"""
.. _tut-freesurfer-mne:
=================================
How MNE uses FreeSurfer's outputs
=================================
This tutorial explains how MRI coordinate frames are handled in MNE-Python,
and how MNE-Python integrates with FreeSurfer for handling MRI data and source
space data in general.
As usual we'll start by importing the necessary packages; for this tutorial
that includes :mod:`nibabel` to handle loading the MRI images (MNE-Python also
uses :mod:`nibabel` under the hood). We'll also use a special :mod:`Matplotlib
` function for adding outlines to text, so that text is
readable on top of an MRI image.
"""
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
# %%
import matplotlib.patheffects as path_effects
import matplotlib.pyplot as plt
import nibabel
import numpy as np
import mne
from mne.io.constants import FIFF
from mne.transforms import apply_trans
# %%
# MRI coordinate frames
# =====================
#
# Let's start out by looking at the ``sample`` subject MRI. Following standard
# FreeSurfer convention, we look at :file:`T1.mgz`, which gets created from the
# original MRI :file:`sample/mri/orig/001.mgz` when you run the FreeSurfer
# command `recon-all `_.
# Here we use :mod:`nibabel` to load the T1 image, and the resulting object's
# :meth:`~nibabel.spatialimages.SpatialImage.orthoview` method to view it.
data_path = mne.datasets.sample.data_path()
subjects_dir = data_path / "subjects"
subject = "sample"
t1_fname = subjects_dir / subject / "mri" / "T1.mgz"
t1 = nibabel.load(t1_fname)
t1.orthoview()
# %%
# Notice that the axes in the
# :meth:`~nibabel.spatialimages.SpatialImage.orthoview` figure are labeled
# L-R, S-I, and P-A. These reflect the standard RAS (right-anterior-superior)
# coordinate system that is widely used in MRI imaging. If you are unfamiliar
# with RAS coordinates, see the excellent nibabel tutorial
# :doc:`nibabel:coordinate_systems`.
#
# Nibabel already takes care of some coordinate frame transformations under the
# hood, so let's do it manually so we understand what is happening. First let's
# get our data as a 3D array and note that it's already a standard size:
data = np.asarray(t1.dataobj)
print(data.shape)
# %%
# These data are voxel intensity values. Here they are unsigned integers in the
# range 0-255, though in general they can be floating point values. A value
# ``data[i, j, k]`` at a given index triplet ``(i, j, k)`` corresponds to some
# real-world physical location ``(x, y, z)`` in space. To get its physical
# location, first we have to choose what coordinate frame we're going to use.
#
# For example, we could choose a geographical coordinate
# frame, with origin is at the center of the earth, Z axis through the north
# pole, X axis through the prime meridian (zero degrees longitude), and Y axis
# orthogonal to these forming a right-handed coordinate system. This would not
# be a very useful choice for defining the physical locations of the voxels
# during the MRI acquisition for analysis, but you could nonetheless figure out
# the transformation that related the ``(i, j, k)`` to this coordinate frame.
#
# Instead, each scanner defines a more practical, native coordinate system that
# it uses during acquisition, usually related to the physical orientation of
# the scanner itself and/or the subject within it. During acquisition the
# relationship between the voxel indices ``(i, j, k)`` and the physical
# location ``(x, y, z)`` in the *scanner's native coordinate frame* is saved in
# the image's *affine transformation*.
#
# .. admonition:: Under the hood
# :class: sidebar note
#
# ``mne.transforms.apply_trans`` effectively does a matrix multiplication
# (i.e., :func:`numpy.dot`), with a little extra work to handle the shape
# mismatch (the affine has shape ``(4, 4)`` because it includes a
# *translation*, which is applied separately).
#
# We can use :mod:`nibabel` to examine this transformation, keeping in mind
# that it processes everything in units of millimeters, unlike MNE where things
# are always in SI units (meters).
#
# This allows us to take an arbitrary voxel or slice of data and know where it
# is in the scanner's native physical space ``(x, y, z)`` (in mm) by applying
# the affine transformation to the voxel coordinates.
print(t1.affine)
vox = np.array([122, 119, 102])
xyz_ras = apply_trans(t1.affine, vox)
print(
"Our voxel has real-world coordinates {}, {}, {} (mm)".format(*np.round(xyz_ras, 3))
)
# %%
# If you have a point ``(x, y, z)`` in scanner-native RAS space and you want
# the corresponding voxel number, you can get it using the inverse of the
# affine. This involves some rounding, so it's possible to end up off by one
# voxel if you're not careful:
ras_coords_mm = np.array([1, -17, -18])
inv_affine = np.linalg.inv(t1.affine)
i_, j_, k_ = np.round(apply_trans(inv_affine, ras_coords_mm)).astype(int)
print(f"Our real-world coordinates correspond to voxel ({i_}, {j_}, {k_})")
# %%
# Let's write a short function to visualize where our voxel lies in an
# image, and annotate it in RAS space (rounded to the nearest millimeter):
def imshow_mri(data, img, vox, xyz, suptitle):
"""Show an MRI slice with a voxel annotated."""
i, j, k = vox
fig, ax = plt.subplots(1, figsize=(6, 6), layout="constrained")
codes = nibabel.orientations.aff2axcodes(img.affine)
# Figure out the title based on the code of this axis
ori_slice = dict(
P="Coronal", A="Coronal", I="Axial", S="Axial", L="Sagittal", R="Sagittal"
)
ori_names = dict(
P="posterior", A="anterior", I="inferior", S="superior", L="left", R="right"
)
title = ori_slice[codes[0]]
ax.imshow(data[i], vmin=10, vmax=120, cmap="gray", origin="lower")
ax.axvline(k, color="y")
ax.axhline(j, color="y")
for kind, coords in xyz.items():
annotation = "{}: {}, {}, {} mm".format(kind, *np.round(coords).astype(int))
text = ax.text(k, j, annotation, va="baseline", ha="right", color=(1, 1, 0.7))
text.set_path_effects(
[
path_effects.Stroke(linewidth=2, foreground="black"),
path_effects.Normal(),
]
)
# reorient view so that RAS is always rightward and upward
x_order = -1 if codes[2] in "LIP" else 1
y_order = -1 if codes[1] in "LIP" else 1
ax.set(
xlim=[0, data.shape[2] - 1][::x_order],
ylim=[0, data.shape[1] - 1][::y_order],
xlabel=f"k ({ori_names[codes[2]]}+)",
ylabel=f"j ({ori_names[codes[1]]}+)",
title=f"{title} view: i={i} ({ori_names[codes[0]]}+)",
)
fig.suptitle(suptitle)
return fig
imshow_mri(data, t1, vox, {"Scanner RAS": xyz_ras}, "MRI slice")
# %%
# Notice that the axis scales (``i``, ``j``, and ``k``) are still in voxels
# (ranging from 0-255); it's only the annotation text that we've translated
# into real-world RAS in millimeters.
#
#
# "MRI coordinates" in MNE-Python: FreeSurfer surface RAS
# -------------------------------------------------------
#
# While :mod:`nibabel` uses **scanner RAS** ``(x, y, z)`` coordinates,
# FreeSurfer uses a slightly different coordinate frame: **MRI surface RAS**.
# The transform from voxels to the FreeSurfer MRI surface RAS coordinate frame
# is known in the `FreeSurfer documentation
# `_ as ``Torig``,
# and in nibabel as :meth:`vox2ras_tkr
# `. This
# transformation sets the center of its coordinate frame in the middle of the
# conformed volume dimensions (``N / 2.``) with the axes oriented along the
# axes of the volume itself. For more information, see
# :ref:`coordinate_systems`.
#
# .. note:: In general, you should assume that the MRI coordinate system for
# a given subject is specific to that subject, i.e., it is not the
# same coordinate MRI coordinate system that is used for any other
# FreeSurfer subject. Even though during processing FreeSurfer will
# align each subject's MRI to ``fsaverage`` to do reconstruction,
# all data (surfaces, MRIs, etc.) get stored in the coordinate frame
# specific to that subject. This is why it's important for group
# analyses to transform data to a common coordinate frame for example
# by :ref:`surface ` or
# :ref:`volumetric ` morphing, or even by just
# applying :ref:`mni-affine-transformation` to points.
#
# Since MNE-Python uses FreeSurfer extensively for surface computations (e.g.,
# white matter, inner/outer skull meshes), internally MNE-Python uses the
# Freeurfer surface RAS coordinate system (not the :mod:`nibabel` scanner RAS
# system) for as many computations as possible, such as all source space
# and BEM mesh vertex definitions.
#
# Whenever you see "MRI coordinates" or "MRI coords" in MNE-Python's
# documentation, you should assume that we are talking about the
# "FreeSurfer MRI surface RAS" coordinate frame!
#
# We can do similar computations as before to convert the given voxel indices
# into FreeSurfer MRI coordinates (i.e., what we call "MRI coordinates" or
# "surface RAS" everywhere else in MNE), just like we did above to convert
# voxel indices to *scanner* RAS:
Torig = t1.header.get_vox2ras_tkr()
print(t1.affine)
print(Torig)
xyz_mri = apply_trans(Torig, vox)
imshow_mri(data, t1, vox, dict(MRI=xyz_mri), "MRI slice")
# %%
# Knowing these relationships and being mindful about transformations, we
# can get from a point in any given space to any other space. Let's start out
# by plotting the Nasion on a sagittal MRI slice:
fiducials = mne.coreg.get_mni_fiducials(subject, subjects_dir=subjects_dir)
nasion_mri = [d for d in fiducials if d["ident"] == FIFF.FIFFV_POINT_NASION][0]
print(nasion_mri) # note it's in Freesurfer MRI coords
# %%
# When we print the nasion, it displays as a ``DigPoint`` and shows its
# coordinates in millimeters, but beware that the underlying data is
# :ref:`actually stored in meters `,
# so before transforming and plotting we'll convert to millimeters:
nasion_mri = nasion_mri["r"] * 1000 # meters → millimeters
nasion_vox = np.round(apply_trans(np.linalg.inv(Torig), nasion_mri)).astype(int)
imshow_mri(
data, t1, nasion_vox, dict(MRI=nasion_mri), "Nasion estimated from MRI transform"
)
# %%
# We can also take the digitization point from the MEG data, which is in the
# "head" coordinate frame.
#
# Let's look at the nasion in the head coordinate frame:
info = mne.io.read_info(data_path / "MEG" / "sample" / "sample_audvis_raw.fif")
nasion_head = [
d
for d in info["dig"]
if d["kind"] == FIFF.FIFFV_POINT_CARDINAL and d["ident"] == FIFF.FIFFV_POINT_NASION
][0]
print(nasion_head) # note it's in "head" coordinates
# %%
# .. admonition:: Head coordinate frame
# :class: sidebar note
#
# The head coordinate frame in MNE is the "Neuromag" head coordinate
# frame. The origin is given by the intersection between a line connecting
# the LPA and RPA and the line orthogonal to it that runs through the
# nasion. It is also in RAS orientation, meaning that +X runs through
# the RPA, +Y goes through the nasion, and +Z is orthogonal to these
# pointing upward. See :ref:`coordinate_systems` for more information.
#
# Notice that in "head" coordinate frame the nasion has values of 0 for the
# ``x`` and ``z`` directions (which makes sense given that the nasion is used
# to define the ``y`` axis in that system).
# To convert from head coordinate frame to voxels, we first apply the head →
# MRI (surface RAS) transform
# from a :file:`trans` file (typically created with the MNE-Python
# coregistration GUI), then convert meters → millimeters, and finally apply the
# inverse of ``Torig`` to get to voxels.
#
# Under the hood, functions like :func:`mne.setup_source_space`,
# :func:`mne.setup_volume_source_space`, and :func:`mne.compute_source_morph`
# make extensive use of these coordinate frames.
trans = mne.read_trans(data_path / "MEG" / "sample" / "sample_audvis_raw-trans.fif")
# first we transform from head to MRI, and *then* convert to millimeters
nasion_dig_mri = apply_trans(trans, nasion_head["r"]) * 1000
# ...then we can use Torig to convert MRI to voxels:
nasion_dig_vox = np.round(apply_trans(np.linalg.inv(Torig), nasion_dig_mri)).astype(int)
imshow_mri(
data,
t1,
nasion_dig_vox,
dict(MRI=nasion_dig_mri),
"Nasion transformed from digitization",
)
# %%
# Using FreeSurfer's surface reconstructions
# ==========================================
# An important part of what FreeSurfer does is provide cortical surface
# reconstructions. For example, let's load and view the ``white`` surface
# of the brain. This is a 3D mesh defined by a set of vertices (conventionally
# called ``rr``) with shape ``(n_vertices, 3)`` and a set of triangles
# (``tris``) with shape ``(n_tris, 3)`` defining which vertices in ``rr`` form
# each triangular facet of the mesh.
fname = subjects_dir / subject / "surf" / "rh.white"
rr_mm, tris = mne.read_surface(fname)
print(f"rr_mm.shape == {rr_mm.shape}")
print(f"tris.shape == {tris.shape}")
print(f"rr_mm.max() = {rr_mm.max()}") # just to show that we are in mm
# %%
# Let's actually plot it:
renderer = mne.viz.backends.renderer.create_3d_figure(
size=(600, 600), bgcolor="w", scene=False
)
gray = (0.5, 0.5, 0.5)
renderer.mesh(*rr_mm.T, triangles=tris, color=gray)
view_kwargs = dict(elevation=90, azimuth=0) # camera at +X with +Z up
mne.viz.set_3d_view(
figure=renderer.figure, distance=350, focalpoint=(0.0, 0.0, 40.0), **view_kwargs
)
renderer.show()
# %%
# We can also plot the mesh on top of an MRI slice. The mesh surfaces are
# defined in millimeters in the MRI (FreeSurfer surface RAS) coordinate frame,
# so we can convert them to voxels by applying the inverse of the ``Torig``
# transform:
rr_vox = apply_trans(np.linalg.inv(Torig), rr_mm)
fig = imshow_mri(data, t1, vox, {"Scanner RAS": xyz_ras}, "MRI slice")
# Based on how imshow_mri works, the "X" here is the last dim of the MRI vol,
# the "Y" is the middle dim, and the "Z" is the first dim, so now that our
# points are in the correct coordinate frame, we need to ask matplotlib to
# do a tricontour slice like:
fig.axes[0].tricontour(
rr_vox[:, 2],
rr_vox[:, 1],
tris,
rr_vox[:, 0],
levels=[vox[0]],
colors="r",
linewidths=1.0,
zorder=1,
)
# %%
# This is the method used by :func:`mne.viz.plot_bem` to show the BEM surfaces.
#
# Cortical alignment (spherical)
# ------------------------------
# A critical function provided by FreeSurfer is spherical surface alignment
# of cortical surfaces, maximizing sulcal-gyral alignment. FreeSurfer first
# expands the cortical surface to a sphere, then aligns it optimally with
# fsaverage. Because the vertex ordering is preserved when expanding to a
# sphere, a given vertex in the source (sample) mesh can be mapped easily
# to the same location in the destination (fsaverage) mesh, and vice-versa.
renderer_kwargs = dict(bgcolor="w")
renderer = mne.viz.backends.renderer.create_3d_figure(
size=(800, 400), scene=False, **renderer_kwargs
)
curvs = [
(
mne.surface.read_curvature(
subjects_dir / subj / "surf" / "rh.curv", binary=False
)
> 0
).astype(float)
for subj in ("sample", "fsaverage")
for _ in range(2)
]
fnames = [
subjects_dir / subj / "surf" / surf
for subj in ("sample", "fsaverage")
for surf in ("rh.white", "rh.sphere")
]
y_shifts = [-450, -150, 450, 150]
z_shifts = [-40, 0, -30, 0]
for name, y_shift, z_shift, curv in zip(fnames, y_shifts, z_shifts, curvs):
this_rr, this_tri = mne.read_surface(name)
this_rr += [0, y_shift, z_shift]
renderer.mesh(
*this_rr.T,
triangles=this_tri,
color=None,
scalars=curv,
colormap="copper_r",
vmin=-0.2,
vmax=1.2,
)
zero = [0.0, 0.0, 0.0]
width = 50.0
y = np.sort(y_shifts)
y = (y[1:] + y[:-1]) / 2.0 - width / 2.0
renderer.quiver3d(zero, y, zero, zero, [1] * 3, zero, "k", width, "arrow")
view_kwargs["focalpoint"] = (0.0, 0.0, 0.0)
mne.viz.set_3d_view(figure=renderer.figure, distance=1050, **view_kwargs)
renderer.show()
# %%
# Let's look a bit more closely at the spherical alignment by overlaying the
# two spherical meshes as wireframes and zooming way in (the vertices of the
# black mesh are separated by about 1 mm):
cyan = "#66CCEE"
black = "k"
renderer = mne.viz.backends.renderer.create_3d_figure(
size=(800, 800), scene=False, **renderer_kwargs
)
surfs = [
mne.read_surface(subjects_dir / subj / "surf" / "rh.sphere")
for subj in ("fsaverage", "sample")
]
colors = [black, cyan]
line_widths = [2, 3]
for surf, color, line_width in zip(surfs, colors, line_widths):
this_rr, this_tri = surf
# cull to the subset of tris with all positive X (toward camera)
this_tri = this_tri[(this_rr[this_tri, 0] > 0).all(axis=1)]
renderer.mesh(
*this_rr.T,
triangles=this_tri,
color=color,
representation="wireframe",
line_width=line_width,
render_lines_as_tubes=True,
)
mne.viz.set_3d_view(figure=renderer.figure, distance=150, **view_kwargs)
renderer.show()
# %%
# You can see that the fsaverage (black) mesh is uniformly spaced, and the
# mesh for subject "sample" (in cyan) has been deformed along the spherical
# surface by
# FreeSurfer. This deformation is designed to optimize the sulcal-gyral
# alignment.
#
# Surface decimation
# ------------------
# These surfaces have a lot of vertices, and in general we only need to use
# a subset of these vertices for creating source spaces. A uniform sampling can
# easily be achieved by subsampling in the spherical space. To do this, we
# use a recursively subdivided icosahedron or octahedron. For example, let's
# load a standard oct-6 source space, and at the same zoom level as before
# visualize how it subsampled (in red) the dense mesh:
src = mne.read_source_spaces(subjects_dir / "sample" / "bem" / "sample-oct-6-src.fif")
print(src)
# sphinx_gallery_thumbnail_number = 10
red = "#EE6677"
renderer = mne.viz.backends.renderer.create_3d_figure(
size=(800, 800), scene=False, **renderer_kwargs
)
rr_sph, _ = mne.read_surface(fnames[1])
for tris, color in [(src[1]["tris"], cyan), (src[1]["use_tris"], red)]:
# cull to the subset of tris with all positive X (toward camera)
tris = tris[(rr_sph[tris, 0] > 0).all(axis=1)]
renderer.mesh(
*rr_sph.T,
triangles=tris,
color=color,
representation="wireframe",
line_width=3,
render_lines_as_tubes=True,
)
mne.viz.set_3d_view(figure=renderer.figure, distance=150, **view_kwargs)
renderer.show()
# %%
# We can also then look at how these two meshes compare by plotting the
# original, high-density mesh as well as our decimated mesh white surfaces.
renderer = mne.viz.backends.renderer.create_3d_figure(
size=(800, 400), scene=False, **renderer_kwargs
)
y_shifts = [-125, 125]
tris = [src[1]["tris"], src[1]["use_tris"]]
for y_shift, tris in zip(y_shifts, tris):
this_rr = src[1]["rr"] * 1000.0 + [0, y_shift, -40]
renderer.mesh(
*this_rr.T,
triangles=tris,
color=None,
scalars=curvs[0],
colormap="copper_r",
vmin=-0.2,
vmax=1.2,
)
renderer.quiver3d([0], [-width / 2.0], [0], [0], [1], [0], "k", width, "arrow")
mne.viz.set_3d_view(figure=renderer.figure, distance=450, **view_kwargs)
renderer.show()
# %%
# .. warning::
# Some source space vertices can be removed during forward computation.
# See :ref:`tut-forward` for more information.
#
# .. _mni-affine-transformation:
#
# FreeSurfer's MNI affine transformation
# --------------------------------------
# In addition to surface-based approaches, FreeSurfer also provides a simple
# affine coregistration of each subject's data to the ``fsaverage`` subject.
# Let's pick a point for ``sample`` and plot it on the brain:
brain = mne.viz.Brain(
"sample", "lh", "white", subjects_dir=subjects_dir, background="w"
)
xyz = np.array([[-55, -10, 35]])
brain.add_foci(xyz, hemi="lh", color="k")
brain.show_view("lat")
# %%
# We can take this point and transform it to MNI space:
mri_mni_trans = mne.read_talxfm(subject, subjects_dir)
print(mri_mni_trans)
xyz_mni = apply_trans(mri_mni_trans, xyz / 1000.0) * 1000.0
print(np.round(xyz_mni, 1))
# %%
# And because ``fsaverage`` is special in that it's already in MNI space
# (its MRI-to-MNI transform is identity), it should land in the equivalent
# anatomical location:
brain = mne.viz.Brain(
"fsaverage", "lh", "white", subjects_dir=subjects_dir, background="w"
)
brain.add_foci(xyz_mni, hemi="lh", color="k")
brain.show_view("lat")
# %%
# Understanding the inflated brain
# --------------------------------
# It takes a minute to interpret data displayed on an inflated brain. This
# visualization is very helpful in showing more of a brain in one image
# since it is difficult to visualize inside the sulci. Below is a video
# relating the pial surface to an inflated surface. If you're interested
# in how this was created, here is the gist used to create the video:
# https://gist.github.com/alexrockhill/b5a1ce6c6ba363cf3f277cd321a763bf.
#
# .. youtube:: mOmfNX-Lkn0