brainiak.utils package

Utilities used by multiple subpackages.

Submodules

brainiak.utils.fmrisim module

fMRI Simulator

Simulate fMRI data for a single subject.

This code provides a set of functions necessary to produce realistic simulations of neural data.

Steps:

generate_signal Specify the volume (or volumes) which represent the signal in the neural data.

generate_stimfunction Create a function to describe the stimulus onsets/durations

export_stimfunction: Generate a three column timing file that can be used with software like FSL

double_gamma_hrf Convolve the stimulus function with the HRF to model when events are expected.

apply_signal Combine the volume and the HRF

calc_noise Estimate the noise properties of a given volume

generate_noise Create the noise for this run. This creates temporal, task and white noise. Various parameters can be tuned depending on need

mask_brain Mask the volume to look like a volume. Based on MNI standard space

plot_brain Show the brain as it unfolds over time with a given opacity.

Authors: Cameron Ellis (Princeton) 2016
brainiak.utils.fmrisim.generate_signal(dimensions, feature_coordinates, feature_size, feature_type, signal_magnitude=[1], signal_constant=1)

Generate volume containing signal

Generate signal in specific regions of the brain with for a single volume. This will then be convolved with the HRF across time

Parameters:
  • dimensions (3 length array, int) – What are the dimensions of the volume you wish to create
  • feature_coordinates (multidimensional array, int) – What are the feature_coordinates of the signal being created. Be aware of clipping: features far from the centre of the brain will be clipped. If you wish to have multiple features then list these as an features x 3 array. To create a signal of a specific shape then supply all the individual feature_coordinates and set the feature_size to 1.
  • feature_size (list, int) – How big is the signal. If m=1 then only one value is accepted, if m>1 then either one value must be supplied or m values
  • feature_type (list, string) – What feature_type of signal is being inserted? Options are cube, loop, cavity, sphere. If features = 1 then only one value is accepted, if features > 1 then either one value must be supplied or m values
  • signal_magnitude (list, float) – What is the (average) magnitude of the signal being generated? A value of 1 means that the signal is one standard deviation from the noise
  • signal_constant (list, bool) – Is the signal constant or is it a random pattern (with the same average magnitude)
Returns:

volume_static – Creates a single volume containing the signal

Return type:

3 dimensional array, float

brainiak.utils.fmrisim.generate_stimfunction(onsets, event_durations, total_time, weights=[1], timing_file=None)

Return the function for the onset of events

When do stimuli onset, how long for and to what extent should you resolve the fMRI time course. There are two ways to create this, either by supplying onset, duration and weight information or by supplying a timing file (in the three column format)

Parameters:
  • onsets (list, int) – What are the timestamps for when an event you want to generate onsets?
  • event_durations (list, int) – What are the durations of the events you want to generate? If there is only one value then this will be assigned to all onsets
  • total_time (int) – How long is the experiment in total.
  • weights (list, float) – How large is the box car for each of the onsets. If there is only one value then this will be assigned to all onsets
  • timing_file (string) – The filename (with path) to a three column timing file (FSL) to make the events. Still requires tr_duration and total_time
Returns:

The time course of stimulus related activation

Return type:

Iterable[float]

brainiak.utils.fmrisim.export_stimfunction(stimfunction, filename)

Output a tab separated timing file

This produces a three column tab separated text file, with the three columns representing onset time, event duration and weight, respectively

Useful if you want to run the simulated data through FEAT analyses

Parameters:
  • stimfunction (list) – The stimulus function describing the time course of events
  • filename (str) – The name of the three column text file to be output
brainiak.utils.fmrisim.double_gamma_hrf(stimfunction, tr_duration, response_delay=6, undershoot_delay=12, response_dispersion=0.9, undershoot_dispersion=0.9, response_scale=1, undershoot_scale=0.035)

Return a double gamma HRF

Parameters:
  • stimfunction (list, bool) – What is the time course of events to be modelled in this experiment
  • response_delay (float) – How many seconds until the peak of the HRF
  • undershoot_delay (float) – How many seconds until the trough of the HRF
  • response_dispersion (float) – How wide is the rising peak dispersion
  • undershoot_dispersion (float) – How wide is the undershoot dispersion
  • response_scale (float) – How big is the response relative to the peak
  • undershoot_scale (float) – How big is the undershoot relative to the trough
Returns:

The time course of the HRF convolved with the stimulus function

Return type:

one dimensional array

brainiak.utils.fmrisim.apply_signal(signal_function, volume_static)

Apply the convolution and stimfunction

Apply the convolution of the HRF and stimulus time course to the volume.

Parameters:
  • signal_function (list, float) – The one dimensional timecourse of the signal over time. Found by convolving the HRF with the stimulus time course.
  • volume_static (multi dimensional array, float) –
Returns:

Generates the spatial noise volume for these parameters

Return type:

multidimensional array, float

brainiak.utils.fmrisim.calc_noise(volume, mask=None)

Calculates the noise properties of the volume supplied. This estimates what noise properties the volume has. For instance it determines the spatial smoothness, the autoregressive noise, system noise etc. Read the doc string for generate_noise to understand how these different types of noise interact.

Parameters:
  • volume (4d numpy array, float) – Take in a functional volume (either the file name or the numpy array) to be used to estimate the noise properties of this
  • mask (4d numpy array, float) – The mask to be used, the same size as the volume
Returns:

  • dict
  • Return a dictionary of the calculated noise parameters of the provided
  • dataset

brainiak.utils.fmrisim.generate_noise(dimensions, stimfunction, tr_duration, mask=None, noise_dict=None)

Generate the noise to be added to the signal. Default noise parameters will create a noise volume with a standard deviation of 0.1 (where the signal defaults to a value of 1). This has built into estimates of how different types of noise mix. All noise values can be set by the user

Parameters:
  • dimensions (n length array, int) – What is the shape of the volume to be generated
  • stimfunction (Iterable, bool) – When do the stimuli events occur
  • tr_duration (float) – What is the duration, in seconds, of each TR?
  • mask (4d array, float) – The mask of the brain volume, using
  • noise_dict (dictionary, float) – This is a dictionary that must contain the key: overall. If there are no other variables provided then it will default values
Returns:

Generates the noise volume for these parameters

Return type:

multidimensional array, float

brainiak.utils.fmrisim.mask_brain(volume, mask_name=None, mask_threshold=0.1, mask_self=0)

Mask the simulated volume This takes in a volume and will output the masked volume. if a one dimensional array is supplied then the output will be a volume of the dimensions specified in the array. The mask can be created from the volume by averaging it. All masks will be bounded to the range of 0 to 1.

Parameters:
  • volume (multidimensional array) – Either numpy array of the volume that has been simulated or a tuple describing the dimensions of the mask to be created
  • mask_name (str) – What is the path to the mask to be loaded? If empty then it defaults to an MNI152 grey matter mask.
  • mask_threshold (float) – What is the threshold (0 -> 1) for including a voxel in the mask?
  • mask_self (bool) – If set to 1 then it makes a mask from the volume supplied (by averaging across time points and changing the range).
Returns:

mask – The masked brain

Return type:

multidimensional array, float

brainiak.utils.fmrisim.plot_brain(fig, brain, mask=None, percentile=99)

Display the brain that has been generated with a given threshold

Parameters:
  • fig (matplotlib object) – The figure to be displayed, generated from matplotlib. import matplotlib.pyplot as plt; fig = plt.figure()
  • brain (3d array) – This is a 3d array with the neural data
  • mask (3d array) – A binary mask describing the location that you want to specify as
  • percentile (float) – What percentage of voxels will be included? Based on the values supplied
Returns:

ax – Object with the information to be plotted

Return type:

matplotlib object

brainiak.utils.utils module

class brainiak.utils.utils.ReadDesign(fname=None, include_orth=True, include_pols=True)

Bases: object

A class which has the ability of reading in design matrix in .1D file, generated by AFNI’s 3dDeconvolve.

Parameters:
  • fname (string, the address of the file to read.) –
  • include_orth (Boollean, whether to include "orthogonal" regressors in) – the nuisance regressors which are usually head motion parameters. All the columns of design matrix are still going to be read in, but the attribute cols_used will reflect whether these orthogonal regressors are to be included for furhter analysis. Note that these are not entered into design_task attribute which include only regressors related to task conditions.
  • include_pols (Boollean, whether to include polynomial regressors in) – the nuisance regressors which are used to capture slow drift of signals.
design

2d array. The design matrix read in from the csv file.

design_task

2d array. The part of design matrix corresponding to – task conditions.

n_col

number of total columns in the design matrix.

column_types

1d array. the types of each column in the design matrix. – 0 for orthogonal regressors (usually head motion parameters), -1 for polynomial basis (capturing slow drift of signals), values > 0 for stimulus conditions

n_basis

scalar. The number of polynomial bases in the designn matrix.

n_stim

scalar. The number of stimulus conditions.

n_orth

scalar. The number of orthogoanal regressors (usually head – motions)

StimLabels

list. The names of each column in the design matrix.

read_afni(fname)
brainiak.utils.utils.concatenate_list(l, axis=0)

Construct a numpy array by stacking arrays in a list

Parameters:
  • data (list of arrays, arrays have same shape in all but one dimension or) –
  • are None (elements) – The list of arrays to be concatenated.
  • axis (int, default = 0) – Axis for the concatenation
Returns:

data_stacked – The resulting concatenated array.

Return type:

array

brainiak.utils.utils.cov2corr(cov)
Calculate the correlation matrix based on a
covariance matrix
Parameters:cov (2D array) –
Returns:corr – correlation converted from the covarince matrix
Return type:2D array
brainiak.utils.utils.fast_inv(a)

to invert a 2D matrix

Parameters:a (2D array) –
Returns:inva – inverse of input matrix a
Return type:2D array
Raises:LinAlgError – If a is singular or not square
brainiak.utils.utils.from_sym_2_tri(symm)
convert a 2D symmetric matrix to an upper
triangular matrix in 1D format
Parameters:symm (2D array) – Symmetric matrix
Returns:tri – Contains elements of upper triangular matrix
Return type:1D array
brainiak.utils.utils.from_tri_2_sym(tri, dim)
convert a upper triangular matrix in 1D format
to 2D symmetric matrix
Parameters:
  • tri (1D array) – Contains elements of upper triangular matrix
  • dim (int) – The dimension of target matrix.
Returns:

symm – Symmetric matrix in shape=[dim, dim]

Return type:

2D array

brainiak.utils.utils.sumexp_stable(data)

Compute the sum of exponents for a list of samples

Parameters:data (array, shape=[features, samples]) – A data array containing samples.
Returns:
  • result_sum (array, shape=[samples,]) – The sum of exponents for each sample divided by the exponent of the maximum feature value in the sample.
  • max_value (array, shape=[samples,]) – The maximum feature value for each sample.
  • result_exp (array, shape=[features, samples]) – The exponent of each element in each sample divided by the exponent of the maximum feature value in the sample.

Note

This function is more stable than computing the sum(exp(v)). It useful for computing the softmax_i(v)=exp(v_i)/sum(exp(v)) function.