Nilearn: Machine learning for Neuro-Imaging in Python

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1. Introduction: nilearn in a nutshell

    1.1. What is nilearn: MVPA, decoding, predictive models, functional connectivity
    1.2. Installing nilearn
    1.3. Python for NeuroImaging, a quick start

2. Decoding and MVPA: predicting from brain images

    2.1. An introduction to decoding
    2.2. Choosing the right predictive model
    2.3. SpaceNet: decoding with spatial structure for better maps
    2.4. Searchlight : finding voxels containing information

3. Functional connectivity and resting state

    3.1. Extracting times series to build a functional connectome
    3.2. Connectome extraction: inverse covariance for direct connections
    3.3. Extracting resting-state networks: ICA and related
    3.4. Region Extraction for better brain parcellations
    3.5. Clustering to parcellate the brain in regions

4. Plotting brain images

    4.1. Different plotting functions
    4.2. Different display modes
    4.3. Adding overlays, edges, contours, contour fillings and markers
    4.4. Displaying or saving to an image file
    4.5. Surface plotting

5. Manipulation brain volumes with nilearn

    5.1. Input and output: neuroimaging data representation
    5.2. Manipulating images: resampling, smoothing, masking, ROIs...
    5.3. From neuroimaging volumes to data matrices: the masker objects

6. Advanced usage: manual pipelines and scaling up

    6.1. Building your own neuroimaging machine-learning pipeline

7. Reference documentation: all nilearn functions

    7.1. nilearn.connectome: Functional Connectivity
    7.2. nilearn.datasets: Automatic Dataset Fetching
    7.3. nilearn.decoding: Decoding
    7.4. nilearn.decomposition: Multivariate decompositions
    7.5. nilearn.image: Image processing and resampling utilities
    7.6. nilearn.input_data: Loading and Processing files easily
    7.7. nilearn.masking: Data Masking Utilities
    7.8. nilearn.regions: Operating on regions
    7.9. nilearn.mass_univariate: Mass-univariate analysis
    7.10. nilearn.plotting: Plotting brain data
    7.11. nilearn.signal: Preprocessing Time Series

8. Nilearn usage examples

    8.1. General examples
    8.2. Visualization of brain images
    8.3. Decoding and predicting from brain images
    8.4. Functional connectivity
    8.5. Manipulating brain image volumes
    8.6. Advanced statistical analysis of brain images

user guide and examples:

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nilearn.github.io-master.zip (23.22 MB)

2017-6-4 16:51:53 上传

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igs816 发表于 2017-6-4 16:52
1. Introduction: nilearn in a nutshell

    1.1. What is nilearn: MVPA, decoding, predictive model ...

楼主好人，谢谢分享