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

/home/alexis/miniconda3/envs/nilearn/lib/python3.10/site-packages/nilearn/datasets/func.py:250: 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/alexis/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()

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/alexis/miniconda3/envs/nilearn/lib/python3.10/site-packages/nilearn/image/image.py:756: FutureWarning: Image data has type int64, which may cause incompatibilities with other tools. This will error in NiBabel 5.0. This warning can be silenced by passing the dtype argument to Nifti1Image().
  return klass(data, affine, header=header)

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()

/home/alexis/miniconda3/envs/nilearn/lib/python3.10/site-packages/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'>

Estimated memory usage: 1042 MB