nilearn

What problem does it solve?

Nilearn skill helps you plan and reason about fMRI statistical modeling, masking, connectome extraction, and machine-learning workflows from neuroimaging inputs without blindly running heavy processing.

Core Features & Use Cases

• GLM modeling guidance: Support for first- and second-level fMRI analyses, including design-matrix planning and contrast interpretation workflows (planning and safe command suggestion).
• Masking and ROI/atlas operations: Guidance for extracting signals using maskers, handling image compatibility assumptions, and preparing expected inputs/outputs.
• Connectomes and image operations: Routing for connectome computation and image-level operations used downstream for statistics and ML.

Quick Start

Ask your question and include your fMRI image type (e.g., 4D runs vs derivatives) and what result you need (e.g., a first-level contrast map or a connectivity matrix), and the skill will propose a safe, documentation-driven plan.