# Map Manager: Longitudinal image analysis with online editing and sharing.

> **NIH NIH RF1** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2021 · $1,158,500

## Abstract

The increasing availability and ease of use of confocal, two-photon, and light-sheet microscopes coupled with
rapid developments in fluorescent protein reporters have made 3D and functional imaging and its analysis a
central component of modern Neuroscience research. Yet, the ease of acquiring 3D and functional images is
creating progressively larger datasets, prompting the need for high-throughput image analysis algorithms and
software that can be both rapid and accurate. Although software to analyze single time-point images has received
substantial attention, tools to analyze multiple time-point longitudinal imaging datasets is currently lacking. This
lack of longitudinal image analysis tools is a major barrier to scientific inquiry with individual labs devising their
own analysis strategies creating a situation where it is difficult for others to verify and reproduce this analysis.
What is needed is a community agreed upon longitudinal image analysis standard that promotes sharing.
 Here, we propose to develop software to create and curate annotations in longitudinal imaging datasets.
This software will solve a major problem by providing the needed rigor and reproducibility while making it easy
for researchers to distribute their data and analysis. Making these important datasets findable, accessible,
interoperable, and reusable. To achieve these goals, we propose to build intuitive web-browser and desktop
graphical-user-interfaces (GUIs) that will work with cloud based data and analysis. These GUIs will be driven
by a Python advanced-programming-interface (API) that is scriptable. For online editing and sharing we will
work with the BRAIN funded Brain Image Library (BIL), and for interoperability with Neurodata Without
Borders (NWB) and Neuroscience Data Interface. We will utilize the BRAIN Initiative NeuroMorpho.Org
and Defining Our Research Methodology (DORY), to ensure our annotations of morphology, connectivity,
and physiological signatures include accepted meta-data nomenclatures and vocabularies.
 We will work closely with a group of "seed" BRAIN funded labs to obtain feedback and make rapid
improvements in the functionality and usability of the front-end GUIs and the back-end API. This will be
achieved by online forums, site visits, and a hack-a-thon hosted at UC Davis. During the Covid pandemic we
have learned that these events work extremely well when done virtually and are prepared to continue this
model. We are committed to providing thorough documentation for the web-browser, desktop GUIs, and
Python API as well as constantly refined and simple to follow recipes with interactive web-based use cases. To
ensure community adoption and use, this proposal also includes working with a number of "seed" labs to run
their data through the entire pipeline from analysis to online sharing.
 The long range goal is to have Map Manager act as a catalyst for data analysis, exploration, and sharing.
Effectively creating a community based approach,...

## Key facts

- **NIH application ID:** 10365810
- **Project number:** 1RF1MH123206-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Robert Harry Cudmore
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,158,500
- **Award type:** 1
- **Project period:** 2021-09-15 → 2025-09-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10365810

## Citation

> US National Institutes of Health, RePORTER application 10365810, Map Manager: Longitudinal image analysis with online editing and sharing. (1RF1MH123206-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10365810. Licensed CC0.

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