.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/04_glm_first_level/plot_bids_features.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` 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_04_glm_first_level_plot_bids_features.py: First level analysis of a complete BIDS dataset from openneuro ============================================================== Full step-by-step example of fitting a :term:`GLM` to perform a first level analysis in an openneuro :term:`BIDS` dataset. We demonstrate how :term:`BIDS` derivatives can be exploited to perform a simple one subject analysis with minimal code. Details about the :term:`BIDS` standard are available at `https://bids.neuroimaging.io/ `_. We also demonstrate how to download individual groups of files from the Openneuro s3 bucket. More specifically: 1. Download an :term:`fMRI` :term:`BIDS` dataset with derivatives from openneuro. 2. Extract first level model objects automatically from the :term:`BIDS` dataset. 3. Demonstrate Quality assurance of Nilearn estimation against available FSL. estimation in the openneuro dataset. 4. Display contrast plot and uncorrected first level statistics table report. .. GENERATED FROM PYTHON SOURCE LINES 26-33 Fetch openneuro :term:`BIDS` dataset ------------------------------------ We download one subject from the stopsignal task in the ds000030 V4 :term:`BIDS` dataset available in openneuro. This dataset contains the necessary information to run a statistical analysis using Nilearn. The dataset also contains statistical results from a previous FSL analysis that we can employ for comparison with the Nilearn estimation. .. GENERATED FROM PYTHON SOURCE LINES 33-62 .. code-block:: Python from nilearn.datasets import ( fetch_ds000030_urls, fetch_openneuro_dataset, select_from_index, ) _, urls = fetch_ds000030_urls() exclusion_patterns = [ "*group*", "*phenotype*", "*mriqc*", "*parameter_plots*", "*physio_plots*", "*space-fsaverage*", "*space-T1w*", "*dwi*", "*beh*", "*task-bart*", "*task-rest*", "*task-scap*", "*task-task*", ] urls = select_from_index( urls, exclusion_filters=exclusion_patterns, n_subjects=1 ) data_dir, _ = fetch_openneuro_dataset(urls=urls) .. rst-class:: sphx-glr-script-out .. code-block:: none [fetch_ds000030_urls] Dataset found in /home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed [fetch_openneuro_dataset] Dataset found in /home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed .. GENERATED FROM PYTHON SOURCE LINES 63-75 Obtain FirstLevelModel objects automatically and fit arguments -------------------------------------------------------------- From the dataset directory we automatically obtain FirstLevelModel objects with their subject_id filled from the :term:`BIDS` dataset. Moreover we obtain, for each model, the list of run images and their respective events and confound regressors. Those are inferred from the confounds.tsv files available in the :term:`BIDS` dataset. To get the first level models we have to specify the dataset directory, the task_label and the space_label as specified in the file names. We also have to provide the folder with the desired derivatives, that in this case were produced by the :term:`fMRIPrep` :term:`BIDS` app. .. GENERATED FROM PYTHON SOURCE LINES 75-95 .. code-block:: Python from nilearn.glm.first_level import first_level_from_bids task_label = "stopsignal" space_label = "MNI152NLin2009cAsym" derivatives_folder = "derivatives/fmriprep" ( models, models_run_imgs, models_events, models_confounds, ) = first_level_from_bids( data_dir, task_label, space_label, smoothing_fwhm=5.0, derivatives_folder=derivatives_folder, n_jobs=2, verbose=1, ) .. rst-class:: sphx-glr-script-out .. code-block:: none /home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_bids_features.py:85: UserWarning: No bold.json found in BIDS folder in: /home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed/derivatives/fmriprep. ) = first_level_from_bids( /home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_bids_features.py:85: 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( [first_level_from_bids] Found the following 1 preprocessed BOLD files - for subject 10159 - for filter: [('task', 'stopsignal'), ('space', 'MNI152NLin2009cAsym')]: - /home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed/derivatives/fmri prep/sub-10159/func/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_desc-pre proc_bold.nii.gz [first_level_from_bids] Found the following 1 events files - for subject 10159 - for filter: [('task', 'stopsignal')]: - /home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed/sub-10159/func/s ub-10159_task-stopsignal_events.tsv [first_level_from_bids] Found the following 1 confounds files - for subject 10159 - for filter: [('task', 'stopsignal')]: - /home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed/derivatives/fmri prep/sub-10159/func/sub-10159_task-stopsignal_desc-confounds_regressors.tsv .. GENERATED FROM PYTHON SOURCE LINES 96-97 Access the model and model arguments of the subject and process events. .. GENERATED FROM PYTHON SOURCE LINES 97-123 .. code-block:: Python model, imgs, events, confounds = ( models[0], models_run_imgs[0], models_events[0], models_confounds[0], ) subject = f"sub-{model.subject_label}" model.minimize_memory = False # override default from pathlib import Path from nilearn.interfaces.fsl import get_design_from_fslmat fsl_design_matrix_path = ( Path(data_dir) / "derivatives" / "task" / subject / "stopsignal.feat" / "design.mat" ) design_matrix = get_design_from_fslmat( fsl_design_matrix_path, column_names=None ) .. GENERATED FROM PYTHON SOURCE LINES 124-127 We identify the columns of the Go and StopSuccess conditions of the design matrix inferred from the FSL file, to use them later for contrast definition. .. GENERATED FROM PYTHON SOURCE LINES 127-134 .. code-block:: Python design_columns = [ f"cond_{int(i):02}" for i in range(len(design_matrix.columns)) ] design_columns[0] = "Go" design_columns[4] = "StopSuccess" design_matrix.columns = design_columns .. GENERATED FROM PYTHON SOURCE LINES 135-138 First level model estimation (one subject) ------------------------------------------ We fit the first level model for one subject. .. GENERATED FROM PYTHON SOURCE LINES 138-140 .. code-block:: Python model.fit(imgs, design_matrices=[design_matrix]) .. rst-class:: sphx-glr-script-out .. code-block:: none /home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_bids_features.py:138: UserWarning: If design matrices are supplied, [t_r] will be ignored. model.fit(imgs, design_matrices=[design_matrix]) [FirstLevelModel.fit] Loading data from /home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed/derivatives/fmri prep/sub-10159/func/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_desc-pre proc_bold.nii.gz [FirstLevelModel.fit] Computing the mask [FirstLevelModel.fit] Resampling mask [FirstLevelModel.fit] Finished fit [FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big one). [FirstLevelModel.fit] Performing mask computation. [FirstLevelModel.fit] Loading data from Nifti1Image('/home/runner/nilearn_data/ds000030/ds000030_R1.0.4/uncompressed/der ivatives/fmriprep/sub-10159/func/sub-10159_task-stopsignal_space-MNI152NLin2009c Asym_desc-preproc_bold.nii.gz') [FirstLevelModel.fit] Smoothing images [FirstLevelModel.fit] Extracting region signals [FirstLevelModel.fit] Cleaning extracted signals [FirstLevelModel.fit] Masking took 2 seconds. [FirstLevelModel.fit] Performing GLM computation. [FirstLevelModel.fit] GLM took 2 seconds. [FirstLevelModel.fit] Computation of 1 runs done in 5 seconds. .. raw:: html 
First Level Model
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.. GENERATED FROM PYTHON SOURCE LINES 141-143 Then we compute the StopSuccess - Go contrast. We can use the column names of the design matrix. .. GENERATED FROM PYTHON SOURCE LINES 143-145 .. code-block:: Python z_map = model.compute_contrast("StopSuccess - Go") .. GENERATED FROM PYTHON SOURCE LINES 146-150 Visualize results ----------------- Let's have a look at the Nilearn estimation and the FSL estimation available in the dataset. .. GENERATED FROM PYTHON SOURCE LINES 150-181 .. code-block:: Python import matplotlib.pyplot as plt import nibabel as nib from scipy.stats import norm from nilearn.plotting import plot_glass_brain, show fsl_z_map = nib.load( Path(data_dir) / "derivatives" / "task" / subject / "stopsignal.feat" / "stats" / "zstat12.nii.gz" ) plot_glass_brain( z_map, threshold=norm.isf(0.001), title='Nilearn Z map of "StopSuccess - Go" (unc p<0.001)', plot_abs=False, display_mode="ortho", ) plot_glass_brain( fsl_z_map, threshold=norm.isf(0.001), title='FSL Z map of "StopSuccess - Go" (unc p<0.001)', plot_abs=False, display_mode="ortho", ) .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/04_glm_first_level/images/sphx_glr_plot_bids_features_001.png :alt: plot bids features :srcset: /auto_examples/04_glm_first_level/images/sphx_glr_plot_bids_features_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/04_glm_first_level/images/sphx_glr_plot_bids_features_002.png :alt: plot bids features :srcset: /auto_examples/04_glm_first_level/images/sphx_glr_plot_bids_features_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 182-183 We show the agreement between the 2 estimations. .. GENERATED FROM PYTHON SOURCE LINES 183-196 .. code-block:: Python from nilearn.plotting import plot_bland_altman, plot_img_comparison plot_img_comparison( z_map, fsl_z_map, model.masker_, ref_label="Nilearn", src_label="FSL" ) plot_bland_altman( z_map, fsl_z_map, model.masker_, ref_label="Nilearn", src_label="FSL" ) show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/04_glm_first_level/images/sphx_glr_plot_bids_features_003.png :alt: Pearson's R: 0.97, Histogram of imgs values :srcset: /auto_examples/04_glm_first_level/images/sphx_glr_plot_bids_features_003.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/04_glm_first_level/images/sphx_glr_plot_bids_features_004.png :alt: plot bids features :srcset: /auto_examples/04_glm_first_level/images/sphx_glr_plot_bids_features_004.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none [NiftiMasker.wrapped] Loading data from Nifti1Image( shape=(65, 77, 49, 1), affine=array([[ 3., 0., -0., -96.], [ 0., 3., -0., -132.], [ 0., 0., 4., -78.], [ 0., 0., 0., 1.]]) ) [NiftiMasker.wrapped] Smoothing images [NiftiMasker.wrapped] Extracting region signals [NiftiMasker.wrapped] Cleaning extracted signals [NiftiMasker.wrapped] Loading data from Nifti1Image( shape=(65, 77, 49, 1), affine=array([[ 3., 0., 0., -96.], [ 0., 3., 0., -132.], [ 0., 0., 4., -78.], [ 0., 0., 0., 1.]]) ) [NiftiMasker.wrapped] Smoothing images [NiftiMasker.wrapped] Extracting region signals [NiftiMasker.wrapped] Cleaning extracted signals [NiftiMasker.wrapped] Loading data from Nifti1Image( shape=(65, 77, 49, 1), affine=array([[ 3., 0., -0., -96.], [ 0., 3., -0., -132.], [ 0., 0., 4., -78.], [ 0., 0., 0., 1.]]) ) [NiftiMasker.wrapped] Smoothing images [NiftiMasker.wrapped] Extracting region signals [NiftiMasker.wrapped] Cleaning extracted signals [NiftiMasker.wrapped] Loading data from Nifti1Image( shape=(65, 77, 49, 1), affine=array([[ 3., 0., 0., -96.], [ 0., 3., 0., -132.], [ 0., 0., 4., -78.], [ 0., 0., 0., 1.]]) ) [NiftiMasker.wrapped] Smoothing images [NiftiMasker.wrapped] Extracting region signals [NiftiMasker.wrapped] Cleaning extracted signals .. GENERATED FROM PYTHON SOURCE LINES 197-203 Saving model outputs to disk ---------------------------- We can now easily save the main results, the model metadata and an HTML report to the disk. .. GENERATED FROM PYTHON SOURCE LINES 203-219 .. code-block:: Python from nilearn.interfaces.bids import save_glm_to_bids output_dir = Path.cwd() / "results" / "plot_bids_features" output_dir.mkdir(exist_ok=True, parents=True) stat_threshold = norm.isf(0.001) save_glm_to_bids( model, contrasts="StopSuccess - Go", contrast_types={"StopSuccess - Go": "t"}, out_dir=output_dir / "derivatives" / "nilearn_glm", threshold=stat_threshold, cluster_threshold=10, ) .. rst-class:: sphx-glr-script-out .. code-block:: none /home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_bids_features.py:210: UserWarning: Contrast name "StopSuccess - Go" changed to "stopsuccessMinusGo" save_glm_to_bids( [save_glm_to_bids] Saving mask... [save_glm_to_bids] Generating design matrices figures... [save_glm_to_bids] Generating contrast matrices figures... [save_glm_to_bids] Saving contrast-level statistical maps... [save_glm_to_bids] Saving model level statistical maps... [save_glm_to_bids] Generating HTML... .. raw:: html 
First Level Model
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.. GENERATED FROM PYTHON SOURCE LINES 220-221 View the generated files .. GENERATED FROM PYTHON SOURCE LINES 221-224 .. code-block:: Python files = sorted((output_dir / "derivatives" / "nilearn_glm").glob("**/*")) print("\n".join([str(x.relative_to(output_dir)) for x in files])) .. rst-class:: sphx-glr-script-out .. code-block:: none derivatives/nilearn_glm/dataset_description.json derivatives/nilearn_glm/sub-10159 derivatives/nilearn_glm/sub-10159/report.html derivatives/nilearn_glm/sub-10159/statmap.json derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_clusters.json derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_clusters.tsv derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_design.png derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_stat-effect_statmap.nii.gz derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_stat-p_statmap.nii.gz derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_stat-t_statmap.nii.gz derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_stat-variance_statmap.nii.gz derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_contrast-stopsuccessMinusGo_stat-z_statmap.nii.gz derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_corrdesign.png derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_design.json derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_design.png derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_design.tsv derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_mask.nii.gz derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_stat-errorts_statmap.nii.gz derivatives/nilearn_glm/sub-10159/sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_stat-rsquared_statmap.nii.gz .. GENERATED FROM PYTHON SOURCE LINES 225-231 Simple statistical report of thresholded contrast ------------------------------------------------- We display the :term:`contrast` plot and table with cluster information. Here we will the image directly from the results saved to disk. .. GENERATED FROM PYTHON SOURCE LINES 231-255 .. code-block:: Python from nilearn.plotting import plot_contrast_matrix plot_contrast_matrix("StopSuccess - Go", design_matrix) z_map = ( output_dir / "derivatives" / "nilearn_glm" / "sub-10159" / ( "sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_" "contrast-stopsuccessMinusGo_stat-z_statmap.nii.gz" ) ) plot_glass_brain( z_map, threshold=stat_threshold, plot_abs=False, display_mode="z", figure=plt.figure(figsize=(4, 4)), ) show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/04_glm_first_level/images/sphx_glr_plot_bids_features_005.png :alt: plot bids features :srcset: /auto_examples/04_glm_first_level/images/sphx_glr_plot_bids_features_005.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/04_glm_first_level/images/sphx_glr_plot_bids_features_006.png :alt: plot bids features :srcset: /auto_examples/04_glm_first_level/images/sphx_glr_plot_bids_features_006.png :class: sphx-glr-multi-img .. GENERATED FROM PYTHON SOURCE LINES 256-282 The saved results include a table of activated clusters. .. note:: This table can also be generated by using the function :func:`nilearn.reporting.get_clusters_table`. .. code-block:: python from nilearn.reporting import get_clusters_table table = get_clusters_table( z_map, stat_threshold=norm.isf(0.001), cluster_threshold=10 ) .. seealso:: The restults saved to disk and the output of get_clusters_table do not contain the anatomical location of the clusters. To get the names of the location of the clusters according to one or several atlases, we recommend using the `atlasreader package `_. .. GENERATED FROM PYTHON SOURCE LINES 282-299 .. code-block:: Python import pandas as pd table_file = ( output_dir / "derivatives" / "nilearn_glm" / "sub-10159" / ( "sub-10159_task-stopsignal_space-MNI152NLin2009cAsym_" "contrast-stopsuccessMinusGo_clusters.tsv" ) ) table = pd.read_csv(table_file, sep="\t") table .. raw:: html
Cluster ID X Y Z Peak Stat Cluster Size (mm3)
0 1 15.0 -93.0 -18.0 122.516815 27900.0
1 1a 6.0 -87.0 -18.0 70.342086 NaN
2 1b 30.0 -87.0 -22.0 69.308056 NaN
3 1c -3.0 -87.0 -18.0 61.796424 NaN
4 2 9.0 69.0 -6.0 92.734506 64908.0
... ... ... ... ... ... ...
78 34 -3.0 42.0 26.0 6.714833 468.0
79 34a -6.0 42.0 18.0 4.408391 NaN
80 35 -3.0 12.0 54.0 5.883419 396.0
81 35a 3.0 18.0 54.0 5.321565 NaN
82 36 -3.0 9.0 6.0 5.772909 468.0

83 rows × 6 columns



.. GENERATED FROM PYTHON SOURCE LINES 300-311 We can get a latex table from a Pandas Dataframe for display and publication purposes. .. note:: This requires to have jinja2 installed: .. code-block:: bash pip install jinja2 .. GENERATED FROM PYTHON SOURCE LINES 311-313 .. code-block:: Python print(table.to_latex()) .. rst-class:: sphx-glr-script-out .. code-block:: none \begin{tabular}{llrrrrr} \toprule & Cluster ID & X & Y & Z & Peak Stat & Cluster Size (mm3) \\ \midrule 0 & 1 & 15.000000 & -93.000000 & -18.000000 & 122.516815 & 27900.000000 \\ 1 & 1a & 6.000000 & -87.000000 & -18.000000 & 70.342086 & NaN \\ 2 & 1b & 30.000000 & -87.000000 & -22.000000 & 69.308056 & NaN \\ 3 & 1c & -3.000000 & -87.000000 & -18.000000 & 61.796424 & NaN \\ 4 & 2 & 9.000000 & 69.000000 & -6.000000 & 92.734506 & 64908.000000 \\ 5 & 2a & 12.000000 & 57.000000 & -22.000000 & 62.999189 & NaN \\ 6 & 2b & -15.000000 & 15.000000 & 74.000000 & 54.897183 & NaN \\ 7 & 2c & 15.000000 & 63.000000 & -10.000000 & 53.770578 & NaN \\ 8 & 3 & 0.000000 & -3.000000 & -18.000000 & 80.143288 & 22788.000000 \\ 9 & 3a & 27.000000 & 9.000000 & -26.000000 & 46.564125 & NaN \\ 10 & 3b & 0.000000 & -15.000000 & -42.000000 & 40.157605 & NaN \\ 11 & 3c & 3.000000 & -18.000000 & -50.000000 & 34.929461 & NaN \\ 12 & 4 & -54.000000 & 42.000000 & 14.000000 & 36.477846 & 4896.000000 \\ 13 & 4a & -45.000000 & 51.000000 & 22.000000 & 19.736986 & NaN \\ 14 & 4b & -33.000000 & 51.000000 & 38.000000 & 17.196909 & NaN \\ 15 & 4c & -27.000000 & 60.000000 & 34.000000 & 16.980278 & NaN \\ 16 & 5 & -3.000000 & 21.000000 & -10.000000 & 26.752451 & 2736.000000 \\ 17 & 5a & 0.000000 & 30.000000 & -6.000000 & 17.541399 & NaN \\ 18 & 5b & -12.000000 & 18.000000 & -14.000000 & 9.857585 & NaN \\ 19 & 5c & 3.000000 & 18.000000 & 2.000000 & 5.270521 & NaN \\ 20 & 6 & -63.000000 & 9.000000 & 2.000000 & 24.590403 & 6552.000000 \\ 21 & 6a & -57.000000 & 18.000000 & -6.000000 & 22.502379 & NaN \\ 22 & 6b & -63.000000 & 27.000000 & 10.000000 & 21.651576 & NaN \\ 23 & 6c & -63.000000 & -3.000000 & 6.000000 & 19.091516 & NaN \\ 24 & 7 & 39.000000 & 6.000000 & 66.000000 & 24.417132 & 3096.000000 \\ 25 & 7a & 51.000000 & 21.000000 & 50.000000 & 14.872285 & NaN \\ 26 & 7b & 45.000000 & 6.000000 & 62.000000 & 14.719380 & NaN \\ 27 & 7c & 51.000000 & 9.000000 & 54.000000 & 11.575969 & NaN \\ 28 & 8 & 12.000000 & 36.000000 & -26.000000 & 20.716392 & 396.000000 \\ 29 & 8a & 18.000000 & 30.000000 & -26.000000 & 14.789520 & NaN \\ 30 & 9 & -45.000000 & 6.000000 & 62.000000 & 20.216765 & 576.000000 \\ 31 & 9a & -51.000000 & 9.000000 & 54.000000 & 17.445403 & NaN \\ 32 & 10 & -39.000000 & -60.000000 & 62.000000 & 18.839163 & 1224.000000 \\ 33 & 10a & -48.000000 & -48.000000 & 62.000000 & 17.127426 & NaN \\ 34 & 10b & -30.000000 & -63.000000 & 66.000000 & 7.893538 & NaN \\ 35 & 10c & -57.000000 & -33.000000 & 58.000000 & 7.533043 & NaN \\ 36 & 11 & -3.000000 & -87.000000 & 46.000000 & 18.639460 & 3132.000000 \\ 37 & 11a & -39.000000 & -75.000000 & 50.000000 & 15.066584 & NaN \\ 38 & 11b & -18.000000 & -81.000000 & 54.000000 & 14.665675 & NaN \\ 39 & 11c & -27.000000 & -81.000000 & 50.000000 & 10.536638 & NaN \\ 40 & 12 & 12.000000 & -105.000000 & -2.000000 & 18.465759 & 864.000000 \\ 41 & 12a & 12.000000 & -93.000000 & -2.000000 & 6.515125 & NaN \\ 42 & 12b & 21.000000 & -105.000000 & -2.000000 & 5.758570 & NaN \\ 43 & 13 & 21.000000 & -93.000000 & 34.000000 & 17.517443 & 792.000000 \\ 44 & 14 & 63.000000 & 12.000000 & 10.000000 & 16.758346 & 828.000000 \\ 45 & 14a & 63.000000 & 21.000000 & 14.000000 & 8.923981 & NaN \\ 46 & 14b & 60.000000 & 12.000000 & -2.000000 & 7.959472 & NaN \\ 47 & 15 & -18.000000 & -60.000000 & -58.000000 & 16.093286 & 504.000000 \\ 48 & 15a & -18.000000 & -72.000000 & -58.000000 & 6.096305 & NaN \\ 49 & 16 & -9.000000 & 33.000000 & -26.000000 & 15.756994 & 900.000000 \\ 50 & 16a & -9.000000 & 42.000000 & -22.000000 & 11.028668 & NaN \\ 51 & 17 & -15.000000 & -108.000000 & -2.000000 & 14.652072 & 936.000000 \\ 52 & 18 & -9.000000 & -48.000000 & 78.000000 & 13.593056 & 720.000000 \\ 53 & 18a & -6.000000 & -57.000000 & 74.000000 & 7.156604 & NaN \\ 54 & 19 & 18.000000 & 18.000000 & 70.000000 & 13.349184 & 468.000000 \\ 55 & 20 & -27.000000 & -93.000000 & 30.000000 & 13.055884 & 540.000000 \\ 56 & 21 & 57.000000 & -51.000000 & -14.000000 & 12.623032 & 612.000000 \\ 57 & 22 & 27.000000 & -78.000000 & 50.000000 & 12.179269 & 612.000000 \\ 58 & 22a & 36.000000 & -75.000000 & 50.000000 & 6.740345 & NaN \\ 59 & 23 & -30.000000 & -102.000000 & 6.000000 & 11.286718 & 360.000000 \\ 60 & 24 & 63.000000 & -12.000000 & 10.000000 & 11.197878 & 648.000000 \\ 61 & 24a & 54.000000 & -15.000000 & 10.000000 & 5.924582 & NaN \\ 62 & 25 & -69.000000 & -30.000000 & -10.000000 & 11.079066 & 468.000000 \\ 63 & 25a & -69.000000 & -30.000000 & 2.000000 & 6.227700 & NaN \\ 64 & 26 & 63.000000 & 0.000000 & 26.000000 & 9.853608 & 576.000000 \\ 65 & 26a & 66.000000 & -3.000000 & 34.000000 & 7.613071 & NaN \\ 66 & 27 & -66.000000 & 9.000000 & 18.000000 & 9.502274 & 1296.000000 \\ 67 & 27a & -66.000000 & 3.000000 & 26.000000 & 7.890005 & NaN \\ 68 & 27b & -60.000000 & -3.000000 & 26.000000 & 7.157832 & NaN \\ 69 & 27c & -66.000000 & -6.000000 & 14.000000 & 5.843833 & NaN \\ 70 & 28 & 30.000000 & -99.000000 & 10.000000 & 8.887969 & 360.000000 \\ 71 & 28a & 33.000000 & -99.000000 & 2.000000 & 7.337295 & NaN \\ 72 & 29 & -66.000000 & -18.000000 & 42.000000 & 8.635179 & 468.000000 \\ 73 & 30 & -48.000000 & 21.000000 & 50.000000 & 8.539273 & 396.000000 \\ 74 & 31 & -36.000000 & -36.000000 & -22.000000 & 8.397082 & 396.000000 \\ 75 & 31a & -27.000000 & -27.000000 & -26.000000 & 4.379355 & NaN \\ 76 & 32 & -45.000000 & 6.000000 & 42.000000 & 7.232974 & 396.000000 \\ 77 & 33 & -15.000000 & -57.000000 & -10.000000 & 7.195093 & 396.000000 \\ 78 & 34 & -3.000000 & 42.000000 & 26.000000 & 6.714833 & 468.000000 \\ 79 & 34a & -6.000000 & 42.000000 & 18.000000 & 4.408391 & NaN \\ 80 & 35 & -3.000000 & 12.000000 & 54.000000 & 5.883419 & 396.000000 \\ 81 & 35a & 3.000000 & 18.000000 & 54.000000 & 5.321565 & NaN \\ 82 & 36 & -3.000000 & 9.000000 & 6.000000 & 5.772909 & 468.000000 \\ \bottomrule \end{tabular} .. GENERATED FROM PYTHON SOURCE LINES 314-325 You can also print the output to markdown, if you have the `tabulate` dependencies installed. .. code-block:: bash pip install tabulate .. code-block:: python table.to_markdown() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 36.746 seconds) **Estimated memory usage:** 1006 MB .. _sphx_glr_download_auto_examples_04_glm_first_level_plot_bids_features.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/0.12.0?urlpath=lab/tree/notebooks/auto_examples/04_glm_first_level/plot_bids_features.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_bids_features.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_bids_features.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_bids_features.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_