# Motion Sequencing for All: pipelining, distribution and training to enable broad adoption of a next-generation platform for behavioral and neurobehavioral analysis

> **NIH NIH U24** · HARVARD MEDICAL SCHOOL · 2021 · $466,672

## Abstract

Understanding the function of the nervous system requires a sophisticated understanding of its main
output, behavior. Although our ability to record from and to manipulate neurons and neural circuits has
accelerated at a spectacular pace over the last decade, progress has lagged in coupling the interrogation of
the nervous system to similarly high-resolution measures of behavior. As a consequence, we lack a
sophisticated understanding of how the brain composes, modifies and controls action.
 We have recently developed a transformative behavioral characterization technology called Motion
Sequencing (MoSeq), which circumvents many of the limitations imposed by typical approaches to behavioral
measurement (e.g., overtraining, head-fixation, limited behavioral flexibility). This analytical system works by
capturing comprehensive and continuous morphometric data about the three-dimensional (3D) posture of a
mouse as it freely behaves. The 3D data are then analyzed using an unsupervised machine learning algorithm
to identify patterns of motion that correspond to stereotyped and reused modules of sub-second behavior
(which by analogy to natural language we refer to as behavioral “syllables”). The output of this fitting procedure
is a parts list for behavior: a limited set of syllables out of which the rodent creates all of its observable action.
In addition, within any given experiment MoSeq identifies the specific transition structure (or “grammar”) that
places individual syllables into sequences; these sequences encode all patterns of spontaneous behavior
expressed by an animal in a given experimental context. We have recently combined this behavioral
assessment technology with techniques for neural recording, allowing us to assess the relationship between
neural activity in behaviorally-relevant circuits and patterns of action. This combined approach allowed us, for
example, to identify a code for elemental 3D pose dynamics in striatum; importantly, these observed
correlations validate MoSeq as a technology that enables accurate inference of internal states from external
states. However, the code that underlies MoSeq is essentially bespoke, inappropriate for distribution, and
difficult for all but expert users to navigate. In addition, implementing MoSeq in its current form requires
extensive prior mathematical and computational experience, limiting its use to a small set of users with
specialized skills. Here we propose Aims to democratize MoSeq by (1) transforming it into an end-to-end
pipeline that can be easily used by graduate-student level neuroscientists with minimal expertise, and which
can be modified on an ongoing basis to accommodate improvements to MoSeq and (2) to offer hands-on
training in the set-up and appropriate use of MoSeq for characterizing behavior and neural-behavioral
relationships. Together these aims will create a vibrant community of MoSeq users; the creation of such a
group has the potential to transform the way behavior i...

## Key facts

- **NIH application ID:** 9902565
- **Project number:** 5U24NS109520-02
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Sandeep R Datta
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $466,672
- **Award type:** 5
- **Project period:** 2019-04-01 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9902565

## Citation

> US National Institutes of Health, RePORTER application 9902565, Motion Sequencing for All: pipelining, distribution and training to enable broad adoption of a next-generation platform for behavioral and neurobehavioral analysis (5U24NS109520-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9902565. Licensed CC0.

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