algorithms.modelgen

SpecifyModel

Link to code

Makes a model specification compatible with spm/fsl designers.

The subject_info field should contain paradigm information in the form of a Bunch or a list of Bunch. The Bunch should contain the following information:

[Mandatory]
- conditions : list of names
- onsets : lists of onsets corresponding to each condition
- durations : lists of durations corresponding to each condition. Should be
left to a single 0 if all events are being modelled as impulses.

[Optional]
- regressor_names : list of str
    list of names corresponding to each column. Should be None if
    automatically assigned.
- regressors : list of lists
   values for each regressor - must correspond to the number of
   volumes in the functional run
- amplitudes : lists of amplitudes for each event. This will be ignored by
  SPM's Level1Design.

The following two (tmod, pmod) will be ignored by any Level1Design class
other than SPM:

- tmod : lists of conditions that should be temporally modulated. Should
  default to None if not being used.
- pmod : list of Bunch corresponding to conditions
  - name : name of parametric modulator
  - param : values of the modulator
  - poly : degree of modulation

Alternatively, you can provide information through event files.

The event files have to be in 1, 2 or 3 column format with the columns corresponding to Onsets, Durations and Amplitudes and they have to have the name event_name.runXXX... e.g.: Words.run001.txt. The event_name part will be used to create the condition names.

Examples

>>> from nipype.interfaces.base import Bunch
>>> s = SpecifyModel()
>>> s.inputs.input_units = 'secs'
>>> s.inputs.functional_runs = ['functional2.nii', 'functional3.nii']
>>> s.inputs.time_repetition = 6
>>> s.inputs.high_pass_filter_cutoff = 128.
>>> info = [Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 180]],                      durations=[[1]]),                 Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]],                       durations=[[1]])]
>>> s.inputs.subject_info = info

Using pmod:

>>> info = [Bunch(conditions=['cond1', 'cond2'],                       onsets=[[2, 50],[100, 180]], durations=[[0],[0]],                       pmod=[Bunch(name=['amp'], poly=[2], param=[[1, 2]]),                      None]),                 Bunch(conditions=['cond1', 'cond2'],                       onsets=[[20, 120],[80, 160]], durations=[[0],[0]],                       pmod=[Bunch(name=['amp'], poly=[2], param=[[1, 2]]),                       None])]
>>> s.inputs.subject_info = info

Inputs:

[Mandatory]
event_files: (a list of items which are a list of items which are an
         existing file name)
        list of event description files 1, 2 or 3 column format
        corresponding to onsets, durations and amplitudes
        mutually_exclusive: subject_info, event_files
functional_runs: (a list of items which are a list of items which are
         an existing file name or an existing file name)
        Data files for model. List of 4D files or list of list of 3D files
        per session
high_pass_filter_cutoff: (a float)
        High-pass filter cutoff in secs
input_units: (u'secs' or u'scans')
        Units of event onsets and durations (secs or scans). Output units
        are always in secs
subject_info: (a list of items which are a Bunch or None)
        Bunch or List(Bunch) subject specific condition information. see
        :ref:`SpecifyModel` or SpecifyModel.__doc__ for details
        mutually_exclusive: subject_info, event_files
time_repetition: (a float)
        Time between the start of one volume to the start of the next image
        volume.

[Optional]
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
outlier_files: (a list of items which are an existing file name)
        Files containing scan outlier indices that should be tossed
realignment_parameters: (a list of items which are an existing file
         name)
        Realignment parameters returned by motion correction algorithm

Outputs:

session_info: (any value)
        session info for level1designs

SpecifySPMModel

Link to code

Adds SPM specific options to SpecifyModel

adds:

• concatenate_runs
• output_units

Examples

>>> from nipype.interfaces.base import Bunch
>>> s = SpecifySPMModel()
>>> s.inputs.input_units = 'secs'
>>> s.inputs.output_units = 'scans'
>>> s.inputs.high_pass_filter_cutoff = 128.
>>> s.inputs.functional_runs = ['functional2.nii', 'functional3.nii']
>>> s.inputs.time_repetition = 6
>>> s.inputs.concatenate_runs = True
>>> info = [Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 180]],                       durations=[[1]]),                 Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]],                       durations=[[1]])]
>>> s.inputs.subject_info = info

Inputs:

[Mandatory]
event_files: (a list of items which are a list of items which are an
         existing file name)
        list of event description files 1, 2 or 3 column format
        corresponding to onsets, durations and amplitudes
        mutually_exclusive: subject_info, event_files
functional_runs: (a list of items which are a list of items which are
         an existing file name or an existing file name)
        Data files for model. List of 4D files or list of list of 3D files
        per session
high_pass_filter_cutoff: (a float)
        High-pass filter cutoff in secs
input_units: (u'secs' or u'scans')
        Units of event onsets and durations (secs or scans). Output units
        are always in secs
subject_info: (a list of items which are a Bunch or None)
        Bunch or List(Bunch) subject specific condition information. see
        :ref:`SpecifyModel` or SpecifyModel.__doc__ for details
        mutually_exclusive: subject_info, event_files
time_repetition: (a float)
        Time between the start of one volume to the start of the next image
        volume.

[Optional]
concatenate_runs: (a boolean, nipype default value: False)
        Concatenate all runs to look like a single session.
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
outlier_files: (a list of items which are an existing file name)
        Files containing scan outlier indices that should be tossed
output_units: (u'secs' or u'scans', nipype default value: secs)
        Units of design event onsets and durations (secs or scans)
realignment_parameters: (a list of items which are an existing file
         name)
        Realignment parameters returned by motion correction algorithm

Outputs:

session_info: (any value)
        session info for level1designs

SpecifySparseModel

Link to code

Specify a sparse model that is compatible with spm/fsl designers

References

[1]	Perrachione TK and Ghosh SS (2013) Optimized design and analysis of

sparse-sampling fMRI experiments. Front. Neurosci. 7:55 http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00055/abstract

Examples

>>> from nipype.interfaces.base import Bunch
>>> s = SpecifySparseModel()
>>> s.inputs.input_units = 'secs'
>>> s.inputs.functional_runs = ['functional2.nii', 'functional3.nii']
>>> s.inputs.time_repetition = 6
>>> s.inputs.time_acquisition = 2
>>> s.inputs.high_pass_filter_cutoff = 128.
>>> s.inputs.model_hrf = True
>>> info = [Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 180]],                       durations=[[1]]),                 Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]],                       durations=[[1]])]
>>> s.inputs.subject_info = info

Inputs:

[Mandatory]
event_files: (a list of items which are a list of items which are an
         existing file name)
        list of event description files 1, 2 or 3 column format
        corresponding to onsets, durations and amplitudes
        mutually_exclusive: subject_info, event_files
functional_runs: (a list of items which are a list of items which are
         an existing file name or an existing file name)
        Data files for model. List of 4D files or list of list of 3D files
        per session
high_pass_filter_cutoff: (a float)
        High-pass filter cutoff in secs
input_units: (u'secs' or u'scans')
        Units of event onsets and durations (secs or scans). Output units
        are always in secs
subject_info: (a list of items which are a Bunch or None)
        Bunch or List(Bunch) subject specific condition information. see
        :ref:`SpecifyModel` or SpecifyModel.__doc__ for details
        mutually_exclusive: subject_info, event_files
time_acquisition: (a float)
        Time in seconds to acquire a single image volume
time_repetition: (a float)
        Time between the start of one volume to the start of the next image
        volume.

[Optional]
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
model_hrf: (a boolean)
        model sparse events with hrf
outlier_files: (a list of items which are an existing file name)
        Files containing scan outlier indices that should be tossed
realignment_parameters: (a list of items which are an existing file
         name)
        Realignment parameters returned by motion correction algorithm
save_plot: (a boolean)
        save plot of sparse design calculation (Requires matplotlib)
scale_regressors: (a boolean, nipype default value: True)
        Scale regressors by the peak
scan_onset: (a float, nipype default value: 0.0)
        Start of scanning relative to onset of run in secs
stimuli_as_impulses: (a boolean, nipype default value: True)
        Treat each stimulus to be impulse like.
use_temporal_deriv: (a boolean)
        Create a temporal derivative in addition to regular regressor
        requires: model_hrf
volumes_in_cluster: (an integer >= 1, nipype default value: 1)
        Number of scan volumes in a cluster

Outputs:

session_info: (any value)
        session info for level1designs
sparse_png_file: (a file name)
        PNG file showing sparse design
sparse_svg_file: (a file name)
        SVG file showing sparse design

gcd()

Link to code

Returns the greatest common divisor of two integers

uses Euclid’s algorithm

>>> gcd(4, 5)
~
>>> gcd(4, 8)
~
>>> gcd(22, 55)
~~

gen_info()

Link to code

Generate subject_info structure from a list of event files

orth()

Link to code

Orthoganlize y_in with respect to x_in

>>> orth_expected = np.array([1.7142857142857144, 0.42857142857142883,                                   -0.85714285714285676])
>>> err = np.abs(np.array(orth([1, 2, 3],[4, 5, 6]) - orth_expected))
>>> all(err < np.finfo(float).eps)
True

scale_timings()

Link to code

Scales timings given input and output units (scans/secs)

Parameters

timelist: list of times to scale input_units: ‘secs’ or ‘scans’ output_units: Ibid. time_repetition: float in seconds

spm_hrf()

Link to code

python implementation of spm_hrf

see spm_hrf for implementation details

% RT - scan repeat time % p - parameters of the response function (two gamma % functions) % defaults (seconds) % p(0) - delay of response (relative to onset) 6 % p(1) - delay of undershoot (relative to onset) 16 % p(2) - dispersion of response 1 % p(3) - dispersion of undershoot 1 % p(4) - ratio of response to undershoot 6 % p(5) - onset (seconds) 0 % p(6) - length of kernel (seconds) 32 ~ % hrf - hemodynamic response function % p - parameters of the response function

the following code using scipy.stats.distributions.gamma doesn’t return the same result as the spm_Gpdf function

hrf = gamma.pdf(u, p[0]/p[2], scale=dt/p[2]) -
      gamma.pdf(u, p[1]/p[3], scale=dt/p[3])/p[4]

>>> print(spm_hrf(2))
[  0.00000000e+00   8.65660810e-02   3.74888236e-01   3.84923382e-01
   2.16117316e-01   7.68695653e-02   1.62017720e-03  -3.06078117e-02
  -3.73060781e-02  -3.08373716e-02  -2.05161334e-02  -1.16441637e-02
  -5.82063147e-03  -2.61854250e-03  -1.07732374e-03  -4.10443522e-04
  -1.46257507e-04]