# DaTscan-based Disease Progression Models for Early-stage Parkinson’s Disease

> **NIH NIH R01** · YALE UNIVERSITY · 2021 · $352,779

## Abstract

Project Summary
Parkinson's disease (PD) is heterogeneous: it has many subtypes and the disease progresses at different
rates in different subtypes. Disease progression in early-stage PD is observed as signal changes in SPECT
imaging with 123I-FP-CIT, which is called DaTscan imaging, or simply DaTscan. The goal of the research
proposed here is to create accurate models of heterogeneous PD progression using DaTscan images. Such
models will not only provide additional insight into PD, but they are also critically important in assessing the
effect of neuro-protective therapy.
A new set of models called mixtures of linear dynamical systems (MLDS) are proposed to model early-state
PD progression as it manifests in DaTscans. MLDS models combine machine-learning methods with linear
dynamical system theory. They capture many features of early-stage PD progression: laterality, non-linear
progression, as well as PD heterogeneity. Preliminary results show that, MLDS is accurate, finds progression
subtypes, relates well to clinical data (MDS-UPDRS motor scores), and gives genuinely new insights about PD
progression.
The proposed research aims to develop the MLDS methodology in region-of-interest as well as voxel-based
frameworks. A detailed discussion of the MLDS theory, model fitting, and the relation to clinical data is
included. Longitudinal DaTscan images as well as MDS-UPDRS motor scores are available for over 440
subjects from the Parkinson's Progression Markers Initiative (PPMI), and this data set will be used along with
MLDS to create the PD progression models.

## Key facts

- **NIH application ID:** 10149422
- **Project number:** 5R01NS107328-04
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Hemant D Tagare
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $352,779
- **Award type:** 5
- **Project period:** 2018-07-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10149422

## Citation

> US National Institutes of Health, RePORTER application 10149422, DaTscan-based Disease Progression Models for Early-stage Parkinson’s Disease (5R01NS107328-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10149422. Licensed CC0.

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