See attached articles for Children and adolescents with anxiety disorders,Children with attention deficit/hyperactivity disorder and Individuals with depressive disorders. 
What is introduction that describes the role of assessment in diagnosis and treatment?Use articles to compare at least two psychological or educational tests and/or assessment procedures for each of the topics chosen? Analyze and describe the psychometric methodologies employed in the development and/or validation of the tests and/or assessment procedures associated with each of the three topics?Debate any relevant approaches to assessment of the constructs being evaluated by any tests and assessments you described?Include an analysis of any challenges related to assessing individuals from diverse social and cultural backgrounds for each of the three topics.?Conclude by evaluating the ethical and professional issues that influence the interpretation of testing and assessment data?
References for articles
Creswell, C., Waite, P., & Hudson, J. (2020). Practitioner Review: Anxiety
disorders in children and young people – assessment and treatment. Journal
of Child Psychology & Psychiatry, 61(6), 628–643. https://doi-org.proxy-
Fox, A., Dishman, S., Valicek, M., Ratcliff, K., & Hilton, C. (2020).
Effectiveness of Social Skills Interventions Incorporating Peer Interactions for
Children With Attention Deficit Hyperactivity Disorder: A Systematic Review.
American Journal of Occupational Therapy, 74(2), 1–19. https://doi-org.proxy-
Kim, M. J., Park, H. Y., Yoo, E.-Y., & Kim, J.-R. (2020). Effects of a Cognitive-
Functional Intervention Method on Improving Executive Function and Self-
Directed Learning in School-Aged Children with Attention Deficit Hyperactivity
Disorder: A Single-Subject Design Study. Occupational Therapy International,
Leightley, D., Lavelle, G., White, K. M., Sun, S., Matcham, F., Ivan, A.,
Oetzmann, C., Penninx, B. W. J. H., Lamers, F., Siddi, S., Haro, J. M., Myin-
Germeys, I., Bruce, S., Nica, R., Wickersham, A., Annas, P., Mohr, D. C.,
Simblett, S., Wykes, T., & Cummins, N. (2021). Investigating the impact of
COVID-19 lockdown on adults with a recent history of recurrent major
depressive disorder: a multi-Centre study using remote measurement
technology. BMC Psychiatry, 21(1), 1–11. https://doi-org.proxy-
Nichols, E. S., Penner, J., Ford, K. A., Wammes, M., Neufeld, R. W. J.,
Mitchell, D. G. V., Greening, S. G., Théberge, J., Williamson, P. C., & Osuch,
E. A. (2021). Emotion regulation in emerging adults with major depressive
disorder and frequent cannabis use. NeuroImage: Clinical, 30. https://doi-
Practitioner Review: Anxiety disorders in children and
young people – assessment and treatment
Cathy Creswell,1,2 Polly Waite,1,2,3 and Jennie Hudson4
1Department of Experimental Psychology, University of Oxford, Oxford, UK; 2Department of Psychiatry, University of
Oxford, Oxford, UK; 3School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK;
4Centre for Emotional Health Macquarie University, Sydney, NSW, Australia
Despite significant advancements in our knowledge of anxiety disorders in children and adolescents, they continue to
be underrecognised and undertreated. It is critical that these disorders are taken seriously in children and young
people as they are highly prevalent, have a negative impact on educational, social and health functioning, create a
risk of ongoing anxiety and other mental health disorders across the life span and are associated with substantial
economic burden. Yet very few children with anxiety disorders access evidence-based treatments, and there is an
urgent need for widespread implementation of effective interventions. This review aimed to provide an overview of
recent research developments that will be relevant to clinicians and policymakers, particularly focusing on the
development and maintenance of child anxiety disorders and considerations for assessment and treatment. Given
the critical need to increase access to effective support, we hope this review will contribute to driving forward a step
change in treatment delivery for children and young people with anxiety disorders and their families. Keywords:
Anxiety disorders; children; adolescents; intervention; treatment; assessment.
Anxiety disorders are the most prevalent mental
health disorders in children and young people (see
Table 1 for DSM classification and prevalence esti-
mates). In their worldwide review of the prevalence of
mental disorders in children and young people,
Polanczyk, Salum, Sugaya, Caye and Rohde (2015)
reported a mean prevalence of 6.5% based on studies
conducted between 1985 and 2012; however, this is
highly likely to be an underestimate of the current
situation given recent findings from consecutive
national surveys in England in which there was a
51% increase in the reported prevalence of anxiety
disorders between 2004 and 2017 (Vizard, Pearce, &
Davis, 2018). Given the significant negative impact
of childhood anxiety disorders on educational, social
and health functioning, the risk of ongoing anxiety
and other mental health disorders in adulthood
(Copeland, Angold, Shanahan, & Costello, 2014)
and the substantial economic burden (Fineberg
et al., 2013), this recent increase in reported preva-
lence is extremely concerning and reflects an urgent
need for effective, early intervention.
Aims of this review
The last three decades have seen a burgeoning of
research into the treatment of anxiety disorders in
children and adolescents, with a number of meta-
analyses published over the last decade. Here, we
have focused on recent developments in the field
that will be of most relevant to clinical practitioners,
specifically, recent literature on the development
and maintenance of anxiety disorders, assessment
and intervention. In line with the bulk of the
literature in this field, we have focused primarily
on school-aged children and young people (4–
18 years).
Development of anxiety disorders in children
and young people
The two most robust predictors of the development of
anxiety disorders in children are inhibited tempera-
ment (the tendency to withdraw, avoid or respond
fearfully to new situations), which increases the risk
of later anxiety disorders more than sevenfold
(Clauss & Blackford, 2012) and having a parent
with an anxiety disorder, which raises the risk
almost twofold (Lawrence, Murayama, & Creswell,
2019). These findings are in keeping with evidence
from twin, family and adoption studies that suggest
heritability rates of between 25% and 50% for child
anxiety symptoms (Cheesman, Rayner, & Eley,
2019), but also highlight the substantial role of the
environment. Because of the inter-familial risks of
anxiety disorders, research on environmental risk
factors has predominantly focused on parenting
behaviours, where there is some longitudinal and
experimental research evidence for a causal role of
parental overinvolvement/control (de Wilde & Rapee,
2008; Hudson & Dodd, 2012; Rubin, Burgess, &
Hastings, 2002; Thirlwall & Creswell, 2010). How-
ever, a growing body of work highlights the reciprocal
relationship between child inhibition/anxiety and
parental involvement/control, in which parental
Conflict of interest statement: See Acknowledgements for full
© 2020 Association for Child and Adolescent Mental Health
Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St, Malden, MA 02148, USA
Journal of Child Psychology and Psychiatry 61:6 (2020), pp 628–643 doi:10.1111/jcpp.13186
PFI_12mmX178mm.pdf + eps format
involvement/control is both elicited by child inhibi-
tion/anxiety and influences it’s development (Eley,
Napolitano, Lau, & Gregory, 2010; Hudson, Doyle, &
Gar, 2009). Experimental and longitudinal evidence
also supports a causal role of parental modelling and
transference of fear information (e.g. Field & Lawson,
2003), although to date these studies have focused
on the development of fear or avoidance, rather than
anxiety disorders per se. Despite quite extensive
research attention, evidence for a role of parental
negativity in the development of child anxiety
disorders is generally lacking (Lawrence, Waite, &
Creswell, 2019).
Notably, where particular parental behaviours
appear to have an effect on child anxiety disorders,
this is likely to vary according to child characteris-
tics, including the child or young person’s age or
stage of development (e.g. Waite & Creswell, 2015),
and the young person’s temperament. For example,
children with higher levels of behavioural inhibition
or trait anxiety have been found to respond to
maternal expressed anxiety or control with a more
fearful response, compared to those with lower levels
(De Rosnay, Cooper, Tsigaras, & Murray, 2006;
Thirlwall & Creswell, 2010). Furthermore, in a recent
longitudinal study behaviourally inhibited
preschoolers only had higher anxiety symptoms at
12 years of age when there had been high maternal
overinvolvement at age 4 years, and effects were
mitigated when mothers demonstrated low overin-
volvement (Hudson, Murayama, Meteyard, Morris, &
Dodd, 2019).
In terms of broader environmental factors, the
role of life events and peer relationships has also
been examined, though to a lesser extent, bringing
further evidence of reciprocal relationships between
‘risk’ factors and childhood anxiety disorders (Broe-
ren, Newall, Dodd, Locker, & Hudson, 2014; Kim,
Conger, Elder, & Lorenz, 2003). Here too, it is likely
that these relationships are influenced by age,
temperament and other moderating factors (Broeren
et al., 2014; Turner, Beidel, & Wolff, 1996).
Research in other areas of the environment, such
as economic adversity, sibling relationships, social
media and school environment, has received limited
attention to date, but it is likely that in some
instances they may create risks for the development
and maintenance of anxiety (Keles, McCrae, &
Grealish, 2019), as well as potential opportunities
for support and intervention (Keeton, Teetsel, Dull,
& Ginsburg, 2015).
Maintenance of anxiety disorders in children
and young people
In contrast to models of anxiety disorders in adults
which have tended to focus on maintenance factors
(i.e. factors that prevent new learning in feared
situations; e.g. Clark, 1986; Clark & Wells, 1995;
Rapee & Heimberg, 1997), models of anxiety
disorders in children and adolescents (e.g. Spence
& Rapee, 2016) have tended to focus more on
developmental risk factors – meaning we have quite
Table 1 Characteristics and prevalence of DSM-5 anxiety
disorders in children and adolescents
disorder Clinical characteristics
Recent example
figures (%)a
Excessive fear of separation
from primary caregiver(s)
Marked fear or anxiety
about a specific object or
situation (e.g. an animal,
injections, vomit) that
almost always provokes
immediate fear or anxiety
Marked fear or anxiety
about social situations in
which the young person is
exposed to possibly
scrutiny by others, and
fears they will act in a way
or show anxiety symptoms
that will be negatively
Excessive and
uncontrollable worry about
a number of events or
activities, associated with
at least 3 symptoms (e.g.
muscle tension, difficulty
concentrating, sleep
Recurrent, unexpected
panic attacks that which
are not restricted to a
particular situation and
concern about future
attacks and/or a change in
behaviour related to the
Agoraphobia Marked fear or anxiety
about 2 or more of the
following situations: using
public transport, being in
open spaces, being in
enclosed spaces, being in a
crowd or standing in a line,
or being outside of the
home alone
Consistent failure to speak
in specific social situations
(e.g. school) where there is
an expectation to speak,
despite speaking in other
Prevalence data are from Vizard et al. (2018) for all anxiety
disorders except selective mutism. We have not combined with
other recent prevalence studies as data are not comparable
due to different time periods covered (e.g. Spence, Zubrick, &
Lawrence, 2018).
aFigures represent point prevalence (proportion who meet
criteria for a diagnosis at a specific point in time).
bFigures taken from Muris and Ollendick’s (2015) review; the
variation in prevalence rates identified is likely to be due to
variability in the strictness of the diagnostic criteria employed.
© 2020 Association for Child and Adolescent Mental Health
doi:10.1111/jcpp.13186 Anxiety disorders in children and young people 629
limited understanding on which to base the content
of treatments (Halldorsson & Creswell, 2017). How-
ever, there is emerging evidence that similar cogni-
tive processes may occur in children and young
people to those described in adult cognitive models,
for example, associations between self-focused
attention and social anxiety (Hodson, McManus,
Clark, & Doll, 2008) and intolerance of uncertainty
and worry (Fialko, Bolton, & Perrin, 2012) in young
people. To date, these studies have largely been
carried out using cross-sectional designs in nonclin-
ical populations in varying, and often wide, age
groups. Going forward, experimental studies to test
causal and maintaining processes in children and
young people are needed, that can take account of
children and young people’s cognitive maturity and
social context, in order to develop specific develop-
mentally tailored interventions (e.g. Leigh & Clark,
There is a high degree of comorbidity among anxiety
disorders in children and young people, particularly
with other anxiety disorders across the age range
(Leyfer, Gallo, Cooper-Vince, & Pincus, 2013), and
mood disorders in adolescence (Essau, 2003). How-
ever, separate anxiety disorders can be adequately
and reliably diagnosed (Spence, 2017). To assess
DSM-5 anxiety disorders, a multimethod and multi-
informant approach is recommended (Hudson, New-
all, Schneider, & Morris, 2014; Kazdin, 2003;
Silverman & Ollendick, 2005) using (a) interview
schedules, (b) questionnaire measures and where
applicable, (c) observational approaches.
Interview schedules
Of these three methods, structured diagnostic inter-
views such as the Anxiety Disorders Interview
Schedule for children and parents (Silverman &
Albano, 1996) are considered to be the ‘gold stan-
dard’. While they are commonly used in research
trials, standardised assessments, such as the ADIS-
C/P, are rarely used systematically in clinical set-
tings, bringing risks that specific anxiety disorders
may be missed or misdiagnosed, and that children
and young people may not respond to the nonspeci-
fic interventions that they often receive (Craddock
et al., 2008). These risks have led to recommenda-
tions that standardised assessments should be used
as an adjunct to clinical assessment (Martin, Fish-
man, Baxter, & Ford, 2011).
Structured diagnostic interviews provide a com-
prehensive assessment of anxiety (including symp-
toms, severity and interference) using independent
information from both the parent or carer and the
child or adolescent. Given the high degree of comor-
bidity among disorders, a comprehensive assess-
ment considers all anxiety and related disorders (e.g.
mood and behaviour disorders) in order to obtain
accurate differential diagnoses at the start of treat-
ment, and also to determine the success of the
treatment approach in reducing the presence and
severity of, not only the most interfering (i.e. primary)
diagnosis, but also all anxiety diagnoses. As anxiety
disorders are associated with increased risk of
suicidal ideation (O’Neil Rodriguez & Kendall, 2014)
and other factors that increase the risk of suicidal
ideation and behaviour (e.g. being bullied by peers,
alcohol and drug problems, and poor academic and
vocational achievement; (Reijntjes, Kamphuis, Prin-
zie, & Telch, 2010; Robinson, Sareen, Cox, & Bolton,
2011), a comprehensive interview assessment
should also include an appropriate assessment of
risk of suicide and self-injury.
One of the significant methodological issues that
arises when using an interview schedule is that
clinicians need to manage differing perspectives
provided by parents and children regarding anxiety
symptom presence, severity and impairment
(Choudhury, Pimentel, & Kendall, 2003; Grills &
Ollendick, 2003) in order to make appropriate clin-
ical decisions. Although clinicians are more likely to
be influenced by the parent than the child’s per-
spective (Grills & Ollendick, 2003), particularly
among preadolescents, it is often difficult to deter-
mine which report is more valid. To ensure equiva-
lent value is placed on both the child or adolescent’s
report and that of the parent, clinicians are encour-
aged to use the ‘OR rule’ (Comer & Kendall, 2004) in
which the diagnostic profile includes clinically inter-
fering symptoms when they are reported by either
the young person or the parent, unless doing so
would lead to double counting of the same symp-
Questionnaire measures
Diagnostic interviews are typically supplemented
with psychometrically reliable and valid question-
naire measures from multiple sources (e.g. parent,
young person, teacher) to assess anxiety symptoms
and/or impairment. Although questionnaire mea-
sures should be used in conjunction with interviews,
they bring advantages of ease of administration and
resulting reductions in time and cost. Further,
combining questionnaire data from parents and the
young person leads to a richer and sometimes more
accurate perspective of the child’s symptoms (Rear-
don, Creswell, et al., 2019). Most youth-reported
questionnaires are designed for children 7 years and
up; however, children’s reading and cognitive ability
at this age vary dramatically and research has
highlighted that a portion of children do not under-
stand the questionnaires presented to them (White &
Hudson, 2016). It is therefore important to consider
whether the measure is appropriate for the child’s
developmental stage when deciding which reporters
to include and which questionnaire measures to
© 2020 Association for Child and Adolescent Mental Health
630 Cathy Creswell et al. J Child Psychol Psychiatr 2020; 61(6): 628–43
choose. Teacher report can also help add to clini-
cian’s understanding of the child’s presenting prob-
lems, particularly for school-specific or classroom-
specific symptoms; however, this may not always be
practical to obtain (e.g. as children move classes/
schools) and there is limited evidence of reliability
and validity (although see Lyneham, Street, Abbott &
Rapee, 2008; Reardon, Spence, Hesse, Shakir, &
Creswell, 2018 for initial promising findings).
A host of measures has been developed to assess
multidimensional anxiety symptoms in children and
adolescents that are available in both parent report
and youth report, such as the Spence Children’s
Anxiety Scale [SCAS: (Nauta et al., 2004; Spence,
Barrett, & Turner, 2003), Screen for Child Anxiety and
Related Emotional Disorders (SCARED); (Birmaher
et al., 2003) and the Multidimensional Anxiety Scale
for Children (MASC; March, Parker, Sullivan, Stal-
lings, & et al., 1997). These measures have typically
been informed by earlier editions of the Diagnostic
and Statistical Manual of Mental Disorders (e.g. DSM-
IV; American Psychiatric Association, 1994), with the
exception of the Youth Anxiety Measure – 5 (Muris
et al., 2017)] which adds selective mutism items
bringing it in line with DSM-5 (American Psychiatric
Association, 2013) and ICD-11 (Reed et al., 2019). To
detect elevated symptoms, the measure needs to have
available established normative data – ideally cultur-
ally relevant – to indicate the degree to which the
symptoms compare to other children of the same age
and gender and the extent to which they can accu-
rately identify children and adolescents with/out
anxiety disorders. Multidimensional measures pro-
vide an overall score for anxiety as well as a subscale
score for symptoms of specific anxiety disorders.
Recent data from a large collaborative study of 10
international child anxiety clinics suggest that the
SCAS can be useful in differentiating some (e.g. social
anxiety disorder and separation anxiety disorder) but
not all of the anxiety disorders (e.g. generalised
anxiety disorders and specific phobias) in children
(Reardon, Creswell, et al., 2019).
Depending on the child’s specific presentation and
the focus of treatment, clinicians may also choose to
include additional disorder-specific measures. For
example, if social anxiety disorder is the focus of
treatment, there are a number of measures specifi-
cally designed to assess social anxiety symptoms
(e.g. Social Phobia and Anxiety Inventory – Children;
Beidel, 1996). Given the common co-occurrence of
depression, particularly in adolescence, and its
likely impact on treatment outcomes (Hudson
et al., 2015), it is also important to include measures
of depressive symptoms such as the Short Mood and
Feelings Questionnaire (SMFQ; Angold, 1995) or
using a combined measure such as the Revised
Child Anxiety and Depression Scale (RCADS; Chor-
pita, Yim, Moffitt, Umemoto, & Francis, 2000).
The assessment of anxiety symptoms in children
with autism spectrum disorders (ASD) has received
increasing attention over the last few years with
evidence questioning the appropriateness of existing
anxiety measures (Glod et al., 2017). Specifically,
parents of ASD children respond differently to par-
ticular items of the SCAS-P compared with parents of
typically developing children (Toscano et al., under
review) and the factor structure differs (e.g. Jitlina
et al., 2017; Magiati et al., 2017). These results
highlight that questionnaire measures designed and
evaluated with typically developing children should
be used with caution in children with ASD and,
although there may some utility in determining a
total anxiety score, clinicians should not rely on the
subscales from multidimensional measures such as
the SCAS-P, particularly those that measure phys-
ical injury fears and obsessive–compulsive disorder
symptoms, when working with children with ASD
(Magiati et al., 2017; Toscano et al., under review).
In addition to symptom severity, a number of
questionnaires have been developed to assess gen-
eral functioning and impairment, such as the
Barkley Functional Impairment Scale for Children
and Adolescents (Barkley, 2012), or the Child and
Adolescent Social and Adaptive Functioning Scale
(Price, Spence, Sheffield, & Donovan, 2002). We have
found the Child Anxiety Life Interference Scale
(Lyneham et al., 2013) and the Child Anxiety Impact
Scale (Langley, Bergman, McCracken, & Piacentini,
2004) particularly useful as they were developed to
assess the specific impact of anxiety symptoms on
the child’s life at home, outside the home as well as
the impact on the parent’s life. Recent evidence
indicates that parent-reported life interference is a
good indicator of child anxiety diagnostic status
(Evans, Thirlwall, Cooper, & Creswell, 2017).
Observational assessment
Observational assessments are infrequently used
outside of research settings but can be used to
determine the level of fear or anxiety experienced
when the child is exposed to threatening stimuli. For
example, behavioural approach tasks (BAT) involve
the child taking steps of increasing difficulty towards
a feared object or situation in a controlled environ-
ment. BATs can provide critical information about
fear levels (Ollendick, Lewis, Cowart, & Davis, 2012)
and can be particularly informative in situations
where there has been inconsistent or unreliable
reporting on diagnostic and questionnaire measures.
Treatment: psychological interventions
The most frequently evaluated psychological treat-
ment for anxiety disorders in children and young
people is cognitive behaviour therapy (CBT), which
typically involves the application of exposure to
enable children and young people to confront feared
situations, typically in a graded fashion, in order to
develop new learning about what really happens when
© 2020 Association for Child and Adolescent Mental Health
doi:10.1111/jcpp.13186 Anxiety disorders in children and young people 631
they enter anxiety-provoking situations. In CBT pro-
grammes, exposure is typically accompanied by cog-
nitive restructuring procedures, to help children
identify and challenge negative automatic thoughts.
Some programmes also include other forms of skills
training, such as relaxation, social skills and prob-
lem-solving training. It has consistently been con-
cluded, across a number of meta-analyses, that CBT
shows clear benefits over waitlist controls, with, for
example, an overall response rate of 59.4% for CBT
versus 17.5% for controls (e.g. James et al., 2013).
While there are some positive indications of sustained
benefits of CBT over the long-term (e.g. Gibby, Cas-
line, & Ginsburg, 2017), others have found high
relapse rates (Ginsburg et al., 2018). Overall, very
few studies have been able to maintain a control
condition over the long-term (e.g. James et al, 2013)
limiting conclusions that can be made.
Does the format of delivery matter?
A recent systematic review of psychotherapies for
childhood anxiety disorders (Zhou et al., 2019) iden-
tified 101 randomised controlled trials (RCTs) includ-
ing 11 categories of psychotherapy, which all involved
CBT (or behavioural therapy) but in a range of
formats (individual, group, bibliotherapy, Internet
assisted – with/out parent involvement or child/
parent only). On the basis of a network meta-analysis
(which compares more than two interventions to each
other in a single meta-analysis), there was some
evidence that groups may be a particularly effective
format. However, these findings need to be inter-
preted with caution, given that group treatments
have not been found to be more effective than
individual treatments when compared directly (e.g.
Manassis et al., 2002) and trials which have taken a
group approach may disproportionately reflect other
important study characteristics, including particular
aged participants and intervention settings (e.g.
clinic versus community). Going forward, we need
sufficiently powered RCTs that allow us to make
head-to-head comparisons between different treat-
ment formats. The inclusion of health economic
analyses to address these questions will be critical,
as it is far from clear that group-based treatments are
necessarily more cost-effective than individual
approaches, as illustrated in the case of social
anxiety disorder in adults (NICE, 2013). On the other
hand, other treatment formats have promising evi-
dence and may bring particular economic advantages
[e.g. bibliotherapy (Yuan et al., 2018)], computerised
and Internet-based interventions (e.g. Ebert et al.,
2015; and see below section: ‘Improving access to
psychological treatments’). Youth and parent prefer-
ences should also be considered; for example, there is
promising evidence for treatment of specific phobias
delivered predominantly within a single (extended)
treatment session (e.g. Ollendick et al., 2009) and
this intensive approach has been found to be highly
motivating and acceptable in adult settings (e.g.
Bevan, Oldfield, & Salkovskis, 2010).
What are the important treatment components?
There has been very little examination of how what is
actually done within the CBT programme relates to
treatment outcome. This is a serious shortcoming,
given recent evidence that certain procedures can
either enhance or inhibit new, adaptive learning (e.g.
Craske, Treanor, Conway, Zbozinek, & Vervliet,
2014). However, the few notable exceptions include
an examination of the trajectory of symptom change in
the large U.S. CAM trial (n = 488; 7–17 years) in
which the introduction of both cognitive restructuring
(which involved changing self-talk) and exposure
tasks significantly accelerated the rate of progress
on measures of symptom severity and global func-
tioning moving forward in treatment, whereas the
introduction of relaxation training had limited impact
(Peris et al., 2015). Notably, improvements in coping
efficacy were a significant mediator of treatment
gains, but improvements in anxious self-talk were
not (Kendall et al., 2016). These findings suggest that
treatments might be more efficiently delivered by
promoting new learning (particularly about coping)
through exposure. This conclusion was also sup-
ported by a recent meta-analysis that concluded that
introducing anxiety management strategies before
exposure does not increase the efficacy of treatment
(Ale, McCarthy, Rothschild, & Whiteside, 2015).
Recent dismantling studies also provide consistent
preliminary findings; for example, exposure therapy
(in which parents are trained how to facilitate expo-
sure outside sessions) achieved greater improve-
ments than an intervention that only involved the
anxiety management strategies that are typically
administered preexposure, such as identifying feel-
ings and anxious cognitions, relaxation and problem-
solving (Whiteside et al., 2015). Notably, different
treatment formats may promote different pathways to
recovery as indicated by Silverman et al.’s recent
(2019) findings that reductions in parental psycho-
logical control mediated outcomes from ‘parent
involvement CBT’ whereas positive peer-youth rela-
tionships mediated outcomes from group CBT with
peers. Together, these findings indicate that the
opportunity to learn through exposure is key and that
this may be optimised in a number of different ways.
What should we deliver to whom?
The majority of trials of CBT for child anxiety
disorders have evaluated outcomes for mixed anxiety
disorders (74% in Zhou et al., 2019), typically
including children presenting with social anxiety
disorder, generalised anxiety disorder, separation
anxiety disorder, obsessive–compulsive disorder and
specific phobias. However, a number of recent stud-
ies have identified that children with social anxiety
© 2020 Association for Child and Adolescent Mental Health
632 Cathy Creswell et al. J Child Psychol Psychiatr 2020; 61(6): 628–43
disorder benefit less from generic CBT approaches
than children with nonsocial forms of anxiety disor-
ders (e.g. posttreatment remission rates of 40.6% vs.
72.0%, Ginsburg et al., 2011; 22.3% vs. 42.1%–
52.7%, Hudson et al., 2015). The reasons for this
remain unclear, with hypotheses including a lack of
focus on relevant exposures (e.g. Ginsburg et al.,
2011), potential disorder-specific maintenance fac-
tors that may not be addressed in current treatments
(e.g. Halldorsson & Creswell, 2017) and/or social
skills deficits (e.g. Beidel, Turner, & Morris, 2000).
To date, RCTs of social anxiety disorder-specific
treatments have predominantly focused on address-
ing potential social skills deficits with consistent
findings that they are effective in comparison with
waitlist control conditions or active, nonspecific
control interventions (e.g. Beidel et al., 2000; Dono-
van & March, 2014; €Ost, Cederlund, & Reuterski€old,
2015; Spence, Donovan, & Brechman-Toussaint,
2000), and in meta-analyses, these treatments have
fared better than generic forms of CBT (e.g. Rey-
nolds, Wilson, Austin, & Hooper, 2012). However, in
a head-to-head comparison of social anxiety disor-
der-specific treatment (including social skills train-
ing and a focus on factors identified in cognitive
models of social anxiety disorder) and traditional
generic CBT (both delivered …
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Daniel Leightley1*, Grace Lavelle1, Katie M. White1, Shaoxiong Sun2, Faith Matcham1, Alina Ivan1,
Carolin Oetzmann1, Brenda W. J. H. Penninx3, Femke Lamers3, Sara Siddi4,5,6, Josep Mario Haro4,5,6,
Inez Myin-Germeys7, Stuart Bruce8, Raluca Nica8,9, Alice Wickersham1, Peter Annas10, David C. Mohr11,
Sara Simblett12, Til Wykes12, Nicholas Cummins2,13, Amos Akinola Folarin2,14,15, Pauline Conde2, Yatharth Ranjan2,
Richard J. B. Dobson2,16, Viabhav A. Narayan17, Mathew Hotopf1,16 and On behalf of the RADAR-CNS Consortium
Background: The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes a
clinical illness Covid-19, has had a major impact on mental health globally. Those diagnosed with major depressive
disorder (MDD) may be negatively impacted by the global pandemic due to social isolation, feelings of loneliness
or lack of access to care. This study seeks to assess the impact of the 1st lockdown – pre-, during and post – in
adults with a recent history of MDD across multiple centres.
Methods: This study is a secondary analysis of an on-going cohort study, RADAR-MDD project, a multi-centre study
examining the use of remote measurement technology (RMT) in monitoring MDD. Self-reported questionnaire and
passive data streams were analysed from participants who had joined the project prior to 1st December 2019 and
had completed Patient Health and Self-esteem Questionnaires during the pandemic (n = 252). We used mixed
models for repeated measures to estimate trajectories of depressive symptoms, self-esteem, and sleep duration.
Results: In our sample of 252 participants, 48% (n = 121) had clinically relevant depressive symptoms shortly before
the pandemic. For the sample as a whole, we found no evidence that depressive symptoms or self-esteem
changed between pre-, during- and post-lockdown. However, we found evidence that mean sleep duration (in
minutes) decreased significantly between during- and post- lockdown (− 12.16; 95% CI − 18.39 to − 5.92; p < 0.001). We also found that those experiencing clinically relevant depressive symptoms shortly before the pandemic showed a decrease in depressive symptoms, self-esteem and sleep duration between pre- and during- lockdown (interaction p = 0.047, p = 0.045 and p < 0.001, respectively) as compared to those who were not. © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data. * Correspondence: [email protected] 1Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK Full list of author information is available at the end of the article Leightley et al. BMC Psychiatry (2021) 21:435 mailto:[email protected] Conclusions: We identified changes in depressive symptoms and sleep duration over the course of lockdown, some of which varied according to whether participants were experiencing clinically relevant depressive symptoms shortly prior to the pandemic. However, the results of this study suggest that those with MDD do not experience a significant worsening in symptoms during the first months of the Covid − 19 pandemic. Keywords: Remote measurement technology, Major depressive disorder, Mobile health Background On the 31st December 2019, the World Health Organ- isation (WHO) documented reports of a cluster of cases of pneumonia of unknown origin in Wuhan, China [1]. The cause was later identified as a novel severe acute re- spiratory syndrome coronavirus 2 (SARS-CoV-2), which caused a clinical illness, Covid-19 [2]. Within weeks of the initial outbreak, the total number of cases and deaths had exceeded those of severe acute respiratory syndrome outbreak in 2003 [3]. In March 2020, a global pandemic was declared by the WHO due to the exponential in- crease in diagnosed cases and deaths, with countries across Europe implementing national lockdowns to re- duce the risk of spread and infection [4]. The ongoing Covid-19 pandemic is predicted to have severe negative global mental health consequences [5, 6], with a review of stressors indicating that quarantine dur- ation, infection fears, frustration, boredom, inadequate information, financial loss, loss of sleep and stigma being the main drivers [7]. The pandemic has disrupted or halted critical mental health services in 93% of countries worldwide, while the demand for mental health care is increasing, according to a recent WHO survey [8]. There have been urgent calls to examine the mental health consequences of Covid-19 at an international level, using high-quality data and robust analysis techniques [9–11]. Gaining a clear indication of population impacts of the pandemic on mental health has been challenging [6]. Pre-existing population studies, which have explicit sam- pling frames and longitudinal data pre-dating the pan- demic, have demonstrated an increase in symptoms of distress within the general population [6, 12]. And in the early stages of the pandemic this was dominated by symptoms of anxiety [12], with symptoms of distress most frequent in young adults [6]. In addition, one population study found sleep to be negatively impacted by the pandemic, with female participants reporting more sleep loss than male participants [13]. A less studied issue has been the impact of public health measures, such as lockdown, on individuals with pre-existing mental disorders who may have less access to care and support services [8]. Adults diagnosed with major depressive disorder (MDD) and experiencing a current episode of depression are particularly susceptible to the challenges raised by lockdown, such as disrupted sleep [14], reduced sociability [15] and changes in mood/self-esteem [16]. Therefore, it is important to understand the trajectories of change in those experien- cing a current episode of depression and how these out- comes are impacted by the pandemic. Across Europe, smartphone ownership and use is high (estimated 76% of adults across Europe [17]), which pro- vides a ready means for accurate and ongoing data col- lection using remote measurement technology (RMT) [18–20]. RMT data collection methods are inexpensive, can gather data in real-time, and crucially considering infection risk, do not require face-to-face contact be- tween the research team and participants. RMT may provide a solution to the need for surveillance at the population level passively, without the need for intrusive study protocols, or continual engagement. This may lead to richer, more objective and holistic characterisation of behaviours and physiology as a result of the Covid-19 pandemic. Remote Assessment of Disease and Relapse in individ- uals with Major Depressive Disorder (RADAR-MDD) is an ongoing study forming a component of the RADAR- Central Nervous System consortium [21]. Participants with MDD from the UK, Spain and The Netherlands were invited to provide longitudinal data via ubiquitous, commercially-available RMT (i.e. phones and activity trackers) [18]. The high-frequency information collected passively includes detail on participants’ sleep quality/ patterns, physical activity, stress, mood, self-esteem, so- ciability, speech patterns, and cognitive function [18]. In addition to passive data collection, self-reported eco- logical momentary assessment data was also collected. This included assessments focusing on depression, speech, self-esteem, and cognitive function. The study provides an opportunity to explore the impact of the pandemic on individuals with a MDD diagnosis and their changes in depressive symptoms across Europe. A strength of RADAR-CNS is the ability to directly com- pare results gathered during- and post- lockdown with previously collected pre-lockdown baseline data. The potential impact of the pandemic on individuals with mental disorders has been recognised as one of a triad of key current global mental health challenges [22]. Relatively high rates of depression have been reported by a number of countries [23], this adding to the existing global burden of depression [24]. However, tackling this in its entirety demands a greater understanding of the Leightley et al. BMC Psychiatry (2021) 21:435 Page 2 of 11 true impact of Covid-19 for those living with pre- existing mental disorders. We therefore aimed to investi- gate the impact of the 1st global lockdown on adults with a recent history of MDD, through the following ob- jectives: 1) To investigate changes in depressive symp- toms, self-esteem and sleep duration pre-, during- and post-lockdown in the period from 1st December 2019 to 1st September 2020; and 2) To investigate whether these changes over time varied according to whether partici- pants were experiencing a depressive episode shortly be- fore the pandemic. Method Data source and participants This study uses data collected between 1st December 2019 and 1st September 2020 (9 months of available data) from the RADAR-MDD project, a multi-centre co- hort, examining the use of RMT in monitoring MDD [18]. Participants were required to meet the following eligibility criteria: 1) DSM-5 diagnostic criteria for diag- nosis of non-psychotic MDD in the last 2 years, 2) recur- rent MDD (lifetime history of at least 2 episodes of depression, 3) willingness and ability to complete self- reported assessment via smartphone, 4) provide in- formed consent, 5) own an Android smartphone, or will- ing to use an Android smartphone provided by the research team, 6) aged 18 years or over, and 7) fluent in English, Spanish, Catalan or Dutch. The study protocol for RADAR-MDD has been previously reported [18]. The data collected via RADAR-MDD project uses RADAR-base, which is an open source platform de- signed to leverage data from wearables and mobile tech- nologies [21]. RADAR-base provides both passive and active data collection via two applications – active and passive. The passive app collects real time monitoring of movement, location, audio and app usage [21]. The ac- tive app collects self-reported user questionnaires. Data from both apps are streamed in real-time to project servers. It is important to note that RADAR-base does not provide a feedback loop to the participant or any clinicians. In total, 623 participants met the eligibility criteria and were recruited between November 2017 and June 2020 across three European countries: United Kingdom (n = 350; 56.2%), Spain (n = 155; 24.9%) and The Netherlands (n = 118; 18.4%). Participants in the UK and The Netherlands were recruited from community samples in- cluding individuals from existing studies on depression and using local clinical services. All participants re- cruited for this study had pre-existing major depressive disorder, and all recruitment sites utilised the same eligi- bility criteria for entry into the study. The Netherlands also recruited through advertisements in general prac- tices and psychologist practices, newspaper advertisements and through ( Spanish participants were recruited from a clinical sample of individuals seeking help for a mental health condition. Each participant was asked to wear a wrist-worn activ- ity tracker (FitBit Charge 2 or 3) and install the active and passive RADAR-base applications onto their smart- phones (see [18, 21] for further details). The project was developed using co-design and in partnership with a Pa- tient Advisory Group. Project apps were used to collect data passively from existing smartphone sensors, and to deliver questionnaires, cognitive tasks, and speech as- sessments. The wrist-worn activity tracker and project apps collected data on participants’ sleep, physical activ- ity, stress, mood, self-esteem, sociability, speech patterns, and cognitive function. The RADAR-MDD project is currently on-going and final data collection is expected in March 2021. Partici- pants were excluded from the current study if they had withdrawn from the RADAR-MDD project at any time (n = 78; 12.5%), enrolled in RADAR-MDD after the 1st December 2019 (n = 200; 32.1%), had not completed a self-reported Patient Health Questionnaire (PHQ) in De- cember 2019, or were missing basic demographics at baseline (n = 93; 14.9%). A total of 252 (40.5%) partici- pants remained after exclusions and their data was used for analysis. The RADAR-MDD project received ethical approval in the United Kingdom from the Camberwell St Giles Re- search Ethics Committee (REC reference: 17/LO/1154); and Spain from the CEIC Fundació Sant Joan de Déu (CI reference: PIC-128-17) and in The Netherlands from the Medische Ethische Toetsingscommissie VUmc (METc VUmc registratienummer: 2018.012 – NL63557.029.17). The research was undertaken in ac- cordance with the Declaration of Helsinki, and all partic- ipants provided informed consent to participate. Measures and features The RADAR-MDD project collects a range of validated measures from participants at different timepoints (see further [18] information) using the RADAR-base active app [25]. RADAR-base sends automatic survey invita- tions (email and in-app push notification). Depressive symptoms The Patient Health Questionnaire (PHQ-8 [26];) was de- livered every 2 weeks via the project app. The PHQ-8 is an 8-item self-report questionnaire which measures the frequency of depressive symptoms over the preceding 2- week period. Each item is rated on a scale of 0–3, produ- cing a range of total scores from 0 to 24. The PHQ-8 has good validity, reliability, sensitivity, and specificity in the general population [26]. In this study, a cut-off score Leightley et al. BMC Psychiatry (2021) 21:435 Page 3 of 11 of 10 or more is defined as a case of clinically relevant depressive symptoms (hereafter ‘depression’) [26]. Self-esteem The Rosenberg Self-Esteem Scale (RSES [27];) was deliv- ered every 2 weeks via the project app alongside the PHQ-8. The RSES is a 10-item self-report instrument for evaluating individual self-esteem [27–29]. Each item is rated on a scale of 1–3 (half the questions are reverse scored), producing a range of scores from 0 to 30. Scores between 15 and 25 are within normal range, with scores below 15 suggesting low self-esteem [27]. Sleep duration Participants enrolled in the RADAR-MDD project were asked to wear a wrist-worn activity tracker (FitBit Charge 2 or 3) over the study duration as much as pos- sible, including when sleeping. The device collected pa- rameters on heart rate and sleep duration. In this study, total sleeping minutes, as computed by the FitBit Charge 2 or 3, was extracted for each participant for each day and a daily feature was calculated to represent the amount slept for each 24-h period. Total sleep duration was calculated between 8:00 pm (20:00) as the starting time point and 11:00 am (11:00) as the finishing time- point (following a procedure reported previously [30]). Where no data was found due to the participant not wearing the device, no features were computed for that day. Data analysis Socio-demographic characteristics were summarised using frequencies and unweighted percentages or me- dians with interquartile ranges (IQR) for the overall sample and for each country individually. Outcome vari- ables (depressive symptoms, self-esteem and sleep dur- ation were then summarised across three timepoints: pre-, during- and post-lockdown (defined as restriction easing in each country). A mean value was computed for depressive symptoms (PHQ-8 score), self-esteem (RSES score) and sleep duration (minutes) for each participant within each of these timepoints. The following dates were used to define these time- points [31]: � United Kingdom: lockdown: 23/03/2020 and easing restrictions: 11/05/2020; � Spain: lockdown: 14/03/2020 and easing restrictions: 02/05/2020; � The Netherlands: lockdown: 17/03/2020 and easing restrictions: 11/05/2020. Changes in the mean total score of each outcome vari- able over these timepoints were analysed using linear mixed models for repeated measures. Linear mixed models are a generalisation of linear regression which permit modelling of repeated measures data by incorp- orating a random effect of ‘participant’. First, we investi- gated the overall changes in each outcome variable using timepoint (pre-, during- and post-lockdown) as the ex- posure variable. We then added pre-pandemic depres- sion caseness into each model as a second exposure variable, including an interaction term between time- point and depression caseness, to investigate whether rate of change in the outcome variables over time varied according to depression caseness. Pre-pandemic depres- sion caseness (denoted as: no depression, depression) was defined as a participant scoring 10 or more on the PHQ-8 during December 2019. This was used to define clinically relevant depressive symptoms shortly before the pandemic. We used post-estimation commands to further ex- plore the associations identified in mixed modelling. Models were fitted using Maximum Likelihood Esti- mation and an unstructured residual-error covariance matrix. Mixed models can produce valid estimates even when data is not missing completely at random, without the need for further missing data techniques like multiple imputation [32]. A participant could have completed a maximum of 18 PHQ-8/RSES self-report questionnaires during the ana- lysis timepoints. RMT offers a unique ability to monitor and track participants, however due to the frequency of data collection, technical issues and daily life, missing data is inevitable, and further information relating to this is presented in Supplement A. Statistical significance was defined as a p-value of less than 0.05. Data process- ing was performed in Python version 3.5. All analyses were performed using STATA MP 16.1. Results Socio-demographic characteristics at baseline The majority of the sample was female (n = 188; 74.6%), had clinically relevant depressive symptoms shortly be- fore the pandemic (n = 121, 48.0%), was cohabiting or married (n = 138; 54.8%) and was on medication for management of depression (n = 166; 65.9%) at baseline (see Table 1). Depressive symptom trajectories Overall, mean depressive symptoms remained stable be- tween pre- and during-lockdown (estimated mean score difference: -0.18; CI: − 0.61 to 0.24, p = 0.339) and be- tween during- and post-lockdown (estimated mean score difference: -0.03; CI: − 0.42 to 0.36, p = 0.882) (Table 2). We then added an interaction term between depres- sion caseness and timepoint to investigate whether these trajectories varied according to depression caseness. Leightley et al. BMC Psychiatry (2021) 21:435 Page 4 of 11 Table 1 Cohort characteristics at baseline (n = 252) stratified by country Variable Overall (n = 252) United Kingdom (n = 140; 55.6%) Spain (n = 70; 27.8%) The Netherlands (n = 42; 16.7%) Sex Male 64 (25.4) 30 (21.3) 24 (34.3) 10 (23.8) Female 188 (74.6) 110 (78.6) 46 (65.7) 32 (76.2) Marital status Single 75 (29.8) 40 (28.6) 10 (14.3) 25 (59.5) Married/cohabiting 138 (54.8) 82 (58.6) 43 (61.4) 13 (30.9) Divorced/Separated/Widowed 39 (15.5) 18 (12.9) 17 (24.3) 4 (9.5) Employment Employed 115 (45.6) 66 (47.1) 28 (40.0) 21 (50.0) Retired 64 (25.4) 35 (25.0) 25 (35.7) 4 (9.5) Student 23 (9.1) 12 (8.8) 1 (1.4) 10 (23.8) Unemployed 26 (10.3) 14 (10.0) 9 (12.9) 3 (7.1) Other 24 (9.5) 13 (9.3) 7 (10.0) 4 (9.5) Age (in years) < 25 16 (6.4) 7 (5.0) – 9 (21.4) 25–34 35 (13.9) 22 (15.7) 2 (2.9) 11 (26.2) 35–44 38 (15.1) 22 (15.7) 12 (17.1) 4 (9.5) 45–54 42 (16.7) 19 (13.6) 18 (25.7) 5 (11.9) 55–64 81 (32.2) 44 (31.4) 28 (40.0) 9 (21.4) 65> 40 (15.9) 26 (18.6) 10 (14.3) 4 (9.5)
Medication for Depression
No 48 (19.1) 36 (25.7) 2 (2.9) 10 (23.8)
Yes 166 (65.9) 80 (57.1) 65 (92.9) 21 (50.0)
Not reported 38 (15.1) 24 (17.1) 3 (4.3) 11 (26.2)
Depressiona (December 2019)
No Depression 131 (52.0) 87 (62.1) 28 (40.0) 16 (38.10)
Depression 121 (48.0) 53 (37.9) 42 (60.0) 26 (61.9)
Length of education (in years) (mean, SD)b 15.9 (6.5) 16.5 (5.5) 12.5 (4.9) 19.3 (8.9)
Length of time in study in days [median,
253 (124 to 327) 285.5 (186.5 to 435) 257.5 (158 to
109.5 (44 to 170)
aAs measured by the Patient Health Questionnaire [26]. Depression defined as scoring 10 or more. bUp to 1st December 2019
Table 2 Estimated overall differences in each outcome variable between each timepoint. Results stratified by country are available
from the corresponding author
Estimated difference between pre- and during- lockdown
(95% CI, p-value)
Estimated difference between during- and post- lockdown
95% CI, p-value)
Mean PHQ-8
-0.18 (− 0.61 to 0.24; p = 0.339) -0.03 (− 0.42 to 0.36; p = 0.882)
-0.06 (− 0.22 to 0.10; p = 0.445) 0.07 (− 0.08 to 0.22; p = 0.381)
Mean sleep
-0.01 (− 5.55 to 5.56; p = 1.000) -12.16 (− 18.39 to − 5.92; p < 0.001) Leightley et al. BMC Psychiatry (2021) 21:435 Page 5 of 11 Perhaps unsurprisingly, those with pre-pandemic depres- sion reported more depressive symptoms at all three timepoints (Table 3). However, there was also some evi- dence for an interaction between depression caseness and timepoint in predicting course of depressive symp- toms between pre- and during-lockdown (p = 0.047; Table 3). We further investigated this using post-estimation commands and found very weak evidence that the de- pressed group showed a decrease in depressive symp- toms between pre- and during-lockdown (estimated mean score: -0.61; CI: − 1.23 to 0.01; p = 0.051), whereas the non-depressed group remained stable (estimated mean score: 0.24; CI: − 0.33 to 0.83, p = 0.409) (Fig. 1). Self-esteem trajectories Overall, mean self-esteem score remained stable between pre- and during-lockdown (estimated mean score differ- ence: -0.06; CI: − 0.22 to 0.10, p = 0.445) and between during- and post-lockdown (estimated mean score dif- ference: 0.07; CI: − 0.08 to 0.22, p = 0.381) (Table 2). We then added an interaction term between depres- sion caseness and timepoint to investigate whether these trajectories varied according to depression caseness. Those with pre-pandemic depression reported lower self-esteem scores throughout the pandemic than those without depression (Table 3). There was also some evidence for an interaction between depression caseness and timepoint in predicting course of self-esteem be- tween pre- and during-lockdown (p = 0.045; Table 3). We further investigated this using post-estimation commands and found evidence that the depressed group showed reducing self-esteem scores between pre- and during-lockdown (estimated mean score: -0.24; CI: − 0.47 to 0.01; p = 0.048), whereas the non-depressed group remained stable (estimated mean score: 0.09; CI: − 0.13 to 0.32, p = 0.409) (Fig. 2). Sleep duration trajectories Overall, mean sleep duration remained stable between pre- and during-lockdown (estimated mean duration dif- ference: -0.01; CI: 5.55 to 5.56, p = 1.000). However, be- tween during- and post-lockdown there was evidence of a significant decrease in mean sleep duration (estimated mean duration difference: -12.16; CI: − 18.39 to − 5.92, p < 0.001) (Table 2). We then added an interaction term between depres- sion caseness and timepoint to investigate whether these trajectories varied according to depression caseness. Those with pre-pandemic depression reported shorter sleep durations during- and post-lockdown relative to those without (Table 3). There was also evidence for an interaction between depression caseness and timepoint Table 3 Estimated difference in each outcome variable between no depression and depression (in December 2019) at each timepoint, and differences in rate of change over time. Results stratified by country are available from the corresponding author Pre-lockdown estimate During- lockdown estimate Post- lockdown estimate Evidence for a difference in the rate of change between pre- and during- lockdown. (Interaction p-value) Evidence for a difference in the rate of change between during- and post- lockdown. (Interaction p-value) n = 252 (mean PHQ-8 score differ- ence, 95% CI) (mean PHQ-8 score differ- ence, 95% CI) (mean PHQ-8 score differ- ence, 95% CI) No Depression Reference group – – – – Depression 9.33 (8.32 to 10.34) 8.47 (7.21 to 9.73) 7.83 (6.70 to 8.96) 0.047 0.112 n = 252 (mean RSES score difference, 95% CI) (mean RSES score difference, 95% CI) (mean RSES score difference, 95% CI) No Depression Reference group – – – – Depression −1.09 (− 1.46 to −0.72) − 1.43 (− 1.85 to − 1.05) − 1.31 (− 1.69 to − 0.92) 0.045 0.461 n = 240 (mean sleep duration difference, 95% CI) (mean sleep duration difference, 95% CI) (mean sleep duration difference, 95% CI) No Depression Reference group – – – – Depression −10.48 (−28.38 to 7.41) −32.98 (−53.32 to − 12.64) −28.26 (−50.67 to −5.85) < 0.001 0.458 Leightley et al. BMC Psychiatry (2021) 21:435 Page 6 of 11 in predicting course of mean sleep duration between pre- and during-lockdown (p < 0.001; Table 3). We further investigated this using post-estimation commands and found strong evidence that the depressed group showed significant decreases in mean sleep dur- ation between pre- and during-lockdown (estimated mean duration difference: -11.64; CI: − 19.33 to − 3.95; p = 0.003), whereas the non-depressed group signifi- cantly increased mean sleep duration (estimated mean sleep duration difference: 10.85; CI: 3.43 to 18.27; p = 0.004) (Fig. 3). However, the interaction between depres- sion caseness and timepoint between during- and post- lockdown was not statistically significant, suggesting that both depression and no depression groups showed a similar rate of decline in sleep duration between these timepoints. Discussion In this study, we investigated the depressive symptom trajectories for a cohort of adults with a recent history of Fig. 2 Mean RSES score trajectories by depression caseness, as estimated from the repeated measures mixed model Fig. 1 Mean PHQ-8 score trajectories by depression caseness, as estimated from the repeated measures mixed model Leightley et al. BMC Psychiatry (2021) 21:435 Page 7 of 11 MDD. For the sample as a whole, we found no evidence that depressive symptoms or self-esteem changed over the course of lockdown. However, we found evidence that mean sleep duration decreased between during- and post- lockdown. We also found that, relative to those who did not show evidence of clinically relevant depres- sive symptom severity shortly before the pandemic, those with pre-pandemic depression showed a significant decrease in sleep duration (in minutes) between pre- and during- lockdown. However, while there were also reductions in symptom and self-esteem scores, this re- duction was not clinically meaningful. The Covid-19 pandemic represents a unique health, social and economic challenge, with the impact on global mental health expected to be high [33], the use of RMT to explore a pre-existing MDD cohort has provided unique insights into behaviours over the duration of the pandemic. The rapid spread and per- sistence of Covid-19 has increased health anxieties, and has resulted in an increase in mental health dis- orders globally [34]. In our study, we focused on a less studied area, those with pre-existing MDD, which has been shown to be negatively impacted as a result of the Covid-19 pandemic, with the severity varying based on occupation, gender, geographical location and physical/mental health comorbidities [33, 35, 36]. There are major differences in the prevalence of de- pression globally, with one US cohort identifying a three-fold increase in depression symptoms during the pandemic than before [35]. This contrasts a Dutch study, which found that while those with de- pression scored highly on … NeuroImage: Clinical 30 (2021) 102575 Available online 26 January 2021 2213-1582/© 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( Emotion regulation in emerging adults with major depressive disorder and frequent cannabis use Emily S. Nichols a, Jacob Penner b, c, d, Kristen A. Ford b, c, d, Michael Wammes b, d, Richard W. J. Neufeld b, e, f, Derek G.V. Mitchell b, e, g, Steven G. Greening h, Jean Théberge b, c, i, Peter C. Williamson b, c, i, Elizabeth A. Osuch b, c, d, i, * a Faculty of Education, University of Western Ontario, London, Canada b Department of Psychiatry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada c Imaging Division, Lawson Health Research Institute, London, Canada d First Episode Mood and Anxiety Program (FEMAP), London Health Sciences Centre, London, Canada e Department of Psychology, University of Western Ontario, London, Canada f Neuroscience Program, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada g Department of Anatomy and Cell Biology, University of Western Ontario, London, Canada h Department of Psychology, University of Manitoba, Winnipeg, Canada i Department of Medical Biophysics, University of Western Ontario, London, Canada A R T I C L E I N F O Keywords: Emotion regulation Major depressive disorder Cannabis Neural activation A B S T R A C T In people with mental health issues, approximately 20% have co-occurring substance use, often involving cannabis. Although emotion regulation can be affected both by major depressive disorder (MDD) and by cannabis use, the relationship among all three factors is unknown. In this study, we used fMRI to evaluate the effect that cannabis use and MDD have on brain activation during an emotion regulation task. Differences were assessed in 74 emerging adults aged 16–23 with and without MDD who either used or did not use cannabis. Severity of depressive symptoms, emotion regulation style, and age of cannabis use onset were also measured. Both MDD and cannabis use interacted with the emotion regulation task in the left temporal lobe, however the location of the interaction differed for each factor. Specifically, MDD showed an interaction with emotion regulation in the middle temporal gyrus, whereas cannabis use showed an interaction in the superior temporal gyrus. Emotion regulation style predicted activity in the right superior frontal gyrus, however, this did not interact with MDD or cannabis use. Severity of depressive symptoms interacted with the emotion regulation task in the left middle temporal gyrus. The results highlight the influence of cannabis use and MDD on emotion regulation processing, suggesting that both may have a broader impact on the brain than previously thought. 1. Introduction Major depressive disorder (MDD) is a potentially debilitating psy- chiatric disorder with an estimated worldwide prevalence in emerging adults of 16–18% (Kessler et al., 2003; Findlay, 2017; Behavioral Health Barometer, 2017). Cannabis is the most commonly used recreational drug after alcohol and the highest prevalence of use is in teens and young adults (Rush et al., 2008). A recent study of Canadian middle- school age youth showed that cannabis use was strongly associated with internalizing mental health problems (viz., depression, anxiety) with an odds ratio of approximately 6.5 (Brownlie et al., 2019). There is some overlap in symptomatology between MDD and heavy cannabis use including anhedonia, changes in weight, sleep disturbance and psy- chomotor problems (Feingold et al., 2017). A recent meta-analysis also found that adolescent cannabis use predicted depression and suicidal behaviour later in life (Gobbi et al., 2019). The link between mood disorders and cannabis use is complex, especially with respect to directionality; cannabis use is predictive of the onset of mood disorders in youth (Henquet et al., 2006; Patton, 2002; van Laar et al., 2007; Wittchen, 2007), even while some individuals use cannabis in an attempt to regulate the symptoms of depression (Ammerman and Tau, 2016; Lake et al., 2020). The likelihood of developing MDD in heavy * Corresponding author at: First Episode Mood and Anxiety Program, London Health Sciences Centre, 860 Richmond Street, London, ON N6A 3H8, Canada. E-mail address: [email protected] (E.A. Osuch). Contents lists available at ScienceDirect NeuroImage: Clinical journal homepage: Received 24 March 2020; Received in revised form 18 September 2020; Accepted 16 January 2021 mailto:[email protected] NeuroImage: Clinical 30 (2021) 102575 2 cannabis users who began at a young age has been estimated to be up to 8.3 times higher than in individuals who do not use cannabis (Schoeler et al., 2018). Emotion regulation, or the ability to modify one’s emotional experience to produce an appropriate response, has been shown to be maladaptive in teenagers and young adults with MDD and who use cannabis (Zimmermann et al., 2017; Cornelis et al., 2019; Stephanou et al., 2017; Dorard et al., 2008). For example, suppression is a maladaptive regulation style in which an individual inhibits expressing emotions, and is correlated with greater depressive symptoms in youth and adults (Gross and John, 2003). In contrast, reappraisal is an adap- tive regulation style in which an individual changes their interpretation of a situation to alter the emotional impact, and is underutilized in emerging adults with MDD (Stephanou et al., 2017) and in those who are cannabis users (Zimmermann et al., 2017). In the context of MDD, studies have shown lower activity in brain areas involved in emotional processing when compared to healthy controls in the dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (vlPFC), anterior cingulate cortex, as well as the basal ganglia (Davidson et al., 2002; Stevens et al., 2011; Mayberg et al., 2005; Koenigs et al., 2008; Fitzgerald et al., 2008; Greening et al., 2014). These findings fit well with models of emotion regulation and of MDD. Emotion regulation is thought to occur through a network of regions, beginning with affective arousal in the amygdala and basal ganglia, then projecting to frontal regions including the vlPFC and the insula, as well as other regions such as the superior temporal gyrus (STG) and angular gyrus (Kohn et al., 2014). The vlPDC then begins the process of emotional appraisal, indicating the need for regulation to the dlPFC. From there, the dlPFC regulates the emotion and feeds forward to the angular gyrus, STG, and back to the amygdala and basal ganglia, all of which create a regulated emotional state (Kohn et al., 2014; Han et al., 2012; Ochsner et al., 2002, 2004; Urry, 2006; Wager et al., 2008). Disruption of the communication among these areas in individuals with MDD has been observed both in measures of resting state connectivity (Brakowski et al., 2017; Kaiser et al., 2015) and in the suppression of activity within these frontal regions in association with over-activation of temporal regions such as the insula and hippocampus (Fitzgerald et al., 2008). The prevalence of depressive symptoms in frequent cannabis users suggests that brain regions involved in emotion regulation may overlap with those affected by cannabis use. A study showing emotion regulation deficits in young, regular recreational cannabis users compared to non- users bolsters this hypothesis (Zimmermann et al., 2017). Indeed, a meta-analysis showed that cannabis use was linked to brain activity abnormalities in the vlPFC, dlPFC, and dmPFC, orbital frontal cortex, ventral striatum, and thalamus (Batalla et al., 2013). A recent review of the imaging literature indicated that adolescent cannabis users showed differences in frontal-parietal networks that mediate cognitive control (Lorenzetti et al., 2017). Further, emotion regulation deficits in frequent cannabis users were associated with abnormal neural activity in bilat- eral frontal networks as well as decreased amygdala-dorsolateral pre- frontal cortex functional connectivity (Zimmermann et al., 2017). Suppressed inferior frontal and medial PFC activation has been found in cannabis users during positive and negative emotional evaluation (Wesley et al., 2016), as has suppressed activity levels in the amygdala (Wesley et al., 2016; Gruber et al., 2009). The overlap in these brain regions, combined with weakened emotional regulation in people with both MDD and cannabis use, suggests that there may be an interaction between MDD and cannabis use on human brain function in the context of emotion regulation. The aim of the present study was to examine the combined effect of MDD and cannabis use on the brain during emotion regulation in emerging adults, as well as how specific characteristics, such as degree of depressive symptoms and age of cannabis use onset, affect emotion processing. To address these questions, we employed an emotion regu- lation task while participants underwent functional magnetic resonance imaging (fMRI). We recruited individuals either with or without MDD, who either did or did not use cannabis frequently, and used a mixed effects approach to identify the unique contributions of each factor on emotion processing. Because both MDD and cannabis use have been shown to suppress activation within frontal regions during emotion regulation, we predicted that combined MDD and cannabis use would interact with emotion regulation within the vlPFC, dlPFC, and dmPFC, above and beyond the contribution of each factor alone. In contrast, we predicted that we would see a dissociation between MDD and cannabis use in temporal regions, with MDD showing increased activity levels and cannabis use showing suppression of activity during emotion processing. Finally, we predicted that severity of depressive symptoms, emotion regulation style, and age of cannabis use onset would each uniquely interact with emotion regulation, further elucidating the relationship between MDD, cannabis use, and the brain. 2. Methods 2.1. Participants and questionnaires Participants were recruited from the local community and through the First Episode Mood and Anxiety Program (FEMAP) in London, Ontario, Canada. The research ethics board at Western University, London, Ontario, Canada provided approval for the protocol. Written informed consent was obtained from participants after a complete description of the study was provided. Data were collected from 77 participants, with four participants removed from the analysis; three due to missing data and one due to an incidental finding, resulting in 73 participants aged 16–23 (M = 19.85, SD = 1.63; 39 female) for further analysis. Although our analyses here did not examine individuals by group, they can be summarized as 20 non-depressed, non/low cannabis- using controls, 20 patients with MDD, 20 non-depressed frequent cannabis users, and 17 frequent cannabis users with either active or recent MDD. Our previous studies used most of the same participants (Ford et al., 2014; Osuch et al., 2016). The treating psychiatrists made the psychiatric diagnoses, confirmed by the Structured Clinical Inter- view for Diagnosis, DSM-IV (Axis I, SCID-CV) (First et al., 1997). Cannabis use intensity has been stratified in numerous ways in previous research (Bava et al., 2013; Bolla et al., 2002); in the current study frequent use was defined as ≥ 4 times per week for at least 3 months preceding the study (Ford et al., 2014). Cannabis use was assessed by self-report and verified by urine screen to confirm all group assignments. Minimal lifetime cannabis use was allowed in the non-cannabis users because complete elimination would have been prohibitively restrictive in this demographic; non-significant use was defined as ≤ 3 times per month for the past year, though most of the non-users had even less frequent use (Ford et al., 2014). These limits were chosen to differentiate “experimentation” in controls from consistent cannabis use in the designated frequent cannabis users. In the current sample, only two “non-frequent users” had used cannabis in the past month; the first used it once, more than two weeks prior to the study. The second used it three times across a three-day period, more than three weeks prior to the study. Both participants tested negative for cannabis in their urine and indicated that they were not regular users. Clinical information was gathered in-person by a member of the research team prior to fMRI data acquisition, as reported previously (Ford et al., 2014; Osuch et al., 2016). Relevant to the present study, the Emotion Regulation Questionnaire (ERQ) (Gross and John, 2003) was used to asses emotion regulation strategies and Hamilton Depression Rating Scale (HAM-D) (Hamilton, 1960) was used to assess severity of depression in all participants. Substance use quantities and age of onset of use were collected by administration of the Youth Risk Behavior Survey (2009) version. Amongst individuals who used cannabis, there was no correlation between frequency of cannabis and alcohol use, measured by the number of days in the past month that they had used each substance (r(31) = − 0.02, p = .899). Study eligibility included absence of head injury or serious medical illness (other than psychiatric E.S. Nichols et al. NeuroImage: Clinical 30 (2021) 102575 3 diagnoses). Thirty-seven participants met the diagnostic criteria for a major depressive episode, with 32 experiencing a current episode and five participants having had one in the recent past (viz., within the last 12 months). Fifteen of these participants were currently on psychoactive medications, primarily selective serotonin reuptake inhibitors (SSRIs), all of whom had current MDD. Medication dose was stable for three weeks before fMRI data acquisition. None of the remaining 40 partici- pants met criteria for a current or past depressive episode. 2.2. Emotion regulation paradigm The emotion regulation fMRI task, adopted from Greening et al. (Greening et al., 2014), was designed to have participants actively alter their feelings elicited by sad (negative) and happy (positive) emotional scenes. Twenty negative and 20 positive emotional scenes were taken from the International Affective Picture System (Lang et al., 2008) for this study. The task involved viewing both negative and positive emotional scenes while being instructed to either simply view the scene (attend) or actively alter their feelings while viewing the scene (reduce negative feelings during negative scenes and enhance positive feelings during positive scenes). The four task conditions were therefore attend- negative, reduce-negative, attend-positive, and enhance-positive. During the reduce-negative task condition participants were instructed to ‘acknowledge that the scene is negative. However, it does not affect you, things do not stay this bad, and the scene does not reflect the whole world’ and during the enhance-positive task condition par- ticipants were instructed to ‘acknowledge that the scene is positive. Further, that it does affect you, things can and do get even better and the scene does reflect the real world’ (Greening et al., 2014). This paradigm attempts to target and modify the negative thought tendencies about self, the world, and the future that are typical for depressed patients (Beck et al., 1979). Participants were trained and practiced the paradigm before being scanned. During 4 imaging runs each participant completed 20 trials of each task condition (80 trials total). The 20 negative and 20 positive emotional scenes were displayed twice, once during the attend condition and again during the regulate condition. Participants never saw the same picture twice in the same run. To help mitigate any order affects, the trial order in each run was set as 4 independent runs and these were counterbalanced across subjects. 2.3. Imaging data acquisition All magnetic resonance imaging (MRI) scans were acquired using the Lawson Health Research Institute’s 3T MRI scanner (Siemens Verio, Erlangen, Germany) with a 32-channel head coil. T1-weighted anatomical images were acquired covering whole brain with 1 mm isotropic resolution; anatomical images were used to orient the func- tional MRI (fMRI) images 6◦ coronal to the AC–PC plane and as a reference for spatial normalization. Blood oxygen level dependent (BOLD) activation was measured using fMRI images acquired with a 2D multi-slice, gradient-echo, echo-planar T2*-weighted scan (TR = 2 s, TE = 20 ms, flip angle = 90◦, FOV = 256 × 256 × 144 mm3, 4 mm isotropic resolution); 4 runs of 200 functional volumes totaled approximately 26 min for the scan. 2.4. Data preprocessing and analysis Results included in this manuscript come from preprocessing per- formed using fMRIPrep 1.3.2 (RRID:SCR_016216) (Esteban et al., 2020, 2019), which is based on Nipype 1.1.9 (RRID:SCR_002502) (Gorgo- lewski et al., 2011; Gorgolewski, 2017). The fMRIPrep pipeline uses a combination of tools from well-known software packages, including FSL, ANTs, FreeSurfer and AFNI. This pipeline was designed to provide the best software implementation for each state of preprocessing (Esteban et al., 2020, 2019). 2.4.1. Anatomical data preprocessing T1-weighted (T1w) images were corrected for intensity non- uniformity (INU) with N4BiasFieldCorrection (Tustison et al., 2010), distributed with ANTs 2.2.0 (AVANTS et al., 2008) (RRID:SCR_004757). The T1w-reference was then skull-stripped with a Nipype implementa- tion of the workflow (from ANTs), using OASI- S30ANTs as target template. A T1w-reference map was computed after registration of 2 T1w images (after INU-correction) using mri_r- obust_template (FreeSurfer 6.0.1) (Reuter et al., 2010). Brain surfaces were reconstructed using recon-all (FreeSurfer 6.0.1, RRID: SCR_001847) (Dale et al., 1999), and the brain mask estimated previ- ously was refined with a custom variation of the method to reconcile ANTs-derived and FreeSurfer-derived segmentations of the cortical gray-matter of Mindboggle (RRID:SCR_002438) (Klein et al., 2017). Spatial normalization to the ICBM 152 Nonlinear Asymmetrical tem- plate version 2009c (RRID:SCR_008796) (Fonov et al., 2009) was per- formed through nonlinear registration with antsRegistration (ANTs 2.2.0), using brain-extracted versions of both T1w volume and template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using fast (FSL 5.0.9, RRID:SCR_002823) (Zhang et al., 2001). 2.4.2. Functional data preprocessing The functional data were also preprocessed according to the fMRI- Prep pipeline. For each of the BOLD runs per subject, the following preprocessing was performed. First, a reference volume and its skull- stripped version were generated using a custom methodology of fMRI- Prep. The BOLD reference was then co-registered to the T1w reference using bbregister (FreeSurfer) which implements boundary-based regis- tration (Greve and Fischl, 2009). Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using mcflirt (FSL 5.0.9) (Jenkinson et al., 2002). BOLD runs were slice-time corrected using 3dTshift from AFNI v16.2.07 (Cox and Hyde, 1997) (RRID:SCR_005927). The BOLD time-series, were resam- pled to surfaces on the following spaces: fsaverage5. The BOLD time- series (including slice-timing correction when applied) were resam- pled onto their original, native space by applying a single, composite transform to correct for head-motion and susceptibility distortions. These resampled BOLD time-series will be referred to as preprocessed BOLD in original space, or just preprocessed BOLD. The BOLD time- series were resampled to MNI152NLin2009cAsym standard space, generating a preprocessed BOLD run in MNI152NLin2009cAsym space. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. Several confounding time- series were calculated based on the preprocessed BOLD: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their imple- mentations in Nipype (following the definitions by (Power et al., 2014). The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction (CompCor) (Behzadi et al., 2007). Principal components are estimated after high- pass filtering the preprocessed BOLD time-series (using a discrete cosine filter with 128 s cut-off) for the two CompCor variants: temporal (tCompCor) and anatomical (aCompCor). Six tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, six components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). The head-motion estimates calculated in the correction step were also E.S. Nichols et al. NeuroImage: Clinical 30 (2021) 102575 4 placed within the corresponding confounds file. All resampling can be performed with a single interpolation step by composing all the perti- nent transformations (i.e. head-motion transform matrices, susceptibil- ity distortion correction when available, and co-registrations to anatomical and template spaces). Gridded (volumetric) resampling was performed using antsApplyTransforms (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels (Lanc- zos, 1964). Non-gridded (surface) resampling was performed using mri_vol2surf (FreeSurfer). Many internal operations of fMRIPrep use Nilearn 0.5.0 (RRID: SCR_001362) (Abraham et al., 2014), mostly within the functional processing workflow. For more details of the pipeline, see the section corresponding to workflows in fMRIPrep’s documentation. 2.4.3. Statistical analysis Data analysis was conducted in AFNI Version AFNI_20.0.18 ’Galba’ (Cox and Hyde, 1997; Cox, 1996; Gold et al., 1998). The first level general linear model was conducted via 3dDeconvolve to generate contrast maps for each individual participant, including a regressor-of- interest for each of the 4 task conditions (attend-negative, reduce- negative, attend-positive, enhance-positive). Six motion parameters (three rotation, three translation) were included as regressors of no- interest, as were the six aCompCor parameters. All regressors were produced by convolving a hemodynamic response function with a standard boxcar design. This generated beta-weight values at each voxel location for each of the four task conditions to carry forward to group analysis (2nd-level). Following first-level analysis, data were smoothed using a 6 mm gaussian kernel (AFNI 3dBlurToFWHM), for a final average smoothing level of 8.18 mm. For each of the following analyses, a whole-brain mask excluding the cerebellum was used. All analyses were performed using the AFNI function 3dLME (Chen et al., 2013), a group analysis program that performs linear mixed effects (LME) analysis on data with multiple measurements per participant. The primary analysis tested the effects of cannabis use and MDD diagnosis on emotion regulation. The model was specified as follows: task condition (attend-negative, reduce-negative, attend-positive, enhance-positive), cannabis use (frequent/low or none), MDD diagnosis (yes/no), including two- and three-way interac- tion terms, were included as variables of interest. Medication use (yes/ no), age, and number of alcoholic drinks consumed in the last 28 days as regressors. Sex was not included as a regressor due to high collinearity with cannabis use. Numeric variables (i.e., age and alcohol use) in this analysis and all subsequent analyses were mean-centered. A random effect of participant was included in the model, and a marginal sum of squares was used. Three secondary analyses were then conducted. First, we examined the interaction between emotion regulation style and task-condition in the full sample. Similar to the main analysis, an LME model was speci- fied with a condition × ERQ score interaction term, and age, alcohol, and medication use included as regressors. The ERQ score involved subtracting the maladaptive emotional style (suppression subscale score) from the adaptive style (reappraisal subscale score). Thus, higher ERQ scores indicated more adaptive emotion regulation than lower scores. Two participants were excluded from this analysis due to missing ERQ score data. Next, we examined the relationship between HAM-D score and BOLD-signal activation during the emotion regulation task. Here, only individuals with an active MDD diagnosis were included (n = 28). The LME model was specified with a condition × HAM-D score interaction, and age, alcohol, and medication were included as regressors. Finally, the effects of early-onset cannabis use on task-related BOLD signal activation were examined. Here, we only included individuals who actively used cannabis (n = 34). We tested our hypothesis that early-onset cannabis use would have pronounced negative effects by grouping subjects into early-onset (under 15 years of age, n = 12) versus late onset (over 15 years of age, n = 22). LME analysis is well-suited for such unbalanced groups (Bagiella et al., 2000; Baayen et al., 2008; Tibon and Levy, 2015). We then identified where early-onset cannabis users had greater or lower activation than late-onset users. The LME model was specified with a condition × age of onset interaction, and age, alcohol, and medication were included as regressors. For second-level analyses, the minimum cluster-size threshold was determined in two steps. First, we estimated the smoothness of the re- siduals for each subject output by 3dDeconvolve using the autocorrela- tion function (ACF) option (AFNI 3dFWHMx), and the mean smoothness level was calculated. Next, minimum cluster size was determined using a 10,000 iteration Monte Carlo simulation (AFNI 3dClustSim) at a voxel- wise alpha level of p = 0.05. Correction for multiple comparisons at p = 0.05 was achieved by setting a minimum cluster size of 64 voxels. Post- hoc contrasts were FDR corrected. 3. Results 3.1. Linear mixed effects – Cannabis Use, MDD, and emotion regulation We first identified regions that showed activity modulated by cannabis use, MDD, and task condition. As reported in Table 1 and Fig. 1A, there was a main effect of MDD in the left supramarginal gyrus, with individuals with MDD showing significantly greater activation than those without MDD (t(51.92) = − 3.07, p = .003). As shown in Fig. 2, there was also a main effect of condition in the left inferior parietal lobe, left middle frontal gyrus, right insula (negative reduce greater than rest), and left inferior frontal gyrus, with the direction of each effect shown in Fig. 2B–H. When examining interaction effects, there was a significant condi- tion × MDD interaction in the left middle temporal gyrus (MTG). As can be seen in Fig. 3B, all conditions showed increased activity in individuals with MDD, except for the positive attend condition in which they showed decreased activity. We also found a significant condition × cannabis use interaction in the left superior temporal gyrus (STG), shown in Fig. 3C. As can be seen in Fig. 3D, while the two emotionally positive conditions led to greater activity in individuals who use cannabis, the opposite was true for the emotionally negative conditions, with individuals who use cannabis showing lower activity. There was no significant 3-way interaction, no cannabis × MDD interaction, and no main effect of cannabis use. 3.2. Linear mixed …

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