# Mozak: Creating an Expert Community to accelerate neuronal reconstruction at scale

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2021 · $639,861

## Abstract

Project Summary
This project aims to leverage the best of both computational and human expertise in neuronal reconstruction
towards the goal of accelerating global neuroscience discovery from internationally-sourced imaging data. We
propose to create a cloud-based unified platform for converging 3-dimensional images of neurons onto a single
analysis platform to (1) train and grow a new expert community of global reconstructors to work across the
data from these groups, to (2) generate a community-sourced neuronal reconstruction database of open
imaging data that can be incorporated into a 3-dimensional map of neuronal interconnectivity - onto which (3)
novel annotations and more complex functional and molecular data can be overlaid. Our approach will evolve
with the growing needs of the neuroscience community over time. To do this, in Aim One (Neuronal
Reconstruction at Scale), we will test if the newly developed crowd-sourced game-based platform Mozak can
develop a collective of new human experts at scale, capable of accelerating the rate of current reconstruction
by at least an order of magnitude, at the same time as increasing the robustness, quality and unbiasedness of
the final reconstructions. In Aim Two (Robust Multi-Purpose Annotation), we will enhance basic neuronal
reconstruction by adding specific semantic annotation— including soma volume and morphological
quantification, volumetric analysis, and ongoing features (e.g. dendritic spines, axonal varicosities) requested
from the neuroscience community. Experienced and high-ranking members will be given the opportunity to
advance through increasingly complex neurons into full arbor brain-wide neuronal projections and multiple
clustered groups of neurons in localized circuits. Finally, in Aim 3 (Creation of a Research-Adaptive Data
Repository), we aim to develop a database of neuronal images reconstructed using the Mozak interface that
will directly serve the general and specific needs of different research groups. Our goal is to make this
database dynamically adaptive — as new research questions will invariably bring new needs for additional
annotations and cross-referencing with other data modalities. This highquality unbiased processing repository
will also be perfectly suited for training sets for automated algorithms, and the generation of a 3-dimensional
maps such as Allen Institute for Brain Science (AIBS) common coordinate framework. We expect that the
computational reconstruction methods will further improve with the new large corpus of “gold standard”
reconstructions. Collectively, the completion of these three aims will create an analysis suite as well as an
online community of experts capable of performing in depth analysis of large-scale datasets that will
significantly accelerate neuroscience research, enhance machine learning for reconstruction analysis, and
create a common platform of baseline neuronal morphology data against which aberrantly functioning neurons
can be anal...

## Key facts

- **NIH application ID:** 10204729
- **Project number:** 5R01MH116247-04
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Zoran Popovic
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $639,861
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10204729

## Citation

> US National Institutes of Health, RePORTER application 10204729, Mozak: Creating an Expert Community to accelerate neuronal reconstruction at scale (5R01MH116247-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10204729. Licensed CC0.

---

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