Supplementary Material for “Resting-State Connectivity Predictors of Response to
Psychotherapy in Major Depressive Disorder”
Andrew Crowther, Moria J. Smoski , Jared Minkel, Tyler Moore, Devin Gibbs, Chris Petty,
Josh Bizzell, Crystal Edler Schiller, John Sideris, Hannah Carl, & Gabriel S. Dichter*
Supplementary Materials I: Cross-correlations among BDI subscale scores in the MDD
sample
Pre-treatment BDI scores:
Total BDI Score
Anhedonia
Subscale
Somatic Subscale
Anhedonia
Subscale
0.80
Somatic
Subscale
0.88
Cognitive
Subscale
0.84
-
0.69
-
0.69
0.49
Post-Treatment BDI scores:
Total BDI Score
Anhedonia
Subscale
Somatic Subscale
Anhedonia
Subscale
0.83
Somatic
Subscale
0.41
Cognitive
Subscale
0.47
-
0.24
-
0.25
0.84
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Supplementary Materials II: Within groups connectivity maps
Figure 1 illustrates, for each seed used in the between groups analysis, the within-groups
rs-fcMRI connectivity maps on the left accompanied by the pattern of group differences in seed-
based connectivity for that seed on the right (color-bars illustrate the degree to which the within
groups maps are greater than 0). This figure illustrates the extent to which group differences in
connectivity are related to connectivity maps within the control and MDD samples.
Figure 1. Within-groups rs-fcMRI connectivity maps for each network and seed region (left);
Group differences for that seed (right)
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Supplementary Materials III: Between-groups results excluding four participants who
completed fewer than eight therapy sessions
Although this study was designed as an intent-to-treat trial, it is possible that participants
who received fewer psychotherapy sessions did not received the intended full benefit of the
treatment. Four patients with MDD completed fewer than eight therapy sessions (i.e., one each
completed 1, 3, 5, and 7 sessions), and Figure 2 and accompany activation tables below we
present findings for group differences in functional connectivity excluding these four patients.
As is evident from the figure, primary findings of group differences in the DAN remained
significant, as did group differences using the right insula and dorsal anterior cingulate seeds in
the salience network. However, group differences in the DMN, ECN, and using the left insula
seed were no longer evident. Although these findings should be interpreted with caution given
the small number of participants who received fewer than eight therapy sessions, these results
suggest that rs-fcMRI-based group differences in the salience network below may be particularly
robust (i.e., they remained significant even after the removal of four participants).
Figure 2. Group differences in rs-fcMRI in the default mode network; the dorsal attention network;
and the salience network (cluster-corrected p<.05; compare to Figure 1 in the main paper).
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Table 3: Between groups differences in rs-fcMRI connectivity (cluster-corrected p<.05) with removal of
four patients with MDD who completed fewer than eight therapy sessions (cluster-corrected p<.05;
compare to Table 4 in the main paper).
MDD>Control
MNI Coordinates
Seed (network)
Region (BA)
X
Y
Z
Peak z-score
Right Anterior Insula (Salience Network)
Left Visual Cortex (BA17)
-2
-88
-8
3.82
Dorsal ACC (Salience Network)
Left Visual Cortex (BA17)
-20
-98
10
3.95
BA: Brodmann Area
Control>MDD
MNI Coordinates
Seed (network)
Region (BA)
X
Y
Z
Peak z-score
Right Anterior Insula (Salience
Network)
Right Anterior Insula (Salience
Network)
Dorsal ACC (Salience Network)
Left Middle temporal lobe (BA21)
-60
-44
4
4.23
Right Middle temporal lobe (BA22)
50
-38
8
4.19
Left Parahippocampal gyrus
-12
-34
-16
4.6
Left IPS (Dorsal Attention
Network)
BA: Brodmann Area
Left Orbitofrontal cortex (BA47)
-32
24
-18
3.71
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Supplementary Materials IV: Predictions of treatment response defined as post-treatment
minus pretreatment BDI scores
To supplement our hierarchical linear
model predictions of treatment response, we
conducted simple correlations between
connectivity in the nine pairs of regions listed in
Table 4 (in the main manuscript) that
differentiated MDD and control groups and
change in total BDI score calculated by
subtracting the BDI acquired on the scan day with
the BDI score collected on the last day of
treatment. One significant association was found.
As illustrated in Figure 3, connectivity between
the right insula and the left middle temporal gyrus
was a significant predictor of treatment response
such that greater pre-treatment connectivity
correlated with greater improvement after BA
psychotherapy. There were no other significant
Figure 3. Pretreatment connectivity between the right
insula and the left middle temporal gyrus was a
significant predictor of treatment response.
associations between baseline connectivity and
treatment response measured this way.
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Supplementary Materials V. Prediction of psychotherapy motivation from
intrinsic connectivity
MDD participants also
completed the Situational
Motivation Scale (SIMS) at
each therapy session to assess
how motivated patients were in
their therapy sessions. The
SIMS is based on selfdetermination theory, which
Figure 4. Biweekly SIMS scores from individual MDD
participants (thin lines) and average biweekly SIMS scores from all
MDD participants (thick line).
posits that the motivations for different human behaviors vary in their level of self-determination,
with self-determination defined as a true sense of choice, a sense of feeling free in doing what
one has chosen to do. The SIMS may be broken down into subscales, reflecting a continuum of
self-determination. From high to low self-determination the subscales are intrinsic motivation,
extrinsic motivation (which is further separated into identified regulation and external
regulation), and amotivation. Intrinsic motivation involves doing an activity for its own sake.
Identified regulation involves choosing to do an activity as a means to an end. External
regulation is when a behavior is determined by rewards or a desire to avoid negative
consequences. Lastly, amotivation involves behavior without a sense of purpose or expectation
of reward (Guay, Vallerand, & Blanchard, 2000). The first SIMS assessment was at week two of
treatment, and biweekly SIMS subscale scores from individual MDD participants (thin lines) and
average biweekly scores from all MDD participants (thick line) are illustrated in the figure
above.
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We used a hierarchical linear model (HLM) estimated via SAS “proc mixed” (SAS
version 9.3, Cary, NC) to analyze the capacity of baseline resting state fMRI data to predict
change in SIMS scores using nine timepoints over 15 weeks. We used a model that tested the
possibility that linear change in SIMS scores over time was moderated by baseline connectivity
included as a time-invariant variable (examining all connectivity metrics that differentiated
MDD and control groups at baseline). The model included main effects for time and
connectivity plus their interaction. Data were centered such that the main effect for connectivity
is estimated at time = 0 (i.e., before treatment) and the main effect for time was estimated at the
mean of connectivity metrics. Three models yielded significant connectivity × time interaction
effects, as described here:
Prediction of change in SIMS identified regulation scores from dACC connectivity with
hippocampus: Tests of random effects indicated significant effects for the intercept (z = 2.80,
p<.003, but not for time (z = 1.15, p>.10). The fixed effects indicated a significant positive
effect for time (t=2.44, p < .03), indicating that SIMS identified regulation scores increased with
time for all participants, and a marginal positive effect for connectivity (t=1.98, p = .051),
indicating that connectivity was marginally positively associated with SIMS identified regulation
scores. The critical connectivity × time interaction was significant as well (t=-2.75p < . 01)
indicating that varying levels of connectivity were associated with differential change in SIMS
identified regulation scores over time, and more specifically that the amount of change in SIMS
identified regulation scores was greatest for those with greater connectivity and least for those
with lower connectivity.
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Prediction of change in SIMS intrinsic motivation scores from intraparietal sulcus connectivity
with orbitofrontal cortex: Tests of random effects indicated significant effects for the intercept (z
= 2.51, p=.006, but not for time (z = 1.10, p>.10). The fixed effects indicated a significant
positive effect for time (t=6.36, p < .0001), indicating that SIMS intrinsic motivation scores
increased with time for all participants, and no effect for connectivity (t=-1.00, p > .30),
indicating that connectivity was not associated with SIMS intrinsic motivation scores. The
critical connectivity × time interaction was significant (t=3.14, p < . 003) indicating that varying
levels of connectivity were associated with differential change in SIMS intrinsic motivation
scores over time, and more specifically that the amount of change in SIMS intrinsic motivation
scores was greatest for those with greater connectivity and least for those with lower
connectivity.
Prediction of change in SIMS external regulation scores from left insula connectivity with
intraparietal sulcus: Tests of random effects indicated significant effects for the intercept (z =
2.86, p<.002, but not for time (z = 0.61, p>.30). The fixed effects indicated a significant
negative effect for time (t=-5.83, p < .0001), indicating that SIMS external regulation scores
decreased with time for all participants, and for connectivity (t=3.79, p > .0003), indicating that
connectivity was not associated with SIMS external regulation scores. The critical connectivity ×
time interaction was significant (t=-3.63 p < . 005) indicating that varying levels of connectivity
were associated with differential change in SIMS external regulation scores over time, and more
specifically that the amount of change in SIMS external regulation scores was greatest for those
with greater connectivity and least for those with lower connectivity.
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