Software and Data

Software

M2M (2023) : A collection of Python tools for working with Allen Mouse Brain Connectivity Atlas in the user's data space (e.g. diffusion MRI data). Our method is based on "round-trip" actions between Allen and user data using the Allen Software Development Kit (AllenSDK) to import data from the Allen Institute and the Python package ANTsPyX for registration. Developed by Mahdi Abou-Amdan and Joël Lefebvre (UQAM) in collaboration with Laurent Petit's group (University of Bordeaux, France) (GitHubConference article to come)
TRAIT2D (2021): A cross-platform Python tool for monitoring, simulating and analyzing particle scattering experiments. Developed in collaboration with the laboratories of Jens Rittscher (Oxford University, UK) and Christian Eggeling (Friedrich Schiller Universität, Jena, Germany)(GitHub, Documentation, Pre-publication)
PyBaSiC (2021): Python implementation of the BaSiC illumination correction method. Developed by Joël Lefebvre in collaboration with Jens Rittscher's laboratory at Oxford University(GitHub, Pypi, Original article).
X-Tract Allen web interface (2020): A web interface to access data from the Allen Institute for brain science's Mouse Brain Connectivity Project. The purpose of this interface is to help identify regions of interest (ROI) containing white matter fiber crossings in the brain. Developed by Philippe Lemieux during an introductory research internship in summer 2020.(GitHub, Project description)

OCT average mouse brain (2017): These average mouse brains were created by combining 4 mouse brains acquired with a serial microscope using optical coherence tomography (OCT). Advanced normalization tools (ANTs) were used to create the normalized brains. Data were also aligned to the common coordinates (CCF) of the Allen mouse. Local attenuation contrast was calculated from OCT reflectivity contrast using a single scatter photon model. These brains were subsampled to an isotropic resolution of 50 microns / voxel(Data, Article).