mne_nirs.statistics.RegressionResults#

class mne_nirs.statistics.RegressionResults(info, data, design)[source]#

Class containing GLM regression results.

Parameters:

infomne.Info: Info.
datadict: Dictionary.
designdataframe: Design matrix.

Attributes:

ch_names: Return the channel names.
compensation_grade: The current gradient compensation grade.
data

Methods

`MSE`()	Return the GLM MSE.
`compute_contrast`(contrast[, contrast_type])	Compute contrasts on regression results.
`copy`()	Return a copy of the GLM results.
`get_channel_types`([picks, unique, only_data_chs])	Get a list of channel type for each channel.
`model`()	Return the GLM model.
`pick`(picks[, exclude])	Pick a subset of channels.
`plot_topo`([conditions, axes, vlim, vmin, ...])	Plot 2D topography of GLM data.
`save`(fname[, overwrite])	Save GLM results to disk.
`scatter`([conditions, exclude_no_interest, ...])	Scatter plot of the GLM results.
`surface_projection`([chroma, condition, ...])	Project GLM results on to the surface of the brain.
`theta`()	Return the GLM theta results.
`to_dataframe`([order])	Return a tidy dataframe representing the GLM results.
`to_dataframe_region_of_interest`(group_by, ...)	Region of interest results as a dataframe.

Returns:

glm_estResultsGLM,: Result class.

MSE()[source]#

Return the GLM MSE.

Returns:

thetasarray: Array of MSEs. A value is provided per channel.

__contains__(ch_type)#

Check channel type membership.

Parameters:

ch_typestr: Channel type to check for. Can be e.g. 'meg', 'eeg', 'stim', etc.

Returns:

inbool: Whether or not the instance contains the given channel type.

Examples

Channel type membership can be tested as:

>>> 'meg' in inst  
True
>>> 'seeg' in inst  
False

__hash__ = None#

property ch_names#

Return the channel names.

Returns:

namesarray: The channel names.

property compensation_grade#: The current gradient compensation grade.

compute_contrast(contrast, contrast_type=None)[source]#

Compute contrasts on regression results.

This is a wrapper function for nilearn.stats.contrasts.

Parameters:

contrastnumpy.ndarray of shape (p) or (q, p),: Where q = number of contrast vectors and p = number of regressors.
contrast_type{None, ‘t’, ‘F’}, optional: Type of the contrast. If None, then defaults to ‘t’ for 1D con_val and ‘F’ for 2D con_val.

Returns:

contrastContrast instance,: Yields the statistics of the contrast (effects, variance, p-values).

copy()#

Return a copy of the GLM results.

Returns:

instinstance of ResultsGLM: A copy of the object.

get_channel_types(picks=None, unique=False, only_data_chs=False)#

Get a list of channel type for each channel.

Parameters:

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 all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
uniquebool: Whether to return only unique channel types. Default is False.
only_data_chsbool: Whether to ignore non-data channels. Default is False.

Returns:

channel_typeslist: The channel types.

model()[source]#

Return the GLM model.

Returns:

modelsarray: Array of models. An model is provided per channel.

pick(picks, exclude=())[source]#

Pick a subset of channels.

Parameters:

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 all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
excludelist | str: Set of channels to exclude, only used when picking based on types (e.g., exclude=”bads” when picks=”meg”).

Returns:

instinstance of ResultsGLM: The modified instance.

plot_topo(conditions=None, axes=None, *, vlim=(None, None), vmin=None, vmax=None, colorbar=True, figsize=(12, 7), sphere=None)[source]#

Plot 2D topography of GLM data.

Parameters:

conditionsarray: Which conditions should be displayed.
axesinstance of Axes | None: The axes to plot to. If None, a new figure is used.
vlimtuple of length 2: Colormap limits to use. If a tuple of floats, specifies the lower and upper bounds of the colormap (in that order); providing None for either entry will set the corresponding boundary at the positive or negative max of the absolute value of the data.
vminfloat | None: Deprecated, use vlim instead.
vmaxfloat | None: Deprecated, use vlim instead.
colorbarBool: Should a colorbar be plotted.
figsizetwo values: Figure size.
sphereAs specified in MNE: Sphere parameter from mne.viz.topomap.plot_topomap.

Returns:

figfigure: Figure of each design matrix component for hbo (top row) and hbr (bottom row).

save(fname, overwrite=False)#

Save GLM results to disk.

Parameters:

fnamestr: The filename to use to write the HDF5 data. Should end in 'glm.h5'.
overwritebool: If True (default False), overwrite the destination file if it exists.

scatter(conditions=(), exclude_no_interest=True, axes=None, no_interest=None)#

Scatter plot of the GLM results.

Parameters:

conditionslist: List of condition names to plot. By default plots all regressors of interest.
exclude_no_interestbool: Exclude regressors of no interest from the figure.
axesAxes: Optional axes on which to plot the data.
no_interestlist: List of regressors that are of no interest. If none are specified then conditions starting with [“drift”, “constant”, “short”, “Short”] will be excluded.

Returns:

pltmatplotlib.Figure: Scatter plot.

surface_projection(chroma='hbo', condition=None, 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)[source]#

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:

chromastr

Chromophore to plot`.

conditionstr

Condition to plot`.

backgroundmatplotlib color

Color of the background of the display window.

figuremayavi.core.api.Scene, matplotlib.figure.Figure, list, 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.

theta()[source]#

Return the GLM theta results.

Returns:

thetasarray: Array of thetas. An array is provided per channel.

to_dataframe(order=None)#

Return a tidy dataframe representing the GLM results.

Parameters:

orderlist: Order in which the rows should be returned by channel name.

Returns:

tidypandas.DataFrame: Dataframe containing GLM results.

to_dataframe_region_of_interest(group_by, condition, weighted=True, demographic_info=False)[source]#

Region of interest results as a dataframe.

Parameters:

group_bydict

Specifies which channels are aggregated into a single ROI. The dict key will be used as the ROI label and the dict values must be lists of picks (either channel names or integer indices of epochs.ch_names). For example:

group_by=dict(Left_ROI=[1, 2, 3, 4], Right_ROI=[5, 6, 7, 8])

conditionstr | list

Name to be used for condition.

weightedBool | dict

Weighting to be applied to each channel in the ROI computation. If False, then all channels will be weighted equally. If True, channels will be weighted by the inverse of the standard error of the GLM fit. For manual specification of the channel weighting a dictionary can be provided. If a dictionary is provided, the keys and length of lists must match the group_by parameters. The weights will be scaled internally to sum to 1.

demographic_infoBool

Add an extra column with demographic information from info[“subject_info”].

Returns:

tidypandas.DataFrame: Dataframe containing GLM results.

Examples using `mne_nirs.statistics.RegressionResults`#

GLM Analysis (Simulated)

GLM Analysis (Measured)

Utilising Anatomical Information

Save and load GLM results

mne_nirs.statistics.RegressionResults#

Examples using mne_nirs.statistics.RegressionResults#

Examples using `mne_nirs.statistics.RegressionResults`#