Introduction

The following notebooks introduce NiSpace step by step, from the basic concept to advanced analysis techniques. They are designed to be read in order, but each notebook can also be used as a standalone reference.

The notebooks are available as downloadable Jupyter notebooks (links at the top of each page).

• Spatial colocalization — concept & motivation
• Getting started: the NiSpace object
• Data: datasets, parcellations & custom inputs
• Working with imaging phenotypes (Y data)
• Null models and permutation testing
• Multiple comparisons correction
• Visualizing colocalization results
• Brain plotting with NiSpace
• NiSpace workflows
• X-Set Enrichment Analysis (XSEA; c.f. ABAnnotate)
• Advanced topics
• A specific null model for coordinate-based meta-analyses
• Paired colocalization: the SPICE test
• Binary spatial colocalization
• Regional influence: leave-one-region-out sensitivity