# Super-Resolved Multimodal Imaging Biomarkers for Frontotemporal Dementia

> **NIH NIH R21** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2024 · $268,730

## Abstract

PROJECT SUMMARY
This proposal is responsive to NIA solicitation PAR-22-094/NOT-AG-21-036 for projects involving the
development of novel approaches to diagnose and study Alzheimer's Disease and Related Dementias (ADRD)
with an emphasis on the need for biomarkers for dementia types other than Alzheimer’s disease in the ADRD
spectrum, including frontotemporal dementia (FTD). In this project, we will leverage our MPI team’s expertise
with MR-based accurate thalamic nuclei segmentation (TNS) and PET super-resolution (SR) to develop
thalamic-nuclei-based measures of atrophy, connectivity, and hypometabolism as possible FTD biomarker
candidates. FTD, the second most common cause of dementia in adults under 65 years of age after AD, is
clinically, genetically, and pathologically heterogeneous. There is an urgent need for antemortem biomarkers for
FTD that are sensitive across its different subtypes. Thalamic atrophy is a common feature of early disease
pathogenesis for all FTD subtypes. We propose to develop an integrated MR and PET imaging framework for
deriving quantitative imaging measures from the thalamic nuclei and validate in secondary-use multimodal
imaging data. Our dataset will include both sporadic FTD and familial C9orf72+ FTD patients. We will use a more
advanced variant of our TNS approach which, as per our preliminary results from an Alzheimer’s disease cohort,
leads to significantly better discrimination between healthy and impaired groups than FreeSurfer’s Bayesian
segmentation method, which is one of the current state-of-the-art TNS methods. We will develop and validate
an SR PET platform that uses MR-based thalamic nuclei labels as additional inputs to the model to enhance
thalamic nuclei contrast. To assess the clinical utility of the thalamic-nuclei-derived multimodal biomarker set for
atrophy, connectivity, and hypometabolism, we will compute receiver operating characteristic curves for FTD
subtype groups vs. cognitively normal subjects. We will also characterize our multimodal biomarker by
establishing a temporal ordering via event-based modeling. To assess the sensitivity of our nuclei-derived
atrophy and connectivity biomarkers, we will conduct cross-sectional spatiotemporal analyses in a larger cohort
with MR-only data that can reveal the age of divergence of each biomarker in a diseased vs. control group.
Finally, we will conduct longitudinal analyses to predict changes in clinical dementia rating and its behavioral and
language subscores from changes in atrophy and connectivity measures from the thalamic nuclei. Unlike
Alzheimer’s disease, for which the ATN research framework is well-developed for biomarker-based
characterization of dementia, there is a pressing need for imaging biomarkers for FTD, which this project can
address. We therefore envision that our proposed research will have high clinical impact on FTD characterization
and could play a role in FTD drug development efforts.

## Key facts

- **NIH application ID:** 10887989
- **Project number:** 1R21AG087392-01
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS AMHERST
- **Principal Investigator:** Joyita Dutta
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $268,730
- **Award type:** 1
- **Project period:** 2024-06-15 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10887989

## Citation

> US National Institutes of Health, RePORTER application 10887989, Super-Resolved Multimodal Imaging Biomarkers for Frontotemporal Dementia (1R21AG087392-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10887989. Licensed CC0.

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