Add coordinate converters between MNI152 and FSAverage

docs/changelog.rst

@@ -28,6 +28,30 @@ Change tags (adopted from `sklearn <https://scikit-learn.org/stable/whats_new/v0
 
 - |API| : you will need to change your code to have the same effect in the future; or a feature will be removed in the future.
 
+Version 2.5.0
+-------------
+- |Feature| : Add functions to convert electrode coordinates between MNI152 and fsaverage space, with ``localization.mni152_to_fsaverage`` and ``localization.fsaverage_to_mni152``.
+
+Version 2.4.0
+-------------
+- |Enhancement| : Adds the ability to set vmin and vmax to ``plot_brain_overlay``.
+
+Version 2.3.0
+-------------
+- |MajorFeature| : Adds the ability to interpolate electrode data onto the brain surface using ``interpolate_electrodes_onto_brain`` as well as enhanced functionality of the ``plot_brain_overlay`` function to plot such data.
+
+Version 2.2.0
+-------------
+- |Enhancement| : Adds the ``del`` keyword functionality to the Data object and its sub-components.
+- |Enhancement| : Enhance the capability of local electrode rereferencing.
+
+
+Version 2.1.0
+-------------
+- |Feature| : Allow the ``Brain`` class to work with MNI152 brains.
+- |Enhancement| : Allow ``plot_brain_elecs`` to work with varied opacity per electrodes.
+- |Fix| : Fix a bug in responsiveness_ttest.
+
 Version 2.0.0
 -------------
 - |MajorFeature| : Added the ``Brain`` class for representing a brain to the ``naplib.localization`` module. This can be used to plot intracranial electrodes on static or interactive 3D brain plots with the ``naplib.visualization module``, estimate anatomical locations of electrodes, calculate statistics on brains, and more. See Examples for illustrations of use-cases.

docs/references/localization.rst

@@ -3,6 +3,17 @@ Localization
 
 .. currentmodule:: naplib.localization
 
+Convert from MNI152 to FSAverage
+--------------------------------
+
+.. autofunction:: mni152_to_fsaverage
+
+Convert from FSAverage to MNI152
+--------------------------------
+
+.. autofunction:: fsaverage_to_mni152
+
+
 Localization on a Freesurfer Brain
 ----------------------------------
 

naplib/localization/__init__.py

@@ -1,3 +1,4 @@
 from .freesurfer import Brain, find_closest_vertices
+from .coordinate_conversions import mni152_to_fsaverage, fsaverage_to_mni152
 
-__all__ = ['Brain', 'find_closest_vertices']
+__all__ = ['Brain', 'find_closest_vertices', 'mni152_to_fsaverage', 'fsaverage_to_mni152']

naplib/localization/coordinate_conversions.py

@@ -0,0 +1,41 @@
+import numpy as np
+
+def mni152_to_fsaverage(coords):
+    """
+    Convert 3D coordinates from MNI152 space to fsaverage space.
+
+    Parameters
+    ----------
+    coords : np.ndarray (elecs, 3)
+        Coordinates in MNI space
+
+    Returns
+    -------
+    new_coords : np.ndarray (elecs, 3)
+        Coordinates in fsaverage space
+    """
+    old_coords = np.concatenate([coords, np.ones((len(coords),1))], axis=1).T
+    xform = np.array([[1.0022, 0.0071, -0.0177, 0.0528], [-0.0146, 0.999, 0.0027, -1.5519], [0.0129, 0.0094, 1.0027, -1.2012]])
+    new_coords = (xform @ old_coords).T
+    return new_coords
+
+def fsaverage_to_mni152(coords):
+    """
+    Convert 3D coordinates from fsaverage space to MNI152 space.
+
+    Parameters
+    ----------
+    coords : np.ndarray (elecs, 3)
+        Coordinates in fsaverage space
+
+    Returns
+    -------
+    new_coords : np.ndarray (elecs, 3)
+        Coordinates in MNI152 space
+    """
+    old_coords = np.concatenate([coords, np.ones((len(coords),1))], axis=1).T
+    xform = np.array([[0.9975, -0.0073, 0.0176, -0.0429], [0.0146, 1.0009, -0.0024, 1.5496], [-0.013, -0.0093, 0.9971, 1.184]])
+    new_coords = (xform @ old_coords).T
+    return new_coords
+
+

tests/test_coordinate_conversions.py

@@ -0,0 +1,14 @@
+import numpy as np
+from naplib.localization import mni152_to_fsaverage, fsaverage_to_mni152
+
+def test_mni152_fsaverage_conversions():
+  coords_tmp = np.array([[13.987, 36.5, 10.067], [-10.54, 24.5, 15.555]])
+  coords_tmp2 = mni152_to_fsaverage(coords_tmp)
+  expected_2 = np.array(
+    [[ 14.15310394,  34.73510469,   9.41733728],
+    [-10.60987978,  23.11927315,  14.49010227]]
+  )
+  assert np.allclose(coords_tmp2, expected_2, rtol=1e-3)
+
+  coords_tmp3 = fsaverage_to_mni152(coords_tmp2)
+  assert np.allclose(coords_tmp3, coords_tmp, rtol=1e-3)