Plot ROIs from PA2 StructureFunction
%matplotlib inline
from nilearn import plotting, image, masking
import os
import numpy as np
import nilearn
roidir = '/fmriNASTest/data00/projects/physact2/data/ROIs/AAL626/'
struct_template='/fmriNASTest/data00/tools/templates/MNI152_T1_1mm_brain.nii'
Seed ROIs
plotting.plot_roi('/fmriDataRaw/fmri_data_raw/DSI_test/AAL626_regions/AAL626_final_195.nii')
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plotting.plot_roi('/fmriDataRaw/fmri_data_raw/DSI_test/AAL626_regions/AAL626_final_198.nii')
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plotting.plot_roi('/fmriDataRaw/fmri_data_raw/DSI_test/AAL626_regions/AAL626_final_200.nii')
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plotting.plot_roi('/fmriDataRaw/fmri_data_raw/DSI_test/AAL626_regions/AAL626_final_202.nii')
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All target nodes (purple = 2 nodes overlap (N=13), yellow = 3 nodes overlap (N=4))
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Overlap in all target nodes
2 nodes overlap
roilist=[193,197,199,228,235,236,238,239,242,272,461,482,532]
roipathlist=[roidir+'AAL626_final_'+str(x)+'.nii' for x in roilist]
imgs = []
for roi in roipathlist:
print('Processing', roi)
imgs.append(nilearn.image.resample_to_img(roi, struct_template))
roi_imgs = image.concat_imgs(imgs)
plotting.plot_prob_atlas(roi_imgs, display_mode='z',colorbar=True)
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plotting.plot_prob_atlas(roi_imgs, display_mode='x',colorbar=True)
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3 nodes overlap
roilist=[177,227,253,566]
roipathlist=[roidir+'AAL626_final_'+str(x)+'.nii' for x in roilist]
imgs = []
for roi in roipathlist:
print('Processing', roi)
imgs.append(nilearn.image.resample_to_img(roi, struct_template))
roi_imgs = image.concat_imgs(imgs)
plotting.plot_prob_atlas(roi_imgs, display_mode='z',colorbar=True)
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plotting.plot_prob_atlas(roi_imgs, display_mode='x',colorbar=True)<nilearn.plotting.displays.XSlicer at 0x7f577705bda0>
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Predicting sedentary change
ROI 195, sedentary
roilist=[197,199,236,242,253,502]
roipathlist=[roidir+'AAL626_final_'+str(x)+'.nii' for x in roilist]
imgs = []
for roi in roipathlist:
print('Processing', roi)
imgs.append(nilearn.image.resample_to_img(roi, struct_template))
roi_imgs = image.concat_imgs(imgs)
plotting.plot_prob_atlas(roi_imgs, display_mode='z',colorbar=True)
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plotting.plot_prob_atlas(roi_imgs, display_mode='x',colorbar=True)
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ROI 198, sedentary
roilist=[177,197]
roipathlist=[roidir+'AAL626_final_'+str(x)+'.nii' for x in roilist]
imgs = []
for roi in roipathlist:
print('Processing', roi)
imgs.append(nilearn.image.resample_to_img(roi, struct_template))
roi_imgs = image.concat_imgs(imgs)
plotting.plot_prob_atlas(roi_imgs, display_mode='z',colorbar=True)
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plotting.plot_prob_atlas(roi_imgs, display_mode='x')
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ROI 200, sedentary
roilist=[177,227,236,239,532,568,65]
roipathlist=[roidir+'AAL626_final_'+str(x)+'.nii' for x in roilist]
imgs = []
for roi in roipathlist:
print('Processing', roi)
imgs.append(nilearn.image.resample_to_img(roi, struct_template))
roi_imgs = image.concat_imgs(imgs)
plotting.plot_prob_atlas(roi_imgs, display_mode='z',colorbar=True)
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plotting.plot_prob_atlas(roi_imgs, display_mode='x',colorbar=True)
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ROI 202, sedentary
roilist=[186,192,264,459,464,506,566,626,63]
roipathlist=[roidir+'AAL626_final_'+str(x)+'.nii' for x in roilist]
imgs = []
for roi in roipathlist:
print('Processing', roi)
imgs.append(nilearn.image.resample_to_img(roi, struct_template))
roi_imgs = image.concat_imgs(imgs)
plotting.plot_prob_atlas(roi_imgs, display_mode='z',colorbar=True)
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plotting.plot_prob_atlas(roi_imgs, display_mode='x',colorbar=True)
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Overlap in significantly predictive nodes - sedentary
roilist=[177,197,236]
roipathlist=[roidir+'AAL626_final_'+str(x)+'.nii' for x in roilist]
imgs = []
for roi in roipathlist:
print('Processing', roi)
imgs.append(nilearn.image.resample_to_img(roi, struct_template))
roi_imgs = image.concat_imgs(imgs)
plotting.plot_prob_atlas(roi_imgs, display_mode='z',colorbar=True)
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plotting.plot_prob_atlas(roi_imgs, display_mode='x',colorbar=True)
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Predicting mod-vig change
ROI 195, mod-vig
roilist=[190,193,194,198,228,236,251,455]
roipathlist=[roidir+'AAL626_final_'+str(x)+'.nii' for x in roilist]
imgs = []
for roi in roipathlist:
print('Processing', roi)
imgs.append(nilearn.image.resample_to_img(roi, struct_template))
roi_imgs = image.concat_imgs(imgs)
plotting.plot_prob_atlas(roi_imgs, display_mode='z',colorbar=True)
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plotting.plot_prob_atlas(roi_imgs, display_mode='x',colorbar=True)
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ROI 198, mod-vig
roilist=[227,238,239]
roipathlist=[roidir+'AAL626_final_'+str(x)+'.nii' for x in roilist]
imgs = []
for roi in roipathlist:
print('Processing', roi)
imgs.append(nilearn.image.resample_to_img(roi, struct_template))
roi_imgs = image.concat_imgs(imgs)
plotting.plot_prob_atlas(roi_imgs, display_mode='z',colorbar=True)
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plotting.plot_prob_atlas(roi_imgs, display_mode='x',colorbar=True)
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ROI 200, mod-vig
roilist=[177,235,238,268,272,457,461,65]
roipathlist=[roidir+'AAL626_final_'+str(x)+'.nii' for x in roilist]
imgs = []
for roi in roipathlist:
print('Processing', roi)
imgs.append(nilearn.image.resample_to_img(roi, struct_template))
roi_imgs = image.concat_imgs(imgs)
plotting.plot_prob_atlas(roi_imgs, display_mode='z',colorbar=True)
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plotting.plot_prob_atlas(roi_imgs, display_mode='x',colorbar=True)
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ROI 202, mod-vig
roilist=[180,182,186,192,193,227,237,271,461,464,481,482,503,63,309]
roipathlist=[roidir+'AAL626_final_'+str(x)+'.nii' for x in roilist]
imgs = []
for roi in roipathlist:
print('Processing', roi)
imgs.append(nilearn.image.resample_to_img(roi, struct_template))
roi_imgs = image.concat_imgs(imgs)
plotting.plot_prob_atlas(roi_imgs, display_mode='z',colorbar=True)
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plotting.plot_prob_atlas(roi_imgs, display_mode='x',colorbar=True)
png
Overlap in significantly predictive nodes - mod-vig
roilist=[193,227,238,461]
roipathlist=[roidir+'AAL626_final_'+str(x)+'.nii' for x in roilist]
imgs = []
for roi in roipathlist:
print('Processing', roi)
imgs.append(nilearn.image.resample_to_img(roi, struct_template))
roi_imgs = image.concat_imgs(imgs)
plotting.plot_prob_atlas(roi_imgs, display_mode='z',colorbar=True)
png
plotting.plot_prob_atlas(roi_imgs, display_mode='x',colorbar=True)
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