Table S1. Family wise error rate and cluster size of fALFF (smoothness: 7.3×7.4×7.2) under corrections of Gaussian Random Field Theory, AFNI
3dClusterSim and DPABI AlphaSim.
Voxel Threshold
Cluster
AFNI 3dClusterSim
DPABI AlphaSim
Gaussian Random Field
Threshold
（One Tailed Twice）
Family Wise
Cluster Size
Error Rate
Family Wise
Cluster Size
Error Rate
Family Wise Error
Cluster Size
Rate
P < 0.01 (Z > 2.33)
P < 0.05
45.0%
65.2±1.3
47.4%
60.2±1.7
41.7%
69.3±1.1
P < 0.005 (Z > 2.58)
P < 0.05
29.2%
42.9±0.9
36.1%
39.5±1.1
25.6%
46.7±0.8
P < 0.001 (Z > 3.09)
P < 0.05
10.8%
19.9±0.4
15.6%
18.4±0.6
12.2%
21.3±0.5
P < 0.0005 (Z > 3.29)
P < 0.05
9.9%
14.2±0.4
11.3%
13.9±0.5
9.2%
15.8±0.4
P < 0.01 (Z > 2.33)
P < 0.025
36.6%
73.8±1.9
41.0%
67.7±2.4
35.5%
79.0±1.2
P < 0.005 (Z > 2.58)
P < 0.025
25.9%
47.1±1.0
29.7%
44.5±1.6
21.7%
53.5±0.8
P < 0.001 (Z > 3.09)
P < 0.025
10.6%
22.4±0.4
13.4%
21.0±0.9
8.3%
24.9±0.4
P < 0.0005 (Z > 3.29)
P < 0.025
6.7%
16.7±0.3
8.5%
16.0±0.7
5.9%
18.5±0.5
Table S2. Family wise error rate and cluster size of ReHo (smoothness: 9.4×8.7×8.4) under corrections of Gaussian Random Field Theory, AFNI
3dClusterSim and DPABI AlphaSim.
Voxel Threshold
Cluster
AFNI 3dClusterSim
DPABI AlphaSim
Gaussian Random Field
Threshold
（One Tailed Twice）
Family Wise
Cluster Size
Error Rate
Family Wise
Cluster Size
Error Rate
Family Wise Error
Cluster Size
Rate
P < 0.01 (Z > 2.33)
P < 0.05
59.3%
65.2±1.3
35.0%
60.2±1.7
25.1%
69.3±1.1
P < 0.005 (Z > 2.58)
P < 0.05
50.7%
42.9±0.9
25.3%
39.5±1.1
19.3%
46.7±0.8
P < 0.001 (Z > 3.09)
P < 0.05
26.7%
19.9±0.4
13.9%
18.4±0.6
8.0%
21.3±0.5
P < 0.0005 (Z > 3.29)
P < 0.05
23.7%
14.2±0.4
11.5%
13.9±0.5
6.5%
15.8±0.4
P < 0.01 (Z > 2.33)
P < 0.025
48.4%
73.8±1.9
27.9%
67.7±2.4
19.8%
79.0±1.2
P < 0.005 (Z > 2.58)
P < 0.025
41.5%
47.1±1.0
20.6%
44.5±1.6
13.4%
53.5±0.8
P < 0.001 (Z > 3.09)
P < 0.025
19.6%
22.4±0.4
11.6%
21.0±0.9
6.2%
24.9±0.4
P < 0.0005 (Z > 3.29)
P < 0.025
15.7%
16.7±0.3
9.7%
16.0±0.7
5.4%
18.5±0.5
Table S3. Family wise error rate and cluster size of Degree Centrality (smoothness: 7.9×8.0×7.8) under corrections of Gaussian Random Field
Theory, AFNI 3dClusterSim and DPABI AlphaSim.
Voxel Threshold
Cluster
AFNI 3dClusterSim
DPABI AlphaSim
Gaussian Random Field
Threshold
（One Tailed Twice）
Family Wise
Cluster Size
Error Rate
Family Wise
Cluster Size
Error Rate
Family Wise Error
Cluster Size
Rate
P < 0.01 (Z > 2.33)
P < 0.05
60.0%
65.2±1.3
53.4%
60.2±1.7
44.8%
69.3±1.1
P < 0.005 (Z > 2.58)
P < 0.05
43.3%
42.9±0.9
39.9%
39.5±1.1
31.4%
46.7±0.8
P < 0.001 (Z > 3.09)
P < 0.05
21.5%
19.9±0.4
17.6%
18.4±0.6
14.6%
21.3±0.5
P < 0.0005 (Z > 3.29)
P < 0.05
14.2%
14.2±0.4
13.4%
13.9±0.5
10.7%
15.8±0.4
P < 0.01 (Z > 2.33)
P < 0.025
51.0%
73.8±1.9
45.9%
67.7±2.4
39.4%
79.0±1.2
P < 0.005 (Z > 2.58)
P < 0.025
38.0%
47.1±1.0
32.3%
44.5±1.6
25.5%
53.5±0.8
P < 0.001 (Z > 3.09)
P < 0.025
18.1%
22.4±0.4
14.8%
21.0±0.9
11.4%
24.9±0.4
P < 0.0005 (Z > 3.29)
P < 0.025
10.2%
16.7±0.3
10.5%
16.0±0.7
6.3%
18.5±0.5
Table S4. Family wise error rate and cluster size of VMHC (smoothness: 6.3×6.9×6.6) under corrections of Gaussian Random Field Theory, AFNI
3dClusterSim and DPABI AlphaSim.
Voxel Threshold
Cluster
AFNI 3dClusterSim
DPABI AlphaSim
Gaussian Random Field
Threshold
（One Tailed Twice）
Family Wise
Cluster Size
Error Rate
Family Wise
Cluster Size
Error Rate
Family Wise Error
Cluster Size
Rate
P < 0.01 (Z > 2.33)
P < 0.05
28.6%
65.2±1.3
46.3%
60.2±1.7
40.3%
69.3±1.1
P < 0.005 (Z > 2.58)
P < 0.05
18.0%
42.9±0.9
36.7%
39.5±1.1
28.6%
46.7±0.8
P < 0.001 (Z > 3.09)
P < 0.05
7.8%
19.9±0.4
17.4%
18.4±0.6
13.3%
21.3±0.5
P < 0.0005 (Z > 3.29)
P < 0.05
5.6%
14.2±0.4
12.5%
13.9±0.5
10.0%
15.8±0.4
P < 0.01 (Z > 2.33)
P < 0.025
24.3%
73.8±1.9
39.4%
67.7±2.4
31.9%
79.0±1.2
P < 0.005 (Z > 2.58)
P < 0.025
16.7%
47.1±1.0
31.1%
44.5±1.6
20.9%
53.5±0.8
P < 0.001 (Z > 3.09)
P < 0.025
5.2%
22.4±0.4
12.3%
21.0±0.9
8.9%
24.9±0.4
P < 0.0005 (Z > 3.29)
P < 0.025
4.1%
16.7±0.3
9.7%
16.0±0.7
7.1%
18.5±0.5
Figure S1. Number of significant voxels (on fALFF) under correction of different strategies of multiple comparison correction in the 10-session
dataset. Voxel number indicates how many voxels that are significant for a given frequency (ranged from 1 to 10, indicated by different color) in
all the 10 sessions. GRF, PT and FDR stand for Guassian Random Field correction, Permutation Test and False Discovery Rate correction,
separately. All versions of GRF correction are one-tailed P values while all versions of PT are two tailed P values.Field correction, Permutation
Test and False Discovery Rate correction, separately. All versions of GRF correction are one-tailed while all versions of PT are two tailed.
Figure S2. Number of significant voxels (on ReHo) under correction of different strategies of multiple comparison correction in the 10-session
dataset. Voxel number indicates how many voxels that are significant for a given frequency (ranged from 1 to 10, indicated by different color) in
all the 10 sessions. GRF, PT and FDR stand for Guassian Random Field correction, Permutation Test and False Discovery Rate correction,
separately. All versions of GRF correction are one-tailed P values while all versions of PT are two tailed P values.
Figure S3. Number of significant voxels (on Degree Centrality) under correction of different strategies of multiple comparison correction in the
10-session dataset. Voxel number indicates how many voxels that are significant for a given frequency (ranged from 1 to 10, indicated by
different color) in all the 10 sessions. GRF, PT and FDR stand for Guassian Random Field correction, Permutation Test and False Discovery Rate
correction, separately. All versions of GRF correction are one-tailed P values while all versions of PT are two tailed P values.
Figure S4. Number of significant voxels (on VMHC) under correction of different strategies of multiple comparison correction in the 10-session
dataset. Voxel number indicates how many voxels that are significant for a given frequency (ranged from 1 to 10, indicated by different color) in
all the 10 sessions. GRF, PT and FDR stand for Guassian Random Field correction, Permutation Test and False Discovery Rate correction,
separately. All versions of GRF correction are one-tailed P values while all versions of PT are two tailed P values.
Figure S5. Sensitivity of fALFF under different multiple comparison correction strategies within the 10-session dataset (different color indicates
the voxels are significant for a given frequency of sessions, ranged from 1 to 10). GRF, PT and FDR stand for Guassian Random Field correction,
Permutation Test and False Discovery Rate correction, separately. All versions of GRF correction are one-tailed P values while all versions of PT
are two tailed P values.
Figure S6. Sensitivity of ReHo under different multiple comparison correction strategies within the 10-session dataset (different color indicates
the voxels are significant for a given frequency of sessions, ranged from 1 to 10). GRF, PT and FDR stand for Guassian Random Field correction,
Permutation Test and False Discovery Rate correction, separately. All versions of GRF correction are one-tailed P values while all versions of PT
are two tailed P values.
Figure S7. Sensitivity of Degree Centrality under different multiple comparison correction strategies within the 10-session dataset (different color
indicates the voxels are significant for a given frequency of sessions, ranged from 1 to 10). GRF, PT and FDR stand for Guassian Random Field
correction, Permutation Test and False Discovery Rate correction, separately. All versions of GRF correction are one-tailed P values while all
versions of PT are two tailed P values.
Figure S8. Sensitivity of VMHC under different multiple comparison correction strategies within the 10-session dataset (different color indicates
the voxels are significant for a given frequency of sessions, ranged from 1 to 10). GRF, PT and FDR stand for Guassian Random Field correction,
Permutation Test and False Discovery Rate correction, separately. All versions of GRF correction are one-tailed P values while all versions of PT
are two tailed P values.
Figure S9. Positive Predictive Value (PPV) of fALFF under different multiple comparison correction strategies within the 10-session dataset
(different color indicates the voxels are significant for a given frequency of sessions, ranged from 1 to 10). GRF, PT and FDR stand for Guassian
Random Field correction, Permutation Test and False Discovery Rate correction, separately. All versions of GRF correction are one-tailed P
values while all versions of PT are two tailed P values.
Figure S10. Positive Predictive Value (PPV) of ReHo under different multiple comparison correction strategies within the 10-session dataset
(different color indicates the voxels are significant for a given frequency of sessions, ranged from 1 to 10). GRF, PT and FDR stand for Guassian
Random Field correction, Permutation Test and False Discovery Rate correction, separately. All versions of GRF correction are one-tailed P
values while all versions of PT are two tailed P values.
Figure S11. Positive Predictive Value (PPV) of Degree Centrality under different multiple comparison correction strategies within the 10-session
dataset (different color indicates the voxels are significant for a given frequency of sessions, ranged from 1 to 10). GRF, PT and FDR stand for
Guassian Random Field correction, Permutation Test and False Discovery Rate correction, separately. All versions of GRF correction are
one-tailed P values while all versions of PT are two tailed P values.
Figure S12. Positive Predictive Value (PPV) of VMHC under different multiple comparison correction strategies within the 10-session dataset
(different color indicates the voxels are significant for a given frequency of sessions, ranged from 1 to 10). GRF, PT and FDR stand for Guassian
Random Field correction, Permutation Test and False Discovery Rate correction, separately. All versions of GRF correction are one-tailed P
values while all versions of PT are two tailed P values.