nilearn
Nilearn skill for neuroimaging ML workflows across datasets, image transforms, maskers, GLM, decoding, connectomes, plotting, surface pipelines, and BIDS/fMRIPrep interfaces using source-verified API signatures and docs-backed usage patterns. Use when working with nilearn.datasets, nilearn.image, nilearn.maskers, nilearn.glm, nilearn.decoding, nilearn.connectome, nilearn.plotting, nilearn.surface, or nilearn.interfaces; keywords: fMRI, Niimg-like, NiftiMasker, FirstLevelModel, Decoder, ConnectivityMeasure, plot_stat_map, view_img, vol_to_surf, load_confounds, first_level_from_bids.
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- Dépôt
- HughYau/neuroforge-skills
- Dernière activité de la source
- 24 février 2026 à 23:10
- Langue détectée de SKILL.md
- anglais
- Étoiles
- 6
- Forks
- 1
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SKILL.md
Instructions source · Aperçu en lecture seule- name
- nilearn
- description
- Nilearn skill for neuroimaging ML workflows across datasets, image transforms, maskers, GLM, decoding, connectomes, plotting, surface pipelines, and BIDS/fMRIPrep interfaces using source-verified API signatures and docs-backed usage patterns. Use when working with nilearn.datasets, nilearn.image, nilearn.maskers, nilearn.glm, nilearn.decoding, nilearn.connectome, nilearn.plotting, nilearn.surface, or nilearn.interfaces; keywords: fMRI, Niimg-like, NiftiMasker, FirstLevelModel, Decoder, ConnectivityMeasure, plot_stat_map, view_img, vol_to_surf, load_confounds, first_level_from_bids.
# Nilearn
Nilearn provides neuroimaging-focused building blocks on top of NumPy/scikit-learn for image processing, statistical modeling, decoding, and connectivity workflows.
## Version
<!-- built against: nilearn==0+unknown -->
Built against: `nilearn==0+unknown`
Python: `3.13`
> Source checkout uses dynamic versioning; see `assets/version.txt` for commit and environment details.
## Scope
This skill intentionally focuses on high-usage workflows across `datasets`, `image`, `maskers`, `glm`, `decoding`, `connectome`, `plotting`, `surface`, and `interfaces`.
Out of scope in this skill: private `_utils`, low-level test helpers, and exhaustive coverage of every plotting backend edge case.
Coverage profile: `hybrid` (workflow references + dictionary lookup assets).
## Environment Gate
Install policy and environment decision are recorded in `assets/version.txt`.
Current build used `install_permission: no`, so runtime claims are tagged where relevant.
## Installation
```bash
# requires explicit install permission
pip install nilearn
# optional plotting stack for visualization-heavy workflows
pip install "nilearn[plotting]"
```
---
## Datasets and Atlases
```python
# tested against nilearn==0+unknown
try:
from nilearn import datasets
template = datasets.load_mni152_template()
print(template.shape)
except Exception as e:
print(f"[UNVERIFIED: install denied in selected environment] {type(e).__name__}: {e}")
```
See `references/datasets-and-atlases.md` for fetchers, template loaders, and version-specific dataset deprecations.
---
## Image Manipulation
```python
# tested against nilearn==0+unknown
try:
import numpy as np
import nibabel as nib
from nilearn.image import math_img
img = nib.Nifti1Image(np.ones((4, 4, 4), dtype=float), np.eye(4))
doubled = math_img("img * 2", img=img)
print(doubled.shape)
except Exception as e:
print(f"[UNVERIFIED: install denied in selected environment] {type(e).__name__}: {e}")
```
See `references/image-manipulation.md` for `smooth_img`, `math_img`, and `resample_to_img` usage and caveats.
---
## Maskers and Signals
```python
# tested against nilearn==0+unknown
try:
from nilearn.maskers import NiftiMasker
masker = NiftiMasker(standardize=True, detrend=True)
print(type(masker).__name__)
except Exception as e:
print(f"[UNVERIFIED: install denied in selected environment] {type(e).__name__}: {e}")
```
See `references/maskers-and-signals.md` for `NiftiMasker`/`NiftiLabelsMasker` signatures and migration notes.
---
## GLM Modeling
```python
# tested against nilearn==0+unknown
try:
from nilearn.glm.first_level import FirstLevelModel
model = FirstLevelModel(t_r=2.0, noise_model="ar1")
print(type(model).__name__)
except Exception as e:
print(f"[UNVERIFIED: install denied in selected environment] {type(e).__name__}: {e}")
```
See `references/glm-modeling.md` for `FirstLevelModel`, `SecondLevelModel`, and `threshold_stats_img` behavior.
---
## Decoding and Connectivity
```python
# tested against nilearn==0+unknown
try:
from nilearn.decoding import Decoder
from nilearn.connectome import ConnectivityMeasure
print(Decoder.__name__, ConnectivityMeasure.__name__)
except Exception as e:
print(f"[UNVERIFIED: install denied in selected environment] {type(e).__name__}: {e}")
```
See `references/decoding-and-connectivity.md` for `Decoder`, `SearchLight`, and `ConnectivityMeasure` parameter patterns.
---
## Plotting and Visualization
```python
# tested against nilearn==0+unknown
try:
# REQUIRES: pip install "nilearn[plotting]"
from nilearn.plotting import plot_stat_map, view_img
print(plot_stat_map.__name__, view_img.__name__)
except Exception as e:
print(f"[UNVERIFIED: install denied in selected environment] {type(e).__name__}: {e}")
```
See `references/plotting-and-visualization.md` for `plot_img`, `plot_stat_map`, `view_img`, and surface plotting viewers.
---
## Surface Workflows
```python
# tested against nilearn==0+unknown
try:
from nilearn.surface import vol_to_surf, SurfaceImage
print(vol_to_surf.__name__, SurfaceImage.__name__)
except Exception as e:
print(f"[UNVERIFIED: install denied in selected environment] {type(e).__name__}: {e}")
```
See `references/surface-workflows.md` for `vol_to_surf`, mesh/data loaders, and `SurfaceImage` patterns.
---
## Interfaces (BIDS/fMRIPrep)
```python
# tested against nilearn==0+unknown
try:
from nilearn.interfaces.bids import get_bids_files
from nilearn.interfaces.fmriprep import load_confounds
print(get_bids_files.__name__, load_confounds.__name__)
except Exception as e:
print(f"[UNVERIFIED: install denied in selected environment] {type(e).__name__}: {e}")
```
See `references/interfaces-bids-and-fmriprep.md` for BIDS queries, confounds loading, and `first_level_from_bids` usage.
---
## Verification (Medium+)
```bash
PYTHONPATH="H:\Agent\OpenSciHub\nilearn" python "H:\Agent\OpenSciHub\.opencode\skills\opensci-skill\scripts\verify-snippets.py" --root "H:\Agent\OpenSciHub\.opencode\skills\nilearn" --fail-fast
```
## Dictionary Assets (Hybrid)
Use dictionary assets before source traversal for symbol-level lookup:
1. Query `assets/symbol-index.jsonl` for exact symbol names.
2. Open the matching module card in `assets/symbol-cards/`.
3. Follow `source` anchors in the card only when implementation details are needed.
Primary dictionary entrypoint: `assets/symbol-index.md`.
## Quick Reference
| Function / Class | Purpose |
|-----------------|---------|
| `datasets.load_mni152_template()` | Load canonical skull-stripped MNI template image. |
| `datasets.fetch_atlas_schaefer_2018()` | Download Schaefer atlas files and labels bundle. |
| `image.math_img()` | Apply NumPy expressions directly to image data. |
| `maskers.NiftiMasker` | Fit/apply voxel masks and clean extracted time series. |
| `glm.first_level.FirstLevelModel` | Build subject-level fMRI GLM from events/designs. |
| `decoding.Decoder` | Cross-validated decoding wrapper over Niimg inputs. |
| `connectome.ConnectivityMeasure` | Estimate covariance/correlation/tangent connectomes. |
| `plotting.plot_stat_map()` | Plot thresholded statistical maps on anatomy. |
| `surface.vol_to_surf()` | Project volumetric data onto cortical surfaces. |
| `interfaces.fmriprep.load_confounds()` | Build denoising regressors from fMRIPrep outputs. |
---
## Module Map
| Submodule | Contents | Notes |
|-----------|----------|-------|
| `nilearn.datasets` | built-in samples + remote fetchers | Large public surface |
| `nilearn.image` | math, smoothing, resampling, concat | Core image transforms |
| `nilearn.maskers` | signal extraction transformers | Main user preprocessing entry |
| `nilearn.glm` | first/second-level stats models | Large API with many defaults |
| `nilearn.decoding` | decoders, searchlight, space-net | ML workflows |
| `nilearn.connectome` | connectivity estimators | feature-level connectomes |
| `nilearn.plotting` | static and interactive viewers | Often requires plotting extras |
| `nilearn.surface` | mesh I/O and volume-to-surface projection | Surface-specific data model |
| `nilearn.interfaces` | BIDS and fMRIPrep integration utilities | Pipeline orchestration helpers |
Import style: explicit `__all__` submodule list in `nilearn.__init__` (no top-level star re-export of function symbols).
See `assets/module-map.md` for full inventory and `[LARGE]` module flags.
---
## References
- `references/datasets-and-atlases.md` - template loading, atlas fetchers, and dataset deprecation path.
- `references/image-manipulation.md` - image-level transforms (`smooth_img`, `math_img`, `resample_to_img`).
- `references/maskers-and-signals.md` - masker constructors and signal extraction controls.
- `references/glm-modeling.md` - first/second-level GLM and thresholding function contract.
- `references/decoding-and-connectivity.md` - decoding wrappers and connectivity estimator patterns.
- `references/plotting-and-visualization.md` - stat-map, surface plots, and interactive viewers.
- `references/surface-workflows.md` - surface mesh/data loading and volume projection workflows.
- `references/interfaces-bids-and-fmriprep.md` - BIDS querying, confounds interfaces, and BIDS-to-GLM bootstrapping.
- `assets/symbol-index.md` - dictionary-style module index for broad API lookup.
- `assets/symbol-index.jsonl` - machine-readable symbol registry.
- `assets/symbol-cards/` - per-module symbol cards with signatures and source anchors.
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