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The SourceEstimate data structure#
Source estimates, commonly referred to as STC (Source Time Courses), are obtained from source localization methods. Source localization method solve the so-called ‘inverse problem’. MNE provides different methods for solving it: dSPM, sLORETA, LCMV, MxNE etc.
Source localization consists in projecting the EEG/MEG sensor data into a 3-dimensional ‘source space’ positioned in the individual subject’s brain anatomy. Hence the data is transformed such that the recorded time series at each sensor location maps to time series at each spatial location of the ‘source space’ where is defined our source estimates.
An STC object contains the amplitudes of the sources over time.
It only stores the amplitudes of activations but
not the locations of the sources. To get access to the locations
you need to have the
src) used to compute the
forward operator (often abbreviated
See Head model and forward computation for more details on forward modeling, and Source localization with MNE, dSPM, sLORETA, and eLORETA for an example of source localization with dSPM, sLORETA or eLORETA.
Source estimates come in different forms:
mne.SourceEstimate: For cortically constrained source spaces.
mne.VolSourceEstimate: For volumetric source spaces
mne.VectorSourceEstimate: For cortically constrained source spaces with vector-valued source activations (strength and orientation)
mne.MixedSourceEstimate: For source spaces formed of a combination of cortically constrained and volumetric sources.
SourceEstimate are surface representations
mostly used together with FreeSurfer
Let’s get ourselves an idea of what a
is. We first set up the environment and load some data:
from mne import read_source_estimate from mne.datasets import sample print(__doc__) # Paths to example data sample_dir_raw = sample.data_path() sample_dir = sample_dir_raw / 'MEG' / 'sample' subjects_dir = sample_dir_raw / 'subjects' fname_stc = sample_dir / 'sample_audvis-meg'
Load and inspect example data#
This data set contains source estimation data from an audio visual task. It has been mapped onto the inflated cortical surface representation obtained from FreeSurfer using the dSPM method. It highlights a noticeable peak in the auditory cortices.
Let’s see how it looks like.
stc = read_source_estimate(fname_stc, subject='sample') # Define plotting parameters surfer_kwargs = dict( hemi='lh', subjects_dir=subjects_dir, clim=dict(kind='value', lims=[8, 12, 15]), views='lateral', initial_time=0.09, time_unit='s', size=(800, 800), smoothing_steps=5) # Plot surface brain = stc.plot(**surfer_kwargs) # Add title brain.add_text(0.1, 0.9, 'SourceEstimate', 'title', font_size=16)
A source estimate contains the time series of a activations
at spatial locations defined by the source space.
In the context of a FreeSurfer surfaces - which consist of 3D triangulations
- we could call each data point on the inflated brain
representation a vertex . If every vertex represents the spatial location
of a time series, the time series and spatial location can be written into a
matrix, where to each vertex (rows) at multiple time points (columns) a value
can be assigned. This value is the strength of our signal at a given point in
space and time. Exactly this matrix is stored in
Let’s have a look at the shape
shape = stc.data.shape print('The data has %s vertex locations with %s sample points each.' % shape)
The data has 7498 vertex locations with 25 sample points each.
We see that stc carries 7498 time series of 25 samples length. Those time series belong to 7498 vertices, which in turn represent locations on the cortical surface. So where do those vertex values come from?
FreeSurfer separates both hemispheres and creates surfaces
representation for left and right hemisphere. Indices to surface locations
are stored in
stc.vertices. This is a list with two arrays of integers,
that index a particular vertex of the FreeSurfer mesh. A value of 42 would
hence map to the x,y,z coordinates of the mesh with index 42.
See next section on how to get access to the positions in a
Since both hemispheres are always represented separately, both attributes
introduced above, can also be obtained by selecting the respective
hemisphere. This is done by adding the correct prefix (
shape_lh = stc.lh_data.shape print('The left hemisphere has %s vertex locations with %s sample points each.' % shape_lh)
The left hemisphere has 3732 vertex locations with 25 sample points each.
Since we did not change the time representation, only the selected subset of
vertices and hence only the row size of the matrix changed. We can check if
the rows of
stc.rh_data sum up to the value we had
is_equal = stc.lh_data.shape + stc.rh_data.shape == stc.data.shape print('The number of vertices in stc.lh_data and stc.rh_data do ' + ('not ' if not is_equal else '') + 'sum up to the number of rows in stc.data')
The number of vertices in stc.lh_data and stc.rh_data do sum up to the number of rows in stc.data
Indeed and as the mindful reader already suspected, the same can be said
stc.lh_vertno thereby maps to the left and
stc.rh_vertno to the right inflated surface representation of
Relationship to SourceSpaces (src)#
As mentioned above,
src carries the mapping from
stc to the surface. The surface is built up from a
for each hemisphere. Each triangle building up a face consists of 3 vertices.
Since src is a list of two source spaces (left and right hemisphere), we can
access the respective data by selecting the source space first. Faces
building up the left hemisphere can be accessed via
the index \(0\) stands for the left and \(1\) for the right
The values in src[‘tris’] refer to row indices in
Here we find the actual coordinates of the surface mesh. Hence every index
value for vertices will select a coordinate from here. Furthermore
src['vertno'] stores the same data as
except when working with sparse solvers such as
mne.inverse_sparse.mixed_norm(), as then only a fraction of
vertices actually have non-zero activations.
In other words
src['vertno']. Thus the Nth time series in
stc.lh_data corresponds to the Nth value in stc.lh_vertno and
src[‘vertno’] respectively, which in turn map the time series to a
specific location on the surface, represented as the set of cartesian
Let’s obtain the peak amplitude of the data as vertex and time point index
peak_vertex, peak_time = stc.get_peak(hemi='lh', vert_as_index=True, time_as_index=True)
The first value thereby indicates which vertex and the second which time
point index from within
stc.lh_data is used. We can
use the respective information to get the index of the surface vertex
resembling the peak and its value.
Let’s visualize this as well, using the same
surfer_kwargs as in the
brain = stc.plot(**surfer_kwargs) # We add the new peak coordinate (as vertex index) as an annotation dot brain.add_foci(peak_vertex_surf, coords_as_verts=True, hemi='lh', color='blue') # We add a title as well, stating the amplitude at this time and location brain.add_text(0.1, 0.9, 'Peak coordinate', 'title', font_size=14)
stc is a class of MNE-Python, representing the
transformed time series obtained from source estimation. For both hemispheres
the data is stored separately in
stc.rh_data in form
of a \(m \times n\) matrix, where \(m\) is the number of spatial
locations belonging to that hemisphere and \(n\) the number of time
stc.rh_vertno correspond to
src['vertno']. Those are the indices of locations on the surface
The surface’s mesh coordinates are stored in
src['rr'] for left and right hemisphere. 3D coordinates can be
accessed by the above logic:
>>> lh_coordinates = src['rr'][stc.lh_vertno] >>> lh_data = stc.lh_data
>>> rh_coordinates = src['rr'][src['vertno']] >>> rh_data = stc.rh_data
Total running time of the script: ( 0 minutes 12.445 seconds)
Estimated memory usage: 34 MB