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.

Fetching data from ADHD dataset#

from nilearn import datasets

adhd_dataset = datasets.fetch_adhd(n_subjects=1)

# plot_carpet can infer TR from the image header, but preprocessing can often
# overwrite that particular header field, so we will be explicit.
t_r = 2.

# Print basic information on the dataset
print('First subject functional nifti image (4D) is at: %s' %
      adhd_dataset.func[0])  # 4D data
First subject functional nifti image (4D) is at: /home/runner/work/nilearn/nilearn/nilearn_data/adhd/data/0010042/0010042_rest_tshift_RPI_voreg_mni.nii.gz

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])

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, t_r=t_r)

display.show()
plot carpet

Deriving a label-based mask#

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

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 = image.new_img_like(atlas_img, discrete_version)
/home/runner/work/nilearn/nilearn/examples/01_plotting/plot_carpet.py:61: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
  discrete_atlas_img = image.new_img_like(atlas_img, discrete_version)

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,
    t_r=t_r,
    mask_labels=map_labels,
    axes=ax,
    cmap="gray",
)

fig.show()
plot carpet
/usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/image/resampling.py:545: 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 9.584 seconds)

Estimated memory usage: 1029 MB

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