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# nipype.interfaces.freesurfer.longitudinal module

Provides interfaces to various longitudinal commands provided by freesurfer

## FuseSegmentations

Link to code

> Bases: FSCommand

> Wrapped executable: `mri_fuse_segmentations`.

> fuse segmentations together from multiple timepoints

> Examples

>     >>> from nipype.interfaces.freesurfer import FuseSegmentations
>     >>> fuse = FuseSegmentations()
>     >>> fuse.inputs.subject_id = 'tp.long.A.template'
>     >>> fuse.inputs.timepoints = ['tp1', 'tp2']
>     >>> fuse.inputs.out_file = 'aseg.fused.mgz'
>     >>> fuse.inputs.in_segmentations = ['aseg.mgz', 'aseg.mgz']
>     >>> fuse.inputs.in_segmentations_noCC = ['aseg.mgz', 'aseg.mgz']
>     >>> fuse.inputs.in_norms = ['norm.mgz', 'norm.mgz', 'norm.mgz']
>     >>> fuse.cmdline
>     'mri_fuse_segmentations -n norm.mgz -a aseg.mgz -c aseg.mgz tp.long.A.template tp1 tp2'

> in_normsa list of items which are a pathlike object or string representing an existing file
>   `-n <filename>`

>     - name of norm file to use (default: norm.mgs) must include the corresponding norm file for all given timepoints as well as for the current subject.

>   Maps to a command-line argument: `-n %s`.

> in_segmentationsa list of items which are a pathlike object or string representing an existing file

>   Name of aseg file to use (default: aseg.mgz) must include the aseg files for all the given timepoints. Maps to a command-line argument: `-a %s`.

> in_segmentations_noCCa list of items which are a pathlike object or string representing an existing file

>   Name of aseg file w/o CC labels (default: aseg.auto_noCCseg.mgz) must include the corresponding file for all the given timepoints. Maps to a command-line argument: `-c %s`.

> out_filea pathlike object or string representing a file

>   Output fused segmentation file.

> timepointsa list of items which are a string

>   Subject_ids or timepoints to be processed. Maps to a command-line argument: `%s` (position: -2).

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> subject_ida string

>   Subject_id being processed. Maps to a command-line argument: `%s` (position: -3).

> subjects_dira pathlike object or string representing an existing directory

>   Subjects directory.

> out_filea pathlike object or string representing a file

>   Output fused segmentation file.

## RobustTemplate

Link to code

> Bases: FSCommandOpenMP

> Wrapped executable: `mri_robust_template`.

> construct an unbiased robust template for longitudinal volumes

> Examples

>     >>> from nipype.interfaces.freesurfer import RobustTemplate
>     >>> template = RobustTemplate()
>     >>> template.inputs.in_files = ['structural.nii', 'functional.nii']
>     >>> template.inputs.auto_detect_sensitivity = True
>     >>> template.inputs.average_metric = 'mean'
>     >>> template.inputs.initial_timepoint = 1
>     >>> template.inputs.fixed_timepoint = True
>     >>> template.inputs.no_iteration = True
>     >>> template.inputs.subsample_threshold = 200
>     >>> template.cmdline  #doctest:
>     'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template mri_robust_template_out.mgz --subsample 200'
>     >>> template.inputs.out_file = 'T1.nii'
>     >>> template.cmdline  #doctest:
>     'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template T1.nii --subsample 200'

>     >>> template.inputs.transform_outputs = ['structural.lta',
>     ...                                      'functional.lta']
>     >>> template.inputs.scaled_intensity_outputs = ['structural-iscale.txt',
>     ...                                             'functional-iscale.txt']
>     >>> template.cmdline
>     'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template T1.nii --iscaleout .../structural-iscale.txt .../functional-iscale.txt --subsample 200 --lta .../structural.lta .../functional.lta'

>     >>> template.inputs.transform_outputs = True
>     >>> template.inputs.scaled_intensity_outputs = True
>     >>> template.cmdline
>     'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template T1.nii --iscaleout .../is1.txt .../is2.txt --subsample 200 --lta .../tp1.lta .../tp2.lta'

>     >>> template.run()

> References

> [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_robust_template]

> auto_detect_sensitivitya boolean

>   Auto-detect good sensitivity (recommended for head or full brain scans). Maps to a command-line argument: `--satit`. Mutually exclusive with inputs: `outlier_sensitivity`.

> in_filesa list of items which are a pathlike object or string representing an existing file

>   Input movable volumes to be aligned to common mean/median template. Maps to a command-line argument: `--mov %s`.

> out_filea pathlike object or string representing a file

>   Output template volume (final mean/median image). Maps to a command-line argument: `--template %s`. (Nipype default value: `mri_robust_template_out.mgz`)

> outlier_sensitivitya float

>   Set outlier sensitivity manually (e.g. “–sat 4.685” ). Higher values mean less sensitivity. Maps to a command-line argument: `--sat %.4f`. Mutually exclusive with inputs: `auto_detect_sensitivity`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> average_metric‘median’ or ‘mean’

>   Construct template from: 0 Mean, 1 Median (default). Maps to a command-line argument: `--average %d`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> fixed_timepointa boolean

>   Map everthing to init TP# (init TP is not resampled). Maps to a command-line argument: `--fixtp`.

> in_intensity_scalesa list of items which are a pathlike object or string representing an existing file

>   Use initial intensity scales. Maps to a command-line argument: `--iscalein %s`.

> initial_timepointan integer (int or long)

>   Use TP# for spacial init (default random), 0: no init. Maps to a command-line argument: `--inittp %d`.

> initial_transformsa list of items which are a pathlike object or string representing an existing file

>   Use initial transforms (lta) on source. Maps to a command-line argument: `--ixforms %s`.

> intensity_scalinga boolean

>   Allow also intensity scaling (default off). Maps to a command-line argument: `--iscale`.

> no_iterationa boolean

>   Do not iterate, just create first template. Maps to a command-line argument: `--noit`.

> num_threadsan integer (int or long)

>   Allows for specifying more threads.

> scaled_intensity_outputsa list of items which are a pathlike object or string representing a file or a boolean

>   Final intensity scales (will activate –iscale). Maps to a command-line argument: `--iscaleout %s`.

> subjects_dira pathlike object or string representing an existing directory

>   Subjects directory.

> subsample_thresholdan integer (int or long)

>   Subsample if dim > # on all axes (default no subs.). Maps to a command-line argument: `--subsample %d`.

> transform_outputsa list of items which are a pathlike object or string representing a file or a boolean

>   Output xforms to template (for each input). Maps to a command-line argument: `--lta %s`.

> out_filea pathlike object or string representing an existing file

>   Output template volume (final mean/median image).

> scaled_intensity_outputsa list of items which are a pathlike object or string representing an existing file

>   Output final intensity scales.

> transform_outputsa list of items which are a pathlike object or string representing an existing file

>   Output xform files from moving to template.