.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_02_first_level_models_plot_adhd_dmn.py: Default Mode Network extraction of AHDH dataset =============================================== This example shows a full step-by-step workflow of fitting a GLM to data extracted from a seed on the Posterior Cingulate Cortex and saving the results. More specifically: 1. A sequence of fMRI volumes are loaded. 2. A design matrix with the Posterior Cingulate Cortex seed is defined. 3. A GLM is applied to the dataset (effect/covariance, then contrast estimation). 4. The Default Mode Network is displayed. .. code-block:: default import numpy as np from nilearn import datasets, plotting from nilearn.input_data import NiftiSpheresMasker from nistats.first_level_model import FirstLevelModel from nistats.design_matrix import make_first_level_design_matrix Prepare data and analysis parameters ------------------------------------- Prepare the data. .. code-block:: default adhd_dataset = datasets.fetch_adhd(n_subjects=1) # Prepare timing t_r = 2. slice_time_ref = 0. n_scans = 176 # Prepare seed pcc_coords = (0, -53, 26) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /home/kshitij/miniconda3/envs/nistats-py36-latest/lib/python3.6/site-packages/nilearn/datasets/func.py:516: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default. dtype=None) Estimate contrasts ------------------ Specify the contrasts. .. code-block:: default seed_masker = NiftiSpheresMasker([pcc_coords], radius=10, detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2., memory='nilearn_cache', memory_level=1, verbose=0) seed_time_series = seed_masker.fit_transform(adhd_dataset.func[0]) frametimes = np.linspace(0, (n_scans - 1) * t_r, n_scans) design_matrix = make_first_level_design_matrix(frametimes, hrf_model='spm', add_regs=seed_time_series, add_reg_names=["pcc_seed"]) dmn_contrast = np.array([1] + [0]*(design_matrix.shape[1]-1)) contrasts = {'seed_based_glm': dmn_contrast} Perform first level analysis ---------------------------- Setup and fit GLM. .. code-block:: default first_level_model = FirstLevelModel(t_r=t_r, slice_time_ref=slice_time_ref) first_level_model = first_level_model.fit(run_imgs=adhd_dataset.func[0], design_matrices=design_matrix) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /home/kshitij/workspace/nistats-org/nistats-repo/nistats-kchawla-pi/nistats/first_level_model.py:73: UserWarning: Mean values of 0 observed.The data have probably been centered.Scaling might not work as expected warn('Mean values of 0 observed.' Estimate the contrast. .. code-block:: default print('Contrast seed_based_glm computed.') z_map = first_level_model.compute_contrast(contrasts['seed_based_glm'], output_type='z_score') # Saving snapshots of the contrasts filename = 'dmn_z_map.png' display = plotting.plot_stat_map(z_map, threshold=3.0, title='Seed based GLM', cut_coords=pcc_coords) display.add_markers(marker_coords=[pcc_coords], marker_color='g',marker_size=300) display.savefig(filename) print("Save z-map in '{0}'.".format(filename)) .. image:: /auto_examples/02_first_level_models/images/sphx_glr_plot_adhd_dmn_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Contrast seed_based_glm computed. Save z-map in 'dmn_z_map.png'. Generating a report ------------------- It can be useful to quickly generate a portable, ready-to-view report with most of the pertinent information. This is easy to do if you have a fitted model and the list of contrasts, which we do here. .. code-block:: default from nistats.reporting import make_glm_report report = make_glm_report(first_level_model, contrasts=contrasts, title='ADHD DMN Report', cluster_threshold=15, min_distance=8., plot_type='glass', ) .. only:: builder_html .. container:: row sg-report .. raw:: html We have several ways to access the report: .. code-block:: default # report # This report can be viewed in a notebook # report.save_as_html('report.html') # report.open_in_browser() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 8.361 seconds) .. _sphx_glr_download_auto_examples_02_first_level_models_plot_adhd_dmn.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_adhd_dmn.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_adhd_dmn.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_