Input and output: neuroimaging data representation#


Inputing data: file names or image objects#

File names and objects, 3D and 4D images#

All Nilearn functions accept file names as arguments:

>>> from nilearn import image
>>> smoothed_img = image.smooth_img('/home/user/t_map001.nii')

Nilearn can operate on either file names or NiftiImage objects. The later represent the data loaded in memory. In the example above, the function smooth_img returns a Nifti1Image object, which can then be readily passed to other nilearn functions.

In nilearn, we often use the term “niimg” as abbreviation that denotes either a file name or a NiftiImage object.

Niimgs can be 3D or 4D. A 4D niimg may for instance represent a time series of 3D images. It can be a list of file names, if these contain 3D information:

>>> # dataset folder contains subject1.nii and subject2.nii
>>> from nilearn.image import smooth_img
>>> result_img = smooth_img(['dataset/subject1.nii', 'dataset/subject2.nii'])

result_img is a 4D in-memory image, containing the data of both subjects.

File name matching: “globbing” and user path expansion#

You can specify files with wildcard matching patterns (as in Unix shell):

  • Matching multiple files: suppose the dataset folder contains subject_01.nii, subject_03.nii, and subject_03.nii; dataset/subject_*.nii is a glob expression matching all filenames:

    >>> # Example with a smoothing process:
>>> from nilearn.image import smooth_img
>>> result_img = smooth_img("dataset/subject_*.nii")

    Note that the resulting is a 4D image.

  • Expanding the home directory ~ is expanded to your home directory:

    >>> result_img = smooth_img("~/dataset/subject_01.nii")

    Using ~ rather than specifying the details of the path is good practice, as it will make it more likely that your script work on different computers.

Python globbing

For more complicated use cases, Python also provides functions to work with file paths, in particular, glob.glob.

Warning

Unlike nilearn’s path expansion, the result of glob.glob is not sorted and, depending on the computer you are running, they might not be in alphabetic order. We advise you to rely on nilearn’s path expansion.

To load data with globbing, we suggest that you use nilearn.image.load_img.

Fetching open datasets from Internet#

Nilearn provides dataset fetching function that automatically downloads reference datasets and atlases. They can be imported from nilearn.datasets:

>>> from nilearn import datasets
>>> haxby_dataset = datasets.fetch_haxby()

They return a data structure that contains different pieces of information on the retrieved dataset, including the file names on hard disk:

>>> # The different files
>>> print(sorted(list(haxby_dataset.keys())))  
['anat', 'description', 'func', 'mask', 'mask_face', 'mask_face_little',
'mask_house', 'mask_house_little', 'mask_vt', 'session_target']
>>> # Path to first functional file
>>> print(haxby_dataset.func[0])  
/.../nilearn_data/haxby2001/subj1/bold.nii.gz

Explanation and further resources of the dataset at hand can be retrieved as follows:

>>> print(haxby_dataset.description)  
Haxby 2001 results


Notes
-----
Results from a classical fMRI study that...

See also

For a list of all the data fetching functions in nilearn, see nilearn.datasets: Automatic Dataset Fetching.


nilearn_data: Where is the downloaded data stored?

The fetching functions download the reference datasets to the disk. They save it locally for future use, in one of the following directories (in order of priority, if present):

  • the folder specified by data_dir parameter in the fetching function

  • the global environment variable NILEARN_SHARED_DATA

  • the user environment variable NILEARN_DATA

  • the nilearn_data folder in the user home folder

The two different environment variables (NILEARN_SHARED_DATA and NILEARN_DATA) are provided for multi-user systems, to distinguish a global dataset repository that may be read-only at the user-level. Note that you can copy that folder to another user’s computers to avoid the initial dataset download on the first fetching call.

You can check in which directory nilearn will store the data with the function nilearn.datasets.get_data_dirs.


Understanding neuroimaging data#

Nifti and Analyze data#

For volumetric data, nilearn works with data stored as in the Nifti structure (via the nibabel package).

The NifTi data structure (also used in Analyze files) is the standard way of sharing data in neuroimaging research. Three main components are:

data

raw scans in form of a numpy array: data = nilearn.image.get_data(img)

affine

returns the transformation matrix that maps from voxel indices of the numpy array to actual real-world locations of the brain: affine = img.affine

header

low-level information about the data (slice duration, etc.): header = img.header

If you need to load the data without using nilearn, read the nibabel documentation.

Note: For older versions of nibabel, affine and header can be retrieved with get_affine() and get_header().

Warning

if you create images directly with nibabel, beware of int64 images. the default integer type used by Numpy is (signed) 64-bit. Several popular neuroimaging tools do not handle int64 Nifti images, so if you build Nifti images directly from Numpy arrays it is recommended to specify a smaller integer type, for example:

np.array([1, 2000, 7], dtype="int32")

Dataset formatting: data shape

It is important to appreciate two main representations for storing and accessing more than one Nifti images, that is sets of MRI scans:

  • a big 4D matrix representing (3D MRI + 1D for time), stored in a single Nifti file. FSL users tend to prefer this format.

  • several 3D matrices representing each time point (single 3D volume) of the session, stored in set of 3D Nifti or analyse files. SPM users tend to prefer this format.

Niimg-like objects#

Nilearn functions take as input argument what we call “Niimg-like objects”:

Niimg: A Niimg-like object can be one of the following:

  • A string with a file path to a Nifti or Analyse image

  • An SpatialImage from nibabel, ie an object exposing get_fdata() method and affine attribute, typically a Nifti1Image from nibabel.

Niimg-4D: Similarly, some functions require 4D Nifti-like data, which we call Niimgs or Niimg-4D. Accepted input arguments are:

  • A path to a 4D Nifti image

  • List of paths to 3D Nifti images

  • 4D Nifti-like object

  • List of 3D Nifti-like objects

Image affines

If you provide a sequence of Nifti images, all of them must have the same affine.

Decreasing memory used when loading Nifti images

When Nifti images are stored compressed (.nii.gz), loading them directly consumes more memory. As a result, large 4D images may raise “MemoryError”, especially on smaller computers and when using Nilearn routines that require intensive 4D matrix operations. One step to improve the situation may be to decompress the data onto disk as an initial step. If multiple images are loaded into memory sequentially, another solution may be to uncache one before loading and performing operations on another.

Text files: phenotype or behavior#

Phenotypic or behavioral data are often provided as text or CSV (Comma Separated Values) file. They can be loaded with pd.read_csv but you may have to specify some options (typically sep if fields aren’t delimited with a comma).

For the Haxby datasets, we can load the categories of the images presented to the subject:

>>> from nilearn import datasets
>>> haxby_dataset = datasets.fetch_haxby()  
>>> import pandas as pd  
>>> labels = pd.read_csv(haxby_dataset.session_target[0], sep=" ")  
>>> stimuli = labels['labels']  
>>> print(stimuli.unique())  
['bottle' 'cat' 'chair' 'face' 'house' 'rest' 'scissors' 'scrambledpix'
 'shoe']

Reading CSV with pandas

Pandas is a powerful package to read data from CSV files and manipulate them.