2.5.1. BIDS dataset first and second level analysis

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

More specifically:

  1. Download an fMRI BIDS dataset with two language conditions to contrast.

  2. Extract automatically from the BIDS dataset first level model objects

  3. Fit a second level model on the fitted first level models. Notice that in this case the preprocessed bold images were already normalized to the same MNI space.

To run this example, you must launch IPython via ipython --matplotlib in a terminal, or use the Jupyter notebook. Fetch example BIDS dataset

We download an simplified BIDS dataset made available for illustrative purposes. It contains only the necessary information to run a statistical analysis using Nistats. The raw data subject folders only contain bold.json and events.tsv files, while the derivatives folder with preprocessed files contain preproc.nii and confounds.tsv files.

Here is the location of the dataset on disk



/home/kshitij/nilearn_data/fMRI-language-localizer-demo-dataset Obtain automatically FirstLevelModel objects and fit arguments

From the dataset directory we obtain automatically FirstLevelModel objects with their subject_id filled from the 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 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.

from nistats.first_level_model import first_level_models_from_bids
task_label = 'languagelocalizer'
models, models_run_imgs, models_events, models_confounds = \
        data_dir, task_label,
        img_filters=[('desc', 'preproc')])


/home/kshitij/workspace/nistats-org/nistats-repo/kchawla-pi/nistats/nistats/first_level_model.py:703: ResourceWarning: unclosed file <_io.TextIOWrapper name='/home/kshitij/nilearn_data/fMRI-language-localizer-demo-dataset/derivatives/sub-01/func/sub-01_task-languagelocalizer_desc-preproc_bold.json' mode='r' encoding='UTF-8'>
  specs = json.load(open(img_specs[0], 'r'))
/home/kshitij/workspace/nistats-org/nistats-repo/kchawla-pi/nistats/nistats/first_level_model.py:718: UserWarning: SliceTimingRef not found in file /home/kshitij/nilearn_data/fMRI-language-localizer-demo-dataset/derivatives/sub-01/func/sub-01_task-languagelocalizer_desc-preproc_bold.json. 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
  img_specs[0]) Quick sanity check on fit arguments

Additional checks or information extraction from pre-processed data can be made here

We just expect one run img per subject.

import os
print([os.path.basename(run) for run in models_run_imgs[0]])



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



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

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.



language    12
string      12
Name: trial_type, dtype: int64 First level model estimation

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

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

from scipy.stats import norm
p001_unc = norm.isf(0.001)

Prepare figure for concurrent plot of individual maps

from nilearn import plotting
import matplotlib.pyplot as plt

fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(8, 4.5))
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, colorbar=False, threshold=p001_unc,
                              title=('sub-' + model.subject_label),
                              axes=axes[int(midx / 5), int(midx % 5)],
                              plot_abs=False, display_mode='x')
fig.suptitle('subjects z_map language network (unc p<0.001)')


/home/kshitij/.programs/anaconda3/envs/nistats-py36-latest/lib/python3.6/site-packages/nilearn/_utils/cache_mixin.py:232: DeprecationWarning: The 'cachedir' attribute has been deprecated in version 0.12 and will be removed in version 0.14.
Use os.path.join(memory.location, 'joblib') attribute instead.
  if (memory.cachedir is None and memory_level is not None
/home/kshitij/.programs/anaconda3/envs/nistats-py36-latest/lib/python3.6/site-packages/scipy/ndimage/measurements.py:272: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index
  return _nd_image.find_objects(input, max_label) 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)

from nistats.second_level_model import SecondLevelModel
second_level_input = models

Note that we apply a smoothing of 8mm.

second_level_model = SecondLevelModel(smoothing_fwhm=8.0)
second_level_model = second_level_model.fit(second_level_input)

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

zmap = second_level_model.compute_contrast(

The group level contrast reveals a left lateralized fronto-temporal language network

plotting.plot_glass_brain(zmap, colorbar=True, threshold=p001_unc,
                          title='Group language network (unc p<0.001)',
                          plot_abs=False, display_mode='x')

Total running time of the script: ( 0 minutes 29.223 seconds)

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