developments in deep learning, and will offer ad...

## Key facts

- **NIH application ID:** 10530196
- **Project number:** 2RF1AG054409-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Christos Davatzikos
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,290,964
- **Award type:** 2
- **Project period:** 2017-04-15 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10530196

## Citation

> US National Institutes of Health, RePORTER application 10530196, Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortium (2RF1AG054409-02). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10530196. Licensed CC0.

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