1. Introduction#

1.1. What is meierlab?#

meierlab is a package built by members of the MCW Meier Lab to help facilitate our day-to-day lab activities and analyses on neuroimaging data. A lot of our processes are common across projects and datasets It contains tools to download data, visualize data for QC, perform outlier analyses, process connectivity data, and more.

If you’re new to programming and/or imaging data, don’t worry. Here, you’ll find plenty of examples and documentation to help you get started.

1.2. Using meierlab for the first time#

meierlab is a Python library. If you have never used Python before, you may find it useful to explore some other tutorials and wikis before going through this guide:

general introduction about Python

introduction to using Python for science

Note

Please check out the internal lab wiki if you’re on campus for additional information about our data specifically.

1.2.1. First steps with meierlab#

At this stage, you should have installed meierlab and opened a Jupyter notebook or an IPython / Python session. First, load meierlab with

import meierlab

1.2.2. Learning from the API references#

All modules are described in the API references.

1.2.3. Learning from the examples#

meierlab comes with a lot of examples/tutorials. Going through them should give you a precise overview of what you can achieve with this package.

1.2.4. Finding help#

We rely pretty heavily on other neuroimaging python and general software packages that may be of interest, but whose documentation is outside the scope of this package…

  • The documentation of scikit-learn explains each method with tips on practical use and examples. While not specific to neuroimaging, it is often a recommended read.

  • (For Python beginners) A quick and gentle introduction to scientific computing with Python with the scipy lecture notes. Moreover, you can use meierlab with Jupyter notebooks or IPython sessions. They provide an interactive environment that greatly facilitates debugging and visualisation.

  • From the Nipy ecosystem: Nibabel is a great tool for manipulating imaging data in various formats and Nilearn contains many useful analysis and visualization modules.

  • Other useful python packages to know: Numpy is especially useful for matrix manipulation and general numeric functions, Pandas has many utilities for handling tabular data, and Networkx is used for graph creation, analysis, and visualization.

  • You will also see references to other neuroimaging packages that aren’t necessarily written in Python: AFNI, FSL, among others.