Track: BrainGlobe#

Modern microscopy techniques allow the acquisition of large-scale multidimensional datasets which enables unbiased investigation of brain structure and function. However, the analysis of such datasets often requires specialised computational tools and expertise.

The BrainGlobe Initiative addresses this by providing easy-to-use tools to analyse large-scale brain histology data and transform it to a common coordinate space in a species agnostic manner. This track will cover basic concepts of image analysis using napari, brain atlases and common coordinate spaces, and guide participants through hands-on tutorials to learn how to use the BrainGlobe ecosystem of computational neuroanatomy tools.

Target audience

This course is designed for researchers who have acquired or expect to acquire large brain histology datasets. It is aimed at those interested in learning about open-source tools for analysing and visualising large microscopy datasets with a specific focus on the BrainGlobe ecosystem.

Course overview#

Core workshop (Monday - Wednesday)#

We will cover the following topics during the first three days:

Introduction: a high level overview of the BrainGlobe ecosystem of computational neuroanatomy tools, and what they enable.
Image analysis in napari: basic concepts of image analysis using napari.
Working in a common coordinate space: a primer on brain atlases, common coordinate spaces and image registration, followed by a hands-on tutorial using brainreg and brainglobe-registration to map data to a BrainGlobe atlas.
Hands-on tutorials: we will work through the following hands-on tutorials using BrainGlobe’s napari graphical user interface:
- Segmenting structures in whole brain microscopy images with brainglobe-segmentation.
- Detecting cells in large 3D images with cellfinder.
- Analysing cell positions in atlas space with brainmapper.
- Visualisation of data in atlas space with brainrender and brainrender-napari.
Programmatic access: an introduction to interacting with the BrainGlobe ecosystem via scripting and the command line.

Collaboration days (Thursday - Friday)#

The final two days are dedicated to collaboration. We will join forces with participants from the Extracellular Electrophysiology track to work together on participant-led projects.

Skill building: we’ll start with a practical workshop on Git and GitHub to equip everyone with the necessary skills for collaborative coding.
Project-based work: participants will self-organise into small teams to tackle projects hands-on. Coding is not a requirement; any idea that benefits from collaboration with other attendees is welcome. Potential project ideas include, but are not limited to:
- Apply a tool: use what you’ve learnt to analyse a new dataset (your own or a public one).
- Give feedback: report bugs and suggest features by raising issues on relevant open-source tools.
- Make a contribution: submit a pull request to an open-source repository.
- Collaborative writing: draft a white paper, blog post, or documentation together.
- Prototype an idea: experiment with a new analysis or method.
Presentation: teams will have the opportunity to share their progress and outcomes on the final afternoon.

Confirmed Instructors#

Prerequisites#

Hardware#

As this is a hands-on workshop, you will need to bring your own laptop. Any fairly recent laptop will be suitable, you don’t need a GPU etc.

Python knowledge#

The only prerequisite is a basic knowledge of programming in Python, and the scientific Python ecosystem. For those without this background, the preparatory month will equip you with all the skills needed to make the most of this course.

Data#

Bringing your own data is encouraged but not required. It’s a great chance to get feedback on your data and learn from others. If you don’t have your own data, we will provide example datasets for you to work with.

We expect that participant-led ideas emerging from this track may inspire collaborative projects during the Collaboration Days on Thursday and Friday.