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NeuroImage: Clinical 34 (2022) 102969
Available online 19 February 2022
2213-1582/© 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Static and treatment-responsive brain biomarkers of depression relapse
vulnerability following prophylactic psychotherapy: Evidence from a
randomized control trial
Norman A.S. Farb
a
,
b
,
*
, Philip Desormeau
b
, Adam K. Anderson
c
, Zindel V. Segal
b
a
Department of Psychology, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, Ontario L5L 1C6, Canada
b
Graduate Department of Psychological Clinical Science, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
c
College of Human Ecology, Cornell University, Ithaca, NY 14853, USA
ARTICLE INFO
Keywords:
Depression
fMRI
Sensory deactivation
Relapse vulnerability
Mood challenge
Dysphoric reactivity
ABSTRACT
Background: Neural reactivity to dysphoric mood induction indexes the tendency for distress to promote
cognitive reactivity and sensory avoidance. Linking these responses to illness prognosis following recovery from
Major Depressive Disorder informs our understanding of depression vulnerability and provides engagement
targets for prophylactic interventions.
Methods: A prospective fMRI neuroimaging design investigated the relationship between dysphoric reactivity and
relapse following prophylactic intervention. Remitted depressed outpatients (N =85) were randomized to
8 weeks of Cognitive Therapy with a Well-Being focus or Mindfulness Based Cognitive Therapy. Participants
were assessed before and after therapy and followed for 2 years to assess relapse status. Neural reactivity
common to both assessment points identied static biomarkers of relapse, whereas reactivity change identied
dynamic biomarkers.
Results: Dysphoric mood induction evoked prefrontal activation and sensory deactivation. Controlling for past
episodes, concurrent symptoms and medication status, somatosensory deactivation was associated with
depression recurrence in a static pattern that was unaffected by prophylactic treatment, HR 0.04, 95% CI [0.01,
0.14], p <.001. Treatment-related prophylaxis was linked to reduced activation of the left lateral prefrontal
cortex (LPFC), HR 3.73, 95% CI [1.33, 10.46], p =.013. Contralaterally, the right LPFC showed dysphoria-
evoked inhibitory connectivity with the right somatosensory biomarker
Conclusions: These ndings support a two-factor model of depression relapse vulnerability, in which: enduring
patterns of dysphoria-evoked sensory deactivation contribute to episode return, but vulnerability may be miti-
gated by targeting prefrontal regions responsive to clinical intervention. Emotion regulation during illness
remission may be enhanced by reducing prefrontal cognitive processes in favor of sensory representation and
integration.
1. Introduction
Relapse and recurrence following guideline indicated pharmacologic
(Rush et al., 2006) or psychotherapeutic (Hollon et al., 2005) treatment
of Major Depressive Disorder (MDD) remain common and debilitating
outcomes (Hardeveld et al., 2009). In contrast to the literature on acute
phase treatment, where promising response indicators have been iden-
tied (Bartlett et al., 2018; Gadad et al., 2018; Godlewska et al., 2018),
efforts to improve clinical prognosis following MDD episode have yiel-
ded few indicators of sustained remission (Kennis et al., 2020). Prior
episodes and residual depressive symptoms remain the best predictors of
future episodes (Eaton et al., 2008; Solomon et al., 2000; Verhoeven
et al., 2018), yet these markers do not capture the cognitive and affective
dynamics that underlie MDD vulnerability, nor are they sensitive to
changes in vulnerability following prophylactic intervention. Identi-
cation of dynamic, treatment-responsive indicators of episode recur-
rence would support greater precision in prophylactic treatment
(Kazdin, 2007).
An emerging neural systems model characterizes MDD vulnerability
as an overemphasis of dysphoric cognition in response to negative
* Corresponding author at: Department of Psychology, 3359 Mississauga Road, Mississauga, Ontario L5L 1C6, Canada.
E-mail address: norman.farb@utoronto.ca (N.A.S. Farb).
Contents lists available at ScienceDirect
NeuroImage: Clinical
journal homepage: www.elsevier.com/locate/ynicl
https://doi.org/10.1016/j.nicl.2022.102969
Received 2 June 2021; Received in revised form 18 January 2022; Accepted 17 February 2022
NeuroImage: Clinical 34 (2022) 102969
2
events, to the detriment of sensory integration of novel, depression-
incongruent events (Disner et al., 2011; Farb et al.). In response to
negative stimuli, acute MDD episodes are often characterized by
reduced engagement of cognitive control in brain regions such as the
dorsolateral prefrontal cortex (PFC), but elevated engagement of
salience detection in medial PFC (Hamilton et al., 2012, 2013). The lack
of dorsolateral PFC response to negative stimuli may be due to chronic
and diffuse hyperactivity observed in resting-state scans of acute MDD
patients relative to healthy controls (Hamilton et al., 2013; Sheline et al.,
2009). Elevated phasic (stimulus-evoked) dorsomedial PFC activity is
also thought to contribute to depressive symptom burden, representing
strategic aspects of depressive self-focus that may extend to the lateral
PFC (Lemogne et al., 2012). Accordingly, response to depression-specic
psychotherapy has been linked to normalization of hyperactivity in the
dorsolateral PFC and related regions (Fonseka et al., 2018; Goldapple
et al., 2004).
As both dorsolateral and dorsomedial PFC reactivity seems to be
provoked by negative stimuli in acute MDD, such reactivity may also
indicate increased self-referential processing and rumination in remis-
sion, a phenotype repeatedly linked to relapse vulnerability (Kruijt et al.,
2013; Segal et al., 2006). Functional connectivity studies corroborate
this account, linking MDD to elevated connectivity within prefrontal
networks (Wang et al., 2016), and remission to reduced connectivity
within these networks (Meyer et al., 2019). Prefrontal hyperactivity may
be a lasting consequence of MDD, overwhelming attentional resources
for engaging in new learning and behaviour even when symptoms have
lessened (Marchetti et al., 2012). Heightened rumination and self-
evaluation seem to lead to an expansion of typically dorsomedial PFC
activity to include the dorsolateral-PFC, thereby reducing opportunities
for adaptive emotion regulation, a foundational premise of the dorsal
nexus hypothesis (Sheline et al., 2010). For example, abnormal activity
in sensory integration regions such as the anterior insula are commonly
observed in MDD, and their normalization may serve as a prognostic
marker of treatment response (Dunlop et al., 2015; McGrath et al.,
2013). According to the dorsal nexus hypothesis, reducing hyperactive
dorsolateral PFC reactivity to negative stimuli in remission may restore
emotion regulation capacity.
Despite its promise, a focus on PFC reactivity addresses cognitive
features of MDD but speaks less to somatic features. Interoceptive
dysfunction, presenting as avoidance of somatic experience, may be a
relatively overlooked facet of depression vulnerability (Harshaw, 2015).
Dysphoric mood induction has been linked to both activation of the PFC
and deactivation of the right middle insula (Farb et al., 2010), a region
supporting sensory integration, including awareness of the body’s in-
ternal state (Craig, 2002). Insula deactivation rather than PFC activation
was associated with concurrent depressive symptoms, afrming the
importance of sensory processing in depression. Furthermore, in one of
the few prospective neuroimaging studies of MDD recurrence, both PFC
activation and sensory deactivation were associated with new MDD
episodes over a subsequent 18-month follow-up (Farb et al., 2011). In
parallel, emerging connectivity studies of depression suggest that
abnormal sensorimotor connectivity may be a powerful but overlooked
feature of depressive symptom burden and treatment response (Ray
et al., 2021). While small sample sizes limit the generalizability of
regional ndings, such studies provide initial evidence for character-
izing MDD relapse vulnerability as an over-reliance on cognitive elab-
oration to the detriment of sensory integration.
A well-powered prospective neuroimaging design may better char-
acterize neural biomarkers of cognitive reactivity and sensory deacti-
vation. Biomarkers identied herein could then be evaluated in future
studies for their predictive utility. Furthermore, given evidence that
prophylactic interventions reduce MDD relapse vulnerability (Guidi and
Fava, 2020; Kuyken et al., 2016), biomarkers can also be evaluated for
their sensitivity to treatment response. This approach also permits
comparing interventions, distinguishing between treatment-specic and
trans-therapeutic mechanisms of prophylaxis. The current study
therefore aimed to 1) identify static biomarkers of relapse vulnerability
over the 24-month clinical follow-up, 2) identify biomarkers sensitive to
prophylactic psychological treatment, and 3) explore mechanistic dif-
ferences between evidence-based prophylactic treatments.
2. Methods
Neuroimaging of a validated dysphoric mood induction task (Farb
et al., 2010, 2011) was conducted as part of a broader RCT reporting
both clinical and psychometric outcomes (Farb et al., 2018; Segal et al.,
2019). In brief, fully remitted participants were randomized to receive
either Mindfulness Based Cognitive Therapy (MBCT) (Segal et al., 2012)
or Cognitive Behavior Therapy with a Well-Being focus (WB-CT) (Fava,
2016) before entering a 2-year follow-up period; participants performed
fMRI and self-report assessment both before and after treatment (Fig. 1).
The study protocol was approved by the institutional review board at the
Centre for Addiction and Mental Health (CAMH) and registered at
clinicaltrials.gov (NCT01178424).
2.1. Participants
Participants were screened for inclusion and exclusion criteria and
provided informed consent. Inclusion criteria were: (1) not currently
meeting a diagnosis of Major Depressive Disorder (MDD) according to
DSM-IV criteria, (2) a score of ≤12 on the Hamilton Depression Rating
Scale (HRSD-17), (3) ≥1 previous episode of MDD, (4) between 18 and
65 years of age and (5) English speaking and the ability to provide
informed consent. Exclusion criteria were: (1) a current diagnosis of
Bipolar Disorder, Substance Abuse Disorder, Schizophrenia or Border-
line Personality Disorder, (2) currently receiving psychotherapy or
practicing meditation >once per week or yoga >twice per week.
Following enrolment in the RCT (n =166), 60% of participants
(n =99) opted to attend the pre-intervention neuroimaging scan, of
whom 86% (n =85) returned for the post-intervention scan. Participants
who failed to complete assessments were interviewed by the research
team to conrm their intention to leave the study. Of the 85 patients
who entered the two-year clinical follow up, data on relapse status was
available for 81% of the sample at one year and 60% of the sample at the
end of two-year follow up. Complete CONSORT information is presented
in Fig. 2, and details on the rates of voluntary withdrawal vs. relapse
over the study period are available in Table S1. The neuroimaging group
did not differ on demographic or clinical history variables compared to
the original RCT clinical sample at either pre- or post-intervention scan
timepoints (Table 1) (Farb et al., 2018; Segal et al., 2019). Relapse status
was not signicantly correlated with any of the demographic variables
listed in Table 1.
2.2. Sample size justication
Required sample size was determined prior to study commencement
using the fMRIPower software package (Mumford, 2012), which used
data from our prior mood-induction prospective relapse study (Farb
et al., 2011) to estimate power to detect relapse from mood induction
responses in the prefrontal cortex and sensory cortices. Detection of
mood-related activation in the medial (BA32) and lateral (BA 46) pre-
frontal cortex achieved 80% power with 24 participants, whereas
detection of mood-related deactivation in somatosensory, insula, and
visual clusters (BA 18) required 35 participants within each group.
Assuming up to 30% attrition over the clinical intervention period, it
was decided to recruit 100 participants to baseline fMRI assessment.
2.3. Randomization and masking
Eligible patients were randomized in blocks of four, using computer
generated quasi-random numbers, to receive eight weekly group ses-
sions of either MBCT or WB-CT. Randomization was performed by the
N.A.S. Farb et al.
NeuroImage: Clinical 34 (2022) 102969
3
study coordinator via a randomization table; investigators were blind to
group allocation during the intervention and follow-up assessment
periods.
2.4. Clinical outcomes
The primary outcome measure was time to relapse/recurrence of
DSM-IV-TR major depressive episode (the study began prior to DSM-5
publication), using the depression module of the SCID. Symptom as-
sessments were conducted at bimonthly intervals alternating between
an eSurvey using the Quick Inventory of Depressive Symptomatology
(QIDS-SR) (Rush et al., 2003) and the HAMD-17 during phone in-
terviews (Fig. 1). A QIDS score of ≥12 also triggered a phone assessment
with HAMD-17 and SCID. Episode return was dened as a score ≥16 on
HAMD and meeting MDD criteria on a subsequent SCID. All interviews
were audio-taped and inter-rater agreement was calculated on a subset
of HAMD-17 interviews, yielding an intraclass correlation coefcient of
0.94 (n =18). Similarly, reliability of MDD diagnoses based on the SCID
yielded a kappa 0.82 (n =22). Relapse diagnoses were conrmed by an
experienced research psychiatrist.
Survival analysis on the Intention to Treat sample indicated that the
2-year relapse rate was 21% with no differences between the groups (7/
37 =19% in WB-CT and 11/48 =23% in MBCT),
χ
2
=0.2, p =.65.
Furthermore, although 64% of participants were on a stable regimen of
antidepressant medication over the study period, the rates of antide-
pressant medication were not signicantly different in relapsers (72%)
than in non-relapsers (61%),
χ
2 =0.35, p =.56. Further details about
assessment and follow-up are available in the published trial protocol
(Farb et al., 2018).
2.5. Dysphoric mood induction
The dysphoric mood induction task has been previously validated
(Farb et al., 2010, 2011), and was programmed for the current study in
the Visual Basic programming language (Microsoft Visual Studio 2012;
Redmond, WA). During fMRI scanning, participants viewed lm clips
and rated their sadness between clips. Participants viewed four sets of
clips over two fMRI acquisition runs, with each run containing one
neutral and one sad set per run. The rst run always featured a neutral
set followed by a sad set, with a sad set followed by neutral set in the
second run. Each set featured an instruction screen (10 sec) prior to
viewing (45–50 sec), rating (6 sec), and reecting upon (50 sec) each of
the four lm clips in sequence. Participants rated their sadness on a scale
of 1 (“Not at All Sad”) to 7 (“Extremely Sad”) using a scanner-compatible
button box. A blank screen reection period followed each rating to
allow washout of lm viewing effects.
2.6. fMRI data acquisition
Neuroimaging was performed at the Rotman Research Institute using
a Siemens Trio 3.0-Tesla scanner, with slew rate of 400 T/m/s and a 12-
channel asymmetric gradient head coil. During the pre- and post-
treatment scans, 2 runs of 434 functional volumes were collected, for
a total of 868 volumes per assessment. Additional details can be found in
the supplementary materials.
2.7. Preprocessing
Data preprocessing was performed using fMRIPrep 20.0.6 (Esteban
et al., 2019, 2020), a consensus standard fMRI preprocessing pipeline;
complete details are provided in the supplementary materials. In sum-
mary, anatomical images from both baseline and post-intervention were
segmented and normalized into a standard Montreal Neurological
Institute (MNI) space through nonlinear registration. Functional runs
were co-registered to the anatomical images, slice-time corrected, and
resampled into 2 mm isotropic voxels in MNI space. Preprocessing and
analysis following the fMRIPrep pipeline were performed using MAT-
LAB 2018a (MathWorks, Natick, MA, USA) and SPM12 (Wellcome
Department of Cognitive Neurology, UK). Functional data were spatially
smoothed using an 8 mm Gaussian kernel, as recommended for opti-
mizing group inference (Mikl et al., 2008).
2.8. fMRI analysis
Whole brain, voxelwise analyses were based on participants (N =85)
who were each scanned both pre- and post-intervention. First level
analysis featured a block design to model neutral and sad lm viewing
periods as separate boxcar regressors across two functional runs. The
standard six motion parameters (3 translation +3 rotation) and global
CSF signals were included as nuisance regressors, as CSF signals act as a
reliable proxy for physiological noise (Birn, 2012; Kong et al., 2012).
Participants did not differ in any of the 6 motion parameters as a func-
tion of Time or Relapse status (Table S2). Sad and neutral lm conditions
were contrasted within each participant session.
The second level analysis employed a mixed linear model to analyze
the dependent factor of Time (Baseline vs. Post-Intervention), and the
independent factors Group (MBCT vs. WB-CT) and Relapse (Relapse vs.
Remitted). Given their documented relationship to depression vulnera-
bility, residual symptoms (Paykel, 2008), number of past episodes
(Bulloch et al., 2014), and antidepressant medication status (Dobson
et al., 2008) were all included as covariates of interest in fMRI factorial
models and in all Cox regressions. Residuals symptom scores were ob-
tained concurrently at the time of each fMRI assessment, and calculated
from data reduction of previously described psychometric data (Segal
et al., 2019).
2.8.1. Statistical thresholds
False positive rates were addressed through statistical thresholding
using clusters determined by a voxel height threshold of z >2.58
(p <.005) and an FWE-corrected cluster threshold of p <.05 (~750
voxels at 2x2x2 resolution). Despite earlier calls for highly conservative
peak threshold of p <.001 (Eklund et al., 2016), more recent research
has advocated for an equitable balance between voxel and cluster
thresholds, an approach that has proved superior to voxelwise p <.001
alone (Cox, 2019). Furthermore, a focus on cluster thresholding over
voxel thresholding appears to lead to superior replicability in larger
Fig. 1. Study design summary. Smartphones and computers alternating within the follow-up period indicate bimonthly assessment points, which alternated between
phone-based and online eSurvey assessments.
N.A.S. Farb et al.
NeuroImage: Clinical 34 (2022) 102969
4
datasets such as ours (170 scans) (Bossier et al., 2020). While novel
adaptive clustering techniques have been introduced to the neuro-
imaging community during the preparation of this manuscript (Cox,
2019; Smith and Nichols, 2009), we followed an a priori analysis plan to
x voxel threshold at p <.005. Our planned threshold was successfully
employed in past studies using this lm-based mood induction paradigm
(Farb et al., 2010, 2011); control for Type-1 error was still maintained
using the cluster correction algorithms built into SPM12.
2.8.2. Relapse biomarker identication
Three a priori analyses supported the study aims:
Static Biomarkers. To identify static biomarkers of relapse vulnera-
bility, the main effect of Relapse (non-relapsers – relapsers) across both
assessment time points (Baseline and Post-Intervention) was computed
as an unbiased estimator of static relapse vulnerability. To test the sta-
bility of the regions identied, post hoc simple effects analyses were
conducted for the main effect of Relapse at both baseline and post-
intervention, as well as an exploratory conjunction analysis between
these separate timepoint whole brain contrasts.
Fig. 2. Study consort diagram.
N.A.S. Farb et al.
NeuroImage: Clinical 34 (2022) 102969
5
Dynamic Biomarkers. To identify biomarkers of treatment response,
all regions demonstrating a main effect of Time [Post-intervention –
Baseline] within the non-relapse group were analyzed for their associ-
ation with future relapse. The analysis was constrained to the non-
relapse group to isolate changes that could plausibly be related to
treatment-induced prophylaxis again depression, as a more conven-
tional whole-brain Time * Relapse interaction effect might be driven by
either baseline differences or changes in the relapse group, neither of
which speak to treatment response. Because changes in the non-relapse
group could be non-specic effects of time rather than relevant for
depression vulnerability, all signicant regions of interest were then
subjected to post-hoc Time * Relapse interactions to determine their
clinical signicance. These post-hoc tests were conducted in the R sta-
tistical programming environment (R Core Team, 2017), using the
‘lme4′library for mixed models (Bates et al., 2015, p. 4), using the
following equation:
% Signal Change ∼Time ∗Group ∗Relapse + (1|Participant ID)
Time * Group interactions within regions identied from the non-
relapse group were also explored to identify differential responses to
prophylactic intervention associated with future relapse. Again, only the
non-relapse group was included in the initial contrasts to provide the
most sensitive estimator of treatment response, as the relapse group, by
denition, did not experience sufcient change over time to achieve
prophylaxis.
Given the typicality of running Relapse ×Time interactions in clin-
ical trials rather than focusing on change in the non-relapse group, an
exploratory Relapse ×Time whole brain analysis was conducted and is
described in the supplementary results, but are not reported in the main
text as they did not inform treatment-related change.
2.8.3. Survival analysis
To better characterize neural reactivity associated with relapse,
survival analysis was performed using Cox proportional hazards models
featuring neural reactivity scores were estimated using the ‘survival’
library (Therneau and Grambsch, 2000) in the R statistical programming
environment (R Core Team, 2017). Cox models were used to estimate
relapse risk at each of the bimonthly time points over the follow-up
period, including the activation levels of neural regions-of-interest
while controlling for past episodes and concurrent depressive symp-
toms as covariates. The analysis also modelled censoring of participants
due to study withdrawal, so that the most complete dataset available at
each time point was used to estimate relapse risk. While neural activity
was a continuous variable in the model, median splits were used for
generating survival curves using the ‘survminer’ and ‘ggplot2′libraries
(Kassambara et al., 2020; Wickham, 2016, p. 2).
2.8.4. Psychophysiological interaction (PPI) analysis
The neural region-of-interest most signicantly associated with
relapse (peak MNI location: x =52, y = − 30, z =64) was used as a seed
region in a post-hoc, generalized psychophysiological interaction (gPPI)
analysis to measure task-evoked changes in whole-brain functional
connectivity with the seed region. Within each participant-session,
signal from the seed region was extracted to construct an interaction
term with a vector contrasting lm task conditions (Sad vs. Neutral). The
resulting rst-level participant-session maps indicated areas where seed
region functional connectivity was signicantly altered as a function of
lm condition. At the group level, relapse status was regressed onto the
participant-session maps to identify where mood-related changes in
somatosensory connectivity were associated with relapse.
3. Results
3.1. Effects of mood induction
Characterization of dysphoric mood induction effects was conducted
prior to analyses supporting the study aims (Table S3). Sadness ratings
were greater for sad than neutral clips, β =1.78, 95% CI [1.57, 2.00],
p <.001; relapse status did not interact with evoked sadness ratings,
β = − 0.11, 95% CI [−0.65, 0.43], p =.69.
The contrast of [Sad Films – Neutral Films] revealed that dysphoric
mood induction was associated with activation along the cortical
midline, including the posterior cingulate, striatum, and medial pre-
frontal cortex, as well as the anterior insula and superior temporal gyrus.
Deactivations were apparent across diverse sensory representation re-
gions, including the primary somatosensory cortex, posterior insula, and
visual regions such as the fusiform gyrus, as well as aspects of the
inferior frontal gyrus (Fig. 3A; Table S4).
3.2. Covariates of interest
Three common indicators of depression relapse vulnerability, past
episodes, residual symptoms and antidepressant status were entered as
covariates to the dysphoric-reactivity analysis. Antidepressant
Table 1
Demographic and clinical characteristics by study stage.
Measure Clinical
Sample
n =166
Baseline
Sample
n =99
Post-
Intervention
n =85
Age, mean (SD) 40.63
(11.77)
39.23
(12.00)
39.08 (12.16)
Gender, n (%)
Female 112
(67.5%)
62 (62.6%) 58 (68.2%)
Ethnicity, n (%)
Caucasian 132
(82.0%)
79 (86.8%) 73 (86.9%)
Afro-Canadian 8 (5.0%) 1 (1.1%) 0
Asian/East-Asian 12 (7.5%) 6 (6.6%) 6 (7.1%)
Hispanic 4 (2.5%) 1 (1.1%) 1 (1.2%)
Other 5 (3.1%) 4 (4.4%) 4 (4.8%)
Education, n (%)
High school 27 (16.3%) 16 (16.2%) 14 (16.5%)
College/University 110
(66.3%)
60 (60.6%) 56 (65.9%)
Graduate school 25 (15.1%) 16 (16.2%) 15 (17.6%)
Other 4 (2.4%) 7 (7.1%) 0
Employment, n (%)
Full time job 91 (59.9%) 49 (57.0%) 46 (58.2%)
Part time job 23 (15.1%) 15 (17.4%) 15 (19.0%)
Unemployed 29 (19.1%) 17 (19.8%) 14 (17.7%)
Student/Other 9 (5.9%) 5 (5.8%) 4 (5.1%)
Age of onset of rst episode of
depression, mean (SD)
22.43
(10.67)
20.87 (9.45) 20.46 (9.41)
Number of past episodes of
depression, mean (SD)
3.86 (2.34) 4.17 (2.60) 4.08 (2.51)
Previous hospitalization n (%) 38 (22.9%) 27 (27.3%) 27 (31.8%)
Suicide attempts, n (%) 29 (17.5%) 19 (19.2%) 16 (18.8%)
Family Hx depression, n (%) 114
(68.7%)
67 (67.7%) 62 (72.9%)
Antidepressant at intake, n (%) 100
(60.2%)
59 (59.6%) 55 (64.7%)
Previous or current
psychotherapy, n (%)
140
(84.3%)
77 (77.8%) 70 (82.4%)
Remission achieved via, n (%)
CBT 39 (28.7%) 16 (20.3%) 16 (21.9%)
Psychotherapy 16 (11.8%) 10 (12.7%) 8 (11.0%)
Medication 34 (25.0%) 23 (29.1%) 20 (27.4%)
Medication & psychotherapy 26 (19.1%) 19 (24.1%) 18 (24.7%)
Other 21 (15.4%) 11 (13.9%) 11 (15.1%)
Number of treatment sessions
attended, mean (SD)
6.27 (2.00) 6.21 (2.14) 6.72 (1.48)
Chi-square tests (for categorical variables) and t-tests for (continuous variables)
across all 3 samples revealed no signicant differences. Please note that par-
ticipants had the option to disclose demographic information at their discretion;
as such, the total number of demographic variable responses may be less than
the full sample size. Percentages are relative to the total number of respondents
for each demographic variable.
N.A.S. Farb et al.
NeuroImage: Clinical 34 (2022) 102969
6
medication status was not associated with neural reactivity to dysphoric
mood provocation.
However, a greater number of past episodes was associated with
greater sadness-evoked deactivation in the medial somatosensory cor-
tex, right anterior insula, and precuneus (Table S5), accounting for
14.4% of the variance in mean signal from these regions. Post-hoc
analysis conrmed that this relationship was signicant both within
non-relapsers, R
2
=0.130, p <.001, and for relapsers, R
2
=0.182,
p =.027 (Fig. 3B).
Furthermore, greater levels of residual symptoms were associated
with greater sadness-evoked deactivation bilaterally within the so-
matosensory cortex and posterior insula (Fig. 3C; Table S4), accounting
for 7.6% of variance in these regions. Post-hoc analysis conrmed this
relationship was signicant for non-relapsers, R
2
=0.075, p =.002, but
failed to reach signicance for relapsers, R
2
=0.046, p =.198.
3.3. Static (Treatment-Invariant) relapse biomarkers
To identify treatment-invariant (static) neural biomarkers of relapse
(Aim 1), dysphoric neural reactivity across both timepoints (baseline
and post-intervention) was regressed onto relapse status over the 24-
month follow-up period. The contrast of [Non-Relapsers – Relapsers]
revealed that relapse was not associated with greater activation in any
region, but were associated with greater deactivation in bilateral
Fig. 3. Neural reactivity to dysphoric mood induction. A) Main Effect of Task (Sad – Neutral); B) past episodes covariate of neural reactivity, with a scatterplot of the
relationship between past episodes and the peak covariate region located in the medial somatosensory cortex; C) residual symptom covariate of neural reactivity,
with a scatterplot of the relationship between residual symptoms and the peak covariate region in the right somatosensory cortex and posterior insula. Scatterplots
use data from both timepoints (baseline and post-intervention) and show linear t within both the non-relapser and relapser sub-groups to illustrate the consistency
of the relationship. Gray shaded areas around the t lines are 95% condence intervals.
N.A.S. Farb et al.
NeuroImage: Clinical 34 (2022) 102969
7
somatosensory cortex, supplementary motor area (SMA), and fusiform
gyrus (Fig. 4A, Table S6). Post-hoc analysis conrmed that all four re-
gions were signicantly associated with relapse at both time points
(Table S7), and an exploratory conjunction analysis that examined the
overlap of relapse associations estimated separately at each time point
replicated these ndings (Fig. S1).
To evaluate the potential independence of the four static relapse
biomarkers, stepwise Cox regressions were performed, beginning with a
baseline model containing number of past episodes and residual symp-
toms. The right somatosensory cortex region was the most signicant
correlate of relapse, demonstrating signicantly greater deactivation in
relapsers compared to non-relapsers, β = − 0.65 [−0.87, −0.44],
p <.001 (Fig. 4B). Including average right somatosensory reactivity in
the Cox model signicantly improved model t, from R
2
=0.201 to
R
2
=0.837,
χ
2
(2) =28.5, p <.001. Survival probability at the end of the
follow-up period was much higher for participants above the median
level of somatosensory deactivation, p =.945; 95% CI [0.874; 1.00],
than for those below the median, p =.566; 95% CI [0.419; 0.765]
(Fig. 4C). Including additional regions did not improve model t
(Table S8), so only the right somatosensory region was retained as a
static vulnerability marker.
3.4. Dynamic (Treatment-Varying) relapse biomarkers
To identify treatment-varying (dynamic) neural biomarkers of
relapse (Aim 2), the effect of Time within non-relapsers was explored.
Time-related reductions were observed in the right inferior parietal lobe,
left DLPFC and left superior occipital regions (Fig. 5A, Table S9). Change
Fig. 4. Main effects of future relapse status on
neural reactivity to dysphoric mood induction. A)
Regions sensitive to future relapse status; B)
Boxplot of right somatosensory reactivity at both
timepoints with 95% condence intervals; C)
survival plot for participants over the follow-up
period as a function of average right somatosen-
sory reactivity (sad – neutral lm clip viewing)
across both time-points. Cross-hatches indicate
participants censored due to relapse or being lost
to follow-up. Please note that due to the context
of sadness-evoked deactivation, ‘Above Median’
scores indicate less deactivation, whereas ‘Below
Median’ scores indicate greater deactivation.
N.A.S. Farb et al.
NeuroImage: Clinical 34 (2022) 102969
8
scores from all three regions were entered into separate Cox regressions
to model relapse risk, controlling for past episodes. Concurrent depres-
sive symptoms and antidepressant status. Of the three regions, only
change in the left DLPFC was associated with future relapse status
(Table S10).
Follow-up multilevel modelling of Time ×Relapse effects suggested
that reductions in left DLPFC reactivity were less pronounced for re-
lapsers than non-relapsers, Relapse ×Time β =0.40 [0.06, 0.73],
p =.022 (Fig. 5B). The interaction was driven by a signicant reduction
for non-relapsers, β = − 0.36 [−0.51, −0.20], p <.001, but no evidence
of change for relapsers, β =0.04 [−0.24, 0.32], p =.773 (Fig. S2).
Compared to the baseline Cox model of past episodes, residual
symptoms and antidepressant status, including change in left DLPFC
reactivity signicantly improved model t, from R
2
=0.201 to
R
2
=0.410,
χ
2
(2) =5.44, p =.020. Including additional regions did not
improve model t (Table S10), so only the left DLPRC region was
retained as a dynamic vulnerability marker. Survival probability at the
end of the follow-up period was much higher for participants below the
median level of DLPFC reactivity change, p =.910; 95% CI [0.815; 1.00],
than for those above the median, p =.567; 95% CI [0.416; 0.773]
(Fig. 5C).
An exploratory, whole-brain Relapse ×Time interaction analysis
suggested an additional dynamic region in the right cerebellum
(Fig. S3A), but follow-up analysis revealed that this interaction was
driven only by change in the relapse group (Fig. S3B, Table S11), and
therefore not a good candidate biomarker of treatment-related change.
3.5. Interactions with treatment group
To identify distinctive neural mechanisms between the MBCT and
WB-CT groups (Aim 3), the overall Group ×Time interaction,
Group ×Time interaction within non-relapsers, and
Group ×Time ×Relapse interactions were explored. However, no group
effects were observed.
3.6. Combined relapse model
To explore biomarker independence, the candidate relapse bio-
markers were combined into a single Cox regression model, controlling
for past episodes, depressive symptoms, and antidepressant medication
status. The static somatosensory and dynamic DLPFC biomarkers both
independently contributed to model t (Fig. 6), with the combined
model accounting for 89.7% of the variance in relapse status, with
excellent concordance (C =0.86).
Fig. 5. Effects of time (baseline vs. post-intervention) on neural reactivity to dysphoric mood induction within the non-relapse group. A) Regions demonstrating
reduced reactivity over time; B) Boxplot of left lateral prefrontal cortex (LPFC) change scores over time with 95% condence intervals; C) survival plot for par-
ticipants over the follow-up period as a function of change in left LPFC change scores. Cross-hatches indicate participants censored due to relapse or being lost to
follow-up. Please note that due to the context of reduced reactivity over time, ‘Above Median’ scores indicate a failure to reduce reactivity, whereas ‘Below Median’
scores indicate reduced reactivity.
N.A.S. Farb et al.
NeuroImage: Clinical 34 (2022) 102969
9
3.7. Psychophysiological interaction (PPI) analysis
Finally, a psychophysiological interaction analysis (PPI) was con-
ducted to explore the whole-brain network arising around sadness-
evoked somatosensory deactivation. The right somatosensory region
identied as a static biomarker of relapse was employed as a seed region.
The analysis indicated that dysphoric mood induction introduced an
inhibitory relationship between the prefrontal cortex/cortical midline
and posterior sensory regions including the somatosensory, auditory,
and visual cortices (Fig. 7A; Table S12). In response to mood induction,
relapse was associated with a shift from positive to negative connectivity
between the somatosensory seed and a right lateral PFC region (Fig. 7B,
Table S12). Examination of the raw (non-PPI) functional connectivity
scores between the right somatosensory seed region and the right LPFC
indicated positive functional connectivity during neutral mood condi-
tions, but negative (inhibitory) connectivity under sad mood (Fig. 7C).
Participants for whom sadness evoked a greater shift from positive to
negative connectivity were more likely to relapse (Fig. 7D).
4. Discussion
Prior research has focused on elevated prefrontal activity in acute
phase depression (Lemogne et al., 2012), which has been interpreted as
a loss of cognitive control over emotion (Sheline et al., 2009), poten-
tially due to the recruitment of cognitive resources in depressive rumi-
nation (Segal et al., 2006). Functional connectivity studies support this
view, as cognitive control networks including the LPFC are observed less
frequently in patients remitted from MDD (Figueroa et al., 2019),
whereas non-relapsers tend to show increased connectivity of the LPFC
with executive control regions following antidepressant discontinuation
(Berwian et al., 2020). While less often the focus of neuroimaging
research, emerging mechanistic accounts of depression have also
implicated sensorimotor dysfunction a reliable correlate of symptom
burden (Ray et al., 2021), which is consistent with established ndings
linking experiential avoidance to depression vulnerability (Barnhofer
et al., 2014; Panayiotou et al., 2015). The present ndings afrm the
role of sensory deactivation in characterizing depression relapse
vulnerability, broadening accounts of depression vulnerability to also
sensorimotor deactivation as a contributing vulnerability biomarker.
Here, neural responses to dysphoric mood induction in remitted
depressed patients implicated sensory deactivation in past, present, and
future depression. Greater numbers of past episodes and concurrent
residual symptoms were each linked with greater deactivation of the
somatosensory cortex and insula, replicating prior ndings (Farb et al.,
2010). After controlling for these effects, sensory deactivation further
accounted for episode return over a 24-month follow-up, characterized
by the deactivation of somatosensory, motor, and visual cortices,
replicating our earlier exploratory work (Farb et al., 2011). Importantly,
the use of two scanning sessions before and after prophylactic treatment
allowed for novel distinctions between static (time-invariant) and
treatment-responsive (time-varying) vulnerability biomarkers. The
pattern of sadness-evoked sensory deactivation in somatomotor and
visual cortices was invariant with respect to treatment in its character-
ization of depressive relapse, with right somatosensory deactivation
providing the strongest measure of association.
To identify relapse-relevant effects of prophylactic treatment, we
Fig. 6. Summary model of relapse risk. The Hazard Ratios for the combined Cox regression model for Relapse that includes neural biomarker activity from both the
static marker of relapse (right somatosensory cortex) and the dynamic marker, wherein activity changed over the intervention period (left lateral prefrontal cortex),
controlling for past episodes, concurrent depressive symptoms, and antidepressant medication status.
N.A.S. Farb et al.
NeuroImage: Clinical 34 (2022) 102969
10
identied changes in reactivity over the intervention period in patients
who sustained remission across the 24-month follow-up (non-relapsers).
Time-related changes in the remission group likely also include non-
specic effects of time such as practice effects, so regions identied as
changing over time were then screened for their association with future
relapse status. Decreased reactivity was observed in the left DLPFC,
occipital cortex and temporal/parietal junction, but only change in the
left DLPFC indicated future relapse. Sustained remission was linked to a
decrease in DLPFC reactivity over the treatment period that was not
apparent in patients who relapsed. DLPFC reductions were most often
observed in patients with elevated baseline DLPFC reactivity (Fig. S2),
consistent with ndings of both greater baseline PFC activity indicating
acute phase treatment response (Godlewska et al., 2018; Goldapple
et al., 2004; Lemogne et al., 2012), and reductions in cortical midline
activity following prophylactic intervention (Williams et al., 2020).
Together, these ndings support a model of prefrontal hyperactivity as a
dynamic, treatment-modiable marker of maladaptive cognitive
reactivity.
The association of relapse with exaggerated DLPFC activation and
somatosensory deactivation also supports a model of depression
vulnerability based on an over-reliance on prefrontal processes such as
cognitive elaboration to the detriment of sensory integration. Accord-
ingly, exploratory PPI analyses using the right somatosensory biomarker
as a seed region illustrated that dysphoric mood induction introduced an
inhibitory relationship with the right DLPFC, contralateral to the left
LPFC dynamic biomarker. This inhibition is understandable given that
strategies such as distraction or reappraisal feature prominently in
treating acute phase depression (Cuijpers et al., 2013; Markowitz,
2008). However, continued attempts to avoid negative affect or down-
regulate negative events may leave patients at insensitive to periods of
relative symptom quiescence, preventing identication of symptom
improvement (Farb et al.; Mellick et al., 2019). A parallel can be found in
the use of somatically-informed approaches to support patients in re-
covery from addiction or in the long term management of chronic pain
(Garland, 2016) and therapies delivered during depression remission,
such as MBCT and WB-CT, encourage the regulation of negative affect
via exposure to its somatic and cognitive features. It is possible that
relapsers were less exible than non-relapsers in adapting their regula-
tory strategies; this view is supported here by reductions in DLPFC
reactivity being limited to the non-relapse group, and in the literature by
ndings of increased perseveration in remitted depressed patients
(Stange et al., 2020).
The nal study aim sought to distinguish MBCT and WB-CT mech-
anisms. However, consistent with psychometric analysis of the larger
clinical trial (Farb et al., 2018; Segal et al., 2019), no neural evidence of
process dissociation was observed. Although at a procedural level,
MBCT and WB-CT emphasize divergent therapeutic strategies, reduction
of DLPFC-related cognitive reactivity represents a common prophylactic
marker. The attenuation of sensory deactivation would also clearly be a
favorable outcome for prophylaxis, but these biomarkers were not
impacted by prophylactic treatment. It remains possible that sensory
deactivation might be addressed as treatment-acquired skills are
consolidated over time (Segal et al., 2019), a potential longitudinal
consequence of reduced prefrontal reactivity.
Fig. 7. Effects of PPI analysis. A) Regions with altered connectivity to the right somatosensory cortex as a function of mood context (sad vs. neutral). Orange areas
are FWE-corrected positive PPI score areas, whereas blue areas are negative scores. B) Sadness-evoked connectivity change with the right lateral prefrontal cortex
(LPFC) is signicantly related to future relapse status. C) Connectivity between the somatosensory cortex and right LPFC is responsive to dysphoric mood induction.
D) The magnitude of sadness-evoked deactivation between right LPFC and somatosensory cortex distinguishes relapsers from non-relapsers. Interpretation of how
this connectivity relates to the right somatosensory seed region may be challenging given that main effect within the somatosensory region was a deactivation; by this
logic, orange areas were more negatively associated with the somatosensory cortex during sad-mood induction, whereas blue areas were more positively associated
with the somatosensory cortex, sharing in the deactivation pattern. (For interpretation of the references to colour in this gure legend, the reader is referred to the
web version of this article.)
N.A.S. Farb et al.
NeuroImage: Clinical 34 (2022) 102969
11
4.1. Limitations and constraints on generalizability
There are several limitations to consider in this work. First, while the
study implicates novel biomarkers of depression vulnerability, it is
limited by the lack of an independent sample by which to test these
biomarkers’ predictive utility (Poldrack et al., 2017). Second, we uti-
lized a dual-criterion threshold for determining clinical relapse (SCID
and HRSD); while commonly used (Klein et al., 2004; Reynolds et al.,
2006), this approach prioritizes condence in relapse events at the
expense of excluding partial relapse phenomena. Third, this study
characterizes MDD vulnerability only in the context of dysphoric reac-
tivity and treatment-response to MBCT and WB-CT. A more compre-
hensive account would require integrating data from pharmacotherapy
and neurostimulation treatments, along with a passive control for the
time elapsed between our baseline and post-intervention scans. Fourth,
while dysphoric-mood induction proved to be a fruitful approach,
probing other aspects of MDD such as anhedonia or self-blame may also
expand the neural proles of relapse and remission (Lythe et al., 2015).
Finally, future research should explore ecologically valid indicators of
cognitive reactivity or sensory deactivation to characterize dynamic
regulatory responses to momentary negative affect.
5. Conclusions
Neural responses to dysphoric mood-induction in remitted depressed
patients support a 2-factor model of MDD relapse vulnerability, in which
i) enduring patterns of dyphoria-evoked sensory deactivation contribute
to MDD vulnerability, but ii) vulnerability may be mitigated by targeting
prefrontal regions where elevated DLPFC reactivity seems responsive to
clinical intervention. These ndings have the potential to inform eval-
uation of prophylactic treatment response and to spur the development
of interventions designed to consolidate clinical remission.
CRediT authorship contribution statement
Norman A.S. Farb: Conceptualization, Data curation, Formal anal-
ysis, Funding acquisition, Investigation, Methodology, Resources, Soft-
ware, Validation, Visualization, Writing – original draft, Writing –
review & editing. Philip Desormeau: Conceptualization, Data curation,
Formal analysis, Methodology, Software, Validation, Visualization,
Writing – original draft, Writing – review & editing. Adam K. Ander-
son: Conceptualization, Funding acquisition, Investigation, Methodol-
ogy, Resources, Software, Supervision, Writing – original draft, Writing
– review & editing. Zindel V. Segal: Conceptualization, Data curation,
Formal analysis, Funding acquisition, Investigation, Methodology,
Project administration, Resources, Supervision, Validation, Visualiza-
tion, Writing – original draft, Writing – review & editing.
Acknowledgments
Arun Ravindran, MD facilitated clinical access to study patients and
Robert Levitan, MD rated the SCID relapse interviews. The study was
funded by the Canadian Institute of Health Research (Grant# 243812).
Disclosures
NF, AA, and PD reported no biomedical nancial interests or po-
tential conicts of interest. ZS disclosed book royalties from Guilford
Press, workshop fees from the Centre for Mindfulness studies, and rev-
enue from online sales at Mindful Noggin Inc., which are all related to
his work as a co-founder of Mindfulness-Based Cognitive Therapy
(MBCT).
Data Sharing Statement
De-identied psychometric and group level neuroimaging data,
including data dictionaries, are available upon request. The study pro-
tocol is available through https://clinicaltrials.gov, #NCT01178424.
Institutional collaboration for publication requires a signed data-access
agreement with the University of Toronto.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.nicl.2022.102969.
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