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Merge pull request #39 from wiheto/stimulationsite_information
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Stimulationsite information
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@wiheto
wiheto authored Mar 9, 2020
2 parents 777c103 + c2b2b86 commit 5db33ba
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1 change: 1 addition & 0 deletions esfmri_connectivity/communitydetection/run_communitydetection.py
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ts = pd.concat(ts)
ts.reset_index(inplace=True, drop=True)
parcel_indices = ts.columns
# Make connectivity matrices
g = ts.corr().values
# Make igrpah variable, make negative connections 0
g[g < 0] = 0
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12 changes: 12 additions & 0 deletions esfmri_connectivity/stimulation_sites/README.md
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# Stimulation sites

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.

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.

Prior to running the next step, the time series should be derived.

The file `stimsite2parcel.tsv` is created by running the `find_stimulation_parcels.tsv`. To replicate the step, run:

`docker run -u esfmri -v $(pwd):/home/esfmri/ -t esfmri python -m esfmri_connectivity.stimulation_sites.find_stimulation_parcel`
Empty file added esfmri_connectivity/stimulation_sites/__init__.py
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82 changes: 82 additions & 0 deletions 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

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')

timeseries = get_timeseries(task='postop', require_stiminfo=True)

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)))

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

semisizes = (radius,) * 3

# 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

# get_parcellation data
parc_data = parc.get_fdata()

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

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)

df = pd.DataFrame(data={'subject': sub, 'run': run, 'overlapping_parcel_index': best_roi, 'parcel_included_in_sub_mask': good_roi})

df.to_csv('./esfmri_connectivity/stimulation_sites/stimsite2parcel.tsv', sep='\t')
71 changes: 71 additions & 0 deletions esfmri_connectivity/stimulation_sites/stimsite2parcel.tsv
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subject run overlapping_parcel_index parcel_included_in_sub_mask
0 357 01 34.0 False
1 405 05 115.0 False
2 335 04 97.0 False
3 294 01 519.0 True
4 316 07 374.0 True
5 316 05 370.0 False
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11 330 03 519.0 True
12 320 04 234.0 True
13 405 01 32.0 False
14 320 05 231.0 True
15 372 07 32.0 False
16 320 01 378.0 True
17 372 01 32.0 False
18 376 04 378.0 False
19 302 01 38.0 True
20 335 02 233.0 True
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22 330 02 366.0 True
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43 357 02 34.0 False
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46 405 02 156.0 False
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64 357 03 100.0 False
65 376 02 519.0 True
66 292 05 32.0 True
67 335 10 117.0 True
68 303 01 519.0 True
69 307 02 155.0 True
138 changes: 138 additions & 0 deletions 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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335 5 82-83 0 82  Left  -29.5872 11.1809 -14.445 83  -34.2767 11.5598 -13.8967
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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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