7.7. Adding Coordinates

Instead of using whole-brain parcellations to aggregate voxel-wise signals from MR images (as for example in the ParcelAggregation marker), junifer allows you to specify a set of coordinates around which to draw spheres to aggregate (for example using the SphereAggregation marker) the MR signals from individual voxels. Now, before you start specifying your own sets of coordinates, check the coordinates that junifer already has built in. If you simply want to use a well known set of coordinates from the literature, there is a reasonable chance, that junifer provides them already.

If you checked the in-built coordinates, and they are not there already (for example if you came up with your own set of coordinates), then junifer provides an easy way for you to register them using the register_coordinates() function, so you can use your own set of coordinates within a junifer pipeline.

From the API reference, we can see that it has 4 positional arguments (name, coordinates, voi_names and space) as well as one optional keyword argument (overwrite).

The name argument takes a string indicating the name you want to give to this set of coordinates. This name can be used to obtain and operate on a set of coordinates in junifer. For example, you can obtain your coordinates after registration by providing name to load_coordinates(). We could simply call it "my_set_of_coordinates", but likely you want a more descriptive and more informative name most of the time.

The coordinates argument takes the actual coordinates as a 2-dimensional numpy.ndarray. It contains one row for every location, and three columns (one for each spatial dimension). That is, the first, second, and third columns indicate the x-, y-, and z-coordinates in MNI space respectively. The number of rows in the array correspond to the number of coordinates that belong to this set.

The voi_names argument takes a list of strings indicating the names of each coordinate (i.e. volume-of-interest) in the coordinates array. Therefore, the length of this list should correspond to the number of rows in the coordinates array. Now, we know everything we need to know to register a set of coordinates.

Lastly, we specify the space that the coordinates are in, for example, "MNI" or "native" (scanner-native space).

7.7.1. Step 1: Prepare code to register a set of coordinates

Let’s make a simple script to register our coordinates. We could simply call it register_custom_coordinates.py. We may start by importing the appropriate packages:

from junifer.data import register_coordinates
import numpy as np

For the sake of this example, we can create a set of coordinates that belong to the Default Mode Network (DMN), and register this set of coordinates with junifer. Note, that junifer already has a set of coordinates built-in (“DMNBuckner”) that is associated with the DMN. Here, we use the DMN coordinates used in a nilearn example.

dmn_coords = np.array(
    [[0, -52, 18],
    [-46, -68, 32],
    [46, -68, 32],
    [1, 50, -5]]
)
voi_names = [
    "Posterior Cingulate Cortex",
    "Left Temporoparietal junction",
    "Right Temporoparietal junction",
    "Medial prefrontal cortex",
]

As you can see, we have four rows, and therefore four locations in the brain associated with this set of coordinates. The variable voi_names reflects this as it contains four strings indicating the name of each location. We can now simply use this to register our coordinates:

register_coordinates(
    name="DMNCustom",
    coordinates=dmn_coords,
    voi_names=voi_names,
    space="MNI"
)

Now, when we run this script, junifer registers these coordinates and we can use them in subsequent analyses. Let’s now consider how to use coordinate registration in combination with codeless configuration using a YAML file.

7.7.2. Step 2: Add coordinate registration to the YAML file

In order to register your coordinates for a pipeline configured by a YAML file, you can use the with keyword provided by junifer:

with:
  - register_custom_coordinates.py

Afterwards continue configuring the rest of your pipeline in this YAML file, and you will be able to use this set of coordinates using the name you gave it during registration (in our example “DMNCustom”). We can add a SphereAggregation to demonstrate how this can be done:

markers:
  - name: DMNCustom_mean
    kind: SphereAggregation
    coords: DMNCustom
    radius: 5
    method: mean

This will aggregate signals from a sphere around each of our coordinates with a radius of 5mm.