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This is the development documentation of nilearn (0.10.5.dev251+gc10b1a5f5) Switch to stable version (0.10.4)

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Quickstart
Examples
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User guide
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- 1. Introduction
- 2. What is nilearn?
- 3. Using nilearn for the first time
- 4. Machine learning applications to Neuroimaging
- 5. Decoding and MVPA: predicting from brain images
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- 6. Functional connectivity and resting state
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  - 6.1. Extracting times series to build a functional connectome
  - 6.2. Connectome extraction: inverse covariance for direct connections
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    - 6.2.3.1. Group-sparse covariance estimation
  - 6.3. Extracting functional brain networks: ICA and related
  - 6.4. Region Extraction for better brain parcellations
  - 6.5. Clustering to parcellate the brain in regions
- 7. Plotting brain images
- 8. Analyzing fMRI using GLMs
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- 9. Manipulation brain volumes with nilearn
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- 10. Advanced usage: manual pipelines and scaling up
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  - 10.1. Building your own neuroimaging machine-learning pipeline
  - 10.2. Downloading statistical maps from the Neurovault repository
API References
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Glossary

Development

Contributing
Maintenance
What’s new
Team
GitHub Repository

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API References¶

This is the class and function reference of nilearn. Please refer to the user guide for more information and usage examples.

nilearn.connectome: Functional Connectivity

10.2. Downloading statistical maps from the Neurovault repository

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