# A Computational Approach for Quantifying Motor Behaviors in Spinocerebellar Ataxias to Improve Early Detection of Motor Signs and Precisely Estimate Disease Severity and Disease Change

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $516,097

## Abstract

ABSTRACT
 The spinocerebellar ataxias (SCA) are debilitating neurodegenerative diseases that impact a range of
human behaviors including arm function, speech, and vision. Tools that can quantify motor deficits in a
granular and objective manner are needed to support early recognition of clinical disease onset, more
sensitively determine efficacy of a therapy, and make personalized predictions about disease progression.
Such tools are needed for upcoming disease modifying clinical trials in SCAs, in order to reduce sample size
and trial duration and better understand how a given therapy modifies human behaviors. Powered off of the
currently available primary outcome measures for these rare ataxias, clinical trials are likely to face patient
recruitment and retention challenges, especially with multiple co-occurring clinical trials. These challenges may
impede or slow our ability to successfully discover therapies for our patients.
 We have recently made substantial progress in capturing multimodal behavioral signals from speech,
eye movement, and arm motor function using everyday technologies: a microphone, iPhone camera, and
computer mouse. Our initial data indicate that these scalable technologies have strong potential to extend
current clinical assessments in ataxia and that our novel machine learning approach for generating disease
severity estimates performs better than the traditional regression model approach. Our algorithms are able to
quantitatively identify signs of ataxia and parkinsonism in SCA individuals' speech and arm movement, even
when absent on clinical assessment. Furthermore, our novel severity estimation algorithm enabled
measurement of disease progression more sensitively than clinical scales. We propose to substantially expand
longitudinal data collection and further develop our novel analytic approaches to train more powerful models for
characterizing and quantifying human motor behavior. The technologies developed have the potential to
facilitate clinical trials aimed at bringing disease modifying therapies to individuals with SCA. While the focus of
this project is on SCA, the novel methodological approaches and data generated are applicable to other
neurodegenerative diseases affecting movement and speech. Furthermore, this project will bring new insight
into how motor abnormalities initially arise and progress.
 The overall goal of this project is to develop widely available systems for improving early detection of
clinical disease onset, severity assessment, and prognostication of spinocerebellar ataxias while
simultaneously learning how these disorders impact fine-grained motor behavior.

## Key facts

- **NIH application ID:** 10817758
- **Project number:** 5R01NS117826-04
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Anoopum Satyawan Gupta
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $516,097
- **Award type:** 5
- **Project period:** 2021-04-15 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10817758

## Citation

> US National Institutes of Health, RePORTER application 10817758, A Computational Approach for Quantifying Motor Behaviors in Spinocerebellar Ataxias to Improve Early Detection of Motor Signs and Precisely Estimate Disease Severity and Disease Change (5R01NS117826-04). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10817758. Licensed CC0.

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