Exploratory Probabilistic Tractography: Clusters (resulting from between group TBSS
analysis) were transformed back to each subject’s native space to create the individualized seed
masks. As discussed in the main text, probabilistic tracking was then carried out separately for
both conditions (i.e. free and constrained) seeding from the clusters. Using bedpostX, estimates
of fiber orientation and their uncertainty were calculated at each voxel. This model also accounts
for the possibility of crossing fibers within each voxel41. We used the default parameters with
5000 sample pathways per each seed voxel with a curvature threshold of 0.2 (corresponding to
±80°). Pathways were also terminated after 2000 steps, using a step length of 0.5 mm. Finally, to
compute percentage of SWM-restricted tracts for each cluster, the resulting waytotals for both
rounds of tractography were then fed into the Equation 1 for each subject.
Further, population probability maps for the tractography outputs seeded from each cluster were
created as follows: first, binary maps were generated by thresholding each subject's individual
tractography result at 0.01 of waytotal. Next, the resulting binary maps were nonlinearly
transformed to the MNI space by applying warp fields generated during TBSS registration stage.
Finally, population probability maps were created by averaging individual binary maps in the
MNI space. For each healthy subject, this process was repeated twice for tractography outputs of
both conditions: i) Free (without any exclusion mask); ii) Constrained (with exclusion mask). For
illustration purposes, the resulting probability maps were thresholded at 10%.
Supplementary Figure 1 depicts population-averaged tractography outcomes for each cluster in
both conditions (free and constrained). The figure shows that fiber tracking constrained with the
SWM mask selectively excluded long range connections by eliminating tracts extending to the
deep white matter.
Figure S1. Thresholded population probability maps of tractography outcomes seeded from different
clusters with and without exclusion mask.
Table S1. Cognitive scores of healthy subjects and patients with schizophrenia.
Cognitive Task
Healthy Subjects
(SD)
Schizophrenia Patients
(SD)
PBonferroni-corrected
Letter Number Sequence
16.5 (3.4)
12.1 (4.2)
<0.0001
Trails B
-2.4 (1.1)
-4.7 (3.0)
0.0002
Digit Span
12.1 (2.3)
10.0 (2.4)
0.0006
Digit Symbol Coding Task
55.5 (10.2)
43.7 (9.3)
<0.0001
Story Recall
10.6 (1.3)
7.8 (2.6)
<0.0001
Table S2. Percentage of superficial white matter restricted streamlines originating from each cluster in
probabilistic tractography.
%SWM-restricted (SD)
Seed Masks
P-value
Schizophrenia
Patients
Healthy
Subjects
Cluster-1
60.6 (5.6)
63.9 (8.0)
0.031
Cluster-2
74.1 (3.0)
79.8 (6.8)
<0.0001*
Cluster-3
86.9 (4.0)
90.7(5.6)
0.0007*
Cluster-4
69.9 (6.8)
68.0 (12.7)
0.37
Cluster-5
39.2 (11.5)
42.9 (28.1)
0.44
Whole-brain
SWM
83.4 (2.3)
83.4 (1.9)
0.97
SWM: superficial white matter
Due to between-group differences in percentage of SWM-restricted streamlines seeding from
Cluster 2 and Cluster 3, we re-analyzed between-group differences in SWM-FA including
SWM-restricted streamline percentage as a covariate for all 5 clusters. SWM-FA in all 5 clusters
remained significantly different between groups, at the Bonferroni-corrected threshold.