Subject to fs-avg

This module provides methods to transform data from subject specific into fs-average space.

src_rec.subj_to_fsavg.prepare(fs_path, anatomy_path, subj_name)

Compute precursors of the mri projections.

Parameters:

• fs_path (string) – Path to the freesurfer folder. Should contain the ‘bin’ folder, your license.txt, and sources.sh.

• anatomy_path (string) – Path to the anatomy folder. This folder should contain a sub-folder for each subject, to be pupulated with the corresponding structural data.

• subj_name (string) – Name of the subject.

src_rec.subj_to_fsavg.compute(cort_mdl, anatomy_path, subj_name, fs_path, overwrite)

Create a subject to fs average transformation matrix.

Parameters:

• cort_mdl (finnpy.src_rec.cort_mdl.Cort_mdl) –

  Container populated with the following items:

  lh_vertnumpy.ndarray, shape(lh_vtx_cnt, 3)

  White matter surface model vertices (left hemisphere).

  lh_facesnumpy.ndarray, shape(lh_face_cnt, 3)

  White matter surface model faces (left hemisphere).

  lh_valid_vertnumpy.ndarray, shape(lh_vtx_cnt,)

  Valid flags for white matter surface model vertices (left hemisphere).

  rh_vertnumpy.ndarray, shape(rh_vtx_cnt, 3)

  White matter surface model vertices (right hemisphere).

  rh_facesnumpy.ndarray, shape(rh_face_cnt, 3)

  White matter surface model faces (right hemisphere).

  lh_valid_vertnumpy.ndarray, shape(rh_vtx_cnt,)

  Valid flags for white matter surface model vertices (right hemisphere).

  octa_model_vertnumpy.ndarray, shape(octa_mdl_vtx_cnt, 3)

  Octamodel vertices (left hemisphere).

  octa_model_facesnumpy.ndarray, shape(octa_mdl_face_cnt, 3)

  Octamodel faces (right hemisphere).

• anatomy_path (string) – Path to the anatomy folder. This folder should contain a sub-folder for each subject, to be pupulated with the corresponding structural data.

• subj_name (string) – Name of the subject.

• fs_path (string) – Path to the freesurfer folder. Should contain the ‘bin’ folder, your license.txt, and sources.sh.

• overwrite (boolean) – Flag whether to overwrite MRI maps.

Returns:

subj_to_fsavg_mdl – Container class, populated with the following items:

transnumpy.ndarray, shape(valid_subj_vtx_cnt, valid_subj_vtx_cnt)

Transformation matrix

lh_valid_vertnumpy.ndarray, shape(fs_avg_vtx_cnt,)

Valid/supporting vertices for left hemisphere.

rh_valid_vertnumpy.ndarray, shape(fs_avg_vtx_cnt,)

Valid/supporting vertices for right hemisphere.

Return type:

finnpy.src_rec.subj_to_fsavg.Subj_to_fsavg_mdl

src_rec.subj_to_fsavg.apply(subj_to_fsavg_mdl, data)

Transforms data from subject space to fs_average space

Parameters:

• trans_mat (scipy.sparse.csr_matrix, shape(source_space_ch_cnt, source_space_ch_cnt)) – Subject to fs-average transformation matrix.

• data (numpy.ndarray, shape(source_space_ch_cnt, samp_cnt)) – Source space (subject) data.

Returns:

transformed_data – Source space (fs-average) data.

Return type:

numpy.ndarray, shape(source_space_ch_cnt, samp_cnt)

src_rec.subj_to_fsavg._calc_small_to_default_vertices_proj(valid_vert, sub_vert, sub_faces)

Calculates projection from subject vertices to small sphere vertices.

Parameters:

• valid_vert (numpy.ndarray, shape(mri_vtx_cnt,)) – Valid vertices

• sub_vert (numpy.ndarray, shape(mri_vtx_cnt, 3)) – High-definition vertices.

• sub_faces (numpy.ndarray, shape(mri_face_cnt, 3)) – High-definition faces.

Returns:

proj – Projection from all vertices to valid vertices.

Return type:

numpy.ndarray, shape(mri_face_cnt, valid_vtx_cnt)

src_rec.subj_to_fsavg._calc_mri_maps(anatomy_path, subj_name, fs_avg_path, hemisphere, overwrite)

Find the subject space points corresponding to fs avg space points.

Parameters:

• anatomy_path (string) – Path to the anatomy folder. This folder should contain a sub-folder for each subject, to be pupulated with the corresponding structural data.

• subj_name (string) – Name of the subject.

• fs_avg_path (string) – Path for fs average freesurfer files.

• hemisphere (string) – Hemisphere to compute for.

• overwrite (boolean) – Flag whether to overwrite preexisting mri maps.

Returns:

• sub_vert (numpy.ndarray, shape(mri_vtx_cnt, 3)) – Vertices of the MRI model.

• sub_faces (numpy.ndarray, shape(mri_face_cnt, 3)) – Faces of the MRI model.

• avg_vert (numpy.ndarray, shape(fs_avg_vtx_cnt, 3)) – Vertices of fs-avg’s sphere model.

• mri_map (numpy.ndarray, shape(fs_avg_vtx_cnt, mri_vtx_cnt)) – Translation from subject mri to fs-average sphere model.