.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/07_advanced/plot_bids_analysis.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_07_advanced_plot_bids_analysis.py>`
        to download the full example code. or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_07_advanced_plot_bids_analysis.py:


BIDS dataset first and second level analysis
============================================

Full step-by-step example of fitting a :term:`GLM`
to perform a first and second level
analysis in a :term:`BIDS` dataset and visualizing the results.
Details about the :term:`BIDS` standard can be consulted at
`https://bids.neuroimaging.io/ <https://bids.neuroimaging.io/>`_.

More specifically:

1. Download an :term:`fMRI` :term:`BIDS` dataset
   with two language conditions to contrast.
2. Extract first level model objects automatically
   from the :term:`BIDS` dataset.
3. Fit a second level model on the fitted first level models.
   Notice that in this case the preprocessed :term:`bold<BOLD>`
   images were already normalized to the same :term:`MNI` space.

.. GENERATED FROM PYTHON SOURCE LINES 21-24

.. code-block:: Python


    from nilearn import plotting








.. GENERATED FROM PYTHON SOURCE LINES 25-33

Fetch example :term:`BIDS` dataset
----------------------------------
We download a simplified :term:`BIDS` dataset made available for illustrative
purposes. It contains only the necessary
information to run a statistical analysis using Nilearn. The raw data
subject folders only contain bold.json and events.tsv files, while the
derivatives folder includes the preprocessed files preproc.nii and the
confounds.tsv files.

.. GENERATED FROM PYTHON SOURCE LINES 33-37

.. code-block:: Python

    from nilearn.datasets import fetch_language_localizer_demo_dataset

    data = fetch_language_localizer_demo_dataset(legacy_output=False)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    [fetch_language_localizer_demo_dataset] Dataset created in 
    /home/runner/nilearn_data/fMRI-language-localizer-demo-dataset
    [fetch_language_localizer_demo_dataset] Downloading data from 
    https://osf.io/3dj2a/download ...
    [fetch_language_localizer_demo_dataset] Downloaded 126386176 of 749503182 bytes 
    (16.9%%,    4.9s remaining)
    [fetch_language_localizer_demo_dataset] Downloaded 226713600 of 749503182 bytes 
    (30.2%%,    4.6s remaining)
    [fetch_language_localizer_demo_dataset] Downloaded 295731200 of 749503182 bytes 
    (39.5%%,    4.6s remaining)
    [fetch_language_localizer_demo_dataset] Downloaded 376307712 of 749503182 bytes 
    (50.2%%,    4.0s remaining)
    [fetch_language_localizer_demo_dataset] Downloaded 476495872 of 749503182 bytes 
    (63.6%%,    2.9s remaining)
    [fetch_language_localizer_demo_dataset] Downloaded 630128640 of 749503182 bytes 
    (84.1%%,    1.1s remaining)
    [fetch_language_localizer_demo_dataset]  ...done. (8 seconds, 0 min)

    [fetch_language_localizer_demo_dataset] Extracting data from 
    /home/runner/nilearn_data/fMRI-language-localizer-demo-dataset/fMRI-language-loc
    alizer-demo-dataset.zip...
    [fetch_language_localizer_demo_dataset] .. done.





.. GENERATED FROM PYTHON SOURCE LINES 38-39

Here is the location of the dataset on disk.

.. GENERATED FROM PYTHON SOURCE LINES 39-41

.. code-block:: Python

    print(data.data_dir)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/runner/nilearn_data/fMRI-language-localizer-demo-dataset




.. GENERATED FROM PYTHON SOURCE LINES 42-53

Obtain automatically FirstLevelModel objects and fit arguments
--------------------------------------------------------------
From the dataset directory we automatically obtain
the FirstLevelModel objects
with their subject_id filled from the :term:`BIDS` dataset.
Moreover, we obtain for each model a dictionary with run_imgs,
events and confounder regressors
since in this case a confounds.tsv file is available
in the :term:`BIDS` dataset.
To get the first level models we only have to specify the dataset directory
and the task_label as specified in the file names.

.. GENERATED FROM PYTHON SOURCE LINES 53-70

.. code-block:: Python

    from nilearn.glm.first_level import first_level_from_bids

    task_label = "languagelocalizer"
    (
        models,
        models_run_imgs,
        models_events,
        models_confounds,
    ) = first_level_from_bids(
        data.data_dir,
        task_label,
        img_filters=[("desc", "preproc")],
        n_jobs=2,
        space_label="",
        smoothing_fwhm=8,
    )





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/runner/work/nilearn/nilearn/examples/07_advanced/plot_bids_analysis.py:61: UserWarning: 'StartTime' not found in file /home/runner/nilearn_data/fMRI-language-localizer-demo-dataset/derivatives/sub-01/func/sub-01_task-languagelocalizer_desc-preproc_bold.json.
      ) = first_level_from_bids(
    /home/runner/work/nilearn/nilearn/examples/07_advanced/plot_bids_analysis.py:61: UserWarning: 'slice_time_ref' not provided and cannot be inferred from metadata.
    It will be assumed that the slice timing reference is 0.0 percent of the repetition time.
    If it is not the case it will need to be set manually in the generated list of models.
      ) = first_level_from_bids(




.. GENERATED FROM PYTHON SOURCE LINES 71-75

Quick sanity check on fit arguments
-----------------------------------
Additional checks or information extraction from pre-processed data can
be made here.

.. GENERATED FROM PYTHON SOURCE LINES 77-78

We just expect one run_img per subject.

.. GENERATED FROM PYTHON SOURCE LINES 78-82

.. code-block:: Python

    from pathlib import Path

    print([Path(run).name for run in models_run_imgs[0]])





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    ['sub-01_task-languagelocalizer_desc-preproc_bold.nii.gz']




.. GENERATED FROM PYTHON SOURCE LINES 83-86

The only confounds stored are regressors obtained from motion correction. As
we can verify from the column headers of the confounds table corresponding
to the only run_img present.

.. GENERATED FROM PYTHON SOURCE LINES 86-88

.. code-block:: Python

    print(models_confounds[0][0].columns)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Index(['RotX', 'RotY', 'RotZ', 'X', 'Y', 'Z'], dtype='object')




.. GENERATED FROM PYTHON SOURCE LINES 89-92

During this acquisition the subject read blocks of sentences and
consonant strings. So these are our only two conditions in events.
We verify there are 12 blocks for each condition.

.. GENERATED FROM PYTHON SOURCE LINES 92-94

.. code-block:: Python

    print(models_events[0][0]["trial_type"].value_counts())





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    trial_type
    language    12
    string      12
    Name: count, dtype: int64




.. GENERATED FROM PYTHON SOURCE LINES 95-102

First level model estimation
----------------------------
Now we simply fit each first level model and plot for each subject the
:term:`contrast` that reveals the language network (language - string).
Notice that we can define a :term:`contrast`
using the names of the conditions specified in the events dataframe.
Sum, subtraction and scalar multiplication are allowed.

.. GENERATED FROM PYTHON SOURCE LINES 104-105

Set the threshold as the z-variate with an uncorrected p-value of 0.001.

.. GENERATED FROM PYTHON SOURCE LINES 105-109

.. code-block:: Python

    from scipy.stats import norm

    p001_unc = norm.isf(0.001)








.. GENERATED FROM PYTHON SOURCE LINES 110-111

Prepare figure for concurrent plot of individual maps.

.. GENERATED FROM PYTHON SOURCE LINES 111-141

.. code-block:: Python

    from math import ceil

    import matplotlib.pyplot as plt
    import numpy as np

    ncols = 2
    nrows = ceil(len(models) / ncols)

    fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(10, 12))
    axes = np.atleast_2d(axes)
    model_and_args = zip(models, models_run_imgs, models_events, models_confounds)
    for midx, (model, imgs, events, confounds) in enumerate(model_and_args):
        # fit the GLM
        model.fit(imgs, events, confounds)
        # compute the contrast of interest
        zmap = model.compute_contrast("language-string")
        plotting.plot_glass_brain(
            zmap,
            threshold=p001_unc,
            title=f"sub-{model.subject_label}",
            axes=axes[int(midx / ncols), int(midx % ncols)],
            plot_abs=False,
            colorbar=True,
            display_mode="x",
            vmin=-12,
            vmax=12,
        )
    fig.suptitle("subjects z_map language network (unc p<0.001)")
    plotting.show()




.. image-sg:: /auto_examples/07_advanced/images/sphx_glr_plot_bids_analysis_001.png
   :alt: subjects z_map language network (unc p<0.001)
   :srcset: /auto_examples/07_advanced/images/sphx_glr_plot_bids_analysis_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 142-148

Second level model estimation
-----------------------------
We just have to provide the list of fitted FirstLevelModel objects
to the SecondLevelModel object for estimation. We can do this because
all subjects share a similar design matrix (same variables reflected in
column names).

.. GENERATED FROM PYTHON SOURCE LINES 148-152

.. code-block:: Python

    from nilearn.glm.second_level import SecondLevelModel

    second_level_input = models








.. GENERATED FROM PYTHON SOURCE LINES 153-154

Note that we apply a smoothing of 8mm.

.. GENERATED FROM PYTHON SOURCE LINES 154-157

.. code-block:: Python

    second_level_model = SecondLevelModel(smoothing_fwhm=8.0, n_jobs=2)
    second_level_model = second_level_model.fit(second_level_input)








.. GENERATED FROM PYTHON SOURCE LINES 158-162

Computing contrasts at the second level is as simple as at the first level.
Since we are not providing confounders we are performing a one-sample test
at the second level with the images determined by the specified first level
contrast.

.. GENERATED FROM PYTHON SOURCE LINES 162-166

.. code-block:: Python

    zmap = second_level_model.compute_contrast(
        first_level_contrast="language-string"
    )








.. GENERATED FROM PYTHON SOURCE LINES 167-169

The group level :term:`contrast` reveals a left lateralized fronto-temporal
language network.

.. GENERATED FROM PYTHON SOURCE LINES 169-179

.. code-block:: Python

    plotting.plot_glass_brain(
        zmap,
        threshold=p001_unc,
        title="Group language network (unc p<0.001)",
        plot_abs=False,
        display_mode="x",
        figure=plt.figure(figsize=(5, 4)),
    )
    plotting.show()




.. image-sg:: /auto_examples/07_advanced/images/sphx_glr_plot_bids_analysis_002.png
   :alt: plot bids analysis
   :srcset: /auto_examples/07_advanced/images/sphx_glr_plot_bids_analysis_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 180-181

Generate and save the GLM report at the group level.

.. GENERATED FROM PYTHON SOURCE LINES 181-188

.. code-block:: Python

    report_slm = second_level_model.generate_report(
        contrasts="intercept",
        first_level_contrast="language-string",
        threshold=p001_unc,
        display_mode="x",
    )





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/runner/work/nilearn/nilearn/examples/07_advanced/plot_bids_analysis.py:181: UserWarning: 'threshold=3.090232306167813' will not be used with 'height_control='fpr''. 'threshold' is only used when 'height_control=None'. Set 'threshold' to '3.09' to avoid this warning.
      report_slm = second_level_model.generate_report(
    /home/runner/work/nilearn/nilearn/examples/07_advanced/plot_bids_analysis.py:181: UserWarning: 'threshold=3.090232306167813' will not be used with 'height_control='fpr''. 'threshold' is only used when 'height_control=None'. Set 'threshold' to '3.0' to avoid this warning.
      report_slm = second_level_model.generate_report(




.. GENERATED FROM PYTHON SOURCE LINES 189-190

View the GLM report at the group level.

.. GENERATED FROM PYTHON SOURCE LINES 190-192

.. code-block:: Python

    report_slm






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        Statistical Report - Second Level Model
        <span class="tooltiptext m-1 p-1 fw-200 fs-3 border border-1 rounded-2"
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    <div class="report">
      <section>
        <h2>Description</h2>
        <p>
      Data were analyzed using <a href="https://nilearn.github.io">Nilearn</a>
      (version= 0.12.1; RRID:SCR_001362).
    </p>
      <p>
        At the group level, a mass univariate analysis was performed with a linear
        regression at each voxel of the brain.
      </p>
      <p>
        Input images were smoothed with gaussian kernel
        <!--  -->
        (full-width at half maximum=8.0 mm).
      </p>
      <p>
        The following contrasts were computed
    :
        <ul>
          <li>intercept</li>
        </ul>
      </p>

      </section>

      <section id="model-details-a80f20cd49584437ae852742358d95d5">
        <h2>Model details</h2>
        <div class="table-container"><table class="pure-table">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Value</th>
        </tr>
        <tr>
          <th>Parameter</th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>smoothing_fwhm (mm)</th>
          <td>8.0</td>
        </tr>
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      </section>


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          <section id="design-matrix-a80f20cd49584437ae852742358d95d5-0">
            <h3>Design Matrix</h3>
            <div class="scroll-y">
              <img class="pure-img" src="data:image/png;base64,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"
                title="Plot of design matrix for 0." alt="Plot of design matrix for 0."
                style="min-width: 600px" />
            </div>

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          </section>

          <section id="contrasts-a80f20cd49584437ae852742358d95d5-0">
            <h3>Contrasts</h3>

            <div class="d-flex flex-column">
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              <div class="scroll-y">
                <img class="pure-img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAG8AAAC8CAYAAAB2b/DhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAXQ0lEQVR4nO2de1QTVx7HvwmQBIUEeROJoFhwlSpqlUX3AAqeoK7KPlSsK5ajbtm22td2tdv6aLscdrHb0m7xsR4Fjy/WLj7W0qorPmoBUVmzpSAgQjdiCFZ5BGx5Jb/9g81swksSSGDCfM6ZQ3Ln3rm/4Zt753fv3PkNj4gIHKyEP9QGcJgPJx6L4cRjMZx4LIYTj8Vw4rEYTjwWw4nHYjjxWAwnHovhxOuBL7/8EkuWLIFUKgWPx8OpU6eeWOby5cuYMWMGhEIhJk6ciIyMDIvbyYnXA48fP8a0adOQlpbWr/xVVVVYvHgx5s2bB4VCgVdeeQXr16/HuXPnLGsocfQJADp58mSfeX73u9/RlClTjNJWrlxJcrncgpYRcS1vgGi1WuTn5yMqKsooXS6XIz8/36J1jwjxWlpa0NjYCI1GY7S1traadTz63120yspK2NnZQa1W48aNG6iqqmLyeHl5QaPR4IcffhiUc+gJmxevpaUF48ePh6+vLyQSidGWnJxs1jF5PB6KiooQFxeHjIwMqFQqlJeXY+zYsYNsfd/YW7W2IaCtrQ1qtRoAcO/ePYjFYmafUCgc0LGnTp2Kt956Cy0tLVixYgUEAgG0Wi3s7OxQW1sLsVgMR0fHAdXRFzbf8gwRi8VG20DEe/rpp7Fs2TI0NTXBxcUFp0+fhlKphJ2dHQDg/PnzCAsLGyzTe2REiddfmpuboVAooFAoAHQOBRQKBZRKJQBg8+bNiI+Ph5+fH86ePYvExEQ0NDRgwYIFyMrKwq5du/Dpp58iPj6eKWMRLOrLDgMaGxsJAAGgxsbGfpW5dOkSU8ZwW7NmDRERxcfHU0REBGk0GqbMm2++SU5OTsTn88nLy4vS0tJIIpFQRkaGRc6LiIhHZNsLkDQaDSQSCQCgsbHR6JpnCjqdDnx+Z0e1Zs0a3L17F99//z3mz5+PrVu3YsyYMTh37hz279+Pa9euAQBmzZqFrKyswTmRHrB5h2UwICJGuNjYWPznP//Bz372M4hEIqSkpODKlSs4duwY5HI5xo0bB6VSiYaGBqxcuRIAGCfGEobZNOZ0m71x+/ZtSkhIoMrKSiatvr6eZsyYQZMnT6bHjx93K9PR0TGgOvuCc1j6yZdffomf/vSnuHjxIjo6OgAAra2tcHFxwZkzZ9DU1NRjF2mRFvc/OPH6SVNTE6ZMmQKVSsVMOAuFQuh0Ojg7O8PNzQ2PHj2yqk3cNa8XDB0UAFi8eDGkUikkEgmSkpJQV1eHbdu2Aeh0iu7duwd3d3er2sh5mz1g6GBUVFSguLgY4eHhEIvFePjwIT766CPs27cP/v7+8PT0hKOjI+zs7PC3v/3NoufSDYtdTYcJpjosWq2W+bxy5Up65plnyMPDg4KDg+nll1+m5uZmevDgAaWmptKkSZPoqaeeos8//5wp09bWZpHz6AlOvF747W9/S4GBgfTNN98QEZG/vz8tXbqUmpubiYiopaWFDh48SNHR0TR37lz617/+ZRH7+4ITrws6nY7UajX9+Mc/plOnThERUXJyMkmlUiovLycioqKiImpubiadTkdnzpyhn//85ySTyeizzz6z6Ll0hROvB1paWig0NJSqq6spOzubxGIxXbx4kYiIHj58SM8//zxlZWUx+a9evUoJCQlMK7UWnHj0/4G0Tqdj0qKioig8PJzEYjFlZmYy6UVFRRQUFETZ2dlGxzCc57QWI36cp/csdTodUlJSmDsJSUlJePz4Mdzd3SGXy9He3o7S0lIsX74c4eHhWLRoEYDOIQUAODs7W932ET3OIyJmSBAdHQ17e3v4+/sjJCQEISEhePXVV5GWlgZfX1/4+fnB3t4eQUFB+Otf/wqg+1jQ2nDjPABbtmzByZMnkZeXBzc3NyZdp9NBqVQiNzcXdXV1CAkJwdy5c8Hn8y032WwCI7rlAZ1rNL/55hts2rQJbm5uTGui/91JGDduHPz9/Y3K6HS6IRcOGKFzm1qtFkDnHXNHR0d89913KC0tBQCmG+TxeFCpVPjwww+7zVkOZVdpyPCwworor3O3b9/GCy+8gDt37mDatGm4efMmKisrYXgVqampwaFDh1BZWTmEFveB1f1bK2M4VKirqyOizimwOXPm0AsvvEBERNXV1SSTyWjWrFl04cIFKioqory8PJowYQK9+OKLQ2l+n4wo8fTjvAsXLtBLL71EJSUlTD61Wk2RkZEkk8nI2dmZpk2bRnFxccx+wznP4cKI8zYvX76M2NhYAMBnn32GRYsWgYjA4/EAALm5uWhvb4erqyumTp0KwILLGAbKEP94LE5PLe/o0aMkFotpyZIlpFariaj3ljUcW5wem3dY9DMgerRaLVatWoWTJ0/i2rVrWL58OSorK3v1IIeLZ9kTw9eyQaCjo8Pon5+QkICtW7eioKAA8+fPR35+PpqamiCXy5GXlzeElpqHzYqn0+lgb2+PxsZGJs3NzQ0KhQLz58/H3bt3ERAQgCtXrmDy5Mn4yU9+gpKSkiG02AyGut+2NMuWLTO65gUEBDBDhPb2dibfkSNHhspEs7Fpb1Oj0SA2NhaXLl0CAERGRsLe3h7Z2dkQCAQ4dOgQPD09IZfLmTLD1rPsAZvtNoHOp4JGjRrFfL9//z4OHz4MgUCApqYmXL16FcXFxUZODVuEA2xAPH3HodPpGBGOHz+OTz75BADwox/9iMl74MABeHl5AQBOnTqFM2fOICYmZlh7lH0yxN32oLFw4ULauXMnXb58mfh8PrP+xHCc98c//pH27t1LH3zwATk5OdHBgweJqOex3CeffEJ+fn4kFApp9uzZVFBQ0Gvd6enp3Z4oEgqFljlRA2xGvD179pBAICAnJyd6++23iajTITEULygoiMaOHUu/+MUvaO/evb0eKzMzkwQCAR04cICKi4tpw4YN5OLiQrW1tT3mT09PJ7FYTDU1NcymH/xbEpsRj4jI0dGReDwexcTEkFKpJCKihoYGRryKigp6+PCh0QMhPbW62bNnG01Ia7VakkqllJyc3GO96enpJJFIBvdk+gFLO/ueyczMxNdff42qqirI5XIUFhYyc5YAUF5eDgcHB3R0dDDRILpe79ra2lBYWIjo6Ggmjc/nIzo6us/QHM3NzfDz84NMJsOyZctQXFw8+CfYBdaKRwYjnMbGRnR0dGDp0qUIDg7GV199hbFjx0Iul+Pzzz9n8i1atOiJ0SAePnwIrVbLODZ6vLy8mMAEXQkKCsKBAwdw+vRpHD58GDqdDnPmzEF1dfUgnW0vWL2tDxL67u7YsWO0dOlSmjJlCn388cdUXFzM5ElMTDRyIs6ePUuNjY3U0NBAjY2N1NLS0u249+/fJwCUl5dnlP7GG2/Q7Nmz+2VbW1sbBQQEMNdeS8G6lked12nw+XzcuHED8fHxCAwMxMyZM5GSkoKkpCRmUL57925kZmYyZcPCwjB69GhIJJJeo0G4u7szoTgMqa2thbe3d79sdHBwwPTp01FRUTGAM30yrBHPcCDN4/HQ2tqK8+fPY8eOHdi5cycOHjyI/fv3o7S0FH/605/w97//HW1tbVi4cKHRcZ40CBcIBJg5cyZycnKM6s7Jyel3aA6tVouioiL4+PiYcIZmYNF2PYi8+uqrRg9zxMXFUWhoKL333ntG+UpKSigqKormzp1Lf/7zn0mtVpu83D0zM5OEQiFlZGRQSUkJ/frXvyYXFxfG/V+zZg1t2bKFyf/OO+/QuXPn6O7du1RYWEhxcXEkEomMunBLwIqlf/X19dBoNJg+fTqT5uHhgePHj0MikaC8vByBgYEAOmdUsrKyEBcXh4qKCrMiEK1cuRLfffcdtm3bBrVajZCQEJw9e5ZxYpRKpZGXWl9fjw0bNkCtVmPMmDGYOXMm8vLyMHny5AGe+ROw6E9jENE/R/CXv/yFPvjgAyIiOnToEHl5edH69eupsLCwW5mOjo5BDSgw3GBFywM6r3NEhG+//Rbnz5+HRqPB73//e0ilUqxfvx5qtRqvvfYaIiIimFZhZ2dnNKSwOYb612MOhw8fpqlTp1JiYiLV1tZSRUUFhYaGUkhICGVmZhrNmthyy2ONt2nI6tWrsXv3bhQUFODFF19ES0sLcnJy4OLiAh6Px967BCbC6puxSqUSa9asgUgkwksvvYQlS5Z0yzNY4auGI6z+iY4bNw7Z2dnQ6XTIzc0danOsDqtbnh6tVgs+n884NYaT0bbc8ljjbfaFftakq3C2Dqu7za6MJOEAGxNvpMGJx2I48VgMJx6LGfbitba2YseOHWa/fcSWGfbjPP04zdwxmi2P84Z9y+PoHU48FmO1GRadTgeVSgVnZ2eTBtMajcborykQEVQqlcnl2ILVxFOpVJDJZGaXH0hZW8Vq4umj4nV9k5Yl0Wg0Ni261cTTd5X6N2hxDBzOYWExnHgshhOPxXDisRhOPBbDicdiOPFYDCdeL6SlpcHf3x8ikQihoaG4fv16n/k//fRTTJo0CSKRCE8//bTRE7kWw1pLs/XLzq255NxwqbspdZsaDSI3N5fs7OwoJSWFSkpK6O233yYHBwcqKioazNPpBideD5gaDWLFihW0ePFio7TQ0FB6/vnnzTe+H3DdZhfMiQaRn59vlB8A5HJ5n9EjBgObWHTbX7reVhIKhd2eS+8rGoQ+fH9X1Gq1SdEjBosR1fJkMtkTQ3mwiRHV8rrejhqsaBDe3t4Dih5hLiOq5elvR+m3nsQzJxpEWFiYUX4A+Oc//9nv6BFmY1F3yAA2eZumRoPIzc0le3t7ev/99+n27du0fft2bqgwWHWaKh5RZ+CCcePGkUAgoNmzZ9O1a9eYfREREbR27Vqj/MePH6fAwEASCAQ0ZcqUbi9HtARWW7c50PWXA6lTD7duk2PYwInHYjjxWAwnHovhxGMxnHgshhOPxVhtblM/nDTngRFzsWZdQ4HVxGtqagLAPTAymFhNPKlUinv37pn1iJdMJjPrARX63yNeFg9aOkRYTTw+nw9fX1+zy5v7gIotB9bhHBYWw4nHYoa9eEKhENu3b+/xxulIx6RbQpGRkQgJCUFqaqoFTRpcbDmUh0kOy4kTJ+Dg4NCvvN9++y3Gjx+PW7duISQkxBzbrM5zzz2HhoYGnDp1aqhN6Rcmiefq6mopO/qkvb293z+aEYUpt90jIiLo5ZdfJiIiPz8/SkpKooSEBHJyciKZTGb0QkF0eZNjREQEs2/fvn00adIkEgqFFBQURGlpacy+qqoqAkCZmZkUHh5OQqGQ0tPTiYho//79NHnyZBIIBOTt7W20qrm+vp7WrVtH7u7u5OzsTPPmzSOFQmG0FCI4OJj27NlDvr6+5OjoSMuXL6eGhgYiItq+fXs3my9dumTKv8fqDEg8V1dXSktLozt37lBycjLx+XwqLS0lIqLr168TALpw4QLV1NTQo0ePiKgzrL6Pjw9lZWVRZWUlZWVlkaurK2VkZBDR/8Xz9/dn8qhUKtq1axeJRCJKTU2lsrIyun79On344YeMbdHR0bRkyRK6ceMGlZeX0+uvv05ubm7M8QDQqFGjaP78+XTr1i26cuUKTZw4kZ599lkiImpqaqIVK1ZQTEwM8/bJ1tbWgf5/LcqAxPvVr37F7NPpdOTp6Um7d+8mov+LcOvWLaNjBAQE0NGjR43S3nvvPQoLCzMql5qaapRHKpXSW2+91aNdV69eJbFY3O2VagEBAZSamsqIx+fzqbq6mtn/xRdfEJ/Pp5qaGiIiWrt2LS1btqx//4xhwIBmWKZOncp85vF48Pb2xoMHD3rN//jxY9y9exfr1q3Dhg0bmPSOjg6jhUIA8MwzzzCfHzx4AJVKhaioqB6P++9//xvNzc1wc3MzSv/hhx9QVVXFfJfJZBg7dizzPSwsDDqdDmVlZRZfIGsJBiReVyeCx+MZvSqtK83NzQCAffv2ITQ01Ghf11ejjR49mvn8pJc5NTc3w8fHB5cvX+62z87ODh999FGf5dmKxeY2BQIBgM6w+Xq8vLwglUpRWVmJ1atX9/tYzs7O8Pf3R05ODubNm9dt/4wZM6BWq2Fvbw9/f3+jfYa3he7duweVSgWpVAoAuHbtGvh8PoKCghibDe0d7lhMPE9PTzg6OuLs2bPw9fWFSCSCRCLBO++8g02bNkEikSAmJgatra24efMm6uvr8dprr/V6vB07diAxMRGenp5YuHAhmpqakJubi40bNyI6OhphYWGIjY1FSkoKAgMDoVKpkJ2djQULFjDHEIlEWLt2Ld5//31oNBps2rQJK1asYLpMf39/nDt3DmVlZXBzc4NEIhneQxRTLpBdHRZDb4+IaNq0abR9+3bm+759+0gmkxGfzzcaKhw5coRCQkJIIBDQmDFjKDw8nE6cOEFEvTs6RJ3vQg8KCiIHBwfy8fGhjRs3Mvs0Gg1t3LiRpFIpOTg4kEwmo9WrV1NxcbHRUGHXrl0klUpJJBLRL3/5S6qrq2OO8eDBA1qwYAE5OTmxYqgw7CPdDhTD6bHg4GAUFRUNsUWDx7CfmOboHU68AVJXV4fVq1dDLBbDxcUF69atY7zq3oiMjASPxzPaEhMTTa57RHWblrirsHDhQtTU1GDv3r1ob29HQkICZs2ahaNHj/ZaJjIyEoGBgXj33XeZtFGjRplu29Beci2PJd9cWVJSQgDoxo0bTNoXX3xBPB6P7t+/32s5Q8dvIHDd5gDIz8+Hi4uL0WxQdHQ0+Hw+CgoK+ix75MgRuLu7Izg4GG+++Sa+//57k+sfUc+k9ycahCmo1Wp4enoapdnb28PV1bXPSBDPPvss/Pz8IJVK8fXXX2Pz5s0oKyvDiRMnTDNgwG13mNP16VjDzXBMasjmzZt7LaPfbt++TUlJSRQYGNitvIeHB+3atavfNubk5BAAqqioMOncRlTL6080CAB4/fXX8dxzz/V5rAkTJvQ4Ed/R0YG6ujqTJrr187wVFRUICAjod7kRJV5/1356eHjAw8PjifnCwsLQ0NCAwsJCzJw5EwBw8eJF6HS6bhPvfaFQKAAAPj4+/S4DYGR1m5YIZhATE0PTp0+ngoIC+uqrr+ipp56iVatWMfurq6spKCiICgoKiIiooqKC3n33Xbp58yZVVVXR6dOnacKECRQeHm5y3Zx4A+TRo0e0atUqcnJyIrFYTAkJCdTU1MTs18/V6udJlUolhYeHk6urKwmFQpo4cSK98cYbZtnGDdJZDDfOYzGceCyGE4/FcOKxGE48FsOJx2I48VgMJx6L4cRjMZx4LIYTj8Vw4rEYTjwWw4nHYjjxWAwnHovhxGMxnHgshhOPxXDisRhOPBbDicdiOPFYDCcei+HEYzGceCyGE4/FcOKxGE68AZKUlIQ5c+Zg1KhRcHFx6VcZIsK2bdvg4+MDR0dHREdH486dOybXzYk3QNra2rB8+XL85je/6XeZlJQUfPzxx9izZw8KCgowevRoyOVytLS0mFb5oDykNoyx9PN5etLT00kikTwxn06nI29vb9q5cyeT1tDQQEKhkI4dO2ZSnVzLszJVVVVQq9WIjo5m0iQSCUJDQ5Gfn2/SsUbUM+mDHcrDHPQhPry8vIzSvby8+gz/0RM23/IEAgG8vb3h5OQEmUwGiUTCbMnJyT2W2bJlS7fYYF230tJSK59Jd2y+5YlEIlRVVaG1tbXbG70GGsrDHPQhPmpra42iP9TW1pr88hCbFw/oFFAkEvU7f39DeZjD+PHj4e3tjZycHEYsjUaDgoICkzxWYAR0m5ZGqVRCoVBAqVRCq9VCoVBAoVAYhW2cNGkSTp48CaAziPorr7yCP/zhD/jHP/6BoqIixMfHQyqVIjY21rTKTXWJOYxZu3Ztj+GtDEMcA2DeykLUOVzYunUreXl5kVAopKioKCorKzO5bpsP5WHLcN0mi+HEYzGceCyGE4/FcOKxGE48FsOJx2I48VgMJx6L4cRjMZx4LIYTj8X8F36nH6gkE02kAAAAAElFTkSuQmCC"
                  title="Plot of the contrast intercept (run 0)."
                  alt="Plot of the contrast intercept (run 0)." style="min-width: 600px" />
              </div>
              <!--  -->
            </div>

          </section>

        </div>


      </div>

      <section id="mask-a80f20cd49584437ae852742358d95d5">
        <h2>Mask</h2>
        <div class="scroll-y">
          <img
            class="pure-img"
            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            alt="Mask image"
            title="Mask image"
            style="min-width: 600px"
          />
        </div>
        <div class="pure-u-1 pure-u-md-3-3">
          <p>
            The mask includes 23640 voxels (23.1 %) of the image.
          </p>
        </div>
      </section>

      <section id="statistical-maps-a80f20cd49584437ae852742358d95d5">
        <h2>Statistical Maps</h2>
        <div class="pure-g">
          <!--  -->
          <!--  -->
          <!--  -->

          <div class="pure-u-1">
            <h3>intercept</h3>

            <div class="d-flex flex-column">
                <div class="scroll-y">
                  <img
                    class="pure-img"
                    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                    alt="Stat map plot for the contrast: intercept"
                    title="Stat map plot for the contrast: intercept"
                    style="min-width: 600px"
                  />
                </div>

              <details>
                <summary class="pure-button button-small contrast-button">
                  Cluster Table
                </summary>
                <!--  -->
                <table class="pure-table">
      <tbody>
        <tr>
          <th>Height control</th>
          <td>fpr</td>
        </tr>
        <tr>
          <th>α</th>
          <td>0.001</td>
        </tr>
        <tr>
          <th>Threshold (computed)</th>
          <td>3.09</td>
        </tr>
        <tr>
          <th>Cluster size threshold (voxels)</th>
          <td>0</td>
        </tr>
        <tr>
          <th>Minimum distance (mm)</th>
          <td>8.0</td>
        </tr>
      </tbody>
    </table>
                <!--  -->
                <!--  -->
                  <table class="pure-table">
      <thead>
        <tr style="text-align: right;">
          <th>Cluster ID</th>
          <th>X</th>
          <th>Y</th>
          <th>Z</th>
          <th>Peak Stat</th>
          <th>Cluster Size (mm3)</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <td>1</td>
          <td>-57.5</td>
          <td>-48.5</td>
          <td>13.5</td>
          <td>4.43</td>
          <td>6652</td>
        </tr>
        <tr>
          <td>1a</td>
          <td>-71.0</td>
          <td>-53.0</td>
          <td>18.0</td>
          <td>3.64</td>
          <td></td>
        </tr>
        <tr>
          <td>2</td>
          <td>-62.0</td>
          <td>-8.0</td>
          <td>49.5</td>
          <td>4.32</td>
          <td>1184</td>
        </tr>
        <tr>
          <td>2a</td>
          <td>-53.0</td>
          <td>-8.0</td>
          <td>45.0</td>
          <td>4.17</td>
          <td></td>
        </tr>
        <tr>
          <td>2b</td>
          <td>-48.5</td>
          <td>-3.5</td>
          <td>54.0</td>
          <td>3.16</td>
          <td></td>
        </tr>
        <tr>
          <td>3</td>
          <td>-48.5</td>
          <td>-30.5</td>
          <td>-22.5</td>
          <td>3.98</td>
          <td>637</td>
        </tr>
        <tr>
          <td>4</td>
          <td>46.0</td>
          <td>5.5</td>
          <td>-27.0</td>
          <td>3.91</td>
          <td>3371</td>
        </tr>
        <tr>
          <td>4a</td>
          <td>50.5</td>
          <td>23.5</td>
          <td>-27.0</td>
          <td>3.80</td>
          <td></td>
        </tr>
        <tr>
          <td>4b</td>
          <td>55.0</td>
          <td>14.5</td>
          <td>-18.0</td>
          <td>3.79</td>
          <td></td>
        </tr>
        <tr>
          <td>5</td>
          <td>-71.0</td>
          <td>-17.0</td>
          <td>-4.5</td>
          <td>3.89</td>
          <td>9841</td>
        </tr>
        <tr>
          <td>5a</td>
          <td>-53.0</td>
          <td>-8.0</td>
          <td>-9.0</td>
          <td>3.82</td>
          <td></td>
        </tr>
        <tr>
          <td>5b</td>
          <td>-66.5</td>
          <td>1.0</td>
          <td>-4.5</td>
          <td>3.82</td>
          <td></td>
        </tr>
        <tr>
          <td>5c</td>
          <td>-48.5</td>
          <td>19.0</td>
          <td>-18.0</td>
          <td>3.69</td>
          <td></td>
        </tr>
        <tr>
          <td>6</td>
          <td>-39.5</td>
          <td>-3.5</td>
          <td>-40.5</td>
          <td>3.60</td>
          <td>455</td>
        </tr>
        <tr>
          <td>7</td>
          <td>50.5</td>
          <td>-12.5</td>
          <td>-9.0</td>
          <td>3.51</td>
          <td>364</td>
        </tr>
        <tr>
          <td>8</td>
          <td>-48.5</td>
          <td>14.5</td>
          <td>18.0</td>
          <td>3.43</td>
          <td>364</td>
        </tr>
        <tr>
          <td>9</td>
          <td>-75.5</td>
          <td>-35.0</td>
          <td>4.5</td>
          <td>3.32</td>
          <td>91</td>
        </tr>
        <tr>
          <td>10</td>
          <td>32.5</td>
          <td>-17.0</td>
          <td>-27.0</td>
          <td>3.26</td>
          <td>91</td>
        </tr>
        <tr>
          <td>11</td>
          <td>-44.0</td>
          <td>-17.0</td>
          <td>-31.5</td>
          <td>3.13</td>
          <td>182</td>
        </tr>
      </tbody>
    </table>
              </details>
            </div>
          </div>
        </div>
      </section>

      <section id="about-a80f20cd49584437ae852742358d95d5" style="text-align: left">
        <h2>About</h2>
        <ul>
          <li>Date preprocessed: <time>2025-09-05T09:32:49</time></li>
        </ul>
      </section>
    </div>

        </div>
      </div>
    </section>

    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 193-195

Or in a separate browser window
report_slm.open_in_browser()

.. GENERATED FROM PYTHON SOURCE LINES 197-198

Save the report to disk

.. GENERATED FROM PYTHON SOURCE LINES 198-201

.. code-block:: Python

    output_dir = Path.cwd() / "results" / "plot_bids_analysis"
    output_dir.mkdir(exist_ok=True, parents=True)
    report_slm.save_as_html(output_dir / "report_slm.html")








.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (1 minutes 25.066 seconds)

**Estimated memory usage:**  1346 MB


.. _sphx_glr_download_auto_examples_07_advanced_plot_bids_analysis.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/nilearn/nilearn/0.12.1?urlpath=lab/tree/notebooks/auto_examples/07_advanced/plot_bids_analysis.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_bids_analysis.ipynb <plot_bids_analysis.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_bids_analysis.py <plot_bids_analysis.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_bids_analysis.zip <plot_bids_analysis.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_