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Stimulationsite information
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# Stimulation sites | ||
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The stimulation site coordinates consisted of two channels (A and B). A got the leading positive phase of stimulation. | ||
We decided to make stimulation sites equidistant between the two channels. We placed a 3mm ROI on the at this coordinate. We then checked which parcels overlapped the most with it. | ||
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The stimulation_information tsv files can be considered part of the raw data should be packaged with raw data at release (how they will be included at release is unclear at time of writing, so they are included here for reproducibility purposes). The stimulation sites in 2009c have been converted from Nlin6 using TemplateFlow and ants. | ||
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Prior to running the next step, the time series should be derived. | ||
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The file `stimsite2parcel.tsv` is created by running the `find_stimulation_parcels.tsv`. To replicate the step, run: | ||
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`docker run -u esfmri -v $(pwd):/home/esfmri/ -t esfmri python -m esfmri_connectivity.stimulation_sites.find_stimulation_parcel` |
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esfmri_connectivity/stimulation_sites/find_stimulation_parcel.py
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import nibabel as nib | ||
import pandas as pd | ||
from esfmri_connectivity.utils.getfiles import get_timeseries | ||
import numpy as np | ||
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parc = nib.load('esfmri_connectivity/parcellation/tpl-MNI152NLin2009cAsym_res-01_atlas-smorgasbord_dseg.nii.gz') | ||
stiminfo = pd.read_csv('./esfmri_connectivity/stimulation_sites/stimulation_infomation_space-MNI152NLin2009cAsym.tsv', sep='\t') | ||
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timeseries = get_timeseries(task='postop', require_stiminfo=True) | ||
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stim_center_mni = [] | ||
stim_center_xyz = [] | ||
for t in timeseries: | ||
sub = int(t.split('sub-')[1].split('_')[0]) | ||
run = int(t.split('run-')[1].split('_')[0]) | ||
# UNCLEAR IN PREREG | ||
# (stiminfo['multiple_contacts_in_run'] == 0, not specified in preregistration that subject will be excluded if multiple stimulations sites at once | ||
stim = stiminfo[(stiminfo['subject']==sub) & (stiminfo['run']==run) & (stiminfo['multiple_contacts_in_run'] == 0)] | ||
tmp = [] | ||
stim_center_mni.append([np.round(np.mean([stim['contact1_mni_' + x].values, stim['contact2_mni_' + x].values]), 2) for x in ['x', 'y', 'z']]) | ||
# centring on the voxel that best aligns with the coordinate and creating a sphere around that and great | ||
coord_xyz = np.linalg.inv(parc.affine).dot([stim_center_mni[-1][0], stim_center_mni[-1][1], stim_center_mni[-1][2], 1])[:-1] | ||
stim_center_xyz.append(np.round(coord_xyz.astype(int))) | ||
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def sphere(shape, radius, position): | ||
""" | ||
taken from https://stackoverflow.com/questions/46626267/how-to-generate-a-sphere-in-3d-numpy-array/46626448 | ||
""" | ||
# assume shape and position are both a 3-tuple of int or float | ||
# the units are pixels / voxels (px for short) | ||
# radius is a int or float in px | ||
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semisizes = (radius,) * 3 | ||
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# genereate the grid for the support points | ||
# centered at the position indicated by position | ||
grid = [slice(-x0, dim - x0) for x0, dim in zip(position, shape)] | ||
position = np.ogrid[grid] | ||
# calculate the distance of all points from `position` center | ||
# scaled by the radius | ||
arr = np.zeros(shape, dtype=float) | ||
for x_i, semisize in zip(position, semisizes): | ||
arr += (np.abs(x_i / semisize) ** 2) | ||
# the inner part of the sphere will have distance below 1 | ||
return arr <= 1.0 | ||
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# get_parcellation data | ||
parc_data = parc.get_fdata() | ||
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best_roi = [] | ||
good_roi = [] | ||
sub = [] | ||
run = [] | ||
# DEVIATION FROM PREREG | ||
# This was increased to 6 instead of 3 so all had some non-0 voxels. | ||
# If this is less than 6, an error will occur as some subjects will have 0 throughout the sphere. | ||
# Expanding the ROI seems to make sense, as multiple transforms have been applied and the centre of the roi is the middle of the two channels | ||
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roi_radius = 6 | ||
for i, xyz in enumerate(stim_center_xyz): | ||
sub.append(int(timeseries[i].split('sub-')[1].split('_')[0])) | ||
run.append(timeseries[i].split('run-')[1].split('_')[0]) | ||
t = pd.read_csv(timeseries[i], sep='\t', index_col=[0]) | ||
# Loop through all stimulations, make a sphere mask | ||
arr = sphere(parc.shape, roi_radius, xyz) | ||
# find the values withithe sphere | ||
vals = parc_data[arr] | ||
# exclude any 0s as these are not a roi | ||
vals = vals[vals!=0] | ||
# get counts of parcels overlapping with the sphere | ||
v, c = np.unique(vals, return_counts=True) | ||
# Find the parcel that overlaps the most | ||
best_roi.append(v[np.argmax(c)]) | ||
#Check if stimulate parcel is in time series for subject | ||
if (v[np.argmax(c)] in t.index): | ||
good_roi.append(True) | ||
else: | ||
good_roi.append(False) | ||
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df = pd.DataFrame(data={'subject': sub, 'run': run, 'overlapping_parcel_index': best_roi, 'parcel_included_in_sub_mask': good_roi}) | ||
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df.to_csv('./esfmri_connectivity/stimulation_sites/stimsite2parcel.tsv', sep='\t') |
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subject run overlapping_parcel_index parcel_included_in_sub_mask | ||
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esfmri_connectivity/stimulation_sites/stimulation_infomation_space-MNI152NLin2009cAsym.tsv
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subject run contactpair multiple_contacts_in_run contact1 side contact1_mni_x contact1_mni_y contact1_mni_z contact2 contact2_mni_x contact2_mni_y contact2_mni_z | ||
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314 1 23-24 0 23 Left -3.92988 -33.7741 28.8333 24 -13.5373 -34.458 27.4403 | ||
314 2 70-71 0 70 Left -41.8096 -33.6572 3.89408 71 -46.9158 -29.7975 3.8408 | ||
314 3 165-170 0 165 Left -58.472 11.2697 2.51656 170 -60.9414 4.19937 9.0508 | ||
314 4 19-20 0 19 Left -11.6302 -9.95544 -20.7476 20 -22.0446 -11.1298 -20.3052 | ||
314 5 39-40 0 39 Left -0.415732 -9.8887 45.7849 40 -5.81944 -9.28185 43.9696 | ||
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400 3 55-56 0 55 Left -19.2377 -11.3899 -23.7174 56 -28.3677 -8.87594 -23.7503 | ||
400 4 97-98 0 97 Left -16.9312 -5.06088 30.2044 98 -26.1689 -4.70711 29.6443 | ||
400 5 66-67 0 66 Left -28.953 -34.5897 -13.3336 67 -35.3382 -27.0147 -13.6173 | ||
403 1 65-66 0 97 Right 33.2111 -38.6691 -19.1712 98 43.9031 -37.0186 -15.3967 | ||
403 3 86-87 0 118 Left -32.4514 -23.2457 -29.42 119 -42.3944 -22.7994 -33.0456 | ||
403 4 44-45 0 44 Left -41.1508 -23.9225 9.34617 45 -45.4986 -19.9375 6.4722 | ||
403 5 68-69 0 100 Right 65.2149 -35.9564 -9.40337 101 76.1483 -35.3087 -8.99223 | ||
413 1 21-22 0 21 Right 12.384 10.9264 -16.3816 22 21.2877 14.5254 -13.6285 | ||
413 2 88-89 0 88 Right 3.95498 40.8802 -2.02315 89 13.9985 41.2767 -1.33539 | ||
413 3 40-41 0 40 Right 51.2291 -14.518 0.414993 41 55.1865 -15.0437 2.43398 | ||
405 1 68-69 0 68 Left -44.2975 -13.261 9.52198 69 -49.8719 -12.4635 7.49852 | ||
405 2 71-72 0 71 Left -60.3158 -11.0596 2.73302 72 -65.5285 -12.0818 1.05697 | ||
405 3 83-84 0 83 Right 18.4157 -6.20823 -17.8396 84 21.2258 -5.86594 -18.3135 | ||
405 4 76-77 0 76 Left -13.1946 -4.63512 -21.6867 77 -15.7012 -3.14316 -21.7677 | ||
405 5 51-52 0 51 Left -12.2637 11.1892 -18.7051 52 -22.1495 11.0533 -15.8479 | ||
405 6 33-34 0 33 Right 25.5355 -26.5993 -17.5925 34 35.2284 -24.5667 -15.9394 |
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