Extracting times series to build a functional connectome#

Page summary

A functional connectome is a set of connections representing brain interactions between regions. Here we show how to extract activation time-series to compute functional connectomes.

References

Time-series from a brain parcellation or “MaxProb” atlas#

Brain parcellations#

Regions used to extract the signal can be defined by a “hard” parcellation. For instance, the nilearn.datasets has functions to download atlases forming reference parcellation, e.g., fetch_atlas_craddock_2012, fetch_atlas_harvard_oxford, fetch_atlas_yeo_2011.

For instance to retrieve the Harvard-Oxford cortical parcellation, sampled at 2mm, and with a threshold of a probability of 0.25:

from nilearn import datasets
dataset = datasets.fetch_atlas_harvard_oxford('cort-maxprob-thr25-2mm')
atlas_filename = dataset.maps
labels = dataset.labels

Plotting can then be done as:

from nilearn import plotting
plotting.plot_roi(atlas_filename)
../_images/sphx_glr_plot_atlas_001.png

See also

Extracting signals on a parcellation#

To extract signal on the parcellation, the easiest option is to use the NiftiLabelsMasker. As any “maskers” in nilearn, it is a processing object that is created by specifying all the important parameters, but not the data:

from nilearn.maskers import NiftiLabelsMasker
masker = NiftiLabelsMasker(labels_img=atlas_filename, standardize=True)

The Nifti data can then be turned to time-series by calling the NiftiLabelsMasker.fit_transform method, that takes either filenames or NiftiImage objects:

time_series = masker.fit_transform(frmi_files,
                                   confounds=confounds_dataframe)

Note that confound signals can be specified in the call. Indeed, to obtain time series that capture well the functional interactions between regions, regressing out noise sources is very important [Varoquaux & Craddock 2013]. For data processed by fMRIPrep, load_confounds and load_confounds_strategy can help you retrieve confound variables. load_confounds_strategy selects confounds based on past literature with limited parameters for customisation. For more freedoms of confounds selection, load_confounds groups confound variables as sets of noise components and one can fine tune each of the parameters.

../_images/sphx_glr_plot_signal_extraction_001.png ../_images/sphx_glr_plot_signal_extraction_002.png

Full example

See the following example for a full file running the analysis: Extracting signals from a brain parcellation.

Exercise: computing the correlation matrix of rest fmri

Try using the information above to compute the correlation matrix of the first subject of the brain development dataset downloaded with nilearn.datasets.fetch_development_fmri.

Hints:


Time-series from a probabilistic atlas#

Probabilistic atlases#

The definition of regions as by a continuous probability map captures better our imperfect knowledge of boundaries in brain images (notably because of inter-subject registration errors). One example of such an atlas well suited to resting-state or naturalistic-stimuli data analysis is the MSDL atlas (nilearn.datasets.fetch_atlas_msdl).

Probabilistic atlases are represented as a set of continuous maps, in a 4D nifti image. Visualization the atlas thus requires to visualize each of these maps, which requires accessing them with nilearn.image.index_img (see the corresponding example).

../_images/sphx_glr_plot_overlay_001.png

Extracting signals from a probabilistic atlas#

As with extraction of signals on a parcellation, extracting signals from a probabilistic atlas can be done with a “masker” object: the NiftiMapsMasker. It is created by specifying the important parameters, in particular the atlas:

from nilearn.maskers import NiftiMapsMasker
masker = NiftiMapsMasker(maps_img=atlas_filename, standardize=True)

The fit_transform method turns filenames or NiftiImage objects to time series:

time_series = masker.fit_transform(frmi_files, confounds=csv_file)

The procedure is the same as with brain parcellations but using the NiftiMapsMasker, and the same considerations on using confounds regressors apply.

../_images/sphx_glr_plot_probabilistic_atlas_extraction_001.png

Full example

A full example of extracting signals on a probabilistic: Extracting signals of a probabilistic atlas of functional regions.

Exercise: correlation matrix of rest fMRI on probabilistic atlas

Try to compute the correlation matrix of the first subject of the brain development dataset downloaded with nilearn.datasets.fetch_development_fmri with the MSDL atlas downloaded via nilearn.datasets.fetch_atlas_msdl.

Hint: The example above has the solution.

A functional connectome: a graph of interactions#

A square matrix, such as a correlation matrix, can also be seen as a “graph”: a set of “nodes”, connected by “edges”. When these nodes are brain regions, and the edges capture interactions between them, this graph is a “functional connectome”.

We can display it with the nilearn.plotting.plot_connectome function that take the matrix, and coordinates of the nodes in MNI space. In the case of the MSDL atlas (nilearn.datasets.fetch_atlas_msdl), the CSV file readily comes with MNI coordinates for each region (see for instance example: Extracting signals of a probabilistic atlas of functional regions).

../_images/sphx_glr_plot_probabilistic_atlas_extraction_002.png

As you can see, the correlation matrix gives a very “full” graph: every node is connected to every other one. This is because it also captures indirect connections. In the next section we will see how to focus on direct connections only.

A functional connectome: extracting coordinates of regions#

For atlases without readily available label coordinates, center coordinates can be computed for each region on hard parcellation or probabilistic atlases.


References