Update ADHD dataset in examples to MAIN

.circleci/clean-cache.py

@@ -9,7 +9,7 @@
 def update_cache_timestamp(timestamp_filename):
     """ Updates the contents of the manual-cache-timestamp file
     with current timestamp.
-    
+
     Returns
     -------
     None

.circleci/manual-cache-timestamp

@@ -1 +1 @@
-2018-12-07 13:09:12.409726
+2019-04-13 20:12:23.519962

doc/connectivity/connectome_extraction.rst

@@ -28,9 +28,10 @@ Connectome extraction: inverse covariance for direct connections
 Sparse inverse covariance for functional connectomes
 =====================================================
 
-Resting-state functional connectivity can be obtained by estimating a
-covariance (or correlation) matrix for signals from different brain
-regions. The same information can be represented as a weighted graph,
+Functional connectivity can be obtained by estimating a covariance
+(or correlation) matrix for signals from different brain
+regions decomposed, for example on resting-state or naturalistic-stimuli datasets.
+The same information can be represented as a weighted graph,
 vertices being brain regions, weights on edges being covariances
 (gaussian graphical model). However, coefficients in a covariance matrix
 reflect direct as well as indirect connections. Covariance matrices form
@@ -104,8 +105,8 @@ of the estimator::
 .. topic:: **Exercise: computing sparse inverse covariance**
    :class: green
 
-   Compute and visualize a connectome on the first subject of the ADHD
-   dataset downloaded with :func:`nilearn.datasets.fetch_adhd`
+   Compute and visualize a connectome on the first subject of the brain
+   development dataset downloaded with :func:`nilearn.datasets.fetch_development_fmri`
 
    **Hints:** The example above has the solution
 
@@ -164,10 +165,11 @@ group analysis only on the non zero coefficients.
    :class: green
 
    Try using the information above to compute a connectome on the
-   first 5 subjects of the ADHD dataset downloaded with
-   :func:`nilearn.datasets.fetch_adhd`
+   first 5 subjects of the brain development dataset downloaded with
+   :func:`nilearn.datasets.fetch_development_fmri`
 
-   **Hint:** The example above has the solution
+   **Hint:** The example above works through the solution for the ADHD dataset. 
+   adhd.
 
 
 .. topic:: **Reference**
@@ -255,8 +257,9 @@ Deviations from this mean in the tangent space are provided in the connectivitie
 .. topic:: **Exercise: computing connectivity in tangent space**
    :class: green
 
-   Compute and visualize the tangent group connectome based on the NYU, OHSU and NeuroImage sites of the ADHD
-   dataset downloaded with :func:`nilearn.datasets.fetch_adhd`
+   Compute and visualize the tangent group connectome based on the brain
+   development
+   dataset downloaded with :func:`nilearn.datasets.fetch_development_fmri`
 
    **Hints:** The example above has the solution
 

doc/connectivity/functional_connectomes.rst

@@ -104,13 +104,13 @@ regions, regressing out noise sources is indeed very important
    :class: green
 
    Try using the information above to compute the correlation matrix of
-   the first subject of the ADHD dataset downloaded with
-   :func:`nilearn.datasets.fetch_adhd`.
+   the first subject of the brain development dataset
+   downloaded with :func:`nilearn.datasets.fetch_development_fmri`.
 
    **Hints:**
 
    * Inspect the '.keys()' of the object returned by
-     :func:`nilearn.datasets.fetch_adhd`.
+     :func:`nilearn.datasets.fetch_development_fmri`.
 
    * :class:`nilearn.connectome.ConnectivityMeasure` can be used to compute
      a correlation matrix (check the shape of your matrices).
@@ -130,7 +130,8 @@ Probabilistic atlases
 The definition of regions as by a continuous probability map captures
 better our imperfect knowledge of boundaries in brain images (notably
 because of inter-subject registration errors). One example of such an
-atlas well suited to resting-state data analysis is the `MSDL atlas
+atlas well suited to resting-state or naturalistic-stimuli data analysis is
+the `MSDL atlas
 <https://team.inria.fr/parietal/18-2/spatial_patterns/spatial-patterns-in-resting-state/>`_
 (:func:`nilearn.datasets.fetch_atlas_msdl`).
 
@@ -181,8 +182,9 @@ the same considerations on using confounds regressors apply.
 .. topic:: **Exercise: correlation matrix of rest fMRI on probabilistic atlas**
    :class: green
 
-   Try to compute the correlation matrix of the first subject of the ADHD
-   dataset downloaded with :func:`nilearn.datasets.fetch_adhd`
+   Try to compute the correlation matrix of the first subject of the
+   brain development
+   dataset downloaded with :func:`nilearn.datasets.fetch_development_fmri`
    with the MSDL atlas downloaded via
    :func:`nilearn.datasets.fetch_atlas_msdl`.
 

doc/connectivity/parcellating.rst

@@ -19,16 +19,17 @@ into homogeneous regions from functional imaging data.
     <http://journal.frontiersin.org/article/10.3389/fnins.2014.00167/full>`_
     Frontiers in neuroscience 8.167 (2014): 13.
 
-Data loading: Resting-state data
+Data loading: movie-watching data
 =================================
 
 .. currentmodule:: nilearn.datasets
 
 Clustering is commonly applied to resting-state data, but any brain
 functional data will give rise of a functional parcellation, capturing
 intrinsic brain architecture in the case of resting-state data.
-In the examples, we use rest data downloaded with the function 
-:func:`fetch_adhd` (see :ref:`loading_data`).
+In the examples, we use naturalistic stimuli-based movie watching
+brain development data downloaded with the function 
+:func:`fetch_development_fmri` (see :ref:`loading_data`).
 
 Applying clustering
 ====================
@@ -51,7 +52,7 @@ Applying clustering
     Both clustering algorithms (as well as many others) are provided by
     this object :class:`nilearn.regions.Parcellations` and full
     code example in
-    :ref:`here<sphx_glr_auto_examples_03_connectivity_plot_rest_parcellations.py>`.
+    :ref:`here<sphx_glr_auto_examples_03_connectivity_plot_data_driven_parcellations.py>`.
     Ward clustering is the easiest to use, as it can be done with the Feature
     agglomeration object. It is also quite fast. We detail it below.
 
@@ -117,8 +118,8 @@ using *ward.fit*. We directly use the result for visualization.
 To visualize the clusters, we assign random colors to each cluster
 for the labels visualization.
 
-.. figure:: ../auto_examples/03_connectivity/images/sphx_glr_plot_rest_parcellations_001.png
-   :target: ../auto_examples/03_connectivity/plot_rest_parcellations.html
+.. figure:: ../auto_examples/03_connectivity/images/sphx_glr_plot_data_driven_parcellations_001.png
+   :target: ../auto_examples/03_connectivity/plot_data_driven_parcellations.html
    :align: center
    :scale: 80
 
@@ -133,12 +134,12 @@ representation, taking the average on each parcel:
 - call *ward.inverse_transform* on the previous result to turn it back into
   the masked picture shape
 
-.. |left_img| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_rest_parcellations_002.png
-   :target: ../auto_examples/03_connectivity/plot_rest_parcellations.html
+.. |left_img| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_data_driven_parcellations_002.png
+   :target: ../auto_examples/03_connectivity/plot_data_driven_parcellations.html
    :width: 49%
 
-.. |right_img| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_rest_parcellations_003.png
-   :target: ../auto_examples/03_connectivity/plot_rest_parcellations.html
+.. |right_img| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_data_driven_parcellations_003.png
+   :target: ../auto_examples/03_connectivity/plot_data_driven_parcellations.html
    :width: 49%
 
 |left_img| |right_img|
@@ -152,6 +153,6 @@ approximated.
 
    All the steps discussed in this section can be seen implemented in
    :ref:`a full code example
-   <sphx_glr_auto_examples_03_connectivity_plot_rest_parcellations.py>`.
+   <sphx_glr_auto_examples_03_connectivity_plot_data_driven_parcellations.py>`.
 
 

doc/connectivity/region_extraction.rst

@@ -24,14 +24,14 @@ Region Extraction for better brain parcellations
 
 .. currentmodule:: nilearn.datasets
 
-Fetching resting state functional datasets
-==========================================
+Fetching movie-watching based functional datasets
+=================================================
 
-We use ADHD resting state functional connectivity datasets of 20 subjects,
-which is already preprocessed and publicly available at
-`<http://fcon_1000.projects.nitrc.org/indi/adhd200/>`_. We use utilities
-:func:`fetch_adhd` implemented in nilearn for automatic fetching of these
-datasets.
+We use a naturalistic stimuli based movie-watching functional connectivity dataset
+of 20 subjects, which is already preprocessed, downsampled to 4mm isotropic resolution, and publicly available at
+`<https://osf.io/5hju4/files/>`_. We use utilities
+:func:`fetch_development_fmri` implemented in nilearn for automatic fetching of this
+dataset.
 
 
 .. literalinclude:: ../../examples/03_connectivity/plot_extract_regions_dictlearning_maps.py
@@ -51,7 +51,7 @@ necessarily converting each file to Nifti1Image object.
 
 .. literalinclude:: ../../examples/03_connectivity/plot_extract_regions_dictlearning_maps.py
     :start-after: # object and fit the model to the functional datasets
-    :end-before: # Visualization of resting state networks
+    :end-before: # Visualization of functional networks
 
 .. currentmodule:: nilearn.plotting
 
@@ -167,7 +167,7 @@ Validating results
 
 Showing only one specific network regions before and after region extraction.
 
-Left image displays the regions of one specific resting network without region extraction
+Left image displays the regions of one specific functional network without region extraction
 and right image displays the regions split apart after region extraction. Here, we can
 validate that regions are nicely separated identified by each extracted region in different
 color.

doc/connectivity/resting_state_networks.rst

@@ -1,13 +1,14 @@
 .. _extracting_rsn:
 
 ==================================================
-Extracting resting-state networks: ICA and related
+Extracting functional brain networks: ICA and related
 ==================================================
 
 .. topic:: **Page summary**
 
    This page demonstrates the use of multi-subject Independent Component
-   Analysis (ICA) of resting-state fMRI data to extract brain networks in
+   Analysis (ICA) of movie-watching fMRI data
+   to extract brain networks in
    an data-driven way. Here we use the 'CanICA' approach, that implements
    a multivariate random effects model across subjects. A newer technique,
    based on dictionary learning, is then described.
@@ -27,16 +28,17 @@ Multi-subject ICA: CanICA
 Data preparation: retrieving example data
 -----------------------------------------
 
-We will use sample data from the `ADHD 200 resting-state dataset
-<http://fcon_1000.projects.nitrc.org/indi/adhd200/>`_ has been
-preprocessed using `CPAC <http://fcp-indi.github.io/>`_. We use nilearn
+We will use down-sampled data from the `brain development dataset
+<https://osf.io/5hju4/files/>`_ has been
+preprocessed using `FMRIPrep and Nilearn <https://osf.io/wjtyq/>`_.
+We use nilearn
 functions to fetch data from Internet and get the filenames (:ref:`more
 on data loading <loading_data>`):
 
 
-.. literalinclude:: ../../examples/03_connectivity/plot_canica_resting_state.py
-    :start-after: # First we load the ADHD200 data
-    :end-before:  ####################################################################
+.. literalinclude:: ../../examples/03_connectivity/plot_canica_analysis.py
+    :start-after: # First we load the brain development fmri data
+    :end-before:  ###############################################
 
 Applying CanICA
 ---------------
@@ -47,7 +49,7 @@ perform a multi-subject ICA decomposition following the CanICA model.
 As with every object in nilearn, we give its parameters at construction,
 and then fit it on the data.
 
-.. literalinclude:: ../../examples/03_connectivity/plot_canica_resting_state.py
+.. literalinclude:: ../../examples/03_connectivity/plot_canica_analysis.py
     :start-after: # Here we apply CanICA on the data
     :end-before: ####################################################################
 
@@ -68,31 +70,31 @@ We can visualize the components as in the previous examples. The first plot
 shows a map generated from all the components. Then we plot an axial cut for
 each component separately.
 
-.. literalinclude:: ../../examples/03_connectivity/plot_canica_resting_state.py
+.. literalinclude:: ../../examples/03_connectivity/plot_canica_analysis.py
     :start-after: # To visualize we plot the outline of all components on one figure
     :end-before: ####################################################################
 
-.. figure:: ../auto_examples/03_connectivity/images/sphx_glr_plot_canica_resting_state_001.png
+.. figure:: ../auto_examples/03_connectivity/images/sphx_glr_plot_canica_analysis_001.png
    :align: center
-   :target: ../auto_examples/03_connectivity/plot_canica_resting_state.html
+   :target: ../auto_examples/03_connectivity/plot_canica_analysis.html
 
 Finally, we can plot the map for different ICA components separately:
 
-.. literalinclude:: ../../examples/03_connectivity/plot_canica_resting_state.py
+.. literalinclude:: ../../examples/03_connectivity/plot_canica_analysis.py
     :start-after: # Finally, we plot the map for each ICA component separately
 
-.. |left_img| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_canica_resting_state_003.png
+.. |left_img| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_canica_analysis_003.png
    :width: 23%
 
-.. |right_img| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_canica_resting_state_004.png
+.. |right_img| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_canica_analysis_004.png
    :width: 23%
 
 .. centered:: |left_img| |right_img|
 
 .. seealso::
 
    The full code can be found as an example:
-   :ref:`sphx_glr_auto_examples_03_connectivity_plot_canica_resting_state.py`
+   :ref:`sphx_glr_auto_examples_03_connectivity_plot_canica_analysis.py`
 
 .. note::
 
@@ -123,13 +125,13 @@ Applying DictLearning
 Sparsity of output map is controlled by a parameter alpha: using a
 larger alpha yields sparser maps.
 
-.. literalinclude:: ../../examples/03_connectivity/plot_compare_resting_state_decomposition.py
+.. literalinclude:: ../../examples/03_connectivity/plot_compare_decomposition.py
     :start-after: # Dictionary learning
     :end-before: ###############################################################################
 
 We can fit both estimators to compare them
 
-.. literalinclude:: ../../examples/03_connectivity/plot_compare_resting_state_decomposition.py
+.. literalinclude:: ../../examples/03_connectivity/plot_compare_decomposition.py
     :start-after: # Fit both estimators
     :end-before: ###############################################################################
 
@@ -138,21 +140,21 @@ Visualizing the results
 
 4D plotting offers an efficient way to compare both resulting outputs
 
-.. literalinclude:: ../../examples/03_connectivity/plot_compare_resting_state_decomposition.py
+.. literalinclude:: ../../examples/03_connectivity/plot_compare_decomposition.py
     :start-after: # Visualize the results
 
-.. |left_img_decomp| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_compare_resting_state_decomposition_001.png
-   :target: ../auto_examples/03_connectivity/plot_compare_resting_state_decomposition.html
+.. |left_img_decomp| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_compare_decomposition_001.png
+   :target: ../auto_examples/03_connectivity/plot_compare_decomposition.html
    :width: 50%
-.. |right_img_decomp| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_compare_resting_state_decomposition_003.png
-   :target: ../auto_examples/03_connectivity/plot_compare_resting_state_decomposition.html
+.. |right_img_decomp| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_compare_decomposition_003.png
+   :target: ../auto_examples/03_connectivity/plot_compare_decomposition.html
    :width: 50%
 
-.. |left_img_decomp_single| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_compare_resting_state_decomposition_002.png
-   :target: ../auto_examples/03_connectivity/plot_compare_resting_state_decomposition.html
+.. |left_img_decomp_single| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_compare_decomposition_002.png
+   :target: ../auto_examples/03_connectivity/plot_compare_decomposition.html
    :width: 50%
-.. |right_img_decomp_single| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_compare_resting_state_decomposition_004.png
-   :target: ../auto_examples/03_connectivity/plot_compare_resting_state_decomposition.html
+.. |right_img_decomp_single| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_compare_decomposition_004.png
+   :target: ../auto_examples/03_connectivity/plot_compare_decomposition.html
    :width: 50%
 
 
@@ -169,4 +171,4 @@ classification tasks.
 .. seealso::
 
    The full code can be found as an example:
-   :ref:`sphx_glr_auto_examples_03_connectivity_plot_compare_resting_state_decomposition.py`
+   :ref:`sphx_glr_auto_examples_03_connectivity_plot_compare_decomposition.py`

doc/index.rst

@@ -30,11 +30,11 @@
 .. |oasis_weights| image:: auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_002.png
    :target: auto_examples/02_decoding/plot_oasis_vbm.html
 
-.. |rest_parcellations| image:: auto_examples/03_connectivity/images/sphx_glr_plot_rest_parcellations_001.png
-   :target: auto_examples/03_connectivity/plot_rest_parcellations.html
+.. |rest_parcellations| image:: auto_examples/03_connectivity/images/sphx_glr_plot_data_driven_parcellations_001.png
+   :target: auto_examples/03_connectivity/plot_data_driven_parcellations.html
 
-.. |canica| image:: auto_examples/03_connectivity/images/sphx_glr_plot_canica_resting_state_011.png
-   :target: auto_examples/03_connectivity/plot_canica_resting_state.html
+.. |canica| image:: auto_examples/03_connectivity/images/sphx_glr_plot_canica_analysis_011.png
+   :target: auto_examples/03_connectivity/plot_canica_analysis.html
 
 .. |tvl1_haxby| image:: auto_examples/02_decoding/images/sphx_glr_plot_haxby_space_net_002.png
    :target: auto_examples/02_decoding/plot_haxby_space_net.html

doc/introduction.rst

@@ -78,8 +78,8 @@ Why is machine learning relevant to NeuroImaging? A few examples!
 
     Data-driven exploration of brain images. This includes the extraction of
     the major brain networks from resting-state data ("resting-state networks")
-    as well as the discovery of connectionally coherent functional modules
-    ("connectivity-based parcellation").
+    or movie-watching data as well as the discovery of connectionally coherent
+    functional modules ("connectivity-based parcellation").
     For example,
     :ref:`extracting_rsn` or :ref:`parcellating_brain` with clustering.
 
@@ -260,14 +260,14 @@ To loop over each individual volume of a 4D image, use :func:`image.iter_img`::
    :class: green
 
    Want to sharpen your skills with nilearn?
-   Compute the mean EPI for first subject of the ADHD
-   dataset downloaded with :func:`nilearn.datasets.fetch_adhd` and
+   Compute the mean EPI for first subject of the brain development
+   dataset downloaded with :func:`nilearn.datasets.fetch_development_fmri` and
    smooth it with an FWHM varying from 0mm to 20mm in increments of 5mm
 
    **Hints:**
 
       * Inspect the '.keys()' of the object returned by
-        :func:`nilearn.datasets.fetch_adhd`
+        :func:`nilearn.datasets.fetch_development_fmri`
 
       * Look at the "reference" section of the documentation: there is a
         function to compute the mean of a 4D image