# PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis

> **NIH NIH R01** · OHIO STATE UNIVERSITY · 2020 · $539,445

## Abstract

PROJECT SUMMARY
 Alzheimer's dementia (AD) is the most common form of dementia in adults over the age of 65, and
Frontotemporal dementia (FTD) is the leading cause of dementia in middle age, with the behavioral variant
subtype (bvFTD) being the most prevalent form. The relationships between clinical syndromes and
pathological causes are complex, which makes accurate diagnosis difficult. For example, multiple studies have
indicated that a significant proportion of cases of AD-like dementia show evidence of non-AD pathology, such
as inclusions of the transactive response DNA-binding protein 43 (TDP-43), a protein associated with clinical
FTD. Also, AD neuropathology has been found in 15–30% of patient with the clinical diagnosis of
frontotemporal dementia (FTD). As treatment agents with potential disease-modifying effects are developed,
sensitive and specific biomarkers will be needed, so that they can be tested and then eventually used in the
appropriate patient populations. In this project, we will focus on clinically diagnosed bvFTD and AD patients,
and apply machine learning to multimodal neuroimaging (T1, FDG-PET) data pooled from large, multisite
studies of AD and FTD. Our goal is to develop novel biomarkers that can differentiate bvFTD, AD and controls.
Our hypothesis is that each neuropathology is associated with a distinct biomarker signature, and these
signatures can be discovered through well-characterized clinical, neurological and neuroanatomical profiles.
We will use available amyloid imaging and cerebrospinal fluid (CSF) measures of β-amyloid and tau to assess
the robustness of our predictions of AD neuropathologies. In Aim 1 we will use cross-sectional and longitudinal
structural imaging to develop predictive biomarker models for differentiating bvFTD vs. AD. In Aim 2 we will
use cross-sectional and longitudinal FDG-PET imaging to develop predictive biomarker models. In Aim 3 we
will evaluate the combination of structural and FDG-PET imaging as predictive biomarker models.
 Relevance: This research supports NIH initiatives on long-term, personalized precision medicine and
big data science. Our predictive biomarker models can inform participant selection in clinical trials so that we
can identify disease-modifying treatments with greater power. Our system-biology approach can enable us to
generate new questions on mechanisms underlying the origin and progression of neuro-pathological
processes, create new data and computational tools that can in turn generate new insights and new
hypotheses.

## Key facts

- **NIH application ID:** 10397226
- **Project number:** 7R01AG055121-05
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** HOWARD J ROSEN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $539,445
- **Award type:** 7
- **Project period:** 2017-09-15 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10397226

## Citation

> US National Institutes of Health, RePORTER application 10397226, PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis (7R01AG055121-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10397226. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*