Vulnerability for New Episodes in Recurrent Major ...€¦ · For peer review only Recurrence in MDD: a Neuroimaging Cohort Study 2 ABSTRACT Introduction Major depressive disorder

For peer review only

Vulnerability for New Episodes in Recurrent Major Depressive Disorder: Protocol for the Longitudinal DELTA-

Neuroimaging Cohort Study

Journal: BMJ Open

Manuscript ID bmjopen-2015-009510

Article Type: Protocol

Date Submitted by the Author: 24-Jul-2015

Complete List of Authors: Mocking, Roel; Academic Medical Center, University of Amsterdam, Department of Psychiatry Figueroa, Caroline; Academic Medical Center, University of Amsterdam, Department of Psychiatry Rive, Maria; Academic Medical Center, University of Amsterdam, Department of Psychiatry Geugies, Hanneke; University of Groningen, University Medical Center Groningen, Neuroimaging Center Servaas, Michelle; University of Groningen, University Medical Center Groningen, Neuroimaging Center Assies, Johanna; Academic Medical Center, University of Amsterdam, Department of Psychiatry Koeter, Maarten; Academic Medical Center, University of Amsterdam, Department of Psychiatry Vaz, Frédéric; Academic Medical Center, University of Amsterdam, Laboratory Genetic Metabolic Disease Wichers, Marieke; University Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE) van Straalen, Jan; Academic Medical Center, University of Amsterdam, Laboratory of General Clinical Chemistry de Raedt, Rudi; Ghent University, Department of Experimental Clinical and Health Psychology Bockting, Claudi; Utrecht University, Department of Clinical Psychology Harmer, Catherine; University of Oxford, Warneford Hospital, Department of Psychiatry Schene, Aart; Radboud University Medical Center, Department of Psychiatry Ruhé, Henricus; University of Groningen, University Medical Center Groningen, Program for Mood and Anxiety Disorders, Department of Psychiatry

<b>Primary Subject Heading</b>:

Mental health

Secondary Subject Heading: Nutrition and metabolism, Radiology and imaging, Epidemiology

Keywords: Adult psychiatry < PSYCHIATRY, Depression & mood disorders < PSYCHIATRY, Magnetic resonance imaging < RADIOLOGY & IMAGING, Neuroradiology < RADIOLOGY & IMAGING, STATISTICS & RESEARCH

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

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METHODS, PREVENTIVE MEDICINE

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Recurrence in MDD: a Neuroimaging Cohort Study 1

Vulnerability for New Episodes in Recurrent Major Depressive

Disorder: Protocol for the Longitudinal DELTA-Neuroimaging

Cohort Study

Roel J.T. Mocking1,#

, Caroline A. Figueroa1, Maria M. Rive

1, Hanneke Geugies

2,3, Michelle N Servaas

2,3,

Johanna Assies1, Maarten W.J. Koeter

1, Frédéric M. Vaz

4, Marieke Wichers

5, Jan P. van Straalen

6, Rudi

de Raedt7, Claudi L.H. Bockting

8,9, Catherine J. Harmer

10, Aart H. Schene

1,11,12, Henricus G. Ruhé

1,2,3,5,#

1 Department of Psychiatry, Academic Medical Center, University of Amsterdam, the Netherlands

2 University of Groningen, Neuroimaging Center, University Medical Center Groningen, the Netherlands

3 University of Groningen, Program for Mood and Anxiety Disorders, Department of Psychiatry, University Medical Center

Groningen, the Netherlands

4 Laboratory Genetic Metabolic Disease, Academic Medical Center, University of Amsterdam, the Netherlands

5 University of Groningen, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), University Medical

Center Groningen, the Netherlands

6 Laboratory of General Clinical Chemistry, Academic Medical Center, University of Amsterdam, the Netherlands

7 Department of Experimental Clinical and Health Psychology, Ghent University, Belgium

8 Department of Clinical Psychology, University of Groningen, Groningen, the Netherlands

9 Department of Clinical and Health Psychology, Utrecht University, Utrecht, The Netherlands

10 Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom

11 Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands

12 Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands

Title character count: 117/xx

Abstract word count: 298/300

Article word count: 7961/recommended 4000 (flexible)

Reference count: 202/xx

Number of Figures: 2

Number of Tables: 0

Total number of Figures and Tables: 2/5

Number and type of Supplementary Materials: 2 tables and text

Running title (46 letters and spaces/xx): Recurrence in MDD: a Neuroimaging Cohort Study

# Corresponding authors:

R.J.T. Mocking, MSc, Department of Psychiatry, Academic Medical Center, Meibergdreef 5, Amsterdam 1105

AZ, The Netherlands, T +31208913695, [email protected].

H.G. Ruhé, MD, PhD, Room 5.16, Mood and Anxiety Disorders, University Center for Psychiatry, University

Medical Center, Hanzeplein 1, Groningen, 9700 RD, The Netherlands, T +31503612367, Fax.: +31503611699,

[email protected].

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ABSTRACT

Introduction

Major depressive disorder (MDD) is widely prevalent and severely disabling, mainly due to its

recurrent nature. A better understanding of the mechanisms underlying MDD-recurrence may help

to identify high-risk patients and improve the preventive treatment they need. MDD-recurrence has

been considered from various levels of perspective including symptomatology, affective

neuropsychology, brain circuitry, and endocrinology/metabolism. However, MDD-recurrence

understanding is limited, because these perspectives have been studied mainly in isolation, cross-

sectionally in depressed patients. Therefore, we aim at improving MDD-recurrence understanding by

studying these four selected perspectives in combination and prospectively during remission.

Methods and analysis

In a cohort design, we will include 60 remitted, unipolar, unmedicated, recurrent MDD-subjects (35-

65yrs) with ≥2 MDD-episodes. At baseline, we will compare the MDD-subjects with 40 matched

controls. Subsequently, we will follow-up the MDD-subjects for 2.5yrs while monitoring recurrences.

We will invite subjects with a recurrence to repeat baseline measurements, together with matched

remitted MDD-subjects. Measurements include questionnaires, sad mood-induction, lifestyle/diet, 3-

Tesla structural (T1-weighted and diffusion tensor imaging) and blood-oxygen-level-dependent

functional magnetic resonance imaging (fMRI) and MR-spectroscopy. fMRI focusses on resting state,

reward/aversive-related learning, and emotion regulation. With affective neuropsychological tasks

we will test emotional processing. Moreover, we will assess endocrinology (salivary hypothalamic-

pituitary-adrenal-axis cortisol and dehydroepiandrosterone-sulfate) and metabolism (metabolomics

including polyunsaturated fatty acids), and store blood for e.g. inflammation analyses, genomics,

proteomics. Finally, we will perform repeated momentary daily assessments using experience

sampling methods at baseline. We will integrate measures to test: (I) differences between MDD-

subjects and controls; (II) associations of baseline measures with retro/prospective recurrence-rates;

and (III) repeated measures changes during follow-up recurrence. This dataset will allow us to study

different predictors of recurrence in combination.

Ethics and dissemination

The local ethics committee approved this study (AMC-METC-Nr.:11/050). We will submit results for

publication in peer-reviewed journals and presentation at (inter)national scientific meetings.

Registration details

This study has been registered at the Dutch Trial Registry (NTR3768).

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Keywords

Major Depressive Disorder; Recurrence; Prevention; Neurobiology; Neuropsychology; Emotions;

Reward; Multimodal Imaging; Diffusion Tensor Imaging; Magnetic Resonance Imaging; Diffusion

Magnetic Resonance Imaging; Limbic System; Prefrontal Cortex; Amygdala; Hippocampus;

Hypothalamus; Default mode network; Gyrus Cinguli; Dopamine; gamma-Aminobutyric Acid;

Glutamic Acid; Fatty Acids; Fatty Acids, Omega-3; Cortisol; Oxidative stress; Lipid Peroxidation; Brain-

Derived Neurotrophic Factor; Genetics; Epigenomics; Inflammation; Longitudinal Studies; Prospective

Studies; Observational Study as Topic; Survival Analysis

Bullet point summary of main strengths and limitations of this study

Strengths

• Strict and specific inclusion-criteria, matching- and recruitment-procedure, leading to

maximal contrast for MDD-vulnerability, without distortion due to important confounders:

MDD-residual symptoms and medication.

• Unique integration of a wide range of measures in a prospective repeated measures design

will allow disentangling of recurrent MDD state- and trait-factors.

Limitations

• The extensive assessment procedure needed to measure all variables of interest and

confounders will potentially lead to inclusion of subjects that are intrinsically aware of the

necessity to perform clinical research and readily willing to cooperate.

• Only including subjects that currently do not use psychotropic drugs may lead to selection of

particular patient subgroups that e.g. previously experienced little benefit or adverse effects

from medication.

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INTRODUCTION

1.1. Rationale

Major depressive disorder (MDD) is a widespread and disabling mental disorder, with estimated

worldwide prevalences of 4.3% annually and 11.1-14.6% during lifetime.1-4

Currently, MDD has the

highest burden of any disorder in high-income countries, and is expected to have the second-highest

burden worldwide in 2030.5 MDD´s (in)direct annual excess costs constitute approximately 1% of the

gross domestic product in these countries.6-8

Next to suicide and cardiovascular comorbidity,9 an

important reason for MDD’s burden is its recurrent course,2 as already indicated by Kraepelin

10 and

formulated by Angst et al.: “Single episodes are extremely rare if the period of observation is

significantly extended”.11

The incidence of recurrencesi varies depending on study-characteristics.

12-15 While recurrent MDD

has been considered as a distinct disease entity (more familiar to bipolar disorder), population

studies show that recurrence is widespread in MDD with ≥40-75% lifetime recurrence in patients

recovered from a first depressive episode,16-19

with even higher rates in clinical samples.20 21

Our 10yr

follow-up study of a specific cohort of recurrent MDD-patients showed an overall 90.3% recurrence-

rate,22

with patients being in a depressed state during 13% of the follow-up time.

During lifetime, MDD-patients are estimated to experience on average about five MDD-episodes.1 21

Therefore, high recurrence rates pose a major health problem. However, depressive episodes seem

to cluster in subpopulations. This also suggests that the most MDD-episodes occur in a relatively

limited number of patients. Consequently, if we could lower recurrence rates in these recurring

cases, we may greatly reduce the overall number of MDD-episodes and thereby MDD’s burden.23

If

we could a-priori identify these patients at high risk for recurrence, this would provide excellent

opportunities for specific, indicated, (secondary) prevention.

For recurrence prevention, antidepressants are most often used,12 24

but unwillingness to take

antidepressants, non-adherence and discontinuation due to adverse effects limit their applicability.25-

27 As an alternative, preventive cognitive psychotherapies have been developed (e.g. mindfulness

based cognitive therapy, preventive cognitive therapy and wellbeing cognitive therapy),28-36

which

i The terms relapse and recurrence are used in the literature and defined as new MDD-episodes within or after

6mths recovery, respectively. However, empirically there is no clear evidence for this distinction. We hereafter

will name both recurrence for clarity.

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seem to produce long-lasting beneficial effects.22 37

Nevertheless, recurrence-rates stay substantial,

urgently calling for further improvements of recurrence preventing therapy.

In that respect, if we better understand the mechanisms underlying vulnerability for recurrence in

MDD, we could (I) use their indicators as (bio)markers to monitor/predict recurrence risk, and/or (II)

use these mechanisms to identify/develop novel targets for improved and personalized preventive

therapy in a precision medicine setting. This early identification and stratified treatment of

recurrence risk38

could potentially reduce recurrent MDD’s disease burden.

However, understanding of mechanisms underlying MDD-recurrence is limited to date. Although

remitted MDD-patients have already been studied for a number of years,21

most studies investigate

MDD during the acute phase. Yet, to be able to differentiate between trait factors (that remain

present during remission and possibly constitute vulnerability for recurrence) versus state factors

(which are only present during an MDD-episode), it is necessary to study patients during remission.

In addition, the actual predictive associations of these possible trait factors with recurrence have to

be tested in long-term prospective follow-ups.

Thus far, the limited research that applied such a prospective approach in remitted MDD-subjects

investigated several factors as predictive of recurrence. While associated with MDD onset,

demographics (e.g. gender) generally do not predict recurrence; clinical and social factors seem to be

more predictive. Regarding clinical factors, the number of previous episodes is amongst the strongest

predictors,39

together with residual depressive symptoms.19

In addition, MDD family history,

comorbid axis I disorders, age of onset and last episode duration and severity have been suggested

as predictors.15 19 40-46

Furthermore, personality characteristics (coping style and personality traits)

and social factors (experiencing daily hassles) have been found to be predictive although findings

remain largely inconsistent. In addition, in our previous study 71% variance in time to 5.5yr

recurrence remained unexplained,47 48

and only few actual predictive factors were potentially

modifiable.

As indicated, the pathophysiology behind these factors’ predictive properties for recurrence remains

far from understood. For example, residual symptoms predict recurrence within a short-term interval

but seem less predictive in the long term.49

This indicates that residual symptoms may not constitute

a vulnerability trait, but rather reflect the early initiation of a new episode or an earlier episode not

yet in full remission. In addition, the predictive effect of previous episodes can be explained due to

scarring (increasing vulnerability directly resulting from experiencing previous episodes) or high

premorbid vulnerability (pre-existing abnormalities leading to both previous episodes and new

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recurrences).50-52

From the prediction perspective, these pathogenetic differences might seem a

merely academic question. However, identifying the mechanisms underlying MDD-recurrence is

essential to discover better potential targets for innovative preventive interventions to increase

resilience.

1.2. Study aims & outline

Based on the above, the present study aims at advancing the knowledge on (I) factors that are

associated with recurrent MDD-vulnerability, (II) how these factors are related with each other, (III)

their predictive association with prospective recurrence, and (IV) their change during recurrence.

In order to do so, we will initially compare fully remitted unmedicated recurrent MDD-subjects to

matched healthy controls. Subsequently, we will monitor recurrence(s) in the MDD-subjects during a

2.5yr follow-up and repeat measurements when an MDD-subject experiences a recurrence during

follow-up. Below, we will first outline our theoretical framework to provide background for our

hypotheses regarding the specifically selected factors that we will investigate.

1.3. Theoretical framework

Based on preliminary findings, theoretical literature, and observations from adjacent fields, several

theories have been developed to explain recurrence pathogenesis. Here, using a stratified approach,

we aim to introduce and integrate theories from four distinct selected levels of perspective:53 54

symptomatology, affective neuropsychology, brain circuits, and endocrinology/metabolism (Figure

1).

1.3.1. Symptom level

A disturbed balance between negative and positive valence systems seems to lie at the heart of MDD

symptomatology.54

Regarding negative valence systems, MDD-patients suffer from e.g. negative

affect, rumination and dysfunctional cognitions. While negative cognition and processing styles as

rumination usually resolve after remission, they may remain present in latent form, and can be

reactivated during (mild) dysphoria, which is conceptualized as ‘cognitive reactivity’.50 55 56

Interestingly, latent dysfunctional attitudes, increased cognitive reactivity and rumination have all

been found to predict recurrence in remitted MDD-subjects.57 58

Relating to negative but also positive

valence systems, anhedonia (inability to experience pleasure) is one of MDD’s core symptoms. Apart

from the ability to experience joy, the rewarding effect of pleasure can also have a motivational

function: pleasurable events appear to reinforce behaviour leading to these events (conditioning).

This implies that experiencing pleasure is a necessary stimulation to learn associations between

stimuli and (pleasurable) outcomes and move an individual to perform certain behaviours. MDD-

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patients have difficulties in experiencing the rewarding effects of positive/pleasurable events when

depressed, particularly relative to aversive stimuli, and indeed have difficulties learning new

beneficial behaviours. This can also be observed in the form of psychomotor retardation and

decreased positive affect. However, anhedonia remains relatively under-investigated during

remission, and it remains largely unknown to what extent anhedonia can predict recurrence (see for

reviews59-63

).

1.3.2. Affective neuropsychological level

This disturbed balance between negative and positive valence systems at the symptom level may

relate to negative biases in emotional processing at the affective (‘hot’) neuropsychological level.

Negative biases manifest themselves when (dis)engaging (i.e. attentional bias), memorizing, error-

monitoring, shifting attention between, or regulating emotional information.64-74

Negative biases are

thought to result from increased negative attention on the self, and are thus related with negative

self-referential processing styles as rumination and cognitive reactivity, which show a reciprocally

reinforcing relationship with negative affect.75

76

Increasing evidence shows that negative self-

referential processing and associated brain alterations contribute greatly to the course and

development of MDD.75

With respect to reward processing, negative biases manifest in decreased

reward sensitivity (negative valence) and increased aversive stimulus sensitivity (positive valence).54

However, the precise relations between these concepts, and to what extent these negative

emotional processing biases with associated brain alterations remain present during remission, and

can predict recurrence, remains largely unknown.77

78-80

1.3.3. Brain circuit level

From a neurobiological brain circuit perspective, disturbed emotional processing at the affective

neuropsychological level may be observed as an imbalance between emotional (limbic/ventral) and

regulating (cognitive/dorsal) regions.81-86

Specifically, emotional brain regions seem hyperactive in

response to negative stimuli but hypoactive to positive.87

In addition, regulating regions are generally

hypoactive but may show compensatory hyperactivity under certain circumstances, e.g. more

automatic emotion regulation.88

This may be explained by altered functional and structural

connectivity between these regions.89

Furthermore, disturbed functioning of the default-mode

network, a network that is involved in self-referential processing and is negatively correlated to

regions that process attention and cognitive control, has consistently been observed in MDD. 90-94

Aberrations in the default-mode network (i.e. failure to de-activate DMN regions)95

during tasks as

well as DMN hyperconnectivity96

during rest have been observed in MDD. DMN aberrations have

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been associated with emotional-cognitive disturbances and increased negative self-focus, such as

rumination.58 75 94 97-106

Especially anhedonic MDD-patients have a decreased ability to change behavior in relation to

rewards, which appears to persist after remission.59 107

This reduced reward responsiveness might be

related to blunted phasic dopaminergic signaling. Indeed, reinforcement learning appeared impaired

in depressed MDD-patients versus controls, with blunted reward signals in the ventral striatum, and

increased compensatory ventral tegmental area activations when thirsty patients were learning

associations between stimuli and water delivery.108

Furthermore, MDD-patients show reduced

reward anticipation and are less prone to exert effort for a potential reward.59

These abnormalities

also appear present in subjects prone to develop MDD.109

Also, recognition of reward-related stimuli

appeared most difficult and associated with most impaired brain activities in the N. accumbens,

anterior cingulate cortex (ACC) and ventromedial prefrontal cortex (vmPFC) in patients with chronic

recurrent MDD.110

Thus, dopaminergic reward-related brain circuits seem to be of importance in

recurrence of MDD. However it remains unclear whether such abnormalities in reward related

learning are also associated with recurrence.

Despite increasing research efforts to delineate these brain circuits, it is hardly investigated how the

default-mode network and its relations to other cognitive networks and emotion-processing and

reward circuits function in remitted recurrent MDD-subjects.111-114

115 116

In addition, it has been

examined scarcely how alterations in these circuits can predict recurrence in remitted MDD-

subjects.117

118

1.3.4. Endocrinology and metabolism

These disturbed brain circuits may be associated with alterations in endocrinology and metabolism.

From an endocrinological viewpoint, the principal stress system -the hypothalamic-pituitary-adrenal

(HPA)-axis- has been studied extensively in MDD.119

In combination with e.g. findings in first degree

relatives, our own research indicates that HPA-axis hyperactivity is an endophenotypic trait, with

higher diurnal cortisol and altered dehydroepiandrosterone-sulfate (DHEAS) that remain during

remission,21 120 121

and potentially predict recurrence.122-125

Interestingly, HPA-axis activity can be

linked with brain circuit alterations. For example, the effects of stress on limbic network structure in

MDD could reflect chronic HPA-axis hyperactivation-induced allostatic load (e.g. reducing

hippocampal volumes), predisposing to MDD(-recurrences). 126 127

Vice versa, the HPA-axis is

controlled by the limbic system,128

through medial prefrontal connections with amygdala and

hypothalamus.129

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Moreover, interestingly, we previously showed a bidirectional relationship between fatty acid

metabolism and HPA-axis activity.130 131

Fatty acids are main constituents of (nerve) cell membranes

and myelin, and so influence important (neuro)physiological mechanisms such as exocytosis,

membrane-anchored protein function, membrane fluidity, second messenger system activity and

white matter integrity.132 133

Furthermore, they are precursors of eicosanoids and are associated with

brain derived neurotrophic factor (BDNF), which regulate inflammatory homeostasis and nervous

system architecture, respectively.9 133-136

We previously showed that besides alterations in omega-3

fatty acids, MDD is additionally associated with more general alterations in overall fatty acid

metabolism, also in recurrent MDD.137-140

However, inconsistencies remain, and recurrent MDD has

only been sparsely investigated. Moreover, given the widespread involvement of fatty acid

metabolism in brain physiology, associations between fatty acid metabolism and brain circuit

alterations can be expected,9 136 141

but remained largely uninvestigated thus far.

Furthermore, glutamate/glutamine and γ-aminobutyric acid (GABA) neurometabolism is currently

considered an interesting additional system in MDD and its recurrence too. Glutamate and GABA are

the major excitatory and inhibitory neurotransmitter, respectively, and have been implicated in

MDD-pathophysiology.142 143

For instance in depressed MDD-patients, excess excitotoxic synaptic

glutamate have been suggested to cause less pregenual ACC deactivation when viewing negative

emotional pictures.144 145

Nevertheless, previous investigations of glutamate/GABA in depressed

MDD-patients remain contradictory,146 147

and while abnormalities might normalize after

remission,148

this is only sparsely investigated,147 149

especially not in recurrent MDD.

1.3.5. Summary of theoretical framework

MDD can be characterized by multiple alterations across systems that remained distinct thus far, but

potentially can be integrated. At the symptom level, MDD-patients show a disturbed balance

between negative and positive valence systems with increased latent negative affect, rumination,

dysfunctional cognitions and cognitive reactivity, together with anhedonia. This may be associated

with negative emotional biases at the affective neuropsychological level. These negative emotional

biases may relate to an imbalance between emotional and regulatory brain circuits, default mode

network hyperconnectivity/activity and might also be associated with a disturbed brain reward

circuit. These brain circuit alterations seem closely connected with HPA-axis alterations, that seem

bidirectionally related with fatty acid and glutamate/GABA-metabolism (Figure 1). However, even if

previous research studied remitted MDD-subjects, these alterations were mostly investigated in

isolation and only cross-sectionally. Consequently, it remains largely unknown to what extent these

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alterations (I) persist during remission, (II) are associated with each other, (III) are predictive for

recurrence and (IV) change during recurrence.

1.4. Hypotheses

With the aim of the current ‘DELTA-neuroimaging’ study to integrate these factors and test their

association with recurrence in a prospective cohort-study of stably remitted unmedicated recurrent

MDD-subjects, we first will compare MDD-subjects with carefully matched controls at baseline, and

subsequently we will follow-up the MDD-subjects for 2.5yrs while monitoring recurrences. Moreover,

we will invite recurring subjects to repeat baseline measurements, together with matched remitted

subjects. Following this line of research we will investigate the following specific hypotheses:

1. Compared to matched never-depressed controls, remitted unmedicated recurrent MDD-subjects

will show (i.e. a trait effect):

a. At the symptom level, a disturbed balance between negative and positive valence

systems with increased rumination, dysfunctional cognitions, cognitive reactivity and

anhedonia.

b. At the affective neuropsychological level, increased negative biases in emotional

processing when (dis)engaging (attentional bias), memorizing, shifting attention

between, and regulating emotionally valenced stimuli.

c. At the brain circuit level, altered grey/white matter structure and function/connectivity

of emotional/regulating regions, reward brain circuits and the default-mode network,

also relative to other networks of the brain, with specifically:

i. More ventral and less dorsal region activation when viewing emotional pictures.

ii. Less connectivity between ventral and dorsal regions.

iii. More activation of dorsal regions during a reappraisal emotion regulation task.

iv. Blunted ventral striatum and increased ventral tegmental area reward-signals.

v. Hyperconnectivity within and dominance of the default-mode network at rest,

which becomes more pronounced after sad mood-induction

d. At the endocrinology and metabolism level, altered HPA-axis activity, fatty acid

metabolism and emotional network GABA/glutamate, with:

i. Higher morning and evening HPA-axis cortisol and relatively lower DHEAS, which

becomes more pronounced after sad mood-induction.

ii. Lower degree of fatty acid unsaturation, chain length, peroxidizability, and ω-

3/ω-6-ratio.

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iii. More glutamate and less glutamine/GABA signals in the basal ganglia and pgACC,

which becomes more pronounced during sad mood-induction.

2. In remitted unmedicated recurrent MDD-subjects the above systems will be related with clinical

characteristics (number of previous episodes, residual symptoms and age of onset) and each

other, and these latter mutual relationships will differ from those in matched never-depressed

controls.

3. In remitted unmedicated recurrent MDD-subjects, above alterations will predict prospective

2.5yr follow-up symptom course, specifically:

a. Time until recurrence

b. Cumulative number and severity of MDD-episodes

c. Course of depressive (residual) symptoms

4. The above alterations will become more pronounced during repeated measures in recurrent

MDD-subjects experiencing a recurrence during follow-up, in comparison to repeated measures

in matched remitted recurrent MDD-subjects (i.e. a state effect).

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2. METHODS

2.1. Design

The present study consists of two stages (Figure 2). First, using a cross-sectional patient-control

design, we will compare remitted recurrent MDD-patients with matched never-depressed controls,

to identify traits that remain present during remission and that are associated with recurrent MDD-

vulnerability. Second, using a prospective cohort-design, we will follow-up the patients. During

follow-up, we will measure depression symptoms every four months, to see whether we can predict

clinical course from baseline measures. Moreover, when we detect a follow-up recurrence, we will

invite the respective patient to repeat several baseline measures. In addition, we will invite remitted

patients (matched on duration of follow-up, gender, age, educational level and working class) to

repeat the measures as well. While this repeated measures design is not required to predict

recurrence, it is of interest as it allows us to identify depression state vs. trait-effects.

In sum, we will first test for trait factors associated with MDD-vulnerability by contrasting vulnerable

(remitted recurrent MDD) vs. resilient (never-depressed controls) subjects. Subsequently, also in

order to further delineate whether these identified factors are causal, consequences or confounders,

we will test their predictive effect of prospective recurrence during follow-up in the remitted

recurrent MDD group. Finally, we aim at disentangling state and trait effects by repeating measures

in patients during recurrence vs. matched patients who are in current remission. Below we will

describe the population, measures, procedure, and analyses in detail in that order, additional

information can be found in the supplementary material.

2.2. Population

2.2.1. Inclusion criteria

In order to maximize contrast for recurrent MDD-vulnerability, without confounding effects of

medication or current MDD-symptoms, we will include recurrent MDD-subjects [≥2 previous MDD-

episodes as assessed using the structured clinical interview for DSM-IV diagnoses (SCID)150

] that are

currently in stable remission [≥8weeks with a 17-item Hamilton Depression Rating Scale (HDRS)≤7

and not fulfilling the criteria for a current MDD episode (as assessed using the SCID during

inclusion)].151

Specifically, we will include subjects aged 35-65yrs, to include a homogeneous age

group, and preclude conversion to bipolar disorder due to later experience of (hypo)manic episodes.

Of note, despite overall high recurrent MDD vulnerability and homogeneity regarding e.g. age, we

expect this group of MDD patients to exhibit considerable variance in prospective recurrence rates.

For example, in our previous research22 30 47 152

the range in previous MDD-episodes was from 2 to 60,

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and we will now include patients with none or only a single episode in the last 10yrs. We expect that

this will lead to a relapse rate of ±50% during the 2.5yrs follow-up, providing excellent within-group

contrasts for prospective recurrence in this overall highly vulnerable group. Second, we will include

relatively resilient controls without personal (SCID) or first degree familial psychiatric history,

carefully matched for age, sex, educational level, working class, and ethnicity.

2.2.2. Exclusion criteria

While comorbidity in general will not be an exclusion criterion because it may be an important

predictor, in order to obtain a homogeneous sample we will exclude subjects with current diagnoses

of alcohol/drug dependence, psychotic or bipolar, predominant anxiety, or severe personality

disorder (all SCID); standard MRI exclusion criteria (e.g. metal objects in the body, claustrophobia);

electroconvulsive therapy within two months before scanning; history of severe head trauma or

neurological disease; severe general physical illness; no Dutch/English proficiency. To minimize

inclusion bias, we will try to familiarize mildly claustrophobic subjects in a mock MRI-scanner to

enable actual MRI-assessments. If this does not succeed, we will only perform non-MRI assessments.

All subjects have to be without psychoactive drugs/medication for >4weeks before assessments. We

will allow incidental benzodiazepine use, but this must be stopped after informed consent and

≥2days before assessments. Despite possible effects of psychotherapy we will not exclude current or

past psychotherapy due to feasibility reasons. However, we will assess all forms of therapy used,

report these and treat them as covariates in our analyses.

2.2.3. Recruitment

To minimize selection biases, we will recruit both groups through identical advertisements in freely

available online and house-to-house papers, posters in public spaces and from previous studies in

our and affiliated research centres. One previous study from which we will recruit subjects is the

Depression Evaluation Longitudinal Therapy Assessment (DELTA)-study.30

We recently completed the

10yr follow-up of this randomized controlled trial assessing the protective effects of 8-weeks

preventive cognitive therapy on recurrence in recurrent MDD.22

In this long-term study we obtained

detailed psychological but also biological measures, which can be linked to data obtained in the

present study in the same subjects. Of note, the original DELTA sample was recruited like the

procedure for new participants for the present recruitment, amongst others through newspaper

advertisements. By DELTA-study design, 50% of the original DELTA sample received randomized

preventive cognitive therapy 10yrs ago, however as (I) previous psychotherapy was not an exclusion

criterion in the present total sample and (II) the preventive cognitive therapy intervention was more

widely implemented in the Netherlands since the DELTA-study, non-DELTA participants could also

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have undergone this treatment. This allows the additional interesting option to collect data on

previous treatments in all subjects in order to estimate the magnitude of this possible treatment

effect. Finally, we will recruit additional recurrent MDD-subjects from patients previously treated by

the AMC or affiliated general practitioners and psychologists.

2.3. Measures

See supplementary material for full details.

2.3.1. Structured interview and questionnaires

The SCID is widely accepted as structured diagnostic interview to adequately assess DSM-IV defined

psychiatric disorders.150 153

Questionnaire-booklets I-IV (see supplement) include questionnaires on

depressive symptoms (e.g. HDRS), stress and life events (trauma, daily hassles), personality

(neuroticism, coping), and lifestyle (physical activity, sleep, diet).

2.3.2. Mood induction

We will prepare a negative and neutral mood-induction procedure by asking subjects to recall and

describe a personal sad and neutral memory,56

from which we will make sad and neutral

personalized scripts. In addition, we will request subjects to listen to and rate five different

fragments of sad/neutral music on a dedicated website (accessible on request). This type of

provocation (combining sad music with autobiographical recall) has been shown to effectively induce

transient dysphoric mood states.56

We used this mood induction to test (I) mood-induced changes in

dysfunctional attitudes (cognitive reactivity), (II) HPA-axis activity, and (III) brain networks.

2.3.3. Affective neuropsychological tests

The affective neuropsychological tests all assess emotional processing. The exogenous cueing task

allows disentangling of attentional engagement and disengagement components in attentional

bias.66 154

The facial expression recognition task measures interpretation of key emotionally valenced

social signals of varying intensity (morphed faces). 69 71 155

The emotional categorization task assesses

response speed to self-referent positive and negative personality descriptors, the emotional memory

task follows up on this task by assessing surprise (free) recollection memory of these personality

descriptors.69 71 155

The internal shift task examines capacity to shift attention between working

memory contents in response to emotional and non-emotional material.156 157

For matching

purposes, we will estimate premorbid intelligence with the Dutch adult reading test.158

2.3.4. Experience sampling method (ESM)

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Momentary assessment techniques are ideal for prospective examination of dynamics of observed

behaviour, and enable to capture the film rather than a snapshot of daily life.159-162

ESM is a

structured diary method developed to study subjects in their daily surroundings, applicable via a

validated interactive ESM-palmtop. We will obtain ESM-ratings regarding positive and negative affect

- hypothesized to be separate but correlated latent factors163

- and possible influencing factors [e.g.

(social) activities], for 6 days with 10 semi-random measurements/day preferably between the first

study-session and MRI-session.

2.3.5. MRI-scans (2 blocks)

In the first block, after locater and reference scans, a structural T1-scan will provide high resolution

3-dimensional anatomical information. Then we will obtain a resting-state scan after neutral mood-

induction,98

followed by a reinforcement learning fMRI-task which applies a Pavlovian-learning

paradigm delivering the thirsty subjects small amounts of sweet or bitter solution at 80-20%

probabilities after conditional stimuli. This enables assessment of reinforcement learning circuitry.108

Subsequently, using a GABA-specific MEGA-PRESS sequence we will obtain an edited 1H J-difference

magnetic resonance spectroscopy (MRS)-scan of the basal ganglia to measure glutamate and

GABA.144 164-166

A Diffusion Weighted Imaging Spin Echo sequence will assess white matter structure

(DTI).167

After a break, in the second block, subjects will perform the cued emotional conflict fMRI-

task, which will test cue related conflict anticipation and response related cognitive control.168

Then,

the emotion regulation task will measure brain activity in emotional and regulatory brain networks

during attending and regulating (distancing technique) positive, negative and neutral emotional

stimuli. Subsequently, we will make another resting-state scan, but this time after a negative mood-

induction. In combination with the neutral resting-state scan from the first block, this sad mood-

induced resting-state scan will allow assessment of mood-induced changes in brain network

interactions.98

Finally, we will make another MRS-scan of the pgACC. In the follow-up MRI-scan-

session we will repeat the structural, resting state (without mood-induction), reinforcement learning

and MRS-scans. During scanning we will record heartbeat and breathing in order to correct for their

movement-effects.

2.3.6. Blood measures

From collected blood tubes, we will use 1×4.5ml ethylenediaminetetraacetic acid (EDTA) blood for

fatty acid analyses in washed erythrocytes (as a model of neuronal membranes),139

which we will

store for future lipidomic analyses. We will use 7ml EDTA and PaxGene blood collection tubes for

future genomic analyses (e.g. serotonin, dopamine, glutamate/GABA-cascades, one-carbon

metabolism, or HPA-axis receptors).38 169 170

We will store platelet-poor plasma from 5 ml citrate

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blood and also store plasma from 4.5ml EDTA and lithium-heparine blood collection tubes for future

use (e.g. metabolomics, inflammation).171

2.3.7. Salivary measures

As described below and in the supplement, we will instruct participants to collect salivary samples

over the day using Salivettes (Sarstedt, Nümbrecht, Germany). Saliva reflects blood cortisol and

DHEAS-concentrations, but enables minimally intrusive and relatively stress free assessment.120 172 173

2.4. Procedure

We will regularly train all assessors and experienced psychiatrists will closely supervise the

assessment procedures. We will discuss difficult assessments; in case of disagreement we will make a

conservative decision (e.g. exclusion).

2.4.1. Preparation

2.4.1.1. Initial assessment & mood-induction

We will telephonically screen recruited subjects for potential eligibility. In a first interview

(telephonically or face-to-face), we will check inclusion and exclusion criteria. After obtaining

informed consent we will register psychiatric and somatic treatment history, covariates of interest

and potential confounders. Furthermore, we will mail questionnaire-booklet I (see supplementary

material) and Salivettes, with detailed instructions. In addition, we will prepare the mood-induction

procedure.

2.4.2. Baseline visits

2.4.2.1. First study-session

We will instruct subjects to arrive after ≥8hrs fasting. First, we will collect blood samples by

venipuncture, which we will directly bring to the laboratories. Subsequently, we will allow subjects to

eat and drink, with the exception of caffeinated drinks.

Next, we will instruct subjects to perform the neuropsychological tests in two blocks with a break in

between, and measure waist circumference140

(see supplementary material). After

neuropsychological testing, we will explain the scanning procedure and train the participant for the

emotion regulation fMRI-task, which will be performed in the scanner (see above and supplement).

After a 15min break, participants will undergo the sad mood-induction. Before and directly after sad

mood-induction, we will request subjects to fill out a Dysfunctional Attitudes Scale (two randomized

counterbalanced versions)56 174 175

, rate their sadness on a visual analogue scale and collect saliva

(using Salivettes).

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Finally, we will explain and instruct participants about the experience sampling method (see above).

In addition, we will provide subjects with questionnaire-booklet II (see supplementary material) to fill

out before the MRI-session.

2.4.2.2. MRI-session

We will instruct subjects to arrive thirsty, i.e. ≥6hrs without drinking and ≥2hrs without eating juicy

food (for the reward learning task). On a Philips Achieva XT 3-Tesla MRI (Philips Medical Systems,

Best, the Netherlands), using a 32-channel receiver headcoil, at the University of Amsterdam,

Spinoza Center, we will scan two consecutive blocks of approximately 60min each (see above),

separated by a break. During the scanning procedure, we will again perform the mood-induction

(neutral/sad) in a slightly modified version as described previously.56

We will ask subjects to listen to

their selected most neutral/sad music piece and meanwhile read their personal sad/neutral

memories presented on a screen in the scanner (during 5min), directly before the resting state scans.

Finally, we will debrief subjects, complete questionnaire-booklet III (see supplementary material),

and obtain post-scan ratings of stimuli presented during the tasks.

2.4.3. Follow-up

2.4.3.1. Monitoring

We will follow-up the recurrent MDD-subjects by regular (every ~4months) phone-calls (SCID and

HDRS) and questionnaire-booklet IV (see supplementary material). To maximize recurrence detection

rates, we will also instruct subjects to contact us at the moment they subjectively experience a

recurrence and inform a person close to them of these instructions.

To allow for the possibility to disentangle state and trait effects, when we detect a recurrence (SCID),

we will invite the respective recurring subject and a matched remitted (MDD-subject to repeat

several baseline measurements (see below). We will preferably scan subjects before they (again)

start antidepressants, but -in order to maintain power- this will not be an exclusion criterion for the

follow-up scan/measurements. Thus, when patients experience a relapse and agree to participate in

the study again, they will be matched with recurrent MDD-participants that are in remission (SCID

and HDRS≤7) and meet matching criteria. We will conduct matching based on group-level

characteristics of relapse patients vs. control patients (mean and distribution of follow-up time, age,

years, sex, educational level and working class). In this way, we also aim to include relatively more

control patients (relapsed:control patients ratio of 1:1.5), with the goal of increasing power. These

matched participants have to be currently euthymic but can have had a prior relapse, thus after the

baseline measurement, or a relapse during follow-up after second participation. The reason for this

approach is that we are interested in comparing the effect of depression (state) vs. depressive

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vulnerability (trait), instead of simply comparing more vulnerable patients to stable patients. This will

give us insight into the pathophysiology of relapse vs. remission; it allows to examine which factors

stay the same, and which factors show change when patients relapse. Potential in-between

recurrences will, however, be examined as a potential confounder in the final analyses. Nevertheless,

a participant will not be included more than once in the follow-up repeated measurements

(scanning/neuropsychology), in order to exclude the possibility of learning effects and habituation in

testing/scanning and prevent complex covariance structures.

2.4.3.2. Repeated measures in recurring and matched MDD-subjects

We will repeat questionnaire-booklets I-III (see supplement), blood sampling and neuropsychological

testing. In addition, we will repeat part of the MRI-scan in an ~1hr scan-session (see above and

supplement). To minimize learning effects, we will use randomized counterbalanced versions of tasks

when applicable. We will not repeat the mood-induction.

2.5. Statistical analysis plan

2.5.1. E-infrastructure and software

We will store raw and cleaned data on dedicated servers and make use of available e-infrastructure

bioinformatics networks where necessary.176

We will use a variety of programs under which SPSS

(IBM SPSS, Chicago, IL, USA).

2.5.2. Data preparation

2.5.2.1. Distributions and missing data

We will inspect distributions and remove (multivariate) outliers and data noncompliant to the

protocol [e.g. saliva samples outside time-range or chance level (neuro)psychological responses]. We

will transform non-normally distributed data where possible, otherwise we will apply non-parametric

tests or bootstrapping if applicable. For extensive missing data at random, we will use multiple

imputation where necessary and possible.139 177 178

2.5.2.2. (Neuro)psychological tests

For the (neuro)psychological tests, we will calculate summation-scores where applicable.71 155

2.5.2.3. ESM

We will prepare ESM-data using developed algorithms. In brief, we will include data in the analyses

for which >30% ESM reports are within 25min after the programmed time of the beep,179

to ensure

reliability.180

From the ESM-data we will test the factor structure of the positive and negative affect

measures using factor analysis, also at the within-subject level (also see supplement).163

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2.5.2.4. MRI-data

We will perform standard pre-processing using dedicated software.181 182

After realignment, we will

co-register functional scans to the structural scan, and thereafter normalize to the standard Monteal

Neurological Institute (MNI) brain or a DARTEL template (Diffeomorphic Anatomical Registration

Through Exponentiated Lie Algebra) for more flexible group normalization, and smooth. For the

different fMRI paradigms, we will perform fixed effect analyses on single subject level with linear

regression techniques (general linear models). For DTI-scans, we will use tract based spatial statics

(TBSS) for general effects and tractography for a priori defined tracts of interest.183

2.5.2.5. Neurometabolism and HPA-axis

We will quantify glutamate and GABA based on acquired MRS-spectra.165

From concentrations of all

measured fatty acids, we will calculate overall fatty acid unsaturation, chain length and

peroxidizability using dedicated indices.139

Finally, we will calculate cortisol/DHEAS-ratio as indication

of HPA-axis balance.184

2.5.3. Statistical analyses

The statistical analysis protocol has been written, and the study statistics will be carried out, under

close supervision of a statistical specialist.

2.5.3.1. Power analyses

Power analyses for continuous and categorical outcomes of the cross-sectional and prospective

analyses show adequate power to detect small to medium effect-sizes (see supplement). This is in

line with previous comparable research that found significant effects in smaller samples.66

Power

calculations for studies involving MRI remain hard and are not used routinely (for an approach see

e.g. Mumford et al.185

, Hayasaka et al.186

and Murphy et al.187

). Currently, there is consensus that

groups of 20 usually yield sufficient power in MRI-studies to detect moderate differences in regions

of interest. Therefore, to ensure adequate power for all our hypotheses, we will test our first

hypotheses on acquired scans from 60 recurrent MDD-subject and 40 controls, which is for baseline

group comparisons a number more than common in MRI studies, also in studies with a comparable

design.188

In a previous study with recurrent MDD-subjects, we observed ~50% recurrence rate in

2.5yrs.30

We therefore expect 2×20-30 subjects to be eligible for a second scan and subsequent

comparisons, allowing for some drop-outs. Based on previous research in comparable samples we

expect low attrition rates.22 30 47 152

Moreover, all participants can be included in the Cox-regression

analyses, since these can adequately deal with attrition (outcome measure incorporates time to

event or censored end of observation). As not all subjects will be identified when the recurrence is

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present and/or not all recurrent patients will be available for a second scan, we expect to obtain two

groups of ±20 patients with or without a recurrence up who will be scanned again during follow.

We perform a large set of measurements, which carries the risk of false positives. However, as we

will perform analyses according to analysis-plans which are a priori specified , we will do so for

independent a priori hypotheses. In addition, we will use multivariate analysis techniques (e.g.

machine-learning) to further reduce the risk of chance findings. Nevertheless, although our sample

size will exceed the level of a pilot-study, especially for the prediction measures that we will identify

we will need new samples to replicate our findings.

2.5.3.2. Descriptive data

We will provide descriptive statistics and compare groups using χ2- and independent samples t-tests

where applicable.

2.5.3.3. First and second hypotheses

For the first hypotheses we will compare the remitted recurrent MDD- with the control-group using

(multiple) general linear models or linear mixed models (e.g. complex repeated measures/covariance

structure, nested data, missing data), where applicable.189

We will present results uncorrected and

corrected for confounders and/or covariates of interest (factors differing between groups with P<.1),

using propensity scores where applicable.190

Independent variables will be group (recurrent MDD vs.

control), potential covariates, their interaction(s), and confounders; the selected outcome(s) for a

given specific hypothesis will be dependent variable(s). If interaction effects do not contribute to the

model, we will remove them to obtain the most parsimonious models. For the second hypotheses we

will use comparable models, except that we will omit the control-group (and consequently the

group-variable and interactions) from the models, and focus on effects of clinical variables of interest

in the remitted recurrent MDD-group.

2.5.3.4. Third and fourth hypotheses

For the prediction analyses, we will use cox-regression models to investigate prospective association

between baseline measures and time until first recurrence. Using time until first recurrence as

primary outcome measure will provide additional modelable variance in the data since such

contrasts not only incorporate 50% recurrence, but also fast vs. slow recurrence which may be highly

relevant from a clinical perspective. Furthermore, in first instance we are planning to only use the

time invariant baseline predictors. However, in a later stage, we will incorporate the variables that

we measure over time, e.g. the HDRS or rumination questionnaires, to see how changes in these

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parameters over time are associated with future recurrence (e.g. mediation) and/or time until

recurrence.

Next, we will model significant univariate associations in multiple regression models, with correction

for other confounders and covariates of interest related to recurrence (e.g. number of previous

episodes, residual symptoms, ‘daily hassles’ and coping style). Moreover, we will analyse secondary

outcomes [cumulative number, length and severity of MDD-episodes and course of depressive

(residual) symptoms] using (multivariate) general linear models or linear mixed models, where

applicable. For the fourth hypotheses, we will investigate change during recurrence using repeated

measures general linear models or linear mixed models where applicable.

2.5.3.5. Additional analyses

To exploit the multimodal and -dimensional character of our data, we plan to apply advanced

statistical methods to identify relevant multivariate patterns, including machine learning, factor and

network analyses.191-193

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3. ETHICS AND DISSEMINATION

3.1. Ethical considerations

3.1.1. Regulation statement

We will conduct this study according to Declaration of Helsinki principles (Seoul, October 2008) and

the Medical Research Involving Human Subjects Act (WMO). The study is approved by the accredited

Medical Ethical Committee (METC) of the Academic Medical Centre (AMC), teaching hospital of the

University of Amsterdam. We will obtain written informed consent beforehand from all participants,

after careful and extensive written and oral information. If desired, we will give subjects up to two

weeks to consider their decision. Investigators will receive Good Clinical Practice training, in

agreement with the AMC research code.

3.1.2. Handling of data and documents

We will encode data and keep this data and blood samples for at least 15yrs. Only researchers

directly involved in the study will have access to encoded data, the key will be with the researcher

only. We will label blood samples with anonymized patient numbers.

3.1.3. Benefits and risk assessment

There is no immediate advantage of participation for participants, there are no interventions

scheduled in this study. MRI is non-invasive, so hardly any risks are associated with this study.

Therefore, the METC determined that no liability insurance is required. We will inform subjects and

the reviewing accredited METC if anything occurs, on the basis of which it appears that

disadvantages of participation may be significantly greater than was foreseen.

Because we recruit unmedicated subjects with moderate to high recurrence risk, it may be

questioned whether follow-up of these subjects is ethically justified. However, we will not actively

propose tapering or discontinuation of antidepressant therapy. Instead we will only include subjects

who decided to stop antidepressants beforehand. In case we detect suicidality during follow-up, we

have a protocol available including a consulting psychiatrist for emergency situations and referral the

most appropriate emergency service. We therefore consider this study ethically justifiable.

In addition, advantages of participation and follow-up will be that MDD-recurrence will be detected

early so prompt psychiatric treatment can be offered. In naturalistic care there might be substantial

patient and institutional delays before recurrence is detected and treatment can be started.194

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3.1.4. Compensation

Participants will receive €75,- for their participation, besides compensation for travel expenses. For

completion of a follow-up scan we will pay €50,-.

3.2. Teaching

This study will provide training of PhD-students, and will involve educational internships of medicine,

psychology and neuroscience bachelor- and master-students of the Universities of Amsterdam,

Nijmegen and Groningen and VU-university.

3.3. Dissemination

3.3.1. Public disclosure and publication policy

We will submit study-results for publication in peer reviewed journals and presentation at

(inter)national meetings, taking into account relevant reporting guidelines (e.g. COPE, STROBE).195-197

We will regularly notify participants of publication. Curated technical appendices, statistical code,

and anonymised data will become freely available from the corresponding authors on request.198

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4. DISCUSSION

4.1. Summary

In summary, the current multimodal DELTA-Neuroimaging study will investigate recurrent MDD

vulnerability by comparing remitted unmedicated recurrent MDD-subjects with carefully matched

controls without personal/1st

degree familial psychiatric history. Biopsychosocial assessments

integrate four distinct levels of perspective: symptomatology, affective neuropsychology, brain

circuits, and endocrinology/metabolism. Subsequently, the cohort of recurrent MDD-subjects will be

followed-up to test to what extent baseline measurements predict, and/or change during

prospective recurrence. This will help to disentangle the pathophysiology behind MDD-recurrence,

and thereby provide (I) (bio)markers identifying high-risk patients needing additional preventive

treatment, and (II) novel targets to improve the treatments preventing against recurrences. Given

MDD’s highly recurrent nature, this knowledge has the potential to substantially reduce MDD’s

disease burden.

4.2. Limitations and strengths

4.2.1. Limitations

Several limitations of the current study should be noted beforehand. First, the extensive assessment

procedure needed to measure all variables of interest and confounders will potentially lead to

inclusion of subjects that are intrinsically aware of the necessity to perform clinical research and

readily willing to cooperate. Nevertheless, this selection bias is inherent to translational

neuroscientific research, and the relatively large number of subjects that will be included will

increase external validity. Moreover, testing the integrated hypotheses of the current study is only

possible by combining the different assessments.

Second, to overcome potential confounding effects of antidepressants and other psychotropic

medication, only subjects that currently do not use these drugs will be included. This may lead to

selection of particular patient subgroups that (I) experienced little benefit from previous medication

trials, (II) are hesitant to use these medications because of adverse effects or for principle reasons,

(III) experience other barriers to care (e.g. financial) or (IV) have an intrinsically lower vulnerability to

have severe recurrences. In addition, it may slow down inclusion. However, this is the only way to

study the hypotheses at hand while eliminating confounding effects of medication use. Furthermore,

the subjects included in the current study may be a clinically relevant representation of patients that

do not want to take antidepressant drugs, for whom knowledge of underlying vulnerability and

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measures to determine this vulnerability might be of help to develop novel alternative treatments to

prevent recurrence risk.

Third, for practical reasons family history will be determined by heteroanamnesis. This may lead to

recall or other biases. However, both under and over-representation can be expected, so we expect

this will not result in systematic biases.

Fourth, DSM-IV diagnostic criteria will be used for current study’s diagnoses, while the DSM-5 has

already been introduced. Since the classification of depressive episodes (i.e. recurrences) have not

changed in DSM-5, this and our specific in- and exclusion criteria will not lead to difficulties in

translating the results when DSM-5 will be used.

Fifth, the current study’s assessments will not include measures of HPA-axis feedback [e.g.

dexamethasone suppression(/corticotropin-releasing hormone-challenge) test].199

This was not

included to prevent overburdening of participants. While consequently the current study will not be

able to directly assess HPA-axis feedback, the study’s seven salivary HPA-axis measures without

pharmacological challenge during the baseline assessments will provide an adequate indication of

HPA-axis activity under natural circumstances, including stress by mood induction.120 172

Sixth, the current study’s MRI-measures will be made using 3-Tesla field strength, while higher field

strengths are also available. Although obviously higher field strengths increase signal to noise ratio,

they may also have several disadvantages.200

Higher costs and specific absorption rates, together

with increased risk for artefacts due to e.g. inhomogeneous transmit fields, more extensive

contraindications and peripheral nerve stimulation limit high field strength applicability. These

disadvantages apply to clinical studies like the present one, but even more to the clinical setting.200

Therefore, 3-Tesla findings may be more readily clinically translated than findings at higher field

strengths, and could therefore be more relevant from the clinical perspective.200

Seventh, while the combined cross-sectional patient-control and prospective follow-up design of the

current study has great advantages, it brings along a balance between two contrasts. First, the

recurrent MDD-vulnerability contrast in the comparison between highly vulnerable patients and

matched resilient controls; and second the within patient-group contrast in time until recurrence of

fast recurrence during follow-up vs. no or late recurrence. Strongly increasing the first contrast by

including only extremely high recurrence risk patients entails the risk of decreasing the second

contrast because all patients will experience fast recurrence. The other way around, by including too

many patients with a low recurrence risk, the first contrast may be disadvantaged because the traits

will not be outspoken enough to be detected. Therefore, also based on our previous research, we

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opted to increase to first contrast by including relatively resilient controls, together with patients

that have proven vulnerability for recurrent MDD (i.e. ≤2 previous MDD episodes). However, we did

not express any additional vulnerability criteria, e.g. time since last episode or higher number of

previous episodes, in order to (I) include recurrent MDD patients that form a naturalistic sample that

is representative regarding vulnerability and (II) not to decrease the second contrast in time until

recurrence.

Of note, we will not include single episode MDD-subjects. While this would enable comparisons

against a relatively low recurrence risk group, instead of controls, this was deemed to be logistically

even more difficult to achieve. Regarding the second contrast in time until recurrence, based on our

previous research and our inclusion procedure, we expect a large spread in the number of previous

episodes (e.g. from 2 up to 60) and time since last episode (e.g. from 8 weeks up to >10yrs), which

both imply modelable variance/contrast in prospective recurrence risk.201

With an expected ‘optimal’

distribution of 50% recurrence-rate during follow-up, we think that our group would be the most

interesting and feasible group to study when looking for factors that can predict imminent

recurrence, in order to (I) select subjects that may benefit from preventive treatment, and (II)

identify pathophysiological mechanisms that can be targeted in these subjects to prevent recurrence

risk.

Finally, the current study does not include (randomized) interventions. Therefore, it will not be

possible to say whether observed effects are causal in nature. Nevertheless, the current study’s

prospective, repeated measures design can optimally select targets for future randomized clinical

trials to test the causal nature of observed effects.

4.2.2. Strengths

The current study also has several distinct strengths. Due to its strict and specific inclusion-criteria,

matching- and recruitment-procedure, the contrast for MDD-vulnerability will be maximal, without

distortion due to important confounders: MDD-residual symptoms and medication. In addition, the

unique integration of a wide range of measures in a prospective repeated measures design will allow

disentangling of recurrent MDD state- and trait-factors.

Furthermore, the study will be performed by an experienced international multi-centre research

group, combining expertise from all measured perspectives. Additionally, the Netherlands’ relative

limited geographic size and high level of social organization make it well suited for long-term follow-

up research.

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Next, ESM-results could set the stage for innovative cost-effective e-health interventions. Moreover,

the focus on lifestyle factors (physical activity/diet) and their biological effects could provide more

insight into recurrent MDD-patients’ increased risk to develop cardiovascular disease,9 as already

acknowledged in the introduction. By combining these lifestyle (biological) assessments with

investigation of (the neurobiology of) motivation, the present study could lead to development of

interventions that help to motivate recurrent MDD-patients to improve their lifestyle. This not only

has the potential to prevent recurrence, but also the highly comorbid cardiovascular risk.202

4.3. Conclusion

By integrating the symptom level, affective neuropsychology, brain circuits, and

endocrinology/metabolism, using a prospective repeated measures design in remitted MDD-subjects,

the present DELTA-Neuroimaging study will provide more insight in recurrent MDD-vulnerability.

Increased insight will lead to novel targets for (I) improved preventive therapy, and/or (II)

(bio)markers to monitor and/or predict recurrence risk. Consequently, ultimately, it holds potential

to alleviate MDD’s highly recurrent course and reduce its currently overwhelming global disease

burden.

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Competing interests

All authors declare that they have no competing interests.

Authors’ contributions

RJTM and HGR designed the study. RJTM and HGR drafted the protocol and the manuscript. All

authors contributed to development and implementation of the study protocol. MWJK provided

statistical advice. RJTM and CAF conduct all participant-related study-procedures. All authors

contributed to editing the manuscript and read and approved the final manuscript.

Acknowledgements

This study is supported by unrestricted personal grants from the AMC to RJTM (AMC PhD

Scholarship) and CAF (AMC MD-PhD Scholarship), and a dedicated grant from the Dutch Brain

Foundation (Hersenstichting Nederland: 2009(2)-72). HGR is supported by a NWO/ZonMW VENI-

Grant #016.126.059.

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FIGURE LEGENDS

FIGURE 1 | Theoretical framework

Schematic representation of the theoretical framework of the present DELTA-Neuroimaging study.

The four selected levels of perspective (endocrinology/metabolism, brain circuits, affective

neuropsycholoy, and symptoms), their respective subdomains, and their connections have been

depicted. The horizontal straight arrows show potential bidirectional relationships (for readability

bidirectional relationships between e.g. anhedonia and cognitive reactivity are not shown), the

horizontal curved arrow shows membrane fluidity balance, colored arrows show potential

connections, dashed arrows show inhibiting effects, and vertical grey arrows show possible

underlying pathways. Abbreviations used: GABA, γ-aminobutyric acid; HPA, hypothalamic-pituitary-

adrenal; PFC, prefrontal cortex; vStr, ventral striatum; VTA, ventral tegmental area; TPN, task positive

network; DMN, default mode network; dACC, dorsal anterior cingulate cortex; pgACC, pregenual

anterior cingulate cortex; Amy, amygdala; ‘Hot’ neuro-Ψ, affective neuropsychology; Cogn. react.,

cognitive reactivity; Dysf. attit., dysfunctional attitudes.

FIGURE 2 | Study design

Figure 2 depicts the study design of the present DELTA-Neuroimaging study. Different part of the

study are shown in chronological order from left to right. For a description of the contents of

questionnaire booklets and tasks we refer to the supplemental information. After screening,

recruited patients and controls participate in the initial assessment where we check in- and exclusion

criteria, register variables and covariates of interest, prepare the mood induction and mail

questionnaire booklet I and Salivettes. During the subsequent first study session we will take fasting

blood samples, perform the affective neuropsychological tests, perform the sad mood-induction,

explain the experience sampling method (ESM) and the emotion regulation functional magnetic

resonance imaging (fMRI) task, and hand out the ESM-psymate and questionnaire booklet II.

Subsequently, subjects come to the MRI-session, where we take structural [T1-weighted and diffuse

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tensor imaging (DTI)] and functional magnetic resonance imaging (fMRI)-scans (neural and sad mood

induction resting state, reinforcement learning, cued emotional conflict, emotion regulation), as well

as γ-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) of the basal ganglia

and pregenual anterior cingulate cortex. Next, we monitor the patients by calling them every ~4

mnths to assess recurrence. In case we detect a recurrence, we invite the respective patient –

together with matched non-recurrent patients – to repeat part of the baseline assessments [blood

samples, affective neuropsychological tests, structural MRI, fMRI (resting state, reinforcement

learning), and MRS].

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‘Ho

t’ ne

uro

-Ψ

Brain

circuits

Amy

sgACC

dACC

PFC

DMN

TPN

Recurrent depression vulnerability &

Depression recurrence

Emotional

biases

Reward

insensitivity

Cognitive

flexibilty

Membrane fatty acids

GABA

Glutamate

End

ocrin

olo

gy/me

tabo

lism

Hypothalamus

Pituitary

Adrenal cortex

HPA-axis

Cortisol & DHEAS

VTA

vStr

PFC

Symp

tom

s

Cogn. react. Anhedonia Rumination Dysf. attit.

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0 yr 2.5 yr

Recruitment Screening

Initial assessment - prepare mood induction

- mail questionnaires & Salivettes

First session - blood samples

- neuropsychology - explain ESM

MRI-session - structural - functional

Recurrence monitoring

Repeated measures - blood samples

- neuropsychology - MRI-scans

Recurrence

Baseline: patients & controls Follow-up: patients only

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SUPPLEMENTARY MATERIAL

1. SUPPLEMENTARY METHODS

1.1. Measures

1.1.1. Questionnaires

1.1.1.1. Not in booklets

We will use the 17-item Hamilton Depression Rating Scale (HDRS) and the Dysfunctional Attitude

Scale (DAS) at various moments during our study procedure (see main article).

- The HDRS is an observer rated major depressive disorder (MDD)-symptom scale to assess the

severity of depression.1 Total scores of the 17-item version range from 0-52 and scores of 0-7 are

considered within the normal range; scores of 8-13 indicate mild depression; 13-18 moderate

depression; 18-22 severe depression and scores ≥23 indicate very severe MDD. Its internal

consistency is high, with Cronbach’s α=.80.2

- The DAS is a self-rated questionnaire to assess general, deeply held, dysfunctional beliefs. Two

40-item versions exists for the Dutch language. Total scores range from 40 to 280, with higher

scores reflecting greater dysfunctional attitudes. The internal consistency and test-retest

reliability are high, with Cronbach’s α’s of respectively .90 and .73.3

1.1.1.2. Questionnaire-booklet I

We will mail questionnaire-booklet I after the initial assessment, participants will take it with them at

the first study-session baseline visit (same for follow-up repeated measure), see main manuscript. It

includes the following questionnaires:

- Inventory of Depressive Symptomatology self-rated (IDS-SR): self-rated MDD-symptom scale to

assess severity of depressive symptoms. The IDS-SR comprises three factors: cognition and

mood, anxiety and arousal, and sleep and appetite regulation. The IDS-SR has 30-items with a

total score range from 0 to 84, with higher scores indicating greater severity of depression.

Scores ≤13 are considered within the normal range; scores of 14-21 indicate mild MDD; 22-38

moderate MDD; ≥39 severe MDD. The IDS-SR has highly acceptable psychometric properties.

Internal consistency was up to 0.92 (Cronbach’s α).4

- Leiden Index of Depression Sensitivity-Revised (LEIDS-R): self-report questionnaire that measures

cognitive reactivity to sad mood.5 Participants are instructed to think about the last time they felt

“somewhat sad”, and to indicate - on a 5-point Likert scale ranging from ‘not at all’ (0) to ‘very

strongly’ (4) - the degree to which a list of statements describe their typical cognitions and

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behaviours in response to sad mood. The LEIDS-R contains 34 items which sum up to the total

score, and subscales (assessing cognitive reactivity in relation to aggression,

hopelessness/suicidality, acceptance/coping, control/perfectionism, risk aversion, and

rumination on sadness). The LEIDS-R has good internal consistency (Cronbach’s α=0.88).6

- Neuroticism-Extraversion-Openness Five-Factor Inventory (NEO-FFI): self-rated questionnaire

measuring five factor model (‘big-five’) dimensions of personality characteristics: Neuroticism,

Agreeableness, Conscientiousness, Extraversion and Openness.7 The NEO-FFI has 60 items on a

five point scale, ranging from "strongly disagree" to "strongly agree". It has sufficient internal

reliability and two-week retest reliability is uniformly high, ranging from 0.86 to 0.90 for the five

scales.8

- Everyday Problem Checklist:9 10

Dutch translation of self-rated questionnaire measuring everyday

(small) stressors (Daily hassles). The questionnaire consists of 114-items which describe

problematic situations and events in daily life.

- Utrecht Coping List (UCL): self-rated measure of coping behaviour while confronted with

problems. The questionnaire consists of 47-item on seven empirically derived subscales: active

tackling, seeking social support, palliative reacting, avoiding, passive reacting, reassuring

thoughts and expression of emotions. The UCL demonstrated strong internal consistency in a

study within the UK population. Five of the seven subscales had good test-retest reliability.11

- Negative life events questionnaire:10 12 self-rated questionnaire asking for recent life-events of

the subject or significant others.

- Childhood Trauma Questionnaire:13 14 Dutch translation of the Childhood Trauma Questionnaire

for assessment of childhood adversity. This 28-item self-report questionnaire retrospectively

assesses childhood trauma and neglect, and consist of five factors; emotional abuse, emotional

neglect, sexual abuse, physical abuse, and physical neglect. Inter-correlations among the five

factors ranged from r=.41 to r=.95.15

Psychometric properties in a sample of Dutch female sex

workers were good.13

- International Physical Activity Questionnaire (Long Form):16-18

Dutch translation of an

internationally validated questionnaire to measure physical activity, developed with support

from the World Health Organization. This 27-item self-report questionnaire assesses the time

that participants spent being physically active in the last 7 days. The reliability of the Dutch

version was good (intra-class correlation coefficient=0.70-0.96) and the validity moderate

(r=0.36-0.49) compared to an accelerometer. Reliability and validity is comparable in psychiatric

populations, e.g. schizophrenia.19

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- Helius Food Frequency Questionnaire (Helius-FFQ):20 21 validated and updated questionnaire to

assess dietary intake. The Helius-FFQ enables detailed, standardized, and comparable

assessment of the diet of five different ethnic groups.

- Women's Health Initiative Insomnia Rating Scale (WHIIRS):22 reliable and internationally validated

questionnaire to assess sleep quality. WHIIRS’ internal consistency is good (Cronbach’s α=.78.).

Test-retest reliability coefficients were .96 for same-day administration and .66 after a year or

more. The WHIIRS has good construct validity.22

- Edinburgh handedness questionnaire:23 24

self-rated questionnaire developed to determine

dominant laterality in executive functions. The questionnaire assesses handedness by of the

preferred hand for carrying out common activities. The 4-item revised structure showed very

high reliability on measures of factorial composite reliability and Cronbach's α. Furthermore, the

estimate of the quality of factor scores was high.24

1.1.1.3. Questionnaire-booklet II

We will hand out questionnaire-booklet II during the first study-session, participants will complete it

before the MRI-session (same for follow-up repeated measure). It includes the following

questionnaires:

- IDS-SR (see above)

- Snaith-Hamilton Pleasure Scale (SHAPS):25 self-rated questionnaire measuring anhedonia. The

SHAPS has 14 items with scores ranging from 0-14. The internal consistency and test-retest

reliability of the SHAPS were adequate, with Cronbach’s α’s of .91 and 0.70 respectively.

Furthermore, the SHAPS was significantly correlated with other validated measures of affect and

personality.

- Ruminative Response Scale (RRS-NL):26 27

validated Dutch adaptation of a self-report rumination

measure. It consists of 26 items that describe responses to a depressed mood that are focused

on the self, symptoms, or consequences of depressed mood. Two separate subscales reflecting

pondering and brooding are distinguished. The RRS-NL possesses good internal consistency and

validity.26

In a recent study examining the Dutch version, Cronbach׳s α for the total RRS-NL was

.94, and .64 for the brooding subscale.28

We adjusted the RRS-NL by slightly reframing the

introductory statement. Instead of referring to what subjects generally do when they feel

depressed, we asked for their answers reflecting the last week. With this adjustment, we aimed

to increase the temporal specificity, by specifically asking for current rumination instead of

general ruminative traits.

- Spielberger State and trait Anxiety Inventory form Dutch Y (STAI-DY):29 self-rated questionnaire

measuring state and trait anxiety. The State Anxiety Scale (40 items) measures subjective feelings

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of apprehension, tension, nervousness, worry, and activation arousal of the autonomic nervous

system. The Trait Anxiety Scale (20 items) evaluates stable aspects of anxiety proneness. Test–

retest reliability coefficients ranged from 0.31 to 0.86 and internal consistency was high, ranging

from 0.86 to 0.95 (Cronbach’s α).29

- Mood and Anxiety Symptoms Questionnaire (MASQ-30D):30 validated short adaptation of the

MASQ, designed to measure the dimensions of Clark and Watson's tripartite model in large-scale

psychopathology research. The MASQ-30D contains 30 items examining mood and anxiety and

has 3 subscales. The scales of the MASQ-D30 showed good internal consistency, with Cronbach’s

α’s >0.87 in patient samples. Correlations of subscales with other measures of mood and anxiety

indicated sufficient convergent validity.

1.1.1.4. Questionnaire-booklet III

We will complete questionnaire-booklet III at the MRI-session (same for follow-up repeated

measure). It includes three observer-rated questionnaires:

- HDRS (see above)

- CORE checklist for psychomotor MDD-symptoms (CORE):31-33 distinguishes dimensions of

psychomotor dysfunction in MDD, all suggestive of a melancholic MDD-subtype. The CORE index

is composed of 18 items, scored on a 4-point scale. Factor analysis showed three interpretable

domains: (I) retardation items (52% of variance), (II) agitation items (15% of variance), and (III)

non-inter-activeness (5% of variance).34 Its translation in Dutch was recently validated.35

- Salpêtrière retardation rating scale (SRRS):36 measures cognitive and motor aspects of

retardation. This scale contains 15 items and has a three-factor solution measuring movement,

speech and cognitive function.34

Correlations between SRRS and measures of cognitive function

and motor abilities show good convergent validity.37

1.1.1.5. Questionnaire-booklet IV

We will use questionnaire-booklet IV during the follow-up measurements. It consists of the IDS-SR,

RRS-NL, and SHAPS, all described above.

1.1.2. Neuropsychological tests

1.1.2.1. Exogenous cueing task (15min)38

In this reaction time task, a target stimulus appears at one of two spatial locations, cued by an

emotional stimulus (emotional face) preceding at the same (‘valid trial’) or opposite spatial location

of the target (‘invalid trial’). When the interval between cue and target onset is short (stimulus onset

asynchrony<300ms), participants typically respond faster to valid compared to invalid trials (‘cue

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validity effect’). In case of emotionally relevant stimulus material, the time course of the cue validity

effect may be extended (‘enhanced cue validity effect’), leading to a larger cue validity effect

compared with neutral information. Secondly, we will measure emotional modification of attentional

engagement and disengagement by comparing speed of responding on valid and invalid emotional

versus neutral trials. Cue emotional valence may (I) lead to response benefits on valid emotional

versus valid neutral trials, which is a measure of attentional engagement towards emotional cues,

and/or (II) delay disengagement of attention, which is indexed by a slower reaction on invalid

emotional trials compared to neutral trials.39-41

1.1.2.2. Facial expression recognition task (20min)

We will use six morphed basic emotions (happiness, surprise, sadness, fear, anger, disgust) from 10

individual characters from the pictures of facial affect series between each prototype and neutral,

and present them in random order for 500ms, replaced by a blank screen. We will record reaction

times to these emotions and the recognition threshold (the intensity level required for successful

recognition of each emotion). This recognition threshold is defined as the level of emotional intensity

at which participants correctly identify ≥75% facial expressions of emotion for four consecutive

intensities.42

1.1.2.3. Emotional categorization (6min)

We will present sixty personality characteristics selected to be disagreeable or agreeable on the

computer screen for 500msec each. These words have been translated from the original English

version to Dutch, matched in terms of word length, ratings of usage frequency, and meaningfulness.

We will ask subjects to categorize the words as likable or dislikeable as quickly and as accurately as

possible. Specifically, we will ask to imagine whether they would be pleased or upset if they

overheard someone else referring to them as possessing this characteristic, so that the judgment is in

part self-referring. We will calculate classification-rates and reaction times to likable and dislikeable

words.40 42

1.1.2.4. Emotional memory (~5 minutes)

Fifteen minutes after completion of the emotional categorization task, we will ask participants to

recall as many of the personality traits as possible. We will compute numbers of positive and

negative words recalled for both correct and false responses.40 42

1.1.2.5. Internal shift task (12min)43

Examines capacity to shift attention between contents of working memory in response to emotional

as well as non-emotional material. We will present Karolinska faces at the centre of the computer

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screen one at a time. We will ask participants to perform two conditions, a non-emotional and an

emotional one. In the non-emotional condition, we will instruct participants to focus on the relevant

stimulus dimension ‘gender’ (male or female), in the second condition, they have to focus on the

‘emotion’ dimension (neutral or angry). All participants complete both conditions in counterbalanced

order. Participant’s task is to keep a silent mental count of the number of items in each category,

presented over a block of items (with random 10 to 14 items) and report numbers at the end of each

block. We will ask participants to update counters of both categories when a face is presented and

report numbers of items at the end of a block in a fixed order, to encourage a consistent counting

strategy (e.g. neutral-angry faces in emotion condition, male-female in gender condition). We will

present each face on the screen until participants press the spacebar to indicate that they have

updated both internal counters. This response latency for updating is the main dependent variable of

the task. The next face appears on the screen after a 200ms inter-stimulus interval. Due to the face-

sequence, there are shifts and no shifts in each block of items (e.g. in the emotion condition shifts

are angry-neutral and neutral-angry and no shifts are angry-angry and neutral-neutral).44

1.1.2.6. Dutch adult reading test (10min)

Dutch version of the national adult reading test. The score is predictive of premorbid intelligence in

brain damaged patients and appeared insensitive to brain deterioration in demented or psychotic

patients.45


Momentary assessment techniques allow for examination of subtle fluctuations of behaviour and

affect over the course of the day, and the prospective nature of the data allows for examination of

the temporal association between different observations. The shift to the micro-level of daily life

showed how subtle dynamic patterns of moment-to-moment affective experiences and responses to

situations constitute the missing link between macro-level risk factors for psychiatric disorders like

MDD and future outcomes.46

Major MDD risk factors interactively impact on reactivity and duration

of momentary experiences in everyday life and the latter patterns in turn predict future course of

symptoms. Therefore, it is relevant to examine mechanisms at the level of these smallest building

blocks. Furthermore, real-life tracking of experiences using ESM might allow for an easy identification

of the concrete bits of real-life affective and behavioural patterns which need remediation.

Furthermore, ESM can provide real-life validity to experimental and imaging results. This is

important, as this clarifies how knowledge of mechanisms connects with real-life intervention

targets.47-49

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The ESM-palmtop (“PsyMate”®) will signal subjects at random moments during the day to answer

questions about affect and daily events. Answering the ESM-palmtop questions after each auditory

signal (“beep”) will take about 30sec. We will program the ESM-palmtop to emit 10 beeps/day at

random intervals in each of the ten 90-minutes time blocks between 7:30h and 22:30h, on 6

consecutive days. After each beep, subjects have to fill out the self-assessment on the ESM-palmtop

to record current context (activity, persons present, location, physical activity), stress appraisals of

this context, and mood. Mood questions include 4 Positive and 5 Negative Affect items.50 Examples

are ‘happy’ and ‘relaxed’ for positive affect and ‘depressed’ and ‘irritated’ for negative affect. The

self-assessments will be rated on 7-point Likert scales (ranging from 1= ‘not at all’ to 7= ‘very’). We

will instruct subjects to complete the ESM-palmtop measurements as quickly as possible after the

beep. This emphasis helps to minimize retrospective memory distortion. In addition, we included a

morning and an evening questionnaire including specific questions regarding sleep and the overall

day, respectively. We will instruct participants to fill in these questionnaires on the ESM-palmtop in

the morning after they wake up, and in the evening before they go to bed. The questions we

included in the ESM-procedure are shown in Supplementary table 1.

Regarding the ESM a standard approach for data cleaning will be used. We will first check for missing

data. Second, we will check whether total response time exceeds 15 minutes or whether time

between the beep and first response exceeds 15 minutes, which observations will be removed. Third,

we will exclude days of measurement when the number of observations was less than five. Fourth,

we will exclude subjects when the number of observations is less than 30. These precautions are

taken to have enough and valid measurements, necessary for valid statistical approaches. We will

thereafter inspect the variables to see whether they contain variation based on the interquartile

range.

Because ESM observations are irregularly spaced (due to the random presentation of measurements

and missing data) and a positive/negative autocorrelation may exist between the expected absolute

successive difference (EASD) and time intervals, we will calculate the mean adjusted absolute

successive difference (MAASD) per ESM variable, taking into account an adjustment parameter λ, to

capture affective instability.51

To avoid night time intervals, successive differences will be calculated

within days.

Because ESM-data will likely be skewed to the left, we will apply nonparametric independent

samples Mann-Whitney U tests when appropriate, to determine significance of differences between

the remitted recurrent MDD and healthy control groups.

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1.1.4. MRI-scanning

We will minimize side-to-side head movements by fitting foam pads between the volunteers' head

and the volume coil. We will obtain scans in the order indicated below; half-way, we will plan a break

of >20min. Due to time constraints we had to perform the emotion regulation task (ERT) in the

second block of scanning, after the break. It could be questioned whether the brain activation by this

ERT might influence the 2nd resting state scan after a mood-induction. If anything, we aimed to have

the subjects maximally experience a sad mood after the mood-induction. As the ERT also provided

negative pictures, (alternated with positive) it can be expected that the ERT might also have primed

people to be more susceptible for the mood induction procedure. As this was a systematic order of

scanning in all subjects, we think that if any effect occurred, this would have only primed all subjects

systematically to be more vulnerable for the mood induction. Supplementary table 2 describes the

experimental designs of all fMRI tasks according to available reporting guidelines,52

the remaining

acquisition parameters are described below.

- Locater scan: a whole brain low resolution 3-dimensional T1-weighted turbo field echo-scan for

anatomical overview. Scan duration=53s; number of slices=100; slice orientation=sagittal; field of

view (FOV)=250×250×220mm; voxel size 2.23×2.23×2.2mm; acquired matrix=112×112; act.

repetition time (TR)=3.1ms; act. echo time (TE) 1.4; flip angle (FA)=8˚; turbo-factor=425.

- Reference scan: to obtain a whole brain sensitivity map for the subsequent SENSitivity Encoding

(SENSE) scans. Scan duration=59s; number of slices=100; slice gap=10mm; slice

orientation=coronal; FOV=530×530mm; voxel size 5.52×7.07×3mm; acquired matrix=96×75;

TR=4ms; TE=0.75; FA=1˚.

- Structural scan (6min): a whole brain high resolution 3-dimensional T1-weighted turbo field

echo-scan for detailed anatomic information. Scan duration=372s; number of slices=220; slice

orientation=transverse; FOV=240×220×188mm; voxel size 1×1×1mm; acquired matrix=240×187;

TR=8.3ms; TE=3.8; FA=8˚; number of averages=2; TURBO-factor=154.

- Resting-state scan: We will give no specific instructions except that all subjects keep their eyes

closed, let their mind wander, lie still and not fall asleep.53

Because we aim to compare resting-

state scans without and with a negative mood induction, we will play neutral or sad music,

respectively, the 5min preceding the resting-state scans. We will combine this with a

personalized neutral and sad script, respectively, which subjects read on the screen, as described

in the main article. We chose not to counterbalance the acquisition of the neutral/mood induced

resting state scans, to prevent potential interference of the subsequent fMRI-tasks by mood-

state after an initial sad mood-induced resting state scan. This scan will be a field echo (FE) echo-

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planar imaging (EPI)-scan with duration=428s; number of slices=37; slice thickness=3mm; act.

slice gap=0.3mm; slice orientation=transverse; slice order=ascending; number of dynamics=210;

FOV=240×220×122mm; voxel size 3×3×3mm; acquired matrix=80×80; TR=2000ms; TE=28ms;

FA=76˚; EPI-factor=43.

- Reinforcement learning task (25min): After instructing the participants to arrive thirsty, a

Pavlovian-learning paradigm will be used, delivering small amounts (0.2ml) of liquid (sweet apple

juice or bitter 3.0M MgSO4) at different probabilities (80-20%) after conditional stimuli. With the

changing probabilities of water delivery, temporal difference reward-learning and aversive-

learning signals can be calculated which will be used as a regressor of interest in the analyses.

This task showed excellent (differential) activations of the reinforcement learning circuitry in

depressed subjects versus controls.54

Of note, the task does not test social stimuli,55

but rather

the persistence of difficulties in temporal difference reward related learning with primary

rewards, as this could be a more general and basic persistent dysfunction in recurrent MDD.

MgSO4 is clinically used as a laxative, and is not harmful to humans. The 15ml solution used in the

experiment will not cause bowel distress. This will be a FEEPI-scan with duration=1693.5s; voxel

size 3×3×3mm; EPI-factor=43.

- Magnetic resonance spectroscopy (MRS): Glutamate, glutamine and GABA only recently became

distinguishable from each other by MRS. We will acquire edited 1H J-difference spectra using a

GABA-specific MEGA-PRESS sequence.56-58 During the odd transients in this sequence, we will

apply a 15.64ms sinc-center editing pulse (64 Hz full width at half maximum) at 1.9ppm and

4.6ppm in an interleaved manner to specifically excite GABA and suppress water, respectively.

We will acquire these spectra in two voxels, one in the left basal ganglia with scan

duration=776s; volume of interest size=30×20×20mm; number of dynamics=384; number of rest

slabs =4; number of samples =2048; TR=2000; TE=73ms; FA=90˚; odd frequency=351; even

frequency=-351; 2nd

order pencil beam-auto shimming; and water suppression. The same in the

pgACC, except with scan duration=328s; volume of interest size=25×20×30mm; number of

dynamics=160.

- Diffusion Tensor Imaging (DTI): measures whole brain fractional anisotropy (FA) and mean

diffusivity which can quantify white matter abnormalities.59 Spin-echo diffusion weighted

imaging DTI-scan duration=333.6s; number of slices=60; slice thickness=2mm; slice gap=0mm;

slice orientation=transverse; FOV=224×224×120mm; voxel size 2×2×2mm; acquired

matrix=112×112; TR=7635ms; TE=88ms; FA=90˚; EPI-factor=59; number of b-factors=2; b-factor

order=ascending; max b-factor=1000.

- Cued Emotional Conflict Task (CECT,60 25min): Participants will be instructed to respond as

quickly as possible with two response buttons indicating happy or sad. In an event-related

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paradigm, each trial starts with one of two word cues (“actual” or “opposite”) presented for

500ms, which instructs participants to respond to the target cue with the identical or opposite

valenced button. After the presentation of the cue word, a fixed interval of 2000ms separates

the presentation of the cue from the target. The target cue is either a happy or sad face

presented in the centre of the screen. This cue-offset period makes it possible to investigate: 1)

cue related conflict anticipation; and 2) response related cognitive control following the

presentation of the emotional target.

Fourteen faces (7 female and 7 male actors) from the Karolinska Directed Emotional Faces

dataset61

will be used. Each face will be shown in a happy or sad expression (matched for

arousal). The assignment of labels to the two response buttons will be counterbalanced across

participants. After the CECT, participants will be asked to rate the faces for valence and arousal

using 9-point Likert scales (valence: 1=unhappy, 5=neutral, 9=happy; arousal: 1=calm,

5=intermediate, 9=excited). This will be a FEEPI-scan with voxel size 3×3×3mm; EPI-factor=43. Six

runs of 24 trials are separated by a short brake.

- Emotion regulation task (ERT, 20min): this will be a modification of the emotion regulation task

described earlier.62 63 The stimulus set will consist of 9 x 4 (sad, happy, fearful, neutral) x 2

(attend, regulate) pictures derived from the International Affective Picture System (IAPS)64 and

http://nl.dreamstime.com; we will match each set for valence, arousal and content. We

preselected the IAPS pictures based on IAPS ratings (scale: 1-9) for valence (neutral: 4-6; positive,

i.e. happy: >6; negative, i.e. fearful and sad: <4); arousal (neutral: <3; emotional, i.e. happy,

fearful, or sad: >6) and furthermore ratings for emotion specificity as assessed by Mikels et al.64

(scale: 1-9) (>7 for each specific emotion category; neutral: <3 for every emotion). In addition, we

will use stock photos from http://nl.dreamstime.com based on emotional content. In total, we

selected 110 pairs of pictures, matched for emotional content. To make matching between IAPS

and Dreamstime pictures possible, we performed an independent pilot study (N=41 healthy

controls). Subjects rated all pictures on valence and arousal [using the same Self-Assessment

Manikin (SAM)65

used for the IAPS database, ranging from unpleasant to pleasant for valence,

and from calm to excited for arousal], emotion type [on a scale from 1 (emotion is not elicited at

all) till 9 (emotion is elicited very strongly)] and complexity [on a scale from 1 (picture is very easy

to interpret) till 9 (picture is very difficult to interpret)]. Based on these ratings, we eventually

selected 36 sets of 2 pictures (9 sets for each emotional category). Within each pair, we matched

the pictures (one for the attend, one for the regulate condition) for valence, arousal, complexity

and emotional content. We will present the pictures in a semi-blocked pseudo-randomized

design. Each block will start with the instruction presented in the middle of the screen (4s),

followed by 3 successive pictures of the same emotional category (10s each). After each picture,

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subjects will indicate the emotional intensity resulting from attending or regulating on a Visual

Analogue Scale (VAS). After each block subjects will also rate their performance (i.e. how well

they were capable of attending or regulating). We will separate blocks by a fixation cross (4s). We

will pseudorandomize and counterbalance the order of stimuli presentation, and instruction

within and between subject groups. We expect this task to show – amongst others- negative bold

responses when viewing pictures relative to the fixation cross (resting state) in the pregenual

anterior cingulate. This will be two FEEPI-scan with max durations=822s; voxel size 3×3×3mm;

EPI-factor=43.


1.1.5.1. Fatty acid metabolism

We will wash erythrocytes of venous EDTA blood three times in isotonic saline, count them by

routine hemocytometric analysis and freeze them overnight in a BHT (2,6-di-tert-butyl-4-

methylphenol)-coated Eppendorf cup. Next, we will transmethylate fifty microliters of the resulting

hemolysate in 1ml 3M HCl by incubating for 4hrs at 90°C in the presence of 10nmol internal

standard; the methyl ester of 18-methylnonadecanoic acid. After cooling, we will extract the aqueous

layer in 2ml hexane, and take this extract to dryness under nitrogen flow and resuspend it in 80μl

hexane. Subsequently, we will inject one microliter of this solution into a Hewlett Packard GC 5890

equipped with an Agilent J&W HP-FFAP, 25m, 0.20mm, 0.33µm GC Column, and detect eluting fatty

acid methylesters by flame ionization detection. Finally, we will calculate fatty acid concentrations

using the known amount of internal standard and express them as pmol/106 cells for erythrocytes.66

67

1.1.5.2. Genetics

We will apply polymerase chain reaction (PCR) and HinfI restriction enzyme digestion as described

previously.68

In short, we will isolate DNA from blood using a filter-based method (QIAamp DNA Mini

Kit, Qiagen Ltd., United Kingdom). Next, we will design PCR-primers using Primer 3 (available at

http://bioinfo.ut.ee/primer3/). Subsequently, we will use a Matrix Assisted Laser Desorption

Ionization Time Of Flight (MALDI-TOF) mass spectrometer from Bruker Daltonics. To increase

reliability, we will genotype all samples in duplicate. Finally, we will save additional genetic material

for future analyses.14

1.1.5.3. Blood storage

We will acquire platelet-poor plasma from lithium-heparine, EDTA and citrate blood tubes using the

following procedure. First, we will centrifuge tubes for 10min at 2680×g (no brake) at 18°C. Next, we

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will pipet plasma of each tube in a separate cryovial®. Subsequently, we will centrifuge the cryovials®

for 5min at 14.000×g (no brake) at 18°C. Finally, we will store the platelet-poor plasma in separate

micronic vials at -80°C until further analyses.


We will instruct participants to provide five saliva samples over a (working, if applicable) day before

the first study-session (at awakening, 30, 45 and 60min thereafter, followed by a fifth measurement

at 22.00h) to diurnally reflect the morning awakening curve and evening HPA-axis activity. In

addition, we will collect saliva using a Salivette before and after sad mood induction to investigate

HPA-axis response to a psychological personal stressor. While Dickerson & Kemeny69

indicated that,

on average, an emotion induction stressor does not elicit a significant cortisol response, this should

be interpreted with caution because of the relatively small numbers of studies that fell in this

category as acknowledged by the authors. Moreover, it could be that some studies observed a

positive, and others a negative effect, that levels out as no effect overall. In addition, Dickerson &

Kemeny excluded studies in which recruitment was based on a physical or psychological diagnosis or

a stressful experience (e.g., diabetes, depression, bereavement). This makes it hard to extrapolate

their findings to our sample of recurrently depressed patients. Of note, several more recent papers

did observe interesting effects of mood on salivary cortisol in recurrent depression (Chopra et al.,

2008;70 Huffziger et al., 2013)71, which makes this assessment of great interest to our study. We will

instruct subjects not to eat, smoke, drink tea or coffee or brush their teeth within 15min before

sampling,10 72 73 to write down exact sampling day and time, and to keep samples refrigerated before

bringing them back to us at the first study-session; we will store them (-20°C) until analysis.

1.1.7. Waist circumference

Increased waist circumference reflects abdominal obesity, which is a metabolic syndrome criterion.74-

77 Abdominal obesity is closely related to insulin resistance and metabolic dysregulation, and a strong

risk factor for development of diabetes type II and cardiovascular disease.78

We will measure waist

circumference at the vertical middle between the lowest palpable rib and upper part of the ilium. We

will use a solid, nonexpendable, measuring tape, which we will apply with light pressure (but without

squeezing underlying tissues) horizontally around the waist. We will instruct subjects to stand with

their feet close together, arms next to their body, and their bodyweight equally distributed. We will

instruct subjects to take of thick clothing, and perform the actual measurement at the end of a

normal expiration.

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1.2. Power analyses

Power analyses were performed using G*Power 3.9.1.2 and package PowerSurvEpi in R.79-83 In the

crosssectional compararison between patients and controls, with 80% power, α=.05 and 5 predictors

in total we can detect small effects (effect size f² = 0.0994846). For the prospective analyses, Cox

proportional hazard regression with our number of subjects and expected outcome distribution, and

a correlated covariate of interest and a moderate effect size results in a power of >80% with α=.05.

Of note, in the initially registered trial protocotol we proposed to include 50 patients and 50 controls.

However, based on data from our previous studies that was analysed since then10 14 66 84-86 we

amended this aspect in our protocol to 60 patients and 40 controls. While yielding an identical total

number of subjects, this provides a more optimized balance between the contrast patients vs.

controls on the one hand, and the prospective analyses in the patients on the other. Analyses of our

previous studies show that there exist rather large effects in the differences between patients and

controls, and relatively smaller effects in the prospective associations. By changing the

patient:control ratio to 60:40, we lose only little power in the cross-sectional analyses (a little less

optimal distribution but equal total number), but gain additional prospective power. In addition,

because the estimates in the control population are expected to be more homogeneous than in the

patient population, also in the cross-sectional analyses the decrease in sample size of the control

population is expected to result in smaller loss of power than the gain in power resulting from the

equal increase of the patient sample size.

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85. Lok A, Assies J, Koeter MW, et al. Sustained medically unexplained physical symptoms in

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86. Lok A, Mocking RJ, Assies J, et al. The one-carbon-cycle and methylenetetrahydrofolate reductase

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morning

SeqNo FieldName

1 mor_asleep

2 mor_nrwakeup

3 mor_lieawake

4 mor_qualsleep

5 mor_lookforw

6 MOR_Phy_Exerc

7 MOR_Fun_Act

8 MOR_Friends

9 MOR_Work

10 MOR_Enjoy_Work

beep

SeqNo FieldName

1 mood_relaxed

2 mood_down

3 Mood_irritat

4 mood_satisfi

5 mood_lonely

6 mood_anxious

7 mood_enthus

8 pat_suspic

9 mood_cheerf

10 mood_guilty

11 mood_restl

12 mood_agitate

13 thou_worry

14 se_selflike

15 se_ashamed

16 se_selfdoub

17 pat_handle

18 soc_who1

19 soc_enjoy_alone

20 soc_prefcomp

21 soc_who2

22 soc_who3

23 soc_nrtot

24 soc_pleasant

25 soc_prefalon

26 soc_interact

27 phy_hungry

28 phy_tired

29 phy_pain

30 phy_dizzy

31 phy_drymouth

32 phy_nauseous

33 act_what1

34 act_what2

35 act_difficul

36 act_well

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37 act_else

38 phy_physact

39 eve_event

40 eve_unpleas

41 eve_important

42 eve_attrib

43 eve_content3

44 eve_specify2

45 eve_specify3

46 Event_Anticipation

47 Event_Like

48 Event_Cat

49 beep_disturb

50 REMOD_BE50

evening

SeqNo FieldName

1 evn_ordinary

2 evn_niceday

3 evn_inflmood

4 evn_pager

5 evn_work

6 PM_txt_thank

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Question (Dutch)

HOE LANG DUURDE HET VOORDAT IK GISTERENAVOND INSLIEP?

HOE VAAK WERD IK AFGELOPEN NACHT WAKKER?

HOE LANG LAG IK VANMORGEN WAKKER VOORDAT IK OPSTOND?

IK HEB GOED GESLAPEN

IK HEB ZIN IN DEZE DAG

IK HEB ZIN OM ME LICHAMELIJK IN TE SPANNEN

IK HEB ZIN IETS LEUKS TE GAAN DOEN VANDAAG

IK HEB ZIN OM MET VRIENDEN TE ZIJN VANDAAG

IK GA VANDAAG WERKEN/STUDEREN

IK HEB ZIN OM TE GAAN WERKEN/STUDEREN

Question (Dutch)

IK VOEL ME ONTSPANNEN

IK VOEL ME SOMBER

IK VOEL ME GEiRRITEERD

IK VOEL ME TEVREDEN

IK VOEL ME EENZAAM

IK VOEL ME ANGSTIG

IK VOEL ME ENTHOUSIAST

IK VOEL ME WANTROUWIG

IK VOEL ME OPGEWEKT

IK VOEL ME SCHULDIG

IK VOEL ME RUSTELOOS

IK VOEL ME PRIKKELBAAR

IK PIEKER

IK MAG MEZELF

IK SCHAAM ME VOOR MEZELF

IK TWIJFEL AAN MEZELF

IK KAN ALLES AAN

MET WIE BEN IK?

IK VIND HET AANGENAAM OM ALLEEN TE ZIJN

IK ZOU LIEVER IN GEZELSCHAP ZIJN

MET WIE BEN IK NOG MEER?

EN...?

MET HOEVEEL MENSEN BEN IK?

IK VIND DIT GEZELSCHAP AANGENAAM

IK ZOU LIEVER ALLEEN ZIJN

WE ZIJN SAMEN IETS AAN HET DOEN

IK HEB HONGER

IK BEN MOE

IK HEB PIJN

IK VOEL ME DUIZELIG

IK HEB EEN DROGE MOND

IK VOEL ME MISSELIJK

WAT DOE IK? (vlak voor de piep)? Dit past in de volgende categorie:

EN DAARNAAST ...?

DIT KOST MIJ MOEITE

DIT KAN IK GOED

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IK ZOU LIEVER WAT ANDERS DOEN

SINDS DE VORIGE PIEP HEB IK ME LICHAMELIJK INGESPANNEN

DENK AAN DE VOOR JOU BELANGRIJKSTE GEBEURTENIS SINDS DE VORIGE PIEP

DEZE GEBEURTENIS WAS:



DIT HAD VOORAL TE MAKEN MET:

DIT GEBEURT GEWOONLIJK:

DIT HAD BETREKKING TOT:

BEDENK WAT JE DE KOMENDE TWEE UUR GAAT DOEN

HOEVEEL ZIN HEB IK HIERIN?

DEZE ACTIVITEIT PAST IN DE VOLGENDE CATEGORIE:

DEZE PIEP STOORDE MIJ

BEDANKT

Question (Dutch)

DEZE DAG WAS EEN GEWONE DAG

IK VOND DIT EEN LEUKE DAG

HET INVULLEN VAN HET APPARAATJE HEEFT MIJN STEMMING BEINVLOED.

ZONDER HET APPARAATJE ZOU IK VANDAAG ANDERE DINGEN HEBBEN GEDAAN

IK BEN VANDAAG GAAN WERKEN/STUDEREN

BEDANKT

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Translation

How long did it take before I fell asleep last night?

How ofter did I wake up last night?

How long did I lay awake in bed before I got up this morning?

I slept well

I am looking forward to this day

I am looking forward to being physically active

I am looking forward to doing something nice today

I am looking forward to being with friends today

I am going to work/study today

I am looking forward to working/studying today

Translation

I feel relaxed

I feel down

I feel irritated

I feel satisfied

I feel lonely

I feel anxious

I feel enthousiastic

I feel suspicious

I feel cheerful

I feel guilty

I feel restless

I feel agitated

I am worrying

I like myself

I feel ashamed for myself

I doubt myself

I can handle anything

With whom am I?

I enjoy being alone

I would prefer to have company

With whom else am I?

And…?

With how many people am I?

I find this company pleasant

I would prefer to be alone

We are doing something together

I am hungy

I am tired

I am having pain

I feel dizzy

I have a dry mouth

I feel nauseous

What do I do (just before the beep)? This fits in the next catagory:

And what else…?

This is difficult to me

This I can do well

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I would prefer to do something else

Since the last beep I have been physically active

Think about the event that was most important to you since the last beep

This event was:

This event was:

This event was:

This event had to do with:

This usually happens:

This was related to:

Think about what you are going to do in the next two hours

How much are you looking forward to this event?

This event fits in the next category:

This beep disturbed me

Thank you

Translation

This day was an ordinary day

I found this a nice day

Responding to this pager influenced my mood

Without the pager I would have done other things today

I went to work/study today

Thank you

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Value Label

1=0-5min<r>2=5-15min<r>3=15-30min<r>4=30-45min<r>5=45m-1u<r>6=1-2uur<r>7=2-4uur<r>8=>4uur

1 - >5

1=0-5min<r>2=5-15min<r>3=15-30min<r>4=30-45min<r>5=45m-1u<r>6=1-2uur<r>7=2-4uur<r>8=>4uur<r>

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Value Label

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

10=partner<r>19=inwonenden<r>29=familie uitwonend<r>30=vrienden<r>40=collega's<r>49=kennissen<r>50=onbekenden/\randeren<r>00=niemand

Likert scale

Likert scale

10=partner<r>19=inwonenden<r>29=familie uitwonend<r>30=vrienden<r>40=collega's<r>49=kennissen<r>50=onbekenden/\randeren<r>00=niemand<r>

10=partner<r>19=inwonenden<r>29=familie uitwonend<r>30=vrienden<r>40=collega's<r>49=kennissen<r>50=onbekenden/\randeren<r>00=niemand

1 - >6

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

43=ontspanning\ractief<r>45=ontspanning\rpassief<r>00=niets/rusten<r>10=werk/studie<r>21=huishouden<r>51=praten<r>27=zelfverzorging<r>21=zorg voor anderen<r>26=medische zorg<r>60=eten/drinken<r>88=onderweg<r>89=anders

43=ontspanning\ractief<r>45=ontspanning\rpassief<r>00=niets<r>10=werk/studie<r>21=huishouden<r>51=praten<r>27=zelfverzorging<r>21=zorg voor anderen<r>26=medische zorg<r>60=eten/drinken<r>88=onderweg<r>89=anders

Likert scale

Likert scale

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Likert scale

Likert scale

1=>>>

-3=zeer onplezierig<r>+3=zeer plezierig<r>

-3=zeer onbelangrijk<r>+3=zeer belangrijk

1=iets wat me overkomen is<r>2=iets waar ik zelf invloed op had<r>3=iets regelmatigs of routine<r>4=een gedachte/gevoel<r>5=anders

1=contact met anderen<r>2=de omgeving waarin ik was<r>3=eigen gesteldheid<r>4=activiteit<r>5=nieuwe informatie<r>6=anders

1=vaker per dag<r>2=dagelijks<r>3=wekelijks<r>4=maandelijks

1=anderen<r>2=mezelf<r>3=concrete dingen<r>4=activiteit<r>5=iets abstracts<r>6=onbekend<r>7=anders

1=>>>

43=ontspanning\ractief<r>45=ontspanning\rpassief<r>00=niets/rusten<r>10=werk/studie<r>21=huishouden<r>51=praten<r>27=zelfverzorging<r>21=zorg voor anderen<r>26=medische zorg<r>60=eten/drinken<r>88=onderweg<r>89=anders

Likert scale

Value Label

Likert scale

Likert scale

Likert scale

Likert scale

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Translation value Label

1=0-5min<r>2=5-15min<r>3=15-30min<r>4=30-45min<r>5=45m-1hr<r>6=1-2hrs<r>7=2-4hrs<r>8=>4hrs

1 - >5

1=0-5min<r>2=5-15min<r>3=15-30min<r>4=30-45min<r>5=45m-1hr<r>6=1-2hrs<r>7=2-4hrs<r>8=>4hrs

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Value Label

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

10=partner<r>19=people I live with<r>29=family I don't live with<r>30=friends<r>40=colleagues<r>49=acquaintances<r>50=unknown people/\others<r>00=no one

Likert scale

Likert scale



1 - >6

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

Likert scale

43=active relaxation<r>45=passive relaxation<r>00=nothing/rest<r>10=work/study<r>21=housekeeping<r>51=speaking<r>27=self-care<r>21=care for others<r>26=medical care<r>60=eating/drinking<r>88=on the way<r>89=otherwise

43=active relaxation<r>45=passive relaxation<r>00=nothing<r>10=work/study<r>21=housekeeping<r>51=speaking<r>27=self-care<r>21=care for others<r>26=medical care<r>60=eating/drinking<r>88=on the way<r>89=otherwise

Likert scale

Likert scale

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Likert scale

Likert scale

1=>>>

-3=very unpleasant<r>+3=very pleasant<r>

-3=very unimportant<r>+3=very important<r>

1=something that happened to me<r>2=something I could influence<r>3=something regular or routine<r>4=a thought/feeling<r>5=otherwise

1=contact with others<r>2=the environment in which I was<r>3=my own state<r>4=activity<r>5=new information<F88r>6=otherwise

1=multiple times per day<r>2=daily<r>3=weekly<r>4=monthly

1=others<r>2=myself<r>3=concrete things<r>4=activity<r>5=something abstract<r>6=unknown<r>7=otherwise

1=>>>


Likert scale

Value Label

Likert scale

Likert scale

Likert scale

Likert scale

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1=something that happened to me<r>2=something I could influence<r>3=something regular or routine<r>4=a thought/feeling<r>5=otherwise

1=contact with others<r>2=the environment in which I was<r>3=my own state<r>4=activity<r>5=new information<F88r>6=otherwise


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Table S2: functional magnetic imaging scan task information according to the form containing the first items from Poldrack et al.’s checklist adapted by Guo et al.

Category Item No Item Description Reinforcement learning task

EXPERIMENTAL

DESIGN -design

specification

1a Describe number of

blocks, trials,

experimental units per

session or per subject

1b State length of each trial

and interval between trials

1c If ISIs are variable, report

the mean and range of

ISIs and how they are

distributed

1d Block-Designs : specify

the length of blocks

1e Event-related Designs :

state whether the design is

optimized for efficiency,

and if so, state how

1f Mixed designs : state

correlation between block

and event regressors

EXPERIMENTAL

DESIGN - task

specification

2a Instructions: state what

subjects are asked to do

See supplement

2b Stimuli: describe what the

Stimuli are and how many

there are

What: see supplement;

How many:

2c Stimuli: state whether

specific stimuli repeated

across trials

See supplement

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EXPERIMENTAL

DESIGN - planned

comparison

3 If the experiment has

multiple conditions, state

what the specific planned

comparisons are, or

whether an omnibus

ANOVA test is used

HUMAN SUBJECTS -

ethics approval

5 State which Institutional

Review Board (IRB)

approved the protocol

See main text

HUMAN SUBJECTS -

behavioral performance

6 State how behavioral

performance was

measured (e.g., response

time, accuracy)

Intensity-, wanting- and liking-

ratings for both tastes on a visual

analogue scale directly before

and after the task

DATA ACQUISITION -

image properties

7a Describe manufacturer,

field strength (in Tesla),

model name

See main text

7b State the number of

experimental sessions and

volumes acquired per

session

Number of dynamics=1125

7c State pulse sequence type

(gradient/spin echo,

EPI/spiral)

EPI

7d State field of view, matrix

size, slice thickness, inter-

slice skip

FOV=240×240×82.2mm; slice

thickness=3mm; act. slice

gap=0.3mm; acquired

matrix=80×80

7e State acquisition

orientation (axial, sagittal,

coronal, oblique; if axials

co-planar with AC-PC,

the volume coverage in

terms of Z in mm)

Slice orientation=transverse/axial

7f State clearly whether it is

on the whole brain. If not,

state area of acquisition

Angulated field of view from

lower edge pons and lower end

prefrontal cortex, 25 slices up to

usually the top of the dorsal

anterior cingulate cortex.

7g State order of acquisition

of slices (sequential or

interleaved)

Sequential, ascending

7h State TE, TR, flip angle TE=28ms; TR=1500ms; FA=70˚

Items 4 and >7 are not applicable because the present manuscript described the study protocol.

Abbreviations: ISIs, inter-stimulus intervals; ANOVA, analysis of variance; EPI, Echo Planar Imaging; FOV, field of view; TE, echo time; TR, repetition time; MRI, magnetic resonance imaging; MNI, Montreal Neurological Institute space; DCT, discrete cosine transform; CC, cubic centimeter; FWE, family-wise error; FDR, false discovery rate; FWHM, full-width at half-maximum; RESEL, resolution element; ROI, region of interest; FIR, finite impulse response

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Cued Emotional Conflict task Emotion regulation task

6 blocks of 24 trials (max.

durations=248s)

24 'semi'-blocks of three similar

trials: 3 attend sad, 3 attend fear,

3 attend happy, 3 attend neutral, 3

regulate sad, 3 regulate fear, 3

regulate happy, 3 regulate neutral

blocks

Each trial started with a cue

presented for 500 ms. After the

presentation of the cue, a fixed

interval of 2000ms separated the

presentation of the cue from the

target.

Each picture was presented for 10

s, followed by an 'ISl' of max. 6

seconds (during which subjects

rated their feelings; this interval

ended as soon as the subject had

finished the rating, i.e. was self-

paced). After every third picture,

subjects also rated how well they

managed to perform during the

previous block (also max. 6

seconds, self-paced), followed by

a 4-seconds inter-block interval

during which a fixation cross was

presented.

The inter-trial interval was jittered

between 3500 and 5500 ms (in

500 ms steps).

See description above.

NA Each 'semi'-block had a duration

of min. 30 s and max. 54 s.

Jittered inter-trial interval

NA

See supplement See supplement


How many: 12 for each cue per

block

See supplement

See supplement See supplement

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Full factorial ANOVA, and

opposite-sad vs. actual-sad,

opposite-happy

vs. actual-happy, and opposite-

sad vs. opposite-happy contrasts.

Full factorial ANOVA

See main text See main text

Response time and accuracy for

face label assignments.

See supplement

See main text See main text

Six sessions with max. number of

dynamics=120

Two sessions with max. number

of dynamics=407

EPI EPI

FOV=240×240×121.8mm; slice


gap=0.3mm; acquired

matrix=80×80

FOV=240×240×121.8mm; slice


gap=0.3mm; acquired

matrix=80×80

Slice orientation=transverse/axial Slice orientation=transverse/axial

Whole brain, 37 slices. Whole brain, 37 slices.

Sequential, ascending Sequential, ascending

TE=28ms; TR=2000ms; FA=76˚ TE=28ms; TR=2000ms; FA=76˚


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STROBE 2007 (v4) checklist of items to be included in reports of observational studies in epidemiology*

Checklist for cohort, case-control, and cross-sectional studies (combined)

Section/Topic Item # Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract 1

(b) Provide in the abstract an informative and balanced summary of what was done and what was found 2

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 4-11

Objectives 3 State specific objectives, including any pre-specified hypotheses 9-11

Methods

Study design 4 Present key elements of study design early in the paper 12

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection 12-19

Participants 6 (a) Cohort study—Give the eligibility criteria, and the sources and methods of selection of participants. Describe

methods of follow-up

Case-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control

selection. Give the rationale for the choice of cases and controls

Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants

12-19

(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposed

Case-control study—For matched studies, give matching criteria and the number of controls per case 12-19

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic

criteria, if applicable 12-21

Data sources/ measurement 8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group 12-21 & supplement

Bias 9 Describe any efforts to address potential sources of bias 12-21

Study size 10 Explain how the study size was arrived at 19-20 & supplement

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen

and why 19-21

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 19-21

(b) Describe any methods used to examine subgroups and interactions 19-21

(c) Explain how missing data were addressed 19-21

(d) Cohort study—If applicable, explain how loss to follow-up was addressed

Case-control study—If applicable, explain how matching of cases and controls was addressed 19-21

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Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy

(e) Describe any sensitivity analyses 19-21

Discussion

Key results 18 Summarise key results with reference to study objectives NA => protocol article

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction

and magnitude of any potential bias 24-26

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results

from similar studies, and other relevant evidence NA => protocol article

Generalisability 21 Discuss the generalisability (external validity) of the study results NA => protocol article

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based 28

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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Vulnerability for New Episodes in Recurrent Major Depressive Disorder: Protocol for the Longitudinal DELTA-

Neuroimaging Cohort Study

Journal: BMJ Open

Manuscript ID bmjopen-2015-009510.R1

Article Type: Protocol

Date Submitted by the Author: 20-Nov-2015

Complete List of Authors: Mocking, Roel; Academic Medical Center, University of Amsterdam, Department of Psychiatry Figueroa, Caroline; Academic Medical Center, University of Amsterdam, Department of Psychiatry Rive, Maria; Academic Medical Center, University of Amsterdam, Department of Psychiatry Geugies, Hanneke; University of Groningen, University Medical Center Groningen, Neuroimaging Center Servaas, Michelle; University of Groningen, University Medical Center Groningen, Neuroimaging Center Assies, Johanna; Academic Medical Center, University of Amsterdam, Department of Psychiatry Koeter, Maarten; Academic Medical Center, University of Amsterdam, Department of Psychiatry Vaz, Frédéric; Academic Medical Center, University of Amsterdam, Laboratory Genetic Metabolic Disease Wichers, Marieke; University Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE) van Straalen, Jan; Academic Medical Center, University of Amsterdam, Laboratory of General Clinical Chemistry de Raedt, Rudi; Ghent University, Department of Experimental Clinical and Health Psychology Bockting, Claudi; Utrecht University, Department of Clinical Psychology Harmer, Catherine; University of Oxford, Warneford Hospital, Department of Psychiatry Schene, Aart; Radboud University Medical Center, Department of Psychiatry Ruhé, Henricus; University of Groningen, University Medical Center Groningen, Program for Mood and Anxiety Disorders, Department of Psychiatry

<b>Primary Subject Heading</b>:

Mental health

Secondary Subject Heading: Nutrition and metabolism, Radiology and imaging, Epidemiology

Keywords: Adult psychiatry < PSYCHIATRY, Depression & mood disorders < PSYCHIATRY, Magnetic resonance imaging < RADIOLOGY & IMAGING, Neuroradiology < RADIOLOGY & IMAGING, STATISTICS & RESEARCH


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METHODS, PREVENTIVE MEDICINE

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Vulnerability for New Episodes in Recurrent Major Depressive

Disorder: Protocol for the Longitudinal DELTA-Neuroimaging

Cohort Study

Roel J.T. Mocking1,#

, Caroline A. Figueroa1, Maria M. Rive

1, Hanneke Geugies

2,3, Michelle N Servaas

2,3,

Johanna Assies1, Maarten W.J. Koeter

1, Frédéric M. Vaz

4, Marieke Wichers

5, Jan P. van Straalen

6, Rudi

de Raedt7, Claudi L.H. Bockting

8,9, Catherine J. Harmer

10, Aart H. Schene

1,11,12, Henricus G. Ruhé

1,2,3,5,#

1 Department of Psychiatry, Academic Medical Center, University of Amsterdam, the Netherlands

2 University of Groningen, Neuroimaging Center, University Medical Center Groningen, the Netherlands

3 University of Groningen, Program for Mood and Anxiety Disorders, Department of Psychiatry, University Medical Center

Groningen, the Netherlands

4 Laboratory Genetic Metabolic Disease, Academic Medical Center, University of Amsterdam, the Netherlands

5 University of Groningen, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), University Medical

Center Groningen, the Netherlands

6 Laboratory of General Clinical Chemistry, Academic Medical Center, University of Amsterdam, the Netherlands

7 Department of Experimental Clinical and Health Psychology, Ghent University, Belgium

8 Department of Clinical Psychology, University of Groningen, Groningen, the Netherlands

9 Department of Clinical and Health Psychology, Utrecht University, Utrecht, The Netherlands

10 Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom

11 Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands

12 Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands

Title character count: 117/xx

Abstract word count: 298/300

Article word count: 7961/recommended 4000 (flexible)

Reference count: 202/xx

Number of Figures: 2

Number of Tables: 0

Total number of Figures and Tables: 2/5

Number and type of Supplementary Materials: 2 tables and text

Running title (46 letters and spaces/xx): Recurrence in MDD: a Neuroimaging Cohort Study

# Corresponding authors:

R.J.T. Mocking, MSc, Department of Psychiatry, Academic Medical Center, Meibergdreef 5, Amsterdam 1105

AZ, The Netherlands, T +31208913695, [email protected].

H.G. Ruhé, MD, PhD, Room 5.16, Mood and Anxiety Disorders, University Center for Psychiatry, University

Medical Center, Hanzeplein 1, Groningen, 9700 RD, The Netherlands, T +31503612367, Fax.: +31503611699,

[email protected].

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ABSTRACT

Introduction

Major depressive disorder (MDD) is widely prevalent and severely disabling, mainly due to its

recurrent nature. A better understanding of the mechanisms underlying MDD-recurrence may help

to identify high-risk patients and improve the preventive treatment they need. MDD-recurrence has

been considered from various levels of perspective including symptomatology, affective

neuropsychology, brain circuitry, and endocrinology/metabolism. However, MDD-recurrence

understanding is limited, because these perspectives have been studied mainly in isolation, cross-

sectionally in depressed patients. Therefore, we aim at improving MDD-recurrence understanding by

studying these four selected perspectives in combination and prospectively during remission.

Methods and analysis

In a cohort design, we will include 60 remitted, unipolar, unmedicated, recurrent MDD-subjects (35-

65yrs) with ≥2 MDD-episodes. At baseline, we will compare the MDD-subjects with 40 matched

controls. Subsequently, we will follow-up the MDD-subjects for 2.5yrs while monitoring recurrences.

We will invite subjects with a recurrence to repeat baseline measurements, together with matched

remitted MDD-subjects. Measurements include questionnaires, sad mood-induction, lifestyle/diet, 3-

Tesla structural (T1-weighted and diffusion tensor imaging) and blood-oxygen-level-dependent

functional magnetic resonance imaging (fMRI) and MR-spectroscopy. fMRI focusses on resting state,

reward/aversive-related learning, and emotion regulation. With affective neuropsychological tasks

we will test emotional processing. Moreover, we will assess endocrinology (salivary hypothalamic-

pituitary-adrenal-axis cortisol and dehydroepiandrosterone-sulfate) and metabolism (metabolomics

including polyunsaturated fatty acids), and store blood for e.g. inflammation analyses, genomics,

proteomics. Finally, we will perform repeated momentary daily assessments using experience

sampling methods at baseline. We will integrate measures to test: (I) differences between MDD-

subjects and controls; (II) associations of baseline measures with retro/prospective recurrence-rates;

and (III) repeated measures changes during follow-up recurrence. This dataset will allow us to study

different predictors of recurrence in combination.

Ethics and dissemination

The local ethics committee approved this study (AMC-METC-Nr.:11/050). We will submit results for

publication in peer-reviewed journals and presentation at (inter)national scientific meetings.

Registration details

This study has been registered at the Dutch Trial Registry (NTR3768).

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Keywords

Major Depressive Disorder; Recurrence; Prevention; Neurobiology; Neuropsychology; Emotions;

Reward; Multimodal Imaging; Diffusion Tensor Imaging; Magnetic Resonance Imaging; Diffusion

Magnetic Resonance Imaging; Limbic System; Prefrontal Cortex; Amygdala; Hippocampus;

Hypothalamus; Default mode network; Gyrus Cinguli; Dopamine; gamma-Aminobutyric Acid;

Glutamic Acid; Fatty Acids; Fatty Acids, Omega-3; Cortisol; Oxidative stress; Lipid Peroxidation; Brain-

Derived Neurotrophic Factor; Genetics; Epigenomics; Inflammation; Longitudinal Studies; Prospective

Studies; Observational Study as Topic; Survival Analysis

Bullet point summary of main strengths and limitations of this study

Strengths

• Strict and specific inclusion-criteria, matching- and recruitment-procedure, leading to

maximal contrast for MDD-vulnerability, without distortion due to important confounders:

MDD-residual symptoms and medication.

• Unique integration of a wide range of measures in a prospective repeated measures design

will allow disentangling of recurrent MDD state- and trait-factors.

Limitations

• The extensive assessment procedure needed to measure all variables of interest and

confounders will potentially lead to inclusion of subjects that are intrinsically aware of the

necessity to perform clinical research and readily willing to cooperate.

• Only including subjects that currently do not use psychotropic drugs may lead to selection of

particular patient subgroups that e.g. previously experienced little benefit or adverse effects

from medication.

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INTRODUCTION

1.1. Rationale

Major depressive disorder (MDD) is a widespread and disabling mental disorder, with estimated

worldwide prevalences of 4.3% annually and 11.1-14.6% during lifetime.1-4

Currently, MDD has the

highest burden of any disorder in high-income countries, and is expected to have the second-highest

burden worldwide in 2030.5 MDD´s (in)direct annual excess costs constitute approximately 1% of the

gross domestic product in these countries.6-8

Next to suicide and cardiovascular comorbidity,9 an

important reason for MDD’s burden is its recurrent course,2 as already indicated by Kraepelin

10 and

formulated by Angst et al.: “Single episodes are extremely rare if the period of observation is

significantly extended”.11

The incidence of recurrencesi varies depending on study-characteristics.

12-15 While recurrent MDD

has been considered as a distinct disease entity (more familiar to bipolar disorder), population

studies show that recurrence is widespread in MDD with ≥40-75% lifetime recurrence in patients

recovered from a first depressive episode,16-19

with even higher rates in clinical samples.20 21

Our 10yr

follow-up study of a specific cohort of recurrent MDD-patients showed an overall 90.3% recurrence-

rate,22

with patients being in a depressed state during 13% of the follow-up time.

During lifetime, MDD-patients are estimated to experience on average about five MDD-episodes.1 21

Therefore, high recurrence rates pose a major health problem. However, depressive episodes seem

to cluster in subpopulations. This also suggests that the most MDD-episodes occur in a relatively

limited number of patients. Consequently, if we could lower recurrence rates in these recurring

cases, we may greatly reduce the overall number of MDD-episodes and thereby MDD’s burden.23

If

we could a-priori identify these patients at high risk for recurrence, this would provide excellent

opportunities for specific, indicated, (secondary) prevention.

For recurrence prevention, antidepressants are most often used,12 24

but unwillingness to take

antidepressants, non-adherence and discontinuation due to adverse effects limit their applicability.25-

27 As an alternative, preventive cognitive psychotherapies have been developed (e.g. mindfulness

based cognitive therapy, preventive cognitive therapy and wellbeing cognitive therapy),28-36

which

i The terms relapse and recurrence are used in the literature and defined as new MDD-episodes within or after

6mths recovery, respectively. However, empirically there is no clear evidence for this distinction. We hereafter

will name both recurrence for clarity.

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seem to produce long-lasting beneficial effects.22 37

Nevertheless, recurrence-rates stay substantial,

urgently calling for further improvements of recurrence preventing therapy.

In that respect, if we better understand the mechanisms underlying vulnerability for recurrence in

MDD, we could (I) use their indicators as (bio)markers to monitor/predict recurrence risk, and/or (II)

use these mechanisms to identify/develop novel targets for improved and personalized preventive

therapy in a precision medicine setting. This early identification and stratified treatment of

recurrence risk38

could potentially reduce recurrent MDD’s disease burden.

However, understanding of mechanisms underlying MDD-recurrence is limited to date. Although

remitted MDD-patients have already been studied for a number of years,21

most studies investigate

MDD during the acute phase. Yet, to be able to differentiate between trait factors (that remain

present during remission and possibly constitute vulnerability for recurrence) versus state factors

(which are only present during an MDD-episode), it is necessary to study patients during remission.

In addition, the actual predictive associations of these possible trait factors with recurrence have to

be tested in long-term prospective follow-ups.

Thus far, the limited research that applied such a prospective approach in remitted MDD-subjects

investigated several factors as predictive of recurrence. While associated with MDD onset,

demographics (e.g. gender) generally do not predict recurrence; clinical and social factors seem to be

more predictive. Regarding clinical factors, the number of previous episodes is amongst the strongest

predictors,39

together with residual depressive symptoms.19

In addition, MDD family history,

comorbid axis I disorders, age of onset and last episode duration and severity have been suggested

as predictors.15 19 40-46

Furthermore, personality characteristics (coping style and personality traits)

and social factors (experiencing daily hassles) have been found to be predictive although findings

remain largely inconsistent. In addition, in our previous study 71% variance in time to 5.5yr

recurrence remained unexplained,47 48

and only few actual predictive factors were potentially

modifiable.

As indicated, the pathophysiology behind these factors’ predictive properties for recurrence remains

far from understood. For example, residual symptoms predict recurrence within a short-term interval

but seem less predictive in the long term.49

This indicates that residual symptoms may not constitute

a vulnerability trait, but rather reflect the early initiation of a new episode or an earlier episode not

yet in full remission. In addition, the predictive effect of previous episodes can be explained due to

scarring (increasing vulnerability directly resulting from experiencing previous episodes) or high

premorbid vulnerability (pre-existing abnormalities leading to both previous episodes and new

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recurrences).50-52

From the prediction perspective, these pathogenetic differences might seem a

merely academic question. However, identifying the mechanisms underlying MDD-recurrence is

essential to discover better potential targets for innovative preventive interventions to increase

resilience.

1.2. Study aims & outline

Based on the above, the present study aims at advancing the knowledge on (I) factors that are

associated with recurrent MDD-vulnerability, (II) how these factors are related with each other, (III)

their predictive association with prospective recurrence, and (IV) their change during recurrence.

In order to do so, we will initially compare fully remitted unmedicated recurrent MDD-subjects to

matched healthy controls. Subsequently, we will monitor recurrence(s) in the MDD-subjects during a

2.5yr follow-up and repeat measurements when an MDD-subject experiences a recurrence during

follow-up. Below, we will first outline our theoretical framework to provide background for our

hypotheses regarding the specifically selected factors that we will investigate.

1.3. Theoretical framework

Based on preliminary findings, theoretical literature, and observations from adjacent fields, several

theories have been developed to explain recurrence pathogenesis. Here, using a stratified approach,

we aim to introduce and integrate theories from four distinct selected levels of perspective:53 54

symptomatology, affective neuropsychology, brain circuits, and endocrinology/metabolism (Figure

1).

1.3.1. Symptom level

A disturbed balance between negative and positive valence systems seems to lie at the heart of MDD

symptomatology.54

Regarding negative valence systems, MDD-patients suffer from e.g. negative

affect, rumination and dysfunctional cognitions. While negative cognition and processing styles as

rumination usually resolve after remission, they may remain present in latent form, and can be

reactivated during (mild) dysphoria, which is conceptualized as ‘cognitive reactivity’.50 55 56

Interestingly, latent dysfunctional attitudes, increased cognitive reactivity and rumination have all

been found to predict recurrence in remitted MDD-subjects.57 58

Relating to negative but also positive

valence systems, anhedonia (inability to experience pleasure) is one of MDD’s core symptoms. Apart

from the ability to experience joy, the rewarding effect of pleasure can also have a motivational

function: pleasurable events appear to reinforce behaviour leading to these events (conditioning).

This implies that experiencing pleasure is a necessary stimulation to learn associations between

stimuli and (pleasurable) outcomes and move an individual to perform certain behaviours. MDD-

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patients have difficulties in experiencing the rewarding effects of positive/pleasurable events when

depressed, particularly relative to aversive stimuli, and indeed have difficulties learning new

beneficial behaviours. This can also be observed in the form of psychomotor retardation and

decreased positive affect. However, anhedonia remains relatively under-investigated during

remission, and it remains largely unknown to what extent anhedonia can predict recurrence (see for

reviews59-63

).

1.3.2. Affective neuropsychological level

This disturbed balance between negative and positive valence systems at the symptom level may

relate to negative biases in emotional processing at the affective (‘hot’) neuropsychological level.

Negative biases manifest themselves when (dis)engaging (i.e. attentional bias), memorizing, error-

monitoring, shifting attention between, or regulating emotional information.64-74

Negative biases are

thought to result from increased negative attention on the self, and are thus related with negative

self-referential processing styles as rumination and cognitive reactivity, which show a reciprocally

reinforcing relationship with negative affect.75

76

Increasing evidence shows that negative self-

referential processing and associated brain alterations contribute greatly to the course and

development of MDD.75

With respect to reward processing, negative biases manifest in decreased

reward sensitivity (negative valence) and increased aversive stimulus sensitivity (positive valence).54

However, the precise relations between these concepts, and to what extent these negative

emotional processing biases with associated brain alterations remain present during remission, and

can predict recurrence, remains largely unknown.77

78-80

1.3.3. Brain circuit level

From a neurobiological brain circuit perspective, disturbed emotional processing at the affective

neuropsychological level may be observed as an imbalance between emotional (limbic/ventral) and

regulating (cognitive/dorsal) regions.81-86

Specifically, emotional brain regions seem hyperactive in

response to negative stimuli but hypoactive to positive.87

In addition, regulating regions are generally

hypoactive but may show compensatory hyperactivity under certain circumstances, e.g. more

automatic emotion regulation.88

This may be explained by altered functional and structural

connectivity between these regions.89

Furthermore, disturbed functioning of the default-mode

network, a network that is involved in self-referential processing and is negatively correlated to

regions that process attention and cognitive control, has consistently been observed in MDD. 90-94

Aberrations in the default-mode network (i.e. failure to de-activate DMN regions)95

during tasks as

well as DMN hyperconnectivity96

during rest have been observed in MDD. DMN aberrations have

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been associated with emotional-cognitive disturbances and increased negative self-focus, such as

rumination.58 75 94 97-106

Especially anhedonic MDD-patients have a decreased ability to change behavior in relation to

rewards, which appears to persist after remission.59 107

This reduced reward responsiveness might be

related to blunted phasic dopaminergic signaling. Indeed, reinforcement learning appeared impaired

in depressed MDD-patients versus controls, with blunted reward signals in the ventral striatum, and

increased compensatory ventral tegmental area activations when thirsty patients were learning

associations between stimuli and water delivery.108

Furthermore, MDD-patients show reduced

reward anticipation and are less prone to exert effort for a potential reward.59

These abnormalities

also appear present in subjects prone to develop MDD.109

Also, recognition of reward-related stimuli

appeared most difficult and associated with most impaired brain activities in the N. accumbens,

anterior cingulate cortex (ACC) and ventromedial prefrontal cortex (vmPFC) in patients with chronic

recurrent MDD.110

Thus, dopaminergic reward-related brain circuits seem to be of importance in

recurrence of MDD. However it remains unclear whether such abnormalities in reward related

learning are also associated with recurrence.

Despite increasing research efforts to delineate these brain circuits, it is hardly investigated how the

default-mode network and its relations to other cognitive networks and emotion-processing and

reward circuits function in remitted recurrent MDD-subjects.111-114

115 116

In addition, it has been

examined scarcely how alterations in these circuits can predict recurrence in remitted MDD-

subjects.117

118

1.3.4. Endocrinology and metabolism

These disturbed brain circuits may be associated with alterations in endocrinology and metabolism.

From an endocrinological viewpoint, the principal stress system -the hypothalamic-pituitary-adrenal

(HPA)-axis- has been studied extensively in MDD.119

In combination with e.g. findings in first degree

relatives, our own research indicates that HPA-axis hyperactivity is an endophenotypic trait, with

higher diurnal cortisol and altered dehydroepiandrosterone-sulfate (DHEAS) that remain during

remission,21 120 121

and potentially predict recurrence.122-125

Interestingly, HPA-axis activity can be

linked with brain circuit alterations. For example, the effects of stress on limbic network structure in

MDD could reflect chronic HPA-axis hyperactivation-induced allostatic load (e.g. reducing

hippocampal volumes), predisposing to MDD(-recurrences). 126 127

Vice versa, the HPA-axis is

controlled by the limbic system,128

through medial prefrontal connections with amygdala and

hypothalamus.129

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Moreover, interestingly, we previously showed a bidirectional relationship between fatty acid

metabolism and HPA-axis activity.130 131

Fatty acids are main constituents of (nerve) cell membranes

and myelin, and so influence important (neuro)physiological mechanisms such as exocytosis,

membrane-anchored protein function, membrane fluidity, second messenger system activity and

white matter integrity.132 133

Furthermore, they are precursors of eicosanoids and are associated with

brain derived neurotrophic factor (BDNF), which regulate inflammatory homeostasis and nervous

system architecture, respectively.9 133-136

We previously showed that besides alterations in omega-3

fatty acids, MDD is additionally associated with more general alterations in overall fatty acid

metabolism, also in recurrent MDD.137-140

However, inconsistencies remain, and recurrent MDD has

only been sparsely investigated. Moreover, given the widespread involvement of fatty acid

metabolism in brain physiology, associations between fatty acid metabolism and brain circuit

alterations can be expected,9 136 141

but remained largely uninvestigated thus far.

Furthermore, glutamate/glutamine and γ-aminobutyric acid (GABA) neurometabolism is currently

considered an interesting additional system in MDD and its recurrence too. Glutamate and GABA are

the major excitatory and inhibitory neurotransmitter, respectively, and have been implicated in

MDD-pathophysiology.142 143

For instance in depressed MDD-patients, excess excitotoxic synaptic

glutamate have been suggested to cause less pregenual ACC deactivation when viewing negative

emotional pictures.144 145

Nevertheless, previous investigations of glutamate/GABA in depressed

MDD-patients remain contradictory,146 147

and while abnormalities might normalize after

remission,148

this is only sparsely investigated,147 149

especially not in recurrent MDD.

1.3.5. Summary of theoretical framework

MDD can be characterized by multiple alterations across systems that remained distinct thus far, but

potentially can be integrated. At the symptom level, MDD-patients show a disturbed balance

between negative and positive valence systems with increased latent negative affect, rumination,

dysfunctional cognitions and cognitive reactivity, together with anhedonia. This may be associated

with negative emotional biases at the affective neuropsychological level. These negative emotional

biases may relate to an imbalance between emotional and regulatory brain circuits, default mode

network hyperconnectivity/activity and might also be associated with a disturbed brain reward

circuit. These brain circuit alterations seem closely connected with HPA-axis alterations, that seem

bidirectionally related with fatty acid and glutamate/GABA-metabolism (Figure 1). However, even if

previous research studied remitted MDD-subjects, these alterations were mostly investigated in

isolation and only cross-sectionally. Consequently, it remains largely unknown to what extent these

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alterations (I) persist during remission, (II) are associated with each other, (III) are predictive for

recurrence and (IV) change during recurrence.

1.4. Hypotheses

With the aim of the current ‘DELTA-neuroimaging’ study to integrate these factors and test their

association with recurrence in a prospective cohort-study of stably remitted unmedicated recurrent

MDD-subjects, we first will compare MDD-subjects with carefully matched controls at baseline, and

subsequently we will follow-up the MDD-subjects for 2.5yrs while monitoring recurrences. Moreover,

we will invite recurring subjects to repeat baseline measurements, together with matched remitted

subjects. Following this line of research we will investigate the following specific hypotheses:

1. Compared to matched never-depressed controls, remitted unmedicated recurrent MDD-subjects

will show (i.e. a trait effect):

a. At the symptom level, a disturbed balance between negative and positive valence

systems with increased rumination, dysfunctional cognitions, cognitive reactivity and

anhedonia.

b. At the affective neuropsychological level, increased negative biases in emotional

processing when (dis)engaging (attentional bias), memorizing, shifting attention

between, and regulating emotionally valenced stimuli.

c. At the brain circuit level, altered grey/white matter structure and function/connectivity

of emotional/regulating regions, reward brain circuits and the default-mode network,

also relative to other networks of the brain, with specifically:

i. More ventral and less dorsal region activation when viewing emotional pictures.

ii. Less connectivity between ventral and dorsal regions.

iii. More activation of dorsal regions during a reappraisal emotion regulation task.

iv. Blunted ventral striatum and increased ventral tegmental area reward-signals.

v. Hyperconnectivity within and dominance of the default-mode network at rest,

which becomes more pronounced after sad mood-induction

d. At the endocrinology and metabolism level, altered HPA-axis activity, fatty acid

metabolism and emotional network GABA/glutamate, with:

i. Higher morning and evening HPA-axis cortisol and relatively lower DHEAS, which

becomes more pronounced after sad mood-induction.

ii. Lower degree of fatty acid unsaturation, chain length, peroxidizability, and ω-

3/ω-6-ratio.

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iii. More glutamate and less glutamine/GABA signals in the basal ganglia and pgACC,

which becomes more pronounced during sad mood-induction.

2. In remitted unmedicated recurrent MDD-subjects the above systems will be related with clinical

characteristics (number of previous episodes, residual symptoms and age of onset) and each

other, and these latter mutual relationships will differ from those in matched never-depressed

controls.

3. In remitted unmedicated recurrent MDD-subjects, above alterations will predict prospective

2.5yr follow-up symptom course, specifically:

a. Time until recurrence

b. Cumulative number and severity of MDD-episodes

c. Course of depressive (residual) symptoms

4. The above alterations will become more pronounced during repeated measures in recurrent

MDD-subjects experiencing a recurrence during follow-up, in comparison to repeated measures

in matched remitted recurrent MDD-subjects (i.e. a state effect).

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2. METHODS

2.1. Design

The present study consists of two stages (Figure 2). First, using a cross-sectional patient-control

design, we will compare remitted recurrent MDD-patients with matched never-depressed controls,

to identify traits that remain present during remission and that are associated with recurrent MDD-

vulnerability. Second, using a prospective cohort-design, we will follow-up the patients. During

follow-up, we will measure depression symptoms every four months, to see whether we can predict

clinical course from baseline measures. Moreover, when we detect a follow-up recurrence, we will

invite the respective patient to repeat several baseline measures. In addition, we will invite remitted

patients (matched on duration of follow-up, gender, age, educational level and working class) to

repeat the measures as well. While this repeated measures design is not required to predict

recurrence, it is of interest as it allows us to identify depression state vs. trait-effects.

In sum, we will first test for trait factors associated with MDD-vulnerability by contrasting vulnerable

(remitted recurrent MDD) vs. resilient (never-depressed controls) subjects. Subsequently, also in

order to further delineate whether these identified factors are causal, consequences or confounders,

we will test their predictive effect of prospective recurrence during follow-up in the remitted

recurrent MDD group. Finally, we aim at disentangling state and trait effects by repeating measures

in patients during recurrence vs. matched patients who are in current remission. Below we will

describe the population, measures, procedure, and analyses in detail in that order, additional

information can be found in the supplementary material (supplementary text, supplementary table

1, and supplementary table 2).

2.2. Population

2.2.1. Inclusion criteria

In order to maximize contrast for recurrent MDD-vulnerability, without confounding effects of

medication or current MDD-symptoms, we will include recurrent MDD-subjects [≥2 previous MDD-

episodes as assessed using the structured clinical interview for DSM-IV diagnoses (SCID)150

] that are

currently in stable remission [≥8weeks with a 17-item Hamilton Depression Rating Scale (HDRS)≤7

and not fulfilling the criteria for a current MDD episode (as assessed using the SCID during

inclusion)].151

Specifically, we will include subjects aged 35-65yrs, to include a homogeneous age

group, and preclude conversion to bipolar disorder due to later experience of (hypo)manic episodes.

Of note, despite overall high recurrent MDD vulnerability and homogeneity regarding e.g. age, we

expect this group of MDD patients to exhibit considerable variance in prospective recurrence rates.

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For example, in our previous research22 30 47 152

the range in previous MDD-episodes was from 2 to 60,

and we will now include patients with none or only a single episode in the last 10yrs. We expect that

this will lead to a relapse rate of ±50% during the 2.5yrs follow-up, providing excellent within-group

contrasts for prospective recurrence in this overall highly vulnerable group. Second, we will include

relatively resilient controls without personal (SCID) or first degree familial psychiatric history,

carefully matched for age, sex, educational level, working class, and ethnicity.

2.2.2. Exclusion criteria

While comorbidity in general will not be an exclusion criterion because it may be an important

predictor, in order to obtain a homogeneous sample we will exclude subjects with current diagnoses

of alcohol/drug dependence, psychotic or bipolar, predominant anxiety, or severe personality

disorder (all SCID); standard MRI exclusion criteria (e.g. metal objects in the body, claustrophobia);

electroconvulsive therapy within two months before scanning; history of severe head trauma or

neurological disease; severe general physical illness; no Dutch/English proficiency. To minimize

inclusion bias, we will try to familiarize mildly claustrophobic subjects in a mock MRI-scanner to

enable actual MRI-assessments. If this does not succeed, we will only perform non-MRI assessments.

All subjects have to be without psychoactive drugs/medication for >4weeks before assessments. We

will allow incidental benzodiazepine use, but this must be stopped after informed consent and

≥2days before assessments. Despite possible effects of psychotherapy we will not exclude current or

past psychotherapy due to feasibility reasons. However, we will assess all forms of therapy used,

report these and treat them as covariates in our analyses.

2.2.3. Recruitment

To minimize selection biases, we will recruit both groups through identical advertisements in freely

available online and house-to-house papers, posters in public spaces and from previous studies in

our and affiliated research centres. One previous study from which we will recruit subjects is the

Depression Evaluation Longitudinal Therapy Assessment (DELTA)-study.30

We recently completed the

10yr follow-up of this randomized controlled trial assessing the protective effects of 8-weeks

preventive cognitive therapy on recurrence in recurrent MDD.22

In this long-term study we obtained

detailed psychological but also biological measures, which can be linked to data obtained in the

present study in the same subjects. Of note, the original DELTA sample was recruited like the

procedure for new participants for the present recruitment, amongst others through newspaper

advertisements. By DELTA-study design, 50% of the original DELTA sample received randomized

preventive cognitive therapy 10yrs ago, however as (I) previous psychotherapy was not an exclusion

criterion in the present total sample and (II) the preventive cognitive therapy intervention was more

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widely implemented in the Netherlands since the DELTA-study, non-DELTA participants could also

have undergone this treatment. This allows the additional interesting option to collect data on

previous treatments in all subjects in order to estimate the magnitude of this possible treatment

effect. Finally, we will recruit additional recurrent MDD-subjects from patients previously treated by

the AMC or affiliated general practitioners and psychologists.

2.3. Measures

See supplementary material (supplementary text, supplementary table 1, and supplementary table

2) for full details.

2.3.1. Structured interview and questionnaires

The SCID is widely accepted as structured diagnostic interview to adequately assess DSM-IV defined

psychiatric disorders.150 153

Questionnaire-booklets I-IV (see supplementary text) include

questionnaires on depressive symptoms (e.g. HDRS), stress and life events (trauma, daily hassles),

personality (neuroticism, coping), and lifestyle (physical activity, sleep, diet).

2.3.2. Mood induction

We will prepare a negative and neutral mood-induction procedure by asking subjects to recall and

describe a personal sad and neutral memory,56

from which we will make sad and neutral

personalized scripts. In addition, we will request subjects to listen to and rate five different

fragments of sad/neutral music on a dedicated website (accessible on request). This type of

provocation (combining sad music with autobiographical recall) has been shown to effectively induce

transient dysphoric mood states.56

We used this mood induction to test (I) mood-induced changes in

dysfunctional attitudes (cognitive reactivity), (II) HPA-axis activity, and (III) brain networks.

2.3.3. Affective neuropsychological tests

The affective neuropsychological tests all assess emotional processing. The exogenous cueing task

allows disentangling of attentional engagement and disengagement components in attentional

bias.66 154

The facial expression recognition task measures interpretation of key emotionally valenced

social signals of varying intensity (morphed faces). 69 71 155

The emotional categorization task assesses

response speed to self-referent positive and negative personality descriptors, the emotional memory

task follows up on this task by assessing surprise (free) recollection memory of these personality

descriptors.69 71 155

The internal shift task examines capacity to shift attention between working

memory contents in response to emotional and non-emotional material.156 157

For matching

purposes, we will estimate premorbid intelligence with the Dutch adult reading test.158

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Momentary assessment techniques are ideal for prospective examination of dynamics of observed

behaviour, and enable to capture the film rather than a snapshot of daily life.159-162

ESM is a

structured diary method developed to study subjects in their daily surroundings, applicable via a

validated interactive ESM-palmtop. We will obtain ESM-ratings regarding positive and negative affect

- hypothesized to be separate but correlated latent factors163

- and possible influencing factors [e.g.

(social) activities], for 6 days with 10 semi-random measurements/day preferably between the first

study-session and MRI-session.

2.3.5. MRI-scans (2 blocks)

In the first block, after locater and reference scans, a structural T1-scan will provide high resolution

3-dimensional anatomical information. Then we will obtain a resting-state scan after neutral mood-

induction,98

followed by a reinforcement learning fMRI-task which applies a Pavlovian-learning

paradigm delivering the thirsty subjects small amounts of sweet or bitter solution at 80-20%

probabilities after conditional stimuli. This enables assessment of reinforcement learning circuitry.108

Subsequently, using a GABA-specific MEGA-PRESS sequence we will obtain an edited 1H J-difference

magnetic resonance spectroscopy (MRS)-scan of the basal ganglia to measure glutamate and

GABA.144 164-166

A Diffusion Weighted Imaging Spin Echo sequence will assess white matter structure

(DTI).167

After a break, in the second block, subjects will perform the cued emotional conflict fMRI-

task, which will test cue related conflict anticipation and response related cognitive control.168

Then,

the emotion regulation task will measure brain activity in emotional and regulatory brain networks

during attending and regulating (distancing technique) positive, negative and neutral emotional

stimuli. Subsequently, we will make another resting-state scan, but this time after a negative mood-

induction. In combination with the neutral resting-state scan from the first block, this sad mood-

induced resting-state scan will allow assessment of mood-induced changes in brain network

interactions.98

Finally, we will make another MRS-scan of the pgACC. In the follow-up MRI-scan-

session we will repeat the structural, resting state (without mood-induction), reinforcement learning

and MRS-scans. During scanning we will record heartbeat and breathing in order to correct for their

movement-effects.


From collected blood tubes, we will use 1×4.5ml ethylenediaminetetraacetic acid (EDTA) blood for

fatty acid analyses in washed erythrocytes (as a model of neuronal membranes),139

which we will

store for future lipidomic analyses. We will use 7ml EDTA and PaxGene blood collection tubes for

future genomic analyses (e.g. serotonin, dopamine, glutamate/GABA-cascades, one-carbon

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metabolism, or HPA-axis receptors).38 169 170

We will store platelet-poor plasma from 5 ml citrate

blood and also store plasma from 4.5ml EDTA and lithium-heparine blood collection tubes for future

use (e.g. metabolomics, inflammation).171


As described below and in the supplementary text, we will instruct participants to collect salivary

samples over the day using Salivettes (Sarstedt, Nümbrecht, Germany). Saliva reflects blood cortisol

and DHEAS-concentrations, but enables minimally intrusive and relatively stress free assessment.120

172 173

2.4. Procedure

We will regularly train all assessors and experienced psychiatrists will closely supervise the

assessment procedures. We will discuss difficult assessments; in case of disagreement we will make a

conservative decision (e.g. exclusion).

2.4.1. Preparation

2.4.1.1. Initial assessment & mood-induction

We will telephonically screen recruited subjects for potential eligibility. In a first interview

(telephonically or face-to-face), we will check inclusion and exclusion criteria. After obtaining

informed consent we will register psychiatric and somatic treatment history, covariates of interest

and potential confounders. Furthermore, we will mail questionnaire-booklet I (see supplementary

text) and Salivettes, with detailed instructions. In addition, we will prepare the mood-induction

procedure.

2.4.2. Baseline visits

2.4.2.1. First study-session

We will instruct subjects to arrive after ≥8hrs fasting. First, we will collect blood samples by

venipuncture, which we will directly bring to the laboratories. Subsequently, we will allow subjects to

eat and drink, with the exception of caffeinated drinks.

Next, we will instruct subjects to perform the neuropsychological tests in two blocks with a break in

between, and measure waist circumference140

(see supplementary text). After neuropsychological

testing, we will explain the scanning procedure and train the participant for the emotion regulation

fMRI-task, which will be performed in the scanner (see above and supplementary text and

supplementary table 2). After a 15min break, participants will undergo the sad mood-induction.

Before and directly after sad mood-induction, we will request subjects to fill out a Dysfunctional

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Attitudes Scale (two randomized counterbalanced versions)56 174 175

, rate their sadness on a visual

analogue scale and collect saliva (using Salivettes).

Finally, we will explain and instruct participants about the experience sampling method (see above).

In addition, we will provide subjects with questionnaire-booklet II (see supplementary text) to fill out

before the MRI-session.

2.4.2.2. MRI-session

We will instruct subjects to arrive thirsty, i.e. ≥6hrs without drinking and ≥2hrs without eating juicy

food (for the reward learning task). On a Philips Achieva XT 3-Tesla MRI (Philips Medical Systems,

Best, the Netherlands), using a 32-channel receiver headcoil, at the University of Amsterdam,

Spinoza Center, we will scan two consecutive blocks of approximately 60min each (see above),

separated by a break. During the scanning procedure, we will again perform the mood-induction

(neutral/sad) in a slightly modified version as described previously.56

We will ask subjects to listen to

their selected most neutral/sad music piece and meanwhile read their personal sad/neutral

memories presented on a screen in the scanner (during 5min), directly before the resting state scans.

Finally, we will debrief subjects, complete questionnaire-booklet III (see supplementary text), and

obtain post-scan ratings of stimuli presented during the tasks.

2.4.3. Follow-up

2.4.3.1. Monitoring

We will follow-up the recurrent MDD-subjects by regular (every ~4months) phone-calls (SCID and

HDRS) and questionnaire-booklet IV (see supplementary text). To maximize recurrence detection

rates, we will also instruct subjects to contact us at the moment they subjectively experience a

recurrence and inform a person close to them of these instructions.

To allow for the possibility to disentangle state and trait effects, when we detect a recurrence (SCID),

we will invite the respective recurring subject and a matched remitted (MDD-subject to repeat

several baseline measurements (see below). We will preferably scan subjects before they (again)

start antidepressants, but -in order to maintain power- this will not be an exclusion criterion for the

follow-up scan/measurements. Thus, when patients experience a relapse and agree to participate in

the study again, they will be matched with recurrent MDD-participants that are in remission (SCID

and HDRS≤7) and meet matching criteria. We will conduct matching based on group-level

characteristics of relapse patients vs. control patients (mean and distribution of follow-up time, age,

years, sex, educational level and working class). In this way, we also aim to include relatively more

control patients (relapsed:control patients ratio of 1:1.5), with the goal of increasing power. These

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matched participants have to be currently euthymic but can have had a prior relapse, thus after the

baseline measurement, or a relapse during follow-up after second participation. The reason for this

approach is that we are interested in comparing the effect of depression (state) vs. depressive

vulnerability (trait), instead of simply comparing more vulnerable patients to stable patients. This will

give us insight into the pathophysiology of relapse vs. remission; it allows to examine which factors

stay the same, and which factors show change when patients relapse. Potential in-between

recurrences will, however, be examined as a potential confounder in the final analyses. Nevertheless,

a participant will not be included more than once in the follow-up repeated measurements

(scanning/neuropsychology), in order to exclude the possibility of learning effects and habituation in

testing/scanning and prevent complex covariance structures.

2.4.3.2. Repeated measures in recurring and matched MDD-subjects

We will repeat questionnaire-booklets I-III (see supplementary text), blood sampling and

neuropsychological testing. In addition, we will repeat part of the MRI-scan in an ~1hr scan-session

(see above, supplementary text, and supplementary table 2). To minimize learning effects, we will

use randomized counterbalanced versions of tasks when applicable. We will not repeat the mood-

induction.

2.5. Statistical analysis plan

2.5.1. E-infrastructure and software

We will store raw and cleaned data on dedicated servers and make use of available e-infrastructure

bioinformatics networks where necessary.176

We will use a variety of programs under which SPSS

(IBM SPSS, Chicago, IL, USA).

2.5.2. Data preparation

2.5.2.1. Distributions and missing data

We will inspect distributions and remove (multivariate) outliers and data noncompliant to the

protocol [e.g. saliva samples outside time-range or chance level (neuro)psychological responses]. We

will transform non-normally distributed data where possible, otherwise we will apply non-parametric

tests or bootstrapping if applicable. For extensive missing data at random, we will use multiple

imputation where necessary and possible.139 177 178

2.5.2.2. (Neuro)psychological tests

For the (neuro)psychological tests, we will calculate summation-scores where applicable.71 155

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2.5.2.3. ESM

We will prepare ESM-data using developed algorithms. In brief, we will include data in the analyses

for which >30% ESM reports are within 25min after the programmed time of the beep,179

to ensure

reliability.180

From the ESM-data we will test the factor structure of the positive and negative affect

measures using factor analysis, also at the within-subject level (also see supplementary text and

supplementary table 1).163

2.5.2.4. MRI-data

We will perform standard pre-processing using dedicated software.181 182

After realignment, we will

co-register functional scans to the structural scan, and thereafter normalize to the standard Monteal

Neurological Institute (MNI) brain or a DARTEL template (Diffeomorphic Anatomical Registration

Through Exponentiated Lie Algebra) for more flexible group normalization, and smooth. For the

different fMRI paradigms, we will perform fixed effect analyses on single subject level with linear

regression techniques (general linear models). For DTI-scans, we will use tract based spatial statics

(TBSS) for general effects and tractography for a priori defined tracts of interest.183

2.5.2.5. Neurometabolism and HPA-axis

We will quantify glutamate and GABA based on acquired MRS-spectra.165

From concentrations of all

measured fatty acids, we will calculate overall fatty acid unsaturation, chain length and

peroxidizability using dedicated indices.139

Finally, we will calculate cortisol/DHEAS-ratio as indication

of HPA-axis balance.184

2.5.3. Statistical analyses

The statistical analysis protocol has been written, and the study statistics will be carried out, under

close supervision of a statistical specialist.

2.5.3.1. Power analyses

Power analyses for continuous and categorical outcomes of the cross-sectional and prospective

analyses show adequate power to detect small to medium effect-sizes with 60 patients and 40

controls (see supplementary text). This is in line with previous comparable research that found

significant effects in smaller samples.66

Power calculations for studies involving MRI remain hard and

are not used routinely (for an approach see e.g. Mumford et al.185

, Hayasaka et al.186

and Murphy et

al.187

). Currently, there is consensus that groups of ≥20 usually yield sufficient power in MRI-studies

to detect moderate differences in regions of interest.

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Based on these power estimations and feasibility aspects, we will test our first hypotheses on

acquired scans from 60 recurrent MDD-subject and 40 controls, which is for baseline group

comparisons a number more than common in MRI studies, also in studies with a comparable

design.188

Regarding feasibility, next to scanning costs which limit subject number, recruitment

efforts were estimated based on our previous studies. These efforts will be manageable with this

sample size of specifically remitted recurrent MDD-patients that have to be medication free.30

Regarding the prospective analyses, in a previous study with recurrent MDD-subjects, we observed

~50% recurrence rate in 2.5yrs.30

We therefore expect 2×20-30 subjects to be eligible for a second

scan and subsequent comparisons, allowing for some drop-outs. Based on previous research in

comparable samples we expect low attrition rates.22 30 47 152

Moreover, all participants can be included

in the Cox-regression analyses, since these can adequately deal with attrition (outcome measure

incorporates time to event or censored end of observation). As not all subjects will be identified

when the recurrence is present and/or not all recurrent patients will be available for a second scan,

we expect to obtain two groups of ±20 patients with or without a recurrence up who will be scanned

again during follow.

We perform a large set of measurements, which carries the risk of false positives. However, as we

will perform analyses according to analysis-plans which are a priori specified , we will do so for

independent a priori hypotheses. In addition, we will use multivariate analysis techniques (e.g.

machine-learning) to further reduce the risk of chance findings. Nevertheless, although our sample

size will exceed the level of a pilot-study, especially for the prediction measures that we will identify

we will need new samples to replicate our findings.

Finally, next to our a priori power analyses, we will perform post-hoc power analyses of our

outcomes once the data have been analysed.

2.5.3.2. Descriptive data

We will provide descriptive statistics and compare groups using χ2- and independent samples t-tests

where applicable.

2.5.3.3. First and second hypotheses

For the first hypotheses we will compare the remitted recurrent MDD- with the control-group using

(multiple) general linear models or linear mixed models (e.g. complex repeated measures/covariance

structure, nested data, missing data), where applicable.189

We will present results uncorrected and

corrected for confounders (factors differing between groups with P<.1) and/or covariates of interest,

using propensity scores where applicable.190

Independent variables will be group (recurrent MDD vs.

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control), potential covariates, their interaction(s), and confounders; the selected outcome(s) for a

given specific hypothesis will be dependent variable(s). If interaction effects do not contribute to the

model, we will remove them to obtain the most parsimonious models. For the second hypotheses we

will use comparable models, except that we will omit the control-group (and consequently the

group-variable and interactions) from the models, and focus on effects of clinical variables of interest

in the remitted recurrent MDD-group.

2.5.3.4. Third and fourth hypotheses

For the prediction analyses, we will use cox-regression models to investigate prospective association

between baseline measures and time until first recurrence. Using time until first recurrence as

primary outcome measure will provide additional modelable variance in the data since such

contrasts not only incorporate 50% recurrence, but also fast vs. slow recurrence which may be highly

relevant from a clinical perspective. Furthermore, in first instance we are planning to only use the

time invariant baseline predictors. However, in a later stage, we will incorporate the variables that

we measure over time, e.g. the HDRS or rumination questionnaires, to see how changes in these

parameters over time are associated with future recurrence (e.g. mediation) and/or time until

recurrence.

Next, we will model significant univariate associations in multiple regression models, with correction

for other confounders and covariates of interest related to recurrence (e.g. number of previous

episodes, residual symptoms, ‘daily hassles’ and coping style). Moreover, we will analyse secondary

outcomes [cumulative number, length and severity of MDD-episodes and course of depressive

(residual) symptoms] using (multivariate) general linear models or linear mixed models, where

applicable. For the fourth hypotheses, we will investigate change during recurrence using repeated

measures general linear models or linear mixed models where applicable.

2.5.3.5. Additional analyses

To exploit the multimodal and -dimensional character of our data, we plan to apply advanced

statistical methods to identify relevant multivariate patterns, including machine learning, factor and

network analyses.191-193

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3. ETHICS AND DISSEMINATION

3.1. Ethical considerations

3.1.1. Regulation statement

We will conduct this study according to Declaration of Helsinki principles (Seoul, October 2008) and

the Medical Research Involving Human Subjects Act (WMO). The study is approved by the accredited

Medical Ethical Committee (METC) of the Academic Medical Centre (AMC), teaching hospital of the

University of Amsterdam. We will obtain written informed consent beforehand from all participants,

after careful and extensive written and oral information. If desired, we will give subjects up to two

weeks to consider their decision. Investigators will receive Good Clinical Practice training, in

agreement with the AMC research code.

3.1.2. Handling of data and documents

We will encode data and keep this data and blood samples for at least 15yrs. Only researchers

directly involved in the study will have access to encoded data, the key will be with the researcher

only. We will label blood samples with anonymized patient numbers.

3.1.3. Benefits and risk assessment

There is no immediate advantage of participation for participants, there are no interventions

scheduled in this study. MRI is non-invasive, so hardly any risks are associated with this study.

Therefore, the METC determined that no liability insurance is required. We will inform subjects and

the reviewing accredited METC if anything occurs, on the basis of which it appears that

disadvantages of participation may be significantly greater than was foreseen.

Because we recruit unmedicated subjects with moderate to high recurrence risk, it may be

questioned whether follow-up of these subjects is ethically justified. However, we will not actively

propose tapering or discontinuation of antidepressant therapy. Instead we will only include subjects

who decided to stop antidepressants beforehand. In case we detect suicidality during follow-up, we

have a protocol available including a consulting psychiatrist for emergency situations and referral the

most appropriate emergency service. We therefore consider this study ethically justifiable.

In addition, advantages of participation and follow-up will be that MDD-recurrence will be detected

early so prompt psychiatric treatment can be offered. In naturalistic care there might be substantial

patient and institutional delays before recurrence is detected and treatment can be started.194

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3.1.4. Compensation

Participants will receive €75,- for their participation, besides compensation for travel expenses. For

completion of a follow-up scan we will pay €50,-.

3.2. Teaching

This study will provide training of PhD-students, and will involve educational internships of medicine,

psychology and neuroscience bachelor- and master-students of the Universities of Amsterdam,

Nijmegen and Groningen and VU-university.

3.3. Dissemination

3.3.1. Public disclosure and publication policy

We will submit study-results for publication in peer reviewed journals and presentation at

(inter)national meetings, taking into account relevant reporting guidelines (e.g. COPE, STROBE).195-197

We will regularly notify participants of publication. Curated technical appendices, statistical code,

and anonymised data will become freely available from the corresponding authors on request.198

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4. DISCUSSION

4.1. Summary

In summary, the current multimodal DELTA-Neuroimaging study will investigate recurrent MDD

vulnerability by comparing remitted unmedicated recurrent MDD-subjects with carefully matched

controls without personal/1st

degree familial psychiatric history. Biopsychosocial assessments

integrate four distinct levels of perspective: symptomatology, affective neuropsychology, brain

circuits, and endocrinology/metabolism. Subsequently, the cohort of recurrent MDD-subjects will be

followed-up to test to what extent baseline measurements predict, and/or change during

prospective recurrence. This will help to disentangle the pathophysiology behind MDD-recurrence,

and thereby provide (I) (bio)markers identifying high-risk patients needing additional preventive

treatment, and (II) novel targets to improve the treatments preventing against recurrences. Given

MDD’s highly recurrent nature, this knowledge has the potential to substantially reduce MDD’s

disease burden.

4.2. Limitations and strengths

4.2.1. Limitations

Several limitations of the current study should be noted beforehand. First, the extensive assessment

procedure needed to measure all variables of interest and confounders will potentially lead to

inclusion of subjects that are intrinsically aware of the necessity to perform clinical research and

readily willing to cooperate. Nevertheless, this selection bias is inherent to translational

neuroscientific research, and the relatively large number of subjects that will be included will

increase external validity. Moreover, testing the integrated hypotheses of the current study is only

possible by combining the different assessments.

Second, to overcome potential confounding effects of antidepressants and other psychotropic

medication, only subjects that currently do not use these drugs will be included. This may lead to

selection of particular patient subgroups that (I) experienced little benefit from previous medication

trials, (II) are hesitant to use these medications because of adverse effects or for principle reasons,

(III) experience other barriers to care (e.g. financial) or (IV) have an intrinsically lower vulnerability to

have severe recurrences. In addition, it may slow down inclusion. However, this is the only way to

study the hypotheses at hand while eliminating confounding effects of medication use. Furthermore,

the subjects included in the current study may be a clinically relevant representation of patients that

do not want to take antidepressant drugs, for whom knowledge of underlying vulnerability and

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measures to determine this vulnerability might be of help to develop novel alternative treatments to

prevent recurrence risk.

Third, for practical reasons family history will be determined by heteroanamnesis. This may lead to

recall or other biases. However, both under and over-representation can be expected, so we expect

this will not result in systematic biases.

Fourth, DSM-IV diagnostic criteria will be used for current study’s diagnoses, while the DSM-5 has

already been introduced. Since the classification of depressive episodes (i.e. recurrences) have not

changed in DSM-5, this and our specific in- and exclusion criteria will not lead to difficulties in

translating the results when DSM-5 will be used.

Fifth, the current study’s assessments will not include measures of HPA-axis feedback [e.g.

dexamethasone suppression(/corticotropin-releasing hormone-challenge) test].199

This was not

included to prevent overburdening of participants. While consequently the current study will not be

able to directly assess HPA-axis feedback, the study’s seven salivary HPA-axis measures without

pharmacological challenge during the baseline assessments will provide an adequate indication of

HPA-axis activity under natural circumstances, including stress by mood induction.120 172

Sixth, the current study’s MRI-measures will be made using 3-Tesla field strength, while higher field

strengths are also available. Although obviously higher field strengths increase signal to noise ratio,

they may also have several disadvantages.200

Higher costs and specific absorption rates, together

with increased risk for artefacts due to e.g. inhomogeneous transmit fields, more extensive

contraindications and peripheral nerve stimulation limit high field strength applicability. These

disadvantages apply to clinical studies like the present one, but even more to the clinical setting.200

Therefore, 3-Tesla findings may be more readily clinically translated than findings at higher field

strengths, and could therefore be more relevant from the clinical perspective.200

Seventh, while the combined cross-sectional patient-control and prospective follow-up design of the

current study has great advantages, it brings along a balance between two contrasts. First, the

recurrent MDD-vulnerability contrast in the comparison between highly vulnerable patients and

matched resilient controls; and second the within patient-group contrast in time until recurrence of

fast recurrence during follow-up vs. no or late recurrence. Strongly increasing the first contrast by

including only extremely high recurrence risk patients entails the risk of decreasing the second

contrast because all patients will experience fast recurrence. The other way around, by including too

many patients with a low recurrence risk, the first contrast may be disadvantaged because the traits

will not be outspoken enough to be detected. Therefore, also based on our previous research, we

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opted to increase to first contrast by including relatively resilient controls, together with patients

that have proven vulnerability for recurrent MDD (i.e. ≤2 previous MDD episodes). However, we did

not express any additional vulnerability criteria, e.g. time since last episode or higher number of

previous episodes, in order to (I) include recurrent MDD patients that form a naturalistic sample that

is representative regarding vulnerability and (II) not to decrease the second contrast in time until

recurrence.

Of note, we will not include single episode MDD-subjects. While this would enable comparisons

against a relatively low recurrence risk group, instead of controls, this was deemed to be logistically

even more difficult to achieve. Regarding the second contrast in time until recurrence, based on our

previous research and our inclusion procedure, we expect a large spread in the number of previous

episodes (e.g. from 2 up to 60) and time since last episode (e.g. from 8 weeks up to >10yrs), which

both imply modelable variance/contrast in prospective recurrence risk.201

With an expected ‘optimal’

distribution of 50% recurrence-rate during follow-up, we think that our group would be the most

interesting and feasible group to study when looking for factors that can predict imminent

recurrence, in order to (I) select subjects that may benefit from preventive treatment, and (II)

identify pathophysiological mechanisms that can be targeted in these subjects to prevent recurrence

risk.

Finally, the current study does not include (randomized) interventions. Therefore, it will not be

possible to say whether observed effects are causal in nature. Nevertheless, the current study’s

prospective, repeated measures design can optimally select targets for future randomized clinical

trials to test the causal nature of observed effects.

4.2.2. Strengths

The current study also has several distinct strengths. Due to its strict and specific inclusion-criteria,

matching- and recruitment-procedure, the contrast for MDD-vulnerability will be maximal, without

distortion due to important confounders: MDD-residual symptoms and medication. In addition, the

unique integration of a wide range of measures in a prospective repeated measures design will allow

disentangling of recurrent MDD state- and trait-factors.

Furthermore, the study will be performed by an experienced international multi-centre research

group, combining expertise from all measured perspectives. Additionally, the Netherlands’ relative

limited geographic size and high level of social organization make it well suited for long-term follow-

up research.

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Next, ESM-results could set the stage for innovative cost-effective e-health interventions. Moreover,

the focus on lifestyle factors (physical activity/diet) and their biological effects could provide more

insight into recurrent MDD-patients’ increased risk to develop cardiovascular disease,9 as already

acknowledged in the introduction. By combining these lifestyle (biological) assessments with

investigation of (the neurobiology of) motivation, the present study could lead to development of

interventions that help to motivate recurrent MDD-patients to improve their lifestyle. This not only

has the potential to prevent recurrence, but also the highly comorbid cardiovascular risk.202

4.3. Conclusion

By integrating the symptom level, affective neuropsychology, brain circuits, and

endocrinology/metabolism, using a prospective repeated measures design in remitted MDD-subjects,

the present DELTA-Neuroimaging study will provide more insight in recurrent MDD-vulnerability.

Increased insight will lead to novel targets for (I) improved preventive therapy, and/or (II)

(bio)markers to monitor and/or predict recurrence risk. Consequently, ultimately, it holds potential

to alleviate MDD’s highly recurrent course and reduce its currently overwhelming global disease

burden.

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Competing interests

All authors declare that they have no competing interests.

Authors’ contributions

RJTM and HGR designed the study. RJTM and HGR drafted the protocol and the manuscript. All

authors contributed to development and implementation of the study protocol. MWJK provided

statistical advice. RJTM and CAF conduct all participant-related study-procedures. All authors

contributed to editing the manuscript and read and approved the final manuscript.

Acknowledgements

This study is supported by unrestricted personal grants from the AMC to RJTM (AMC PhD

Scholarship) and CAF (AMC MD-PhD Scholarship), and a dedicated grant from the Dutch Brain

Foundation (Hersenstichting Nederland: 2009(2)-72). HGR is supported by a NWO/ZonMW VENI-

Grant #016.126.059.

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FIGURE LEGENDS


Schematic representation of the theoretical framework of the present DELTA-Neuroimaging study.

The four selected levels of perspective (endocrinology/metabolism, brain circuits, affective

neuropsycholoy, and symptoms), their respective subdomains, and their connections have been

depicted. The horizontal straight arrows show potential bidirectional relationships (for readability

bidirectional relationships between e.g. anhedonia and cognitive reactivity are not shown), the

horizontal curved arrow shows membrane fluidity balance, colored arrows show potential

connections, dashed arrows show inhibiting effects, and vertical grey arrows show possible

underlying pathways. Abbreviations used: GABA, γ-aminobutyric acid; HPA, hypothalamic-pituitary-

adrenal; PFC, prefrontal cortex; vStr, ventral striatum; VTA, ventral tegmental area; TPN, task positive

network; DMN, default mode network; dACC, dorsal anterior cingulate cortex; pgACC, pregenual

anterior cingulate cortex; Amy, amygdala; ‘Hot’ neuro-Ψ, affective neuropsychology; Cogn. react.,

cognitive reactivity; Dysf. attit., dysfunctional attitudes.


Figure 2 depicts the study design of the present DELTA-Neuroimaging study. Different part of the

study are shown in chronological order from left to right. For a description of the contents of

questionnaire booklets and tasks we refer to the supplementary text. After screening, recruited

patients and controls participate in the initial assessment where we check in- and exclusion criteria,

register variables and covariates of interest, prepare the mood induction and mail questionnaire

booklet I and Salivettes. During the subsequent first study session we will take fasting blood samples,

perform the affective neuropsychological tests, perform the sad mood-induction, explain the

experience sampling method (ESM) and the emotion regulation functional magnetic resonance

imaging (fMRI) task, and hand out the ESM-psymate and questionnaire booklet II. Subsequently,

subjects come to the MRI-session, where we take structural [T1-weighted and diffuse tensor imaging

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(DTI)] and functional magnetic resonance imaging (fMRI)-scans (neural and sad mood induction

resting state, reinforcement learning, cued emotional conflict, emotion regulation), as well as γ-

aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) of the basal ganglia and

pregenual anterior cingulate cortex. Next, we monitor the patients by calling them every ~4 mnths to

assess recurrence. In case we detect a recurrence, we invite the respective patient – together with

matched non-recurrent patients – to repeat part of the baseline assessments [blood samples,

affective neuropsychological tests, structural MRI, fMRI (resting state, reinforcement learning), and

MRS].

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228x212mm (300 x 300 DPI)

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79x29mm (300 x 300 DPI)

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SUPPLEMENTARY MATERIAL

1. SUPPLEMENTARY METHODS

1.1. Measures

1.1.1. Questionnaires

1.1.1.1. Not in booklets

We will use the 17-item Hamilton Depression Rating Scale (HDRS) and the Dysfunctional Attitude

Scale (DAS) at various moments during our study procedure (see main article).

- The HDRS is an observer rated major depressive disorder (MDD)-symptom scale to assess the

severity of depression.1 Total scores of the 17-item version range from 0-52 and scores of 0-7 are

considered within the normal range; scores of 8-13 indicate mild depression; 13-18 moderate

depression; 18-22 severe depression and scores ≥23 indicate very severe MDD. Its internal

consistency is high, with Cronbach’s α=.80.2

- The DAS is a self-rated questionnaire to assess general, deeply held, dysfunctional beliefs. Two

40-item versions exists for the Dutch language. Total scores range from 40 to 280, with higher

scores reflecting greater dysfunctional attitudes. The internal consistency and test-retest

reliability are high, with Cronbach’s α’s of respectively .90 and .73.3

1.1.1.2. Questionnaire-booklet I

We will mail questionnaire-booklet I after the initial assessment, participants will take it with them at

the first study-session baseline visit (same for follow-up repeated measure), see main manuscript. It

includes the following questionnaires:

- Inventory of Depressive Symptomatology self-rated (IDS-SR): self-rated MDD-symptom scale to

assess severity of depressive symptoms. The IDS-SR comprises three factors: cognition and

mood, anxiety and arousal, and sleep and appetite regulation. The IDS-SR has 30-items with a

total score range from 0 to 84, with higher scores indicating greater severity of depression.

Scores ≤13 are considered within the normal range; scores of 14-21 indicate mild MDD; 22-38

moderate MDD; ≥39 severe MDD. The IDS-SR has highly acceptable psychometric properties.

Internal consistency was up to 0.92 (Cronbach’s α).4

- Leiden Index of Depression Sensitivity-Revised (LEIDS-R): self-report questionnaire that measures

cognitive reactivity to sad mood.5 Participants are instructed to think about the last time they felt

“somewhat sad”, and to indicate - on a 5-point Likert scale ranging from ‘not at all’ (0) to ‘very

strongly’ (4) - the degree to which a list of statements describe their typical cognitions and

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behaviours in response to sad mood. The LEIDS-R contains 34 items which sum up to the total

score, and subscales (assessing cognitive reactivity in relation to aggression,

hopelessness/suicidality, acceptance/coping, control/perfectionism, risk aversion, and

rumination on sadness). The LEIDS-R has good internal consistency (Cronbach’s α=0.88).6

- Neuroticism-Extraversion-Openness Five-Factor Inventory (NEO-FFI): self-rated questionnaire

measuring five factor model (‘big-five’) dimensions of personality characteristics: Neuroticism,

Agreeableness, Conscientiousness, Extraversion and Openness.7 The NEO-FFI has 60 items on a

five point scale, ranging from "strongly disagree" to "strongly agree". It has sufficient internal

reliability and two-week retest reliability is uniformly high, ranging from 0.86 to 0.90 for the five

scales.8

- Everyday Problem Checklist:9 10

Dutch translation of self-rated questionnaire measuring everyday

(small) stressors (Daily hassles). The questionnaire consists of 114-items which describe

problematic situations and events in daily life.

- Utrecht Coping List (UCL): self-rated measure of coping behaviour while confronted with

problems. The questionnaire consists of 47-item on seven empirically derived subscales: active

tackling, seeking social support, palliative reacting, avoiding, passive reacting, reassuring

thoughts and expression of emotions. The UCL demonstrated strong internal consistency in a

study within the UK population. Five of the seven subscales had good test-retest reliability.11

- Negative life events questionnaire:10 12 self-rated questionnaire asking for recent life-events of

the subject or significant others.

- Childhood Trauma Questionnaire:13 14 Dutch translation of the Childhood Trauma Questionnaire

for assessment of childhood adversity. This 28-item self-report questionnaire retrospectively

assesses childhood trauma and neglect, and consist of five factors; emotional abuse, emotional

neglect, sexual abuse, physical abuse, and physical neglect. Inter-correlations among the five

factors ranged from r=.41 to r=.95.15

Psychometric properties in a sample of Dutch female sex

workers were good.13

- International Physical Activity Questionnaire (Long Form):16-18

Dutch translation of an

internationally validated questionnaire to measure physical activity, developed with support

from the World Health Organization. This 27-item self-report questionnaire assesses the time

that participants spent being physically active in the last 7 days. The reliability of the Dutch

version was good (intra-class correlation coefficient=0.70-0.96) and the validity moderate

(r=0.36-0.49) compared to an accelerometer. Reliability and validity is comparable in psychiatric

populations, e.g. schizophrenia.19

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- Helius Food Frequency Questionnaire (Helius-FFQ):20 21 validated and updated questionnaire to

assess dietary intake. The Helius-FFQ enables detailed, standardized, and comparable

assessment of the diet of five different ethnic groups.

- Women's Health Initiative Insomnia Rating Scale (WHIIRS):22 reliable and internationally validated

questionnaire to assess sleep quality. WHIIRS’ internal consistency is good (Cronbach’s α=.78.).

Test-retest reliability coefficients were .96 for same-day administration and .66 after a year or

more. The WHIIRS has good construct validity.22

- Edinburgh handedness questionnaire:23 24

self-rated questionnaire developed to determine

dominant laterality in executive functions. The questionnaire assesses handedness by of the

preferred hand for carrying out common activities. The 4-item revised structure showed very

high reliability on measures of factorial composite reliability and Cronbach's α. Furthermore, the

estimate of the quality of factor scores was high.24

1.1.1.3. Questionnaire-booklet II

We will hand out questionnaire-booklet II during the first study-session, participants will complete it

before the MRI-session (same for follow-up repeated measure). It includes the following

questionnaires:

- IDS-SR (see above)

- Snaith-Hamilton Pleasure Scale (SHAPS):25 self-rated questionnaire measuring anhedonia. The

SHAPS has 14 items with scores ranging from 0-14. The internal consistency and test-retest

reliability of the SHAPS were adequate, with Cronbach’s α’s of .91 and 0.70 respectively.

Furthermore, the SHAPS was significantly correlated with other validated measures of affect and

personality.

- Ruminative Response Scale (RRS-NL):26 27

validated Dutch adaptation of a self-report rumination

measure. It consists of 26 items that describe responses to a depressed mood that are focused

on the self, symptoms, or consequences of depressed mood. Two separate subscales reflecting

pondering and brooding are distinguished. The RRS-NL possesses good internal consistency and

validity.26

In a recent study examining the Dutch version, Cronbach׳s α for the total RRS-NL was

.94, and .64 for the brooding subscale.28

We adjusted the RRS-NL by slightly reframing the

introductory statement. Instead of referring to what subjects generally do when they feel

depressed, we asked for their answers reflecting the last week. With this adjustment, we aimed

to increase the temporal specificity, by specifically asking for current rumination instead of

general ruminative traits.

- Spielberger State and trait Anxiety Inventory form Dutch Y (STAI-DY):29 self-rated questionnaire

measuring state and trait anxiety. The State Anxiety Scale (40 items) measures subjective feelings

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of apprehension, tension, nervousness, worry, and activation arousal of the autonomic nervous

system. The Trait Anxiety Scale (20 items) evaluates stable aspects of anxiety proneness. Test–

retest reliability coefficients ranged from 0.31 to 0.86 and internal consistency was high, ranging

from 0.86 to 0.95 (Cronbach’s α).29

- Mood and Anxiety Symptoms Questionnaire (MASQ-30D):30 validated short adaptation of the

MASQ, designed to measure the dimensions of Clark and Watson's tripartite model in large-scale

psychopathology research. The MASQ-30D contains 30 items examining mood and anxiety and

has 3 subscales. The scales of the MASQ-D30 showed good internal consistency, with Cronbach’s

α’s >0.87 in patient samples. Correlations of subscales with other measures of mood and anxiety

indicated sufficient convergent validity.

1.1.1.4. Questionnaire-booklet III

We will complete questionnaire-booklet III at the MRI-session (same for follow-up repeated

measure). It includes three observer-rated questionnaires:

- HDRS (see above)

- CORE checklist for psychomotor MDD-symptoms (CORE):31-33 distinguishes dimensions of

psychomotor dysfunction in MDD, all suggestive of a melancholic MDD-subtype. The CORE index

is composed of 18 items, scored on a 4-point scale. Factor analysis showed three interpretable

domains: (I) retardation items (52% of variance), (II) agitation items (15% of variance), and (III)

non-inter-activeness (5% of variance).34 Its translation in Dutch was recently validated.35

- Salpêtrière retardation rating scale (SRRS):36 measures cognitive and motor aspects of

retardation. This scale contains 15 items and has a three-factor solution measuring movement,

speech and cognitive function.34

Correlations between SRRS and measures of cognitive function

and motor abilities show good convergent validity.37

1.1.1.5. Questionnaire-booklet IV

We will use questionnaire-booklet IV during the follow-up measurements. It consists of the IDS-SR,

RRS-NL, and SHAPS, all described above.

1.1.2. Neuropsychological tests

1.1.2.1. Exogenous cueing task (15min)38

In this reaction time task, a target stimulus appears at one of two spatial locations, cued by an

emotional stimulus (emotional face) preceding at the same (‘valid trial’) or opposite spatial location

of the target (‘invalid trial’). When the interval between cue and target onset is short (stimulus onset

asynchrony<300ms), participants typically respond faster to valid compared to invalid trials (‘cue

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validity effect’). In case of emotionally relevant stimulus material, the time course of the cue validity

effect may be extended (‘enhanced cue validity effect’), leading to a larger cue validity effect

compared with neutral information. Secondly, we will measure emotional modification of attentional

engagement and disengagement by comparing speed of responding on valid and invalid emotional

versus neutral trials. Cue emotional valence may (I) lead to response benefits on valid emotional

versus valid neutral trials, which is a measure of attentional engagement towards emotional cues,

and/or (II) delay disengagement of attention, which is indexed by a slower reaction on invalid

emotional trials compared to neutral trials.39-41

1.1.2.2. Facial expression recognition task (20min)

We will use six morphed basic emotions (happiness, surprise, sadness, fear, anger, disgust) from 10

individual characters from the pictures of facial affect series between each prototype and neutral,

and present them in random order for 500ms, replaced by a blank screen. We will record reaction

times to these emotions and the recognition threshold (the intensity level required for successful

recognition of each emotion). This recognition threshold is defined as the level of emotional intensity

at which participants correctly identify ≥75% facial expressions of emotion for four consecutive

intensities.42

1.1.2.3. Emotional categorization (6min)

We will present sixty personality characteristics selected to be disagreeable or agreeable on the

computer screen for 500msec each. These words have been translated from the original English

version to Dutch, matched in terms of word length, ratings of usage frequency, and meaningfulness.

We will ask subjects to categorize the words as likable or dislikeable as quickly and as accurately as

possible. Specifically, we will ask to imagine whether they would be pleased or upset if they

overheard someone else referring to them as possessing this characteristic, so that the judgment is in

part self-referring. We will calculate classification-rates and reaction times to likable and dislikeable

words.40 42

1.1.2.4. Emotional memory (~5 minutes)

Fifteen minutes after completion of the emotional categorization task, we will ask participants to

recall as many of the personality traits as possible. We will compute numbers of positive and

negative words recalled for both correct and false responses.40 42

1.1.2.5. Internal shift task (12min)43

Examines capacity to shift attention between contents of working memory in response to emotional

as well as non-emotional material. We will present Karolinska faces at the centre of the computer

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screen one at a time. We will ask participants to perform two conditions, a non-emotional and an

emotional one. In the non-emotional condition, we will instruct participants to focus on the relevant

stimulus dimension ‘gender’ (male or female), in the second condition, they have to focus on the

‘emotion’ dimension (neutral or angry). All participants complete both conditions in counterbalanced

order. Participant’s task is to keep a silent mental count of the number of items in each category,

presented over a block of items (with random 10 to 14 items) and report numbers at the end of each

block. We will ask participants to update counters of both categories when a face is presented and

report numbers of items at the end of a block in a fixed order, to encourage a consistent counting

strategy (e.g. neutral-angry faces in emotion condition, male-female in gender condition). We will

present each face on the screen until participants press the spacebar to indicate that they have

updated both internal counters. This response latency for updating is the main dependent variable of

the task. The next face appears on the screen after a 200ms inter-stimulus interval. Due to the face-

sequence, there are shifts and no shifts in each block of items (e.g. in the emotion condition shifts

are angry-neutral and neutral-angry and no shifts are angry-angry and neutral-neutral).44

1.1.2.6. Dutch adult reading test (10min)

Dutch version of the national adult reading test. The score is predictive of premorbid intelligence in

brain damaged patients and appeared insensitive to brain deterioration in demented or psychotic

patients.45


Momentary assessment techniques allow for examination of subtle fluctuations of behaviour and

affect over the course of the day, and the prospective nature of the data allows for examination of

the temporal association between different observations. The shift to the micro-level of daily life

showed how subtle dynamic patterns of moment-to-moment affective experiences and responses to

situations constitute the missing link between macro-level risk factors for psychiatric disorders like

MDD and future outcomes.46

Major MDD risk factors interactively impact on reactivity and duration

of momentary experiences in everyday life and the latter patterns in turn predict future course of

symptoms. Therefore, it is relevant to examine mechanisms at the level of these smallest building

blocks. Furthermore, real-life tracking of experiences using ESM might allow for an easy identification

of the concrete bits of real-life affective and behavioural patterns which need remediation.

Furthermore, ESM can provide real-life validity to experimental and imaging results. This is

important, as this clarifies how knowledge of mechanisms connects with real-life intervention

targets.47-49

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The ESM-palmtop (“PsyMate”®) will signal subjects at random moments during the day to answer

questions about affect and daily events. Answering the ESM-palmtop questions after each auditory

signal (“beep”) will take about 30sec. We will program the ESM-palmtop to emit 10 beeps/day at

random intervals in each of the ten 90-minutes time blocks between 7:30h and 22:30h, on 6

consecutive days. After each beep, subjects have to fill out the self-assessment on the ESM-palmtop

to record current context (activity, persons present, location, physical activity), stress appraisals of

this context, and mood. Mood questions include 4 Positive and 5 Negative Affect items.50 Examples

are ‘happy’ and ‘relaxed’ for positive affect and ‘depressed’ and ‘irritated’ for negative affect. The

self-assessments will be rated on 7-point Likert scales (ranging from 1= ‘not at all’ to 7= ‘very’). We

will instruct subjects to complete the ESM-palmtop measurements as quickly as possible after the

beep. This emphasis helps to minimize retrospective memory distortion. In addition, we included a

morning and an evening questionnaire including specific questions regarding sleep and the overall

day, respectively. We will instruct participants to fill in these questionnaires on the ESM-palmtop in

the morning after they wake up, and in the evening before they go to bed. The questions we

included in the ESM-procedure are shown in Supplementary table 1.

Regarding the ESM a standard approach for data cleaning will be used. We will first check for missing

data. Second, we will check whether total response time exceeds 15 minutes or whether time

between the beep and first response exceeds 15 minutes, which observations will be removed. Third,

we will exclude days of measurement when the number of observations was less than five. Fourth,

we will exclude subjects when the number of observations is less than 30. These precautions are

taken to have enough and valid measurements, necessary for valid statistical approaches. We will

thereafter inspect the variables to see whether they contain variation based on the interquartile

range.

Because ESM observations are irregularly spaced (due to the random presentation of measurements

and missing data) and a positive/negative autocorrelation may exist between the expected absolute

successive difference (EASD) and time intervals, we will calculate the mean adjusted absolute

successive difference (MAASD) per ESM variable, taking into account an adjustment parameter λ, to

capture affective instability.51

To avoid night time intervals, successive differences will be calculated

within days.

Because ESM-data will likely be skewed to the left, we will apply nonparametric independent

samples Mann-Whitney U tests when appropriate, to determine significance of differences between

the remitted recurrent MDD and healthy control groups.

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1.1.4. MRI-scanning

We will minimize side-to-side head movements by fitting foam pads between the volunteers' head

and the volume coil. We will obtain scans in the order indicated below; half-way, we will plan a break

of >20min. Due to time constraints we had to perform the emotion regulation task (ERT) in the

second block of scanning, after the break. It could be questioned whether the brain activation by this

ERT might influence the 2nd resting state scan after a mood-induction. If anything, we aimed to have

the subjects maximally experience a sad mood after the mood-induction. As the ERT also provided

negative pictures, (alternated with positive) it can be expected that the ERT might also have primed

people to be more susceptible for the mood induction procedure. As this was a systematic order of

scanning in all subjects, we think that if any effect occurred, this would have only primed all subjects

systematically to be more vulnerable for the mood induction. Supplementary table 2 describes the

experimental designs of all fMRI tasks according to available reporting guidelines,52

the remaining

acquisition parameters are described below.

- Locater scan: a whole brain low resolution 3-dimensional T1-weighted turbo field echo-scan for

anatomical overview. Scan duration=53s; number of slices=100; slice orientation=sagittal; field of

view (FOV)=250×250×220mm; voxel size 2.23×2.23×2.2mm; acquired matrix=112×112; act.

repetition time (TR)=3.1ms; act. echo time (TE) 1.4; flip angle (FA)=8˚; turbo-factor=425.

- Reference scan: to obtain a whole brain sensitivity map for the subsequent SENSitivity Encoding

(SENSE) scans. Scan duration=59s; number of slices=100; slice gap=10mm; slice

orientation=coronal; FOV=530×530mm; voxel size 5.52×7.07×3mm; acquired matrix=96×75;

TR=4ms; TE=0.75; FA=1˚.

- Structural scan (6min): a whole brain high resolution 3-dimensional T1-weighted turbo field

echo-scan for detailed anatomic information. Scan duration=372s; number of slices=220; slice

orientation=transverse; FOV=240×220×188mm; voxel size 1×1×1mm; acquired matrix=240×187;

TR=8.3ms; TE=3.8; FA=8˚; number of averages=2; TURBO-factor=154.

- Resting-state scan: We will give no specific instructions except that all subjects keep their eyes

closed, let their mind wander, lie still and not fall asleep.53

Because we aim to compare resting-

state scans without and with a negative mood induction, we will play neutral or sad music,

respectively, the 5min preceding the resting-state scans. We will combine this with a

personalized neutral and sad script, respectively, which subjects read on the screen, as described

in the main article. We chose not to counterbalance the acquisition of the neutral/mood induced

resting state scans, to prevent potential interference of the subsequent fMRI-tasks by mood-

state after an initial sad mood-induced resting state scan. This scan will be a field echo (FE) echo-

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planar imaging (EPI)-scan with duration=428s; number of slices=37; slice thickness=3mm; act.

slice gap=0.3mm; slice orientation=transverse; slice order=ascending; number of dynamics=210;

FOV=240×220×122mm; voxel size 3×3×3mm; acquired matrix=80×80; TR=2000ms; TE=28ms;

FA=76˚; EPI-factor=43.

- Reinforcement learning task (25min): After instructing the participants to arrive thirsty, a

Pavlovian-learning paradigm will be used, delivering small amounts (0.2ml) of liquid (sweet apple

juice or bitter 3.0M MgSO4) at different probabilities (80-20%) after conditional stimuli. With the

changing probabilities of water delivery, temporal difference reward-learning and aversive-

learning signals can be calculated which will be used as a regressor of interest in the analyses.

This task showed excellent (differential) activations of the reinforcement learning circuitry in

depressed subjects versus controls.54

Of note, the task does not test social stimuli,55

but rather

the persistence of difficulties in temporal difference reward related learning with primary

rewards, as this could be a more general and basic persistent dysfunction in recurrent MDD.

MgSO4 is clinically used as a laxative, and is not harmful to humans. The 15ml solution used in the

experiment will not cause bowel distress. This will be a FEEPI-scan with duration=1693.5s; voxel

size 3×3×3mm; EPI-factor=43.

- Magnetic resonance spectroscopy (MRS): Glutamate, glutamine and GABA only recently became

distinguishable from each other by MRS. We will acquire edited 1H J-difference spectra using a

GABA-specific MEGA-PRESS sequence.56-58 During the odd transients in this sequence, we will

apply a 15.64ms sinc-center editing pulse (64 Hz full width at half maximum) at 1.9ppm and

4.6ppm in an interleaved manner to specifically excite GABA and suppress water, respectively.

We will acquire these spectra in two voxels, one in the left basal ganglia with scan

duration=776s; volume of interest size=30×20×20mm; number of dynamics=384; number of rest

slabs =4; number of samples =2048; TR=2000; TE=73ms; FA=90˚; odd frequency=351; even

frequency=-351; 2nd

order pencil beam-auto shimming; and water suppression. The same in the

pgACC, except with scan duration=328s; volume of interest size=25×20×30mm; number of

dynamics=160.

- Diffusion Tensor Imaging (DTI): measures whole brain fractional anisotropy (FA) and mean

diffusivity which can quantify white matter abnormalities.59 Spin-echo diffusion weighted

imaging DTI-scan duration=333.6s; number of slices=60; slice thickness=2mm; slice gap=0mm;

slice orientation=transverse; FOV=224×224×120mm; voxel size 2×2×2mm; acquired

matrix=112×112; TR=7635ms; TE=88ms; FA=90˚; EPI-factor=59; number of b-factors=2; b-factor

order=ascending; max b-factor=1000.

- Cued Emotional Conflict Task (CECT,60 25min): Participants will be instructed to respond as

quickly as possible with two response buttons indicating happy or sad. In an event-related

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paradigm, each trial starts with one of two word cues (“actual” or “opposite”) presented for

500ms, which instructs participants to respond to the target cue with the identical or opposite

valenced button. After the presentation of the cue word, a fixed interval of 2000ms separates

the presentation of the cue from the target. The target cue is either a happy or sad face

presented in the centre of the screen. This cue-offset period makes it possible to investigate: 1)

cue related conflict anticipation; and 2) response related cognitive control following the

presentation of the emotional target.

Fourteen faces (7 female and 7 male actors) from the Karolinska Directed Emotional Faces

dataset61

will be used. Each face will be shown in a happy or sad expression (matched for

arousal). The assignment of labels to the two response buttons will be counterbalanced across

participants. After the CECT, participants will be asked to rate the faces for valence and arousal

using 9-point Likert scales (valence: 1=unhappy, 5=neutral, 9=happy; arousal: 1=calm,

5=intermediate, 9=excited). This will be a FEEPI-scan with voxel size 3×3×3mm; EPI-factor=43. Six

runs of 24 trials are separated by a short brake.

- Emotion regulation task (ERT, 20min): this will be a modification of the emotion regulation task

described earlier.62 63 The stimulus set will consist of 9 x 4 (sad, happy, fearful, neutral) x 2

(attend, regulate) pictures derived from the International Affective Picture System (IAPS)64 and

http://nl.dreamstime.com; we will match each set for valence, arousal and content. We

preselected the IAPS pictures based on IAPS ratings (scale: 1-9) for valence (neutral: 4-6; positive,

i.e. happy: >6; negative, i.e. fearful and sad: <4); arousal (neutral: <3; emotional, i.e. happy,

fearful, or sad: >6) and furthermore ratings for emotion specificity as assessed by Mikels et al.64

(scale: 1-9) (>7 for each specific emotion category; neutral: <3 for every emotion). In addition, we

will use stock photos from http://nl.dreamstime.com based on emotional content. In total, we

selected 110 pairs of pictures, matched for emotional content. To make matching between IAPS

and Dreamstime pictures possible, we performed an independent pilot study (N=41 healthy

controls). Subjects rated all pictures on valence and arousal [using the same Self-Assessment

Manikin (SAM)65

used for the IAPS database, ranging from unpleasant to pleasant for valence,

and from calm to excited for arousal], emotion type [on a scale from 1 (emotion is not elicited at

all) till 9 (emotion is elicited very strongly)] and complexity [on a scale from 1 (picture is very easy

to interpret) till 9 (picture is very difficult to interpret)]. Based on these ratings, we eventually

selected 36 sets of 2 pictures (9 sets for each emotional category). Within each pair, we matched

the pictures (one for the attend, one for the regulate condition) for valence, arousal, complexity

and emotional content. We will present the pictures in a semi-blocked pseudo-randomized

design. Each block will start with the instruction presented in the middle of the screen (4s),

followed by 3 successive pictures of the same emotional category (10s each). After each picture,

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subjects will indicate the emotional intensity resulting from attending or regulating on a Visual

Analogue Scale (VAS). After each block subjects will also rate their performance (i.e. how well

they were capable of attending or regulating). We will separate blocks by a fixation cross (4s). We

will pseudorandomize and counterbalance the order of stimuli presentation, and instruction

within and between subject groups. We expect this task to show – amongst others- negative bold

responses when viewing pictures relative to the fixation cross (resting state) in the pregenual

anterior cingulate. This will be two FEEPI-scan with max durations=822s; voxel size 3×3×3mm;

EPI-factor=43.


1.1.5.1. Fatty acid metabolism

We will wash erythrocytes of venous EDTA blood three times in isotonic saline, count them by

routine hemocytometric analysis and freeze them overnight in a BHT (2,6-di-tert-butyl-4-

methylphenol)-coated Eppendorf cup. Next, we will transmethylate fifty microliters of the resulting

hemolysate in 1ml 3M HCl by incubating for 4hrs at 90°C in the presence of 10nmol internal

standard; the methyl ester of 18-methylnonadecanoic acid. After cooling, we will extract the aqueous

layer in 2ml hexane, and take this extract to dryness under nitrogen flow and resuspend it in 80μl

hexane. Subsequently, we will inject one microliter of this solution into a Hewlett Packard GC 5890

equipped with an Agilent J&W HP-FFAP, 25m, 0.20mm, 0.33µm GC Column, and detect eluting fatty

acid methylesters by flame ionization detection. Finally, we will calculate fatty acid concentrations

using the known amount of internal standard and express them as pmol/106 cells for erythrocytes.66

67

1.1.5.2. Genetics

We will apply polymerase chain reaction (PCR) and HinfI restriction enzyme digestion as described

previously.68

In short, we will isolate DNA from blood using a filter-based method (QIAamp DNA Mini

Kit, Qiagen Ltd., United Kingdom). Next, we will design PCR-primers using Primer 3 (available at

http://bioinfo.ut.ee/primer3/). Subsequently, we will use a Matrix Assisted Laser Desorption

Ionization Time Of Flight (MALDI-TOF) mass spectrometer from Bruker Daltonics. To increase

reliability, we will genotype all samples in duplicate. Finally, we will save additional genetic material

for future analyses.14

1.1.5.3. Blood storage

We will acquire platelet-poor plasma from lithium-heparine, EDTA and citrate blood tubes using the

following procedure. First, we will centrifuge tubes for 10min at 2680×g (no brake) at 18°C. Next, we

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will pipet plasma of each tube in a separate cryovial®. Subsequently, we will centrifuge the cryovials®

for 5min at 14.000×g (no brake) at 18°C. Finally, we will store the platelet-poor plasma in separate

micronic vials at -80°C until further analyses.


We will instruct participants to provide five saliva samples over a (working, if applicable) day before

the first study-session (at awakening, 30, 45 and 60min thereafter, followed by a fifth measurement

at 22.00h) to diurnally reflect the morning awakening curve and evening HPA-axis activity. In

addition, we will collect saliva using a Salivette before and after sad mood induction to investigate

HPA-axis response to a psychological personal stressor. While Dickerson & Kemeny69

indicated that,

on average, an emotion induction stressor does not elicit a significant cortisol response, this should

be interpreted with caution because of the relatively small numbers of studies that fell in this

category as acknowledged by the authors. Moreover, it could be that some studies observed a

positive, and others a negative effect, that levels out as no effect overall. In addition, Dickerson &

Kemeny excluded studies in which recruitment was based on a physical or psychological diagnosis or

a stressful experience (e.g., diabetes, depression, bereavement). This makes it hard to extrapolate

their findings to our sample of recurrently depressed patients. Of note, several more recent papers

did observe interesting effects of mood on salivary cortisol in recurrent depression (Chopra et al.,

2008;70 Huffziger et al., 2013)71, which makes this assessment of great interest to our study. We will

instruct subjects not to eat, smoke, drink tea or coffee or brush their teeth within 15min before

sampling,10 72 73 to write down exact sampling day and time, and to keep samples refrigerated before

bringing them back to us at the first study-session; we will store them (-20°C) until analysis.

1.1.7. Waist circumference

Increased waist circumference reflects abdominal obesity, which is a metabolic syndrome criterion.74-

77 Abdominal obesity is closely related to insulin resistance and metabolic dysregulation, and a strong

risk factor for development of diabetes type II and cardiovascular disease.78

We will measure waist

circumference at the vertical middle between the lowest palpable rib and upper part of the ilium. We

will use a solid, nonexpendable, measuring tape, which we will apply with light pressure (but without

squeezing underlying tissues) horizontally around the waist. We will instruct subjects to stand with

their feet close together, arms next to their body, and their bodyweight equally distributed. We will

instruct subjects to take of thick clothing, and perform the actual measurement at the end of a

normal expiration.

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1.2. Power analyses

Power analyses were performed using G*Power 3.9.1.2 and package PowerSurvEpi in R.79-83 In the

cross-sectional compararison between patients (n = 60) and controls (n =40), with 80% power, α=.05

and 5 predictors in total we can detect small effects (effect size f² = 0.0994846). For the prospective

analyses, Cox proportional hazard regression with 60 patients of which 2×±20 eligble for a second

scan , a correlated covariate of interest and a moderate effect size results in a power of >80% with

α=.05.

Of note, in the initially registered trial protocotol we proposed to include 50 patients and 50 controls.

However, based on data from our previous studies that was analysed since then10 14 66 84-86 we

amended this aspect in our protocol to 60 patients and 40 controls. While yielding an identical total

number of subjects, this provides a more optimized balance between the contrast patients vs.

controls on the one hand, and the prospective analyses in the patients on the other. Analyses of our

previous studies show that there exist rather large effects in the differences between patients and

controls, and relatively smaller effects in the prospective associations. By changing the

patient:control ratio to 60:40, we lose only little power in the cross-sectional analyses (a little less

optimal distribution but equal total number), but gain additional prospective power. In addition,

because the estimates in the control population are expected to be more homogeneous than in the

patient population, also in the cross-sectional analyses the decrease in sample size of the control

population is expected to result in smaller loss of power than the gain in power resulting from the

equal increase of the patient sample size.

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morning

SeqNo FieldName Question (Dutch) Translation Value Label Translation value Label

1 mor_asleep HOE LANG DUURDE HET VOORDAT IK GISTERENAVOND INSLIEP? How long did it take before I fell asleep last night? 1=0-5min<r>2=5-15min<r>3=15-30min<r>4=30-45min<r>5=45m-1u<r>6=1-2uur<r>7=2-4uur<r>8=>4uur 1=0-5min<r>2=5-15min<r>3=15-30min<r>4=30-45min<r>5=45m-1hr<r>6=1-2hrs<r>7=2-4hrs<r>8=>4hrs

2 mor_nrwakeup HOE VAAK WERD IK AFGELOPEN NACHT WAKKER? How ofter did I wake up last night? 1 - >5 1 - >5

3 mor_lieawake HOE LANG LAG IK VANMORGEN WAKKER VOORDAT IK OPSTOND? How long did I lay awake in bed before I got up this morning? 1=0-5min<r>2=5-15min<r>3=15-30min<r>4=30-45min<r>5=45m-1u<r>6=1-2uur<r>7=2-4uur<r>8=>4uur<r> 1=0-5min<r>2=5-15min<r>3=15-30min<r>4=30-45min<r>5=45m-1hr<r>6=1-2hrs<r>7=2-4hrs<r>8=>4hrs

4 mor_qualsleep IK HEB GOED GESLAPEN I slept well Likert scale Likert scale

5 mor_lookforw IK HEB ZIN IN DEZE DAG I am looking forward to this day Likert scale Likert scale

6 MOR_Phy_Exerc IK HEB ZIN OM ME LICHAMELIJK IN TE SPANNEN I am looking forward to being physically active Likert scale Likert scale

7 MOR_Fun_Act IK HEB ZIN IETS LEUKS TE GAAN DOEN VANDAAG I am looking forward to doing something nice today Likert scale Likert scale

8 MOR_Friends IK HEB ZIN OM MET VRIENDEN TE ZIJN VANDAAG I am looking forward to being with friends today Likert scale Likert scale

9 MOR_Work IK GA VANDAAG WERKEN/STUDEREN I am going to work/study today

10 MOR_Enjoy_Work IK HEB ZIN OM TE GAAN WERKEN/STUDEREN I am looking forward to working/studying today

beep

SeqNo FieldName Question (Dutch) Translation Value Label Value Label

1 mood_relaxed IK VOEL ME ONTSPANNEN I feel relaxed Likert scale Likert scale

2 mood_down IK VOEL ME SOMBER I feel down Likert scale Likert scale

3 Mood_irritat IK VOEL ME GEiRRITEERD I feel irritated Likert scale Likert scale

4 mood_satisfi IK VOEL ME TEVREDEN I feel satisfied Likert scale Likert scale

5 mood_lonely IK VOEL ME EENZAAM I feel lonely Likert scale Likert scale

6 mood_anxious IK VOEL ME ANGSTIG I feel anxious Likert scale Likert scale

7 mood_enthus IK VOEL ME ENTHOUSIAST I feel enthousiastic Likert scale Likert scale

8 pat_suspic IK VOEL ME WANTROUWIG I feel suspicious Likert scale Likert scale

9 mood_cheerf IK VOEL ME OPGEWEKT I feel cheerful Likert scale Likert scale

10 mood_guilty IK VOEL ME SCHULDIG I feel guilty Likert scale Likert scale

11 mood_restl IK VOEL ME RUSTELOOS I feel restless Likert scale Likert scale

12 mood_agitate IK VOEL ME PRIKKELBAAR I feel agitated Likert scale Likert scale

13 thou_worry IK PIEKER I am worrying Likert scale Likert scale

14 se_selflike IK MAG MEZELF I like myself Likert scale Likert scale

15 se_ashamed IK SCHAAM ME VOOR MEZELF I feel ashamed for myself Likert scale Likert scale

16 se_selfdoub IK TWIJFEL AAN MEZELF I doubt myself Likert scale Likert scale

17 pat_handle IK KAN ALLES AAN I can handle anything Likert scale Likert scale

18 soc_who1 MET WIE BEN IK? With whom am I? 10=partner<r>19=inwonenden<r>29=familie uitwonend<r>30=vrienden<r>40=collega's<r>49=kennissen<r>50=onbekenden/\randeren<r>00=niemand 10=partner<r>19=people I live with<r>29=family I don't live with<r>30=friends<r>40=colleagues<r>49=acquaintances<r>50=unknown people/\others<r>00=no one

19 soc_enjoy_alone IK VIND HET AANGENAAM OM ALLEEN TE ZIJN I enjoy being alone Likert scale Likert scale

20 soc_prefcomp IK ZOU LIEVER IN GEZELSCHAP ZIJN I would prefer to have company Likert scale Likert scale

21 soc_who2 MET WIE BEN IK NOG MEER? With whom else am I? 10=partner<r>19=inwonenden<r>29=familie uitwonend<r>30=vrienden<r>40=collega's<r>49=kennissen<r>50=onbekenden/\randeren<r>00=niemand<r> 10=partner<r>19=people I live with<r>29=family I don't live with<r>30=friends<r>40=colleagues<r>49=acquaintances<r>50=unknown people/\others<r>00=no one

22 soc_who3 EN...? And…? 10=partner<r>19=inwonenden<r>29=familie uitwonend<r>30=vrienden<r>40=collega's<r>49=kennissen<r>50=onbekenden/\randeren<r>00=niemand 10=partner<r>19=people I live with<r>29=family I don't live with<r>30=friends<r>40=colleagues<r>49=acquaintances<r>50=unknown people/\others<r>00=no one

23 soc_nrtot MET HOEVEEL MENSEN BEN IK? With how many people am I? 1 - >6 1 - >6

24 soc_pleasant IK VIND DIT GEZELSCHAP AANGENAAM I find this company pleasant Likert scale Likert scale

25 soc_prefalon IK ZOU LIEVER ALLEEN ZIJN I would prefer to be alone Likert scale Likert scale

26 soc_interact WE ZIJN SAMEN IETS AAN HET DOEN We are doing something together Likert scale Likert scale

27 phy_hungry IK HEB HONGER I am hungy Likert scale Likert scale

28 phy_tired IK BEN MOE I am tired Likert scale Likert scale

29 phy_pain IK HEB PIJN I am having pain Likert scale Likert scale

30 phy_dizzy IK VOEL ME DUIZELIG I feel dizzy Likert scale Likert scale

31 phy_drymouth IK HEB EEN DROGE MOND I have a dry mouth Likert scale Likert scale

32 phy_nauseous IK VOEL ME MISSELIJK I feel nauseous Likert scale Likert scale

33 act_what1 WAT DOE IK? (vlak voor de piep)? Dit past in de volgende categorie: What do I do (just before the beep)? This fits in the next catagory: 43=ontspanning\ractief<r>45=ontspanning\rpassief<r>00=niets/rusten<r>10=werk/studie<r>21=huishouden<r>51=praten<r>27=zelfverzorging<r>21=zorg voor anderen<r>26=medische zorg<r>60=eten/drinken<r>88=onderweg<r>89=anders 43=active relaxation<r>45=passive relaxation<r>00=nothing/rest<r>10=work/study<r>21=housekeeping<r>51=speaking<r>27=self-care<r>21=care for others<r>26=medical care<r>60=eating/drinking<r>88=on the way<r>89=otherwise

34 act_what2 EN DAARNAAST ...? And what else…? 43=ontspanning\ractief<r>45=ontspanning\rpassief<r>00=niets<r>10=werk/studie<r>21=huishouden<r>51=praten<r>27=zelfverzorging<r>21=zorg voor anderen<r>26=medische zorg<r>60=eten/drinken<r>88=onderweg<r>89=anders 43=active relaxation<r>45=passive relaxation<r>00=nothing<r>10=work/study<r>21=housekeeping<r>51=speaking<r>27=self-care<r>21=care for others<r>26=medical care<r>60=eating/drinking<r>88=on the way<r>89=otherwise

35 act_difficul DIT KOST MIJ MOEITE This is difficult to me Likert scale Likert scale

36 act_well DIT KAN IK GOED This I can do well Likert scale Likert scale

37 act_else IK ZOU LIEVER WAT ANDERS DOEN I would prefer to do something else Likert scale Likert scale

38 phy_physact SINDS DE VORIGE PIEP HEB IK ME LICHAMELIJK INGESPANNEN Since the last beep I have been physically active Likert scale Likert scale

39 eve_event DENK AAN DE VOOR JOU BELANGRIJKSTE GEBEURTENIS SINDS DE VORIGE PIEP Think about the event that was most important to you since the last beep 1=>>> 1=>>>

40 eve_unpleas DEZE GEBEURTENIS WAS: This event was: -3=zeer onplezierig<r>+3=zeer plezierig<r> -3=very unpleasant<r>+3=very pleasant<r>

41 eve_important DEZE GEBEURTENIS WAS: This event was: -3=zeer onbelangrijk<r>+3=zeer belangrijk -3=very unimportant<r>+3=very important<r>

42 eve_attrib DEZE GEBEURTENIS WAS: This event was: 1=iets wat me overkomen is<r>2=iets waar ik zelf invloed op had<r>3=iets regelmatigs of routine<r>4=een gedachte/gevoel<r>5=anders 1=something that happened to me<r>2=something I could influence<r>3=something regular or routine<r>4=a thought/feeling<r>5=otherwise

43 eve_content3 DIT HAD VOORAL TE MAKEN MET: This event had to do with: 1=contact met anderen<r>2=de omgeving waarin ik was<r>3=eigen gesteldheid<r>4=activiteit<r>5=nieuwe informatie<r>6=anders 1=contact with others<r>2=the environment in which I was<r>3=my own state<r>4=activity<r>5=new information<F88r>6=otherwise

44 eve_specify2 DIT GEBEURT GEWOONLIJK: This usually happens: 1=vaker per dag<r>2=dagelijks<r>3=wekelijks<r>4=maandelijks 1=multiple times per day<r>2=daily<r>3=weekly<r>4=monthly

45 eve_specify3 DIT HAD BETREKKING TOT: This was related to: 1=anderen<r>2=mezelf<r>3=concrete dingen<r>4=activiteit<r>5=iets abstracts<r>6=onbekend<r>7=anders 1=others<r>2=myself<r>3=concrete things<r>4=activity<r>5=something abstract<r>6=unknown<r>7=otherwise

46 Event_Anticipation BEDENK WAT JE DE KOMENDE TWEE UUR GAAT DOEN Think about what you are going to do in the next two hours 1=>>> 1=>>>

47 Event_Like HOEVEEL ZIN HEB IK HIERIN? How much are you looking forward to this event?

48 Event_Cat DEZE ACTIVITEIT PAST IN DE VOLGENDE CATEGORIE: This event fits in the next category: 43=ontspanning\ractief<r>45=ontspanning\rpassief<r>00=niets/rusten<r>10=werk/studie<r>21=huishouden<r>51=praten<r>27=zelfverzorging<r>21=zorg voor anderen<r>26=medische zorg<r>60=eten/drinken<r>88=onderweg<r>89=anders 43=active relaxation<r>45=passive relaxation<r>00=nothing/rest<r>10=work/study<r>21=housekeeping<r>51=speaking<r>27=self-care<r>21=care for others<r>26=medical care<r>60=eating/drinking<r>88=on the way<r>89=otherwise

49 beep_disturb DEZE PIEP STOORDE MIJ This beep disturbed me Likert scale Likert scale

50 REMOD_BE50 BEDANKT Thank you

evening

SeqNo FieldName Question (Dutch) Translation Value Label Value Label

1 evn_ordinary DEZE DAG WAS EEN GEWONE DAG This day was an ordinary day Likert scale Likert scale

2 evn_niceday IK VOND DIT EEN LEUKE DAG I found this a nice day Likert scale Likert scale

3 evn_inflmood HET INVULLEN VAN HET APPARAATJE HEEFT MIJN STEMMING BEINVLOED. Responding to this pager influenced my mood Likert scale Likert scale

4 evn_pager ZONDER HET APPARAATJE ZOU IK VANDAAG ANDERE DINGEN HEBBEN GEDAAN Without the pager I would have done other things today Likert scale Likert scale

5 evn_work IK BEN VANDAAG GAAN WERKEN/STUDEREN I went to work/study today

6 PM_txt_thank BEDANKT Thank you

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Category Item No Item Description Reinforcement learning task Cued Emotional Conflict task Emotion regulation task

EXPERIMENTAL

DESIGN -design

specification

1a Describe number of

blocks, trials,

experimental units per

session or per subject

6 blocks of 24 trials (max.

durations=248s)

24 'semi'-blocks of three similar

trials: 3 attend sad, 3 attend fear,

3 attend happy, 3 attend neutral, 3

regulate sad, 3 regulate fear, 3

regulate happy, 3 regulate neutral

blocks

1b State length of each trial

and interval between trials

Each trial started with a cue

presented for 500 ms. After the

presentation of the cue, a fixed

interval of 2000ms separated the

presentation of the cue from the

target.

Each picture was presented for 10

s, followed by an 'ISl' of max. 6

seconds (during which subjects

rated their feelings; this interval

ended as soon as the subject had

finished the rating, i.e. was self-

paced). After every third picture,

subjects also rated how well they

managed to perform during the

previous block (also max. 6

seconds, self-paced), followed by

a 4-seconds inter-block interval

during which a fixation cross was

presented.

1c If ISIs are variable, report

the mean and range of

ISIs and how they are

distributed

The inter-trial interval was

jittered between 3500 and 5500

ms (in 500 ms steps).

See description above.

1d Block-Designs : specify

the length of blocks

NA Each 'semi'-block had a duration

of min. 30 s and max. 54 s.

1e Event-related Designs :

state whether the design is

optimized for efficiency,

and if so, state how

Jittered inter-trial interval

1f Mixed designs : state

correlation between block

and event regressors

NA

EXPERIMENTAL

DESIGN - task

specification

2a Instructions: state what

subjects are asked to do

See supplement See supplement See supplement

2b Stimuli: describe what the

Stimuli are and how many

there are


How many:


How many: 12 for each cue per

block

See supplement

2c Stimuli: state whether

specific stimuli repeated

across trials

See supplement See supplement See supplement

EXPERIMENTAL

DESIGN - planned

comparison

3 If the experiment has

multiple conditions, state

what the specific planned

comparisons are, or

whether an omnibus

ANOVA test is used

Full factorial ANOVA, and

opposite-sad vs. actual-sad,

opposite-happy

vs. actual-happy, and opposite-

sad vs. opposite-happy contrasts.

Full factorial ANOVA

HUMAN SUBJECTS -

ethics approval

5 State which Institutional

Review Board (IRB)

approved the protocol

See main text See main text See main text

HUMAN SUBJECTS -

behavioral performance

6 State how behavioral

performance was

measured (e.g., response

time, accuracy)

Intensity-, wanting- and liking-

ratings for both tastes on a visual

analogue scale directly before

and after the task

Response time and accuracy for

face label assignments.

See supplement

DATA ACQUISITION -

image properties

7a Describe manufacturer,

field strength (in Tesla),

model name

See main text See main text See main text

7b State the number of

experimental sessions and

volumes acquired per

session

Number of dynamics=1125 Six sessions with max. number of

dynamics=120

Two sessions with max. number

of dynamics=407

7c State pulse sequence type

(gradient/spin echo,

EPI/spiral)

EPI EPI EPI

7d State field of view, matrix

size, slice thickness, inter-

slice skip

FOV=240×240×82.2mm; slice


gap=0.3mm; acquired

matrix=80×80

FOV=240×240×121.8mm; slice


gap=0.3mm; acquired

matrix=80×80

FOV=240×240×121.8mm; slice


gap=0.3mm; acquired

matrix=80×80

7e State acquisition

orientation (axial, sagittal,

coronal, oblique; if axials

co-planar with AC-PC,

the volume coverage in

terms of Z in mm)

Slice

orientation=transverse/axial

Slice orientation=transverse/axial Slice orientation=transverse/axial

7f State clearly whether it is

on the whole brain. If not,

state area of acquisition

Angulated field of view from

lower edge pons and lower end

prefrontal cortex, 25 slices up to

usually the top of the dorsal

anterior cingulate cortex.

Whole brain, 37 slices. Whole brain, 37 slices.

7g State order of acquisition

of slices (sequential or

interleaved)

Sequential, ascending Sequential, ascending Sequential, ascending

7h State TE, TR, flip angle TE=28ms; TR=1500ms; FA=70˚ TE=28ms; TR=2000ms; FA=76˚ TE=28ms; TR=2000ms; FA=76˚

Items 4 and >7 are not applicable because the present manuscript described the study protocol.

Abbreviations: ISIs, inter-stimulus intervals; ANOVA, analysis of variance; EPI, Echo Planar Imaging; FOV, field of view; TE, echo time; TR, repetition time;

MRI, magnetic resonance imaging; MNI, Montreal Neurological Institute space; DCT, discrete cosine transform; CC, cubic centimeter; FWE, family-wise error; impulse response

FDR, false discovery rate; FWHM, full-width at half-maximum; RESEL, resolution element; ROI, region of interest; FIR, finite

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STROBE 2007 (v4) checklist of items to be included in reports of observational studies in epidemiology*

Checklist for cohort, case-control, and cross-sectional studies (combined)

Section/Topic Item # Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract 1

(b) Provide in the abstract an informative and balanced summary of what was done and what was found 2

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 4-11

Objectives 3 State specific objectives, including any pre-specified hypotheses 9-11

Methods

Study design 4 Present key elements of study design early in the paper 12

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection 12-19

Participants 6 (a) Cohort study—Give the eligibility criteria, and the sources and methods of selection of participants. Describe

methods of follow-up

Case-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control

selection. Give the rationale for the choice of cases and controls

Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants

12-19

(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposed

Case-control study—For matched studies, give matching criteria and the number of controls per case 12-19

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic

criteria, if applicable 12-21

Data sources/ measurement 8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group 12-21 & supplement

Bias 9 Describe any efforts to address potential sources of bias 12-21

Study size 10 Explain how the study size was arrived at 19-20 & supplement

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen

and why 19-21

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 19-21

(b) Describe any methods used to examine subgroups and interactions 19-21

(c) Explain how missing data were addressed 19-21

(d) Cohort study—If applicable, explain how loss to follow-up was addressed

Case-control study—If applicable, explain how matching of cases and controls was addressed 19-21

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Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy

(e) Describe any sensitivity analyses 19-21

Discussion

Key results 18 Summarise key results with reference to study objectives NA => protocol article

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction

and magnitude of any potential bias 24-26

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results

from similar studies, and other relevant evidence NA => protocol article

Generalisability 21 Discuss the generalisability (external validity) of the study results NA => protocol article

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based 28

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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