Utility functions for mutli-subjectParcellation: this basically uses nipy io lib to perform IO opermation in parcel definition processes
Fixed parcellation of a given dataset
Parameters : | domain/mask_image : betas: list of paths to activation images from the subject : nbparcel, int : number fo desired parcels nn=6: number of nearest neighbors to define the image topology :
method=’ward’: clustering method used, to be chosen among :
write_di: string, topional, write directory. :
mu = 10., float: the relative weight of anatomical information : verbose=0: verbosity mode : fullpath=None, string, :
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Performs the parcellation of a certain mask
Parameters : | mask_images: string or Nifti1Image or list of strings/Nifti1Images, :
nb_parcel: int, :
threshold: float, optional, :
output_image: string, optional :
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Returns : | wim: Nifti1Imagine instance, representing the resulting parcellation : |
Instantiating a Parcel structure from a give set of input
Parameters : | mask_images: string or Nifti1Image or list of strings/Nifti1Images, :
learning_images: (nb_subject-) list of (nb_feature-) list of strings, :
ths=.5: threshold to select the regions that are common across subjects. :
fdim: int, optional :
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This function computes parcel averages and RFX at the parcel-level
Parameters : | Pa: MultiSubjectParcellation instance :
test_images: (Pa.nb_subj-) list of paths :
test_id: string, optional, :
rfx_path: string optional, :
swd: string, optional :
condition_id: string, optional, :
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Returns : | test_data: array of shape(Pa.nb_parcel, Pa.nb_subj) :
prfx: array of shape(Pa.nb_parcel), :
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Write images that describe the spatial structure of the parcellation
Parameters : | Pa : MultiSubjectParcellation instance,
template_path: string, optional, :
indiv_path: list of strings, optional :
subject_id: list of strings of length Pa.nb_subj :
swd: string, optional :
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