mne_nirs.visualisation.plot_glm_surface_projection

mne_nirs.visualisation.plot_glm_surface_projection(inst, statsmodel_df, picks='hbo', value='Coef.', background='w', figure=None, clim='auto', mode='weighted', colormap='RdBu_r', surface='pial', hemi='both', size=800, view=None, colorbar=True, distance=0.03, subjects_dir=None, src=None, verbose=False)

Project GLM results on to the surface of the brain.

Note: This function provides a convenient wrapper around low level MNE-Python functions. It is convenient if you wish to use a generic head model. If you have acquired fMRI images you may wish to use the underlying lower level functions.

Note: This function does not conduct a forward model analysis with photon migration etc. It simply projects the values from each channel to the local cortical surface. It is useful for visualisation, but users should take this in to consideration when drawing conclusions from the visualisation.

Parameters:

instinstance of Raw

Haemoglobin data.

statsmodel_dfdataframe

As produced by produced by statsmodels_to_results.

picksstr | array-like | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick good sEEG, ECoG, and DBS channels.

valuestr

Column from dataframe to plot.

backgroundmatplotlib color

Color of the background of the display window.

figuremayavi.core.api.Scene, matplotlib.figure.Figure, list, int, None

If None, a new figure will be created. If multiple views or a split view is requested, this must be a list of the appropriate length. If int is provided it will be used to identify the Mayavi figure by it’s id or create a new figure with the given id. If an instance of matplotlib figure, mpl backend is used for plotting.

climstr | dict

Colorbar properties specification. If ‘auto’, set clim automatically based on data percentiles. If dict, should contain:

kind‘value’ | ‘percent’

Flag to specify type of limits.

limslist | np.ndarray | tuple of float, 3 elements

Lower, middle, and upper bounds for colormap.

pos_limslist | np.ndarray | tuple of float, 3 elements

Lower, middle, and upper bound for colormap. Positive values will be mirrored directly across zero during colormap construction to obtain negative control points.

Note

Only one of lims or pos_lims should be provided. Only sequential colormaps should be used with lims, and only divergent colormaps should be used with pos_lims.

modestr

Can be “sum” to do a linear sum of weights, “weighted” to make this a weighted sum, “nearest” to use only the weight of the nearest sensor, or “single” to do a distance-weight of the nearest sensor. Default is “sum”.

colormapstr

Colormap to use.

surfacestr

The type of surface (inflated, white etc.).

hemistr

Hemisphere id (ie ‘lh’, ‘rh’, ‘both’, or ‘split’). In the case of ‘both’, both hemispheres are shown in the same window. In the case of ‘split’ hemispheres are displayed side-by-side in different viewing panes.

sizefloat or tuple of float

The size of the window, in pixels. can be one number to specify a square window, or the (width, height) of a rectangular window. Has no effect with mpl backend.

viewstr

View to set brain to.

colorbarbool

If True, display colorbar on scene.

distancefloat

Distance (m) defining the activation “ball” of the sensor.

subjects_dirpath-like | None

The path to the directory containing the FreeSurfer subjects reconstructions. If None, defaults to the SUBJECTS_DIR environment variable.

srcinstance of SourceSpaces

The source space.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

figureinstance of mne.viz.Brain | matplotlib.figure.Figure

An instance of mne.viz.Brain or matplotlib figure.

Examples using mne_nirs.visualisation.plot_glm_surface_projection

Group Level GLM Analysis

Group Level GLM Analysis

Utilising Anatomical Information

Utilising Anatomical Information