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.algorithms import modelgen
>>> from nipype.interfaces.base import Bunch
>>> s = modelgen.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.
>>> evs_run2 = Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 180]], durations=[[1]])
>>> evs_run3 = Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]])
>>> s.inputs.subject_info = [evs_run2, evs_run3]

Using pmod:

>>> evs_run2 = Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 50], [100, 180]], durations=[[0], [0]], pmod=[Bunch(name=['amp'], poly=[2], param=[[1, 2]]), None])
>>> evs_run3 = 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 = [evs_run2, evs_run3]

Inputs:

[Mandatory]
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, bids_event_file
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, bids_event_file
bids_event_file: (a list of items which are an existing file name)
        TSV event file containing common BIDS fields: `onset`,`duration`,
        and categorization and amplitude columns
        mutually_exclusive: subject_info, event_files, bids_event_file
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
input_units: ('secs' or 'scans')
        Units of event onsets and durations (secs or scans). Output units
        are always in secs
high_pass_filter_cutoff: (a float)
        High-pass filter cutoff in secs
time_repetition: (a float)
        Time between the start of one volume to the start of the next image
        volume.

[Optional]
bids_condition_column: (a unicode string, nipype default value:
          trial_type)
        Column of the file passed to `bids_event_file` to the unique values
        of which events will be assignedto regressors
bids_amplitude_column: (a unicode string)
        Column of the file passed to `bids_event_file` according to which to
        assign amplitudes to events
realignment_parameters: (a list of items which are an existing file
          name)
        Realignment parameters returned by motion correction algorithm
parameter_source: ('SPM' or 'FSL' or 'AFNI' or 'FSFAST' or 'NIPY',
          nipype default value: SPM)
        Source of motion parameters
outlier_files: (a list of items which are an existing file name)
        Files containing scan outlier indices that should be tossed

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.algorithms import modelgen
>>> from nipype.interfaces.base import Bunch
>>> s = modelgen.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
>>> evs_run2 = Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 180]], durations=[[1]])
>>> evs_run3 = Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]])
>>> s.inputs.subject_info = [evs_run2, evs_run3]

Inputs:

[Mandatory]
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, bids_event_file
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, bids_event_file
bids_event_file: (a list of items which are an existing file name)
        TSV event file containing common BIDS fields: `onset`,`duration`,
        and categorization and amplitude columns
        mutually_exclusive: subject_info, event_files, bids_event_file
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
input_units: ('secs' or 'scans')
        Units of event onsets and durations (secs or scans). Output units
        are always in secs
high_pass_filter_cutoff: (a float)
        High-pass filter cutoff in secs
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.
output_units: ('secs' or 'scans', nipype default value: secs)
        Units of design event onsets and durations (secs or scans)
bids_condition_column: (a unicode string, nipype default value:
          trial_type)
        Column of the file passed to `bids_event_file` to the unique values
        of which events will be assignedto regressors
bids_amplitude_column: (a unicode string)
        Column of the file passed to `bids_event_file` according to which to
        assign amplitudes to events
realignment_parameters: (a list of items which are an existing file
          name)
        Realignment parameters returned by motion correction algorithm
parameter_source: ('SPM' or 'FSL' or 'AFNI' or 'FSFAST' or 'NIPY',
          nipype default value: SPM)
        Source of motion parameters
outlier_files: (a list of items which are an existing file name)
        Files containing scan outlier indices that should be tossed

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.algorithms import modelgen
>>> from nipype.interfaces.base import Bunch
>>> s = modelgen.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
>>> evs_run2 = Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 180]], durations=[[1]])
>>> evs_run3 = Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]])
>>> s.inputs.subject_info = [evs_run2, evs_run3]

Inputs:

[Mandatory]
time_acquisition: (a float)
        Time in seconds to acquire a single image volume
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, bids_event_file
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, bids_event_file
bids_event_file: (a list of items which are an existing file name)
        TSV event file containing common BIDS fields: `onset`,`duration`,
        and categorization and amplitude columns
        mutually_exclusive: subject_info, event_files, bids_event_file
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
input_units: ('secs' or 'scans')
        Units of event onsets and durations (secs or scans). Output units
        are always in secs
high_pass_filter_cutoff: (a float)
        High-pass filter cutoff in secs
time_repetition: (a float)
        Time between the start of one volume to the start of the next image
        volume.

[Optional]
volumes_in_cluster: (a long integer >= 1, nipype default value: 1)
        Number of scan volumes in a cluster
model_hrf: (a boolean)
        Model sparse events with hrf
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
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
save_plot: (a boolean)
        Save plot of sparse design calculation (requires matplotlib)
bids_condition_column: (a unicode string, nipype default value:
          trial_type)
        Column of the file passed to `bids_event_file` to the unique values
        of which events will be assignedto regressors
bids_amplitude_column: (a unicode string)
        Column of the file passed to `bids_event_file` according to which to
        assign amplitudes to events
realignment_parameters: (a list of items which are an existing file
          name)
        Realignment parameters returned by motion correction algorithm
parameter_source: ('SPM' or 'FSL' or 'AFNI' or 'FSFAST' or 'NIPY',
          nipype default value: SPM)
        Source of motion parameters
outlier_files: (a list of items which are an existing file name)
        Files containing scan outlier indices that should be tossed

Outputs:

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

bids_gen_info()

Link to code

Generate subject_info structure from a list of BIDS .tsv event files.

Parameters

bids_event_files : list of str

Filenames of BIDS .tsv event files containing columns including: ‘onset’, ‘duration’, and ‘trial_type’ or the condition_column value.

condition_column : str

Column of files in bids_event_files based on the values of which events will be sorted into different regressors

amplitude_column : str

Column of files in bids_event_files based on the values of which to apply amplitudes to events. If unspecified, all events will be represented with an amplitude of 1.

Returns

list of Bunch

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

Orthogonalize 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]