Study 2

Brief description of neuroimaging study.

For group-level statistical inference, we ran a sign permutation test using the following bash command with AFNI to run 3dttest++ with -Clustsim:

dirA=mnt/data/study/firstlevel_glm/
3dttest++ -prefix /mnt/data/study/results/Condition_A.nii.gz \
          -mask /mnt/data/study/code/modeling/MNI152-graymatter-thr50-2mm.nii.gz \
          -Clustsim \
          -setA Condition_A \
             101 "$dirA/sub-101_stats.nii.gz[0]" \
             102 "$dirA/sub-102_stats.nii.gz[0]" \
             103 "$dirA/sub-103_stats.nii.gz[0]" \
             104 "$dirA/sub-104_stats.nii.gz[0]" \
             105 "$dirA/sub-105_stats.nii.gz[0]" \
             106 "$dirA/sub-106_stats.nii.gz[0]" \
             107 "$dirA/sub-107_stats.nii.gz[0]" \
             108 "$dirA/sub-108_stats.nii.gz[0]" \
             109 "$dirA/sub-109_stats.nii.gz[0]" \
             110 "$dirA/sub-110_stats.nii.gz[0]" \
             111 "$dirA/sub-111_stats.nii.gz[0]" \
             112 "$dirA/sub-112_stats.nii.gz[0]" \
             113 "$dirA/sub-113_stats.nii.gz[0]" \
             115 "$dirA/sub-115_stats.nii.gz[0]" \
             201 "$dirA/sub-201_stats.nii.gz[0]" \
             202 "$dirA/sub-202_stats.nii.gz[0]" \
             203 "$dirA/sub-203_stats.nii.gz[0]" \
             204 "$dirA/sub-204_stats.nii.gz[0]" \
             205 "$dirA/sub-205_stats.nii.gz[0]" \
             206 "$dirA/sub-206_stats.nii.gz[0]" \
             207 "$dirA/sub-207_stats.nii.gz[0]" \
             208 "$dirA/sub-208_stats.nii.gz[0]" \
             209 "$dirA/sub-209_stats.nii.gz[0]" \
             210 "$dirA/sub-210_stats.nii.gz[0]" \
             211 "$dirA/sub-211_stats.nii.gz[0]" \
             212 "$dirA/sub-212_stats.nii.gz[0]" \
             213 "$dirA/sub-213_stats.nii.gz[0]" \
             215 "$dirA/sub-215_stats.nii.gz[0]"

We thresholded our image using the following bash command with AFNI to run 3dClusterize:

3dClusterize -nosum -1Dformat -inset /mnt/data/study/results/Condition_A.nii.gz -idat 1 -ithr 1 -NN 2 -clust_nvox 9 -bisided -2.8 2.8 -pref_map Clust_mask

Figure 2

Rather than selecting a few slices, let’s allow readers to interact with the entire statistical map!

import nibabel as nib
from nilearn.plotting import view_img

zmap = nib.load('data/unthresh_Z.nii.gz')
view_img(zmap, threshold=2.8)

Here is the statistic map on a surface mesh.

Supplemental Figure 3

from nilearn.plotting import view_img_on_surf

view_img_on_surf(zmap,threshold=2.8)

Table 2

We can also generate a coordinate table from the statistical map.

from nilearn.reporting import get_clusters_table
table = get_clusters_table(zmap, 
                           stat_threshold=2.8,
                           cluster_threshold=10).set_index('Cluster ID',
                                                           drop=True)
display(table)
X Y Z Peak Stat Cluster Size (mm3)
Cluster ID
1 -34.0 20.0 8.0 3.961450 832
2 -40.0 -54.0 50.0 3.916769 2800
2a -34.0 -64.0 38.0 3.624071
2b -36.0 -56.0 40.0 3.511222
2c -46.0 -42.0 44.0 3.082136
3 10.0 36.0 22.0 3.871496 512
4 -40.0 24.0 32.0 3.761007 1648
4a -48.0 28.0 34.0 3.689187
4b -42.0 30.0 26.0 3.413833
4c -54.0 12.0 34.0 3.204023
5 0.0 24.0 36.0 3.636167 3432
5a -6.0 24.0 42.0 3.557699
5b -8.0 32.0 44.0 3.462496
6 -30.0 10.0 60.0 3.617460 272
6a -30.0 2.0 64.0 3.185505
7 -38.0 44.0 0.0 3.396527 600
7a -42.0 50.0 6.0 3.363012
8 -18.0 26.0 56.0 3.380708 240
9 -10.0 38.0 18.0 3.372366 208
10 14.0 2.0 -8.0 3.150095 80
11 42.0 -56.0 52.0 3.104519 416
11a 38.0 -64.0 46.0 3.018312