[Mne_analysis] Errors when morphing labels

Broman Emilia emilia.broman at aalto.fi
Wed Jun 26 10:11:36 EDT 2019
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Hi all,


I have a problem with morphing labels from "fsaverage" to subject space. I am trying to run the following code:


import mne
import numpy as np
import scipy.io as sio
from mne import datasets
from mne.minimum_norm import read_inverse_operator

datasets.fetch_hcp_mmp_parcellation(subjects_dir=subjects_dir,
                                        verbose=True)
datasets.fetch_aparc_sub_parcellation(subjects_dir=subjects_dir,
                                          verbose=True)
label_list =['superiortemporal_9-lh']
labels = mne.read_labels_from_annot('fsaverage', 'aparc_sub', 'lh', subjects_dir=subjects_dir)

for l in label_list:
    subj_list = ['p9u']
    frequency_band='4Hz8Hz'
    for run in range(1, 11):
        for subject in subj_list:
            this_label = [label for label in labels if label.name == l][0]
            inverse_operator = read_inverse_operator(fname_inv + subject +'/'+ subject + '_run' + str(run) + '-'+ frequency_band+'-inv.fif')
            src = inverse_operator['src']
            morphed_label=this_label.copy().morph('fsaverage', subject, 5, 5, subjects_dir, 1,None)
            fname= '/m/nbe/scratch/alex/private/emilia/stc-meg/stc/' + subject +'/'+ subject + '_run' + str(run) + '_'+ frequency_band+'_source_estimate-lh.stc'
            stc = mne.read_source_estimate(fname)
            label_data= stc.extract_label_time_course(morphed_label,src,mode='mean_flip')
            label_mean=np.mean(label_data,axis=0)
            sio.savemat(output_path + subject + '_'+frequency_band+'_run' + str(run) +'_'+ this_label.name + '_mean.mat', {'vect':label_mean})


However, when I try to run the command "morphed_label=this_label.copy().morph('fsaverage', subject, 5, 5, subjects_dir, 1,None)" I get the following error:



Traceback (most recent call last):
  File "<ipython-input-2-231684f5069e>", line 27, in <module>
    morphed_label=this_label.copy().morph('fsaverage', subject, 5, 5, subjects_dir, 1,None)
  File "</home/bromane1/.local/lib/python3.6/site-packages/mne/externals/decorator.py:decorator-gen-216>", line 2, in morph
  File "/home/bromane1/.local/lib/python3.6/site-packages/mne/utils/_logging.py", line 89, in wrapper
    return function(*args, **kwargs)
  File "/home/bromane1/.local/lib/python3.6/site-packages/mne/label.py", line 583, in morph
    subjects_dir=subjects_dir, warn=False).apply(stc)
  File "</home/bromane1/.local/lib/python3.6/site-packages/mne/externals/decorator.py:decorator-gen-280>", line 2, in compute_source_morph
  File "/home/bromane1/.local/lib/python3.6/site-packages/mne/utils/_logging.py", line 88, in wrapper
    return function(*args, **kwargs)
  File "/home/bromane1/.local/lib/python3.6/site-packages/mne/morph.py", line 188, in compute_source_morph
    xhemi=xhemi)
  File "/home/bromane1/.local/lib/python3.6/site-packages/mne/morph.py", line 865, in _compute_morph_matrix
    vertices_to[hemi_to], maps[hemi_from], warn=warn)
  File "/home/bromane1/.local/lib/python3.6/site-packages/mne/morph.py", line 1069, in _morph_buffer
    data_morphed = maps[nearest, :] * data
  File "/share/apps2/anaconda/anaconda3/latest/envs/neuroimaging/lib/python3.6/site-packages/scipy/sparse/csr.py", line 316, in __getitem__
    P = extractor(row, self.shape[0])     # [[1,2],j] or [[1,2],1:2]
  File "/share/apps2/anaconda/anaconda3/latest/envs/neuroimaging/lib/python3.6/site-packages/scipy/sparse/csr.py", line 270, in extractor
    min_indx, max_indx = check_bounds(indices, N)
  File "/share/apps2/anaconda/anaconda3/latest/envs/neuroimaging/lib/python3.6/site-packages/scipy/sparse/csr.py", line 256, in check_bounds
    raise IndexError('index (%d) out of range' % max_indx)
IndexError: index (160155) out of range

The dataset consists of 48 subjects out of which 4 subjects have this problem. I have checked the Freesurfer reconstruction of these subjects and they look OK. Do you happen to know what could have gone wrong here? Or do you have any ideas on where I should start looking for errors?

Thank you!
- Emilia
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