9.2.10. Visualizing global patterns with a carpet plot

A common quality control step for functional MRI data is to visualize the data over time in a carpet plot (also known as a Power plot or a grayplot).

The nilearn.plotting.plot_carpet function generates a carpet plot from a 4D functional image.

9.2.10.1. Fetching data from ADHD dataset

from nilearn import datasets

adhd_dataset = datasets.fetch_adhd(n_subjects=1)

# Print basic information on the dataset
print('First subject functional nifti image (4D) is at: %s' %
      adhd_dataset.func[0])  # 4D data

Out:

/home/nicolas/GitRepos/nilearn-fork/nilearn/datasets/func.py:240: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
  phenotypic = np.genfromtxt(phenotypic, names=True, delimiter=',',
First subject functional nifti image (4D) is at: /home/nicolas/nilearn_data/adhd/data/0010042/0010042_rest_tshift_RPI_voreg_mni.nii.gz

9.2.10.2. Deriving a mask

from nilearn import masking

# Build an EPI-based mask because we have no anatomical data
mask_img = masking.compute_epi_mask(adhd_dataset.func[0])

9.2.10.3. Visualizing global patterns over time

import matplotlib.pyplot as plt

from nilearn.plotting import plot_carpet

display = plot_carpet(adhd_dataset.func[0], mask_img)

display.show()
plot carpet

9.2.10.4. Deriving a label-based mask

Create a gray matter/white matter/cerebrospinal fluid mask from ICBM152 tissue probability maps.

import nibabel as nib
import numpy as np
from nilearn import image

atlas = datasets.fetch_icbm152_2009()
atlas_img = image.concat_imgs((atlas["gm"], atlas["wm"], atlas["csf"]))
map_labels = {"Gray Matter": 1, "White Matter": 2, "Cerebrospinal Fluid": 3}

atlas_data = atlas_img.get_fdata()
discrete_version = np.argmax(atlas_data, axis=3) + 1
discrete_version[np.max(atlas_data, axis=3) == 0] = 0
discrete_atlas_img = nib.Nifti1Image(
    discrete_version,
    atlas_img.affine,
    atlas_img.header,
)

9.2.10.5. Visualizing global patterns, separated by tissue type

import matplotlib.pyplot as plt

from nilearn.plotting import plot_carpet

fig, ax = plt.subplots(figsize=(10, 10))

display = plot_carpet(
    adhd_dataset.func[0],
    discrete_atlas_img,
    mask_labels=map_labels,
    axes=ax,
)

fig.show()
plot carpet

Out:

/home/nicolas/GitRepos/nilearn-fork/nilearn/image/resampling.py:531: UserWarning: Casting data from int32 to float32
  warnings.warn("Casting data from %s to %s" % (data.dtype.name, aux))
Coercing atlas_values to <class 'int'>

Total running time of the script: ( 0 minutes 9.177 seconds)

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