algorithms.modelgen¶
SpecifyModel¶
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¶
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¶
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()
¶
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()
¶
Returns the greatest common divisor of two integers
uses Euclid’s algorithm
>>> gcd(4, 5)
~
>>> gcd(4, 8)
~
>>> gcd(22, 55)
~~
orth()
¶
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()
¶
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()
¶
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]