Cohort profile for the STratifying Resilience and Depression

STratifying Resilience and Depression Longitudinally (STRADL) is a population-based study built on the Generation Scotland: Scottish Family Health Study (GS:SFHS) resource. The aim of STRADL is to subtype major depressive disorder (MDD) on the basis of its aetiology, using detailed clinical, cognitive, and brain imaging assessments. The GS:SFHS provides 1 1 1 1 2 3 4 1 1 1 4 4 4 2 1 2 2 2 1 2 4 4 4 4 4 4 4 5 6 7 7 4 4 4 4 4 4 4 1*

clinical, cognitive, and brain imaging assessments.The GS:SFHS provides an important opportunity to study complex gene-environment interactions, incorporating linkage to existing datasets and inclusion of early-life variables for two longitudinal birth cohorts.Specifically, data collection in STRADL included: socio-economic and lifestyle variables; physical measures; questionnaire data that assesses resilience, early-life adversity, personality, psychological health, and lifetime history of mood disorder; laboratory samples; cognitive tests; and brain magnetic resonance imaging.Some of the questionnaire and cognitive data were first assessed at the GS:SFHS baseline assessment between 2006-2011, thus providing longitudinal measures of depression and resilience.Similarly, routine NHS data and early-life variables are linked to STRADL data, further providing opportunities for longitudinal analysis.Recruitment has been completed and we consented and tested 1,188 participants.

Introduction
Why was the study set up? Major depressive disorder (MDD) affects approximately 13% of the population at least once in their lifetime 1 , and remains a leading cause of economic burden and non-lethal global disability 2,3 due to its recurrent or chronic nature.At present, MDD diagnosis is based on arbitrary and clinically heterogeneous criteria 4 .Consequently, and even with optimal management, much of the disability caused by MDD persists 5 because of the absence of targeted disease-modifying treatments.The underlying pathophysiology of MDD is believed to be heterogeneous 6 , with genetic and environmental factors acting to influence disease expression.Thus, it is important for treatment to shift from the current "trial and error" approach, towards personalised and preventative forms of treatment for individuals with markedly different disease mechanisms.However, progress in this area has been severely restricted because the aetiology of MDD is complex, and remains poorly understood.

STratifying
Resilience and Depression Longitudinally (STRADL) aims to subtype MDD on the basis of its aetiology using detailed clinical, cognitive, and brain imaging assessments.STRADL will examine the interaction between genetic and environmental factors that increase risk and occurrence of different MDD subtypes, and assess common and distinct mechanisms and clinical trajectories of MDD phenotypes.Additionally, STRADL aims to assess individual resilience, or the ability to adapt positively and 'avoid' psychopathology despite exposure to known risk factors such as stress, early-life adversity, and family history 7 .
STRADL was built on the Generation Scotland: Scottish Family Health Study resource (GS:SFHS) 8 , which undertook its first major baseline assessments between 2006 and 2011.GS:SFHS is a population-based study of genetic health and complex disease in a cohort of 24,096 individuals, who have been extensively phenotyped for MDD and related traits.This cohort provides an important opportunity to study geneenvironment interactions, and remains one of the richest sources of data available, incorporating linkage of existing phenotypic and genomic data, detailed lifestyle and socioeconomic characterisation, extensive eHealth Record linkage 9 , and the inclusion of two longitudinal birth cohorts -the Walker birth cohort 10 , and Aberdeen Children of the 1950s (ACONF) 11 .The first wave of STRADL included depression-focused follow-up assessment of GS:SFHS, which involved remote questionnaires; study protocol and cohort characteristics are described elsewhere 12 .Here, we describe the second wave of STRADL, a depression-focused deep phenotyping face-to-face assessment, using detailed clinical and cognitive tests, and neuroimaging.The results describe the cohort profile and baseline questionnaire and cognitive data, and we provide a summary of key demographic data from the current wave of STRADL, compared to STRADL remote follow-up and wider GS:SFHS baseline assessment.A summary of all data available and the proportion of valid and useable data is also provided.

Who is in the cohort?
We aim to study people both with and without depression, and therefore our recruitment targeted the whole GS:SFHS population, not merely people with a depression history.Participants in the Tayside and Grampian areas who had already taken part in GS:SFHS between 2006-2011, and who were eligible for re-contact, were sent a postal invitation by the University of Dundee Health Informatics Centre (HIC).Included in the invitation was a reply slip to indicate whether the participant would be willing to undergo face-to-face assessment and brain magnetic resonance imaging (MRI), described here.Those who replied positively were contacted by telephone by a researcher at the most local recruitment centre.In Dundee (Tayside) recruitment targeted members of the Walker cohort, and in Aberdeen recruitment initially targeted members of ACONF, due to the rich early-life data already available for these cohorts.
In total, 5,649 potential participants were invited to take part in the study; 576 (10.2%) were members of ACONF; 1,103 (19.5%) were members of the Walker cohort; and 3,970 (70.3%) were members of the wider GS:SFHS population.Out of these potential participants, 646 (11.4%) people declined participation at first point of contact with HIC, and we received no reply from 3,358 (59.4%) people, even after sending up to three reminders.Initially, 1,645 (29.1%) people responded positively; however, a further 170 (3.0%) declined once they were contacted by our research team or withdrew before consenting.Recruitment ended in May 2019 and we consented and tested 1,188 (72.2%) of positive respondents across Aberdeen (n = 582) and Dundee (n = 606) sites.Figure 1 shows the recruitment process and attrition.

What has been measured and when?
Table 1 shows all variables collected in STRADL face-to-face assessments, and those that were repetitions of the GS:SFHS baseline assessment and STRADL remote questionnaire follow-up.Before any new data were collected, participants signed a consent form permitting data and samples to be shared with other researchers through a secure data management system, and provided permission to be re-contacted in the future for additional research.Consent for linkage of participant data and samples to routine NHS records was previously obtained as part of the original GS:SFHS (05/S1401/89).All subsequent procedures were conducted following an independent, but linked, ethics application (14/SS/0039).
At each site participants attended three testing 'stations', which involved i) collection of clinical and questionnaire data, and biological samples ii) cognitive assessment, and iii) neuroimaging, the order of which varied at random between participants.Data from the clinical station were collected without a set order; however, cognitive tests were administered in the same order for each participant, and the MRI sequences also remained the same -except for one fMRI task, which was counterbalanced (details described in section Brain magnetic resonance imaging).All measures were administered in accordance with rigorous standard operating procedures based on best practice.

Clinical assessment
Medical history was updated from previous GS:SFHS baseline assessment and any new diagnoses or medical episodes were recorded.General health and lifestyle data were also collected, as were the physical measurements of height, weight, two automated measures of blood pressure, and left-and righthand grip strength (using a Patterson Medical Jamar hand dynamometer).We collected laboratory samples for repeat genetic analysis and additional genomic analyses, and for analysis of epigenetic status including DNA methylation.Additionally, detailed questionnaire data were collected that will be used to test the structure of depressive symptoms and their association with each measure.Laboratory samples.A small sample of hair was collected from the posterior vertex region for longitudinal cumulative cortisol.Cortisol assays from hair samples provide a more stable marker of chronic cortisol exposure compared to cross-sectional blood or urine samples, which show considerable diurnal variation 13 .Other available assays include cortisone, testosterone, progesterone, and dehydroepiandrosterone.Venepuncture was carried out using a butterfly needle kit.Blood was extracted into the following vacutainers types (analyses in parentheses): 1) EDTA (Full blood count; FBC); 2) clot activator gel for serum separation (C-reactive protein CRP); 3) EDTA (DNA extraction); 4) 2 x Tempus RNA (RNA extraction); 5) EDTA (plasma biomarkers); 6) Lithium Heparin (plasma biomarkers).FBC and CRP samples were taken and sent to NHS laboratories for screening of clinically significant markers of anaemia and inflammation.When blood samples could not be collected, a saliva DNA collection kit (Oragene or GeneFiX) was used instead.These laboratory samples were temporarily stored at each collection  Clinical interview and questionnaire data.All participants were assessed for a lifetime history of MDD.We used a research version of the Structured Clinical Interview for DSM-IV disorders (SCID) 14 to assess symptoms of mood disorder (including MDD and episodes of mania and hypomania), repeating the GS:SFHS baseline assessment.Diagnostic criteria were based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR).For participants who met full criteria for MDD, we assessed if any episode had a post-partum onset, and if criteria for melancholic or atypical MDD subtypes were met.The research version of the SCID was designed to allow assessors to systematically evaluate individuals against the key DSM-IV-TR criteria for unipolar depression and bipolar disorder.The SCID has good reliability, and it is considered the "gold standard" in determining clinical diagnoses and their accuracy 15 .
Participants also completed a series of short questionnaires that assessed resilience, psychological well-being and mild psychiatric problems, and personality, some of which were repeated after first being completed for GS:SFHS (see Table 1).The Brief Resilience Scale (BRS) 16 is a six-item questionnaire used as a measure of psychological resilience, or the ability to 'bounce back' from stress.Participants were assessed for a life history of cannabis use using the Drug Use questionnaire developed for UK Biobank 17 .Those who used cannabis more than once were asked follow-up questions about the frequency and functional impact of their use.Three mood questionnaires were administered: the Mood Disorder Questionnaire (MDQ) 18 , which is a sensitive screen for bipolar spectrum disorders; the Quick Inventory of Depressive Symptomology (QIDS) 19 , which is a 16-item inventory designed to assess the severity of depressive symptoms; and the Hospital Anxiety and Depression Scale (HADS) 20 anxiety subscale (seven items) was used to screen for symptoms of anxiety.In addition, the General Health Questionnaire (GHQ) 21   psychological well-being on four scales: somatic symptoms; anxiety; social dysfunction; and depression.We used a Likert scoring system for the GHQ to calculate scores for each scale separately, as well as a total score.
We administered two measures that assess core personality traits: we used the neuroticism and extraversion scales from the Eysenck Personality Questionnaire -Revised Short Form (EPQ-R) 22 , each of which has 12 items; and the International Personality Item Pool (IPIP), Five-Factor Personality Inventory 23 , which is a 50-item questionnaire that assesses the following core personality traits: extraversion; agreeableness; conscientiousness; emotional stability (the reverse of neuroticism); and imagination/intellect (similar to openness).Additionally, the General Causality Orientations Scale 24 , which consists of 12 vignettes describing scenarios to determine each person's orientation of causality 25 , was used to assess one's inclination towards being motivated autonomously, externally, or passively.
Finally, we assessed early-life adversity (childhood or adolescent abuse or neglect) using the Childhood Trauma Questionnaire (CTQ) 26 .This is a 28-item retrospective inventory that assesses three areas of abuse (emotional, physical, and sexual) and two areas of neglect (emotional and physical).The CTQ also includes a minimisation/denial scale that identifies potential underreporting of maltreatment.A mean score was calculated for each measure by totalling the item responses, with appropriate reverse scoring (e.g., GHQ, BRS).Higher scores represent higher levels of psychological distress, personality trait, or childhood trauma, except for the BRS where higher scores indicate greater resilience.Scoring and interpretation of data were based on the administration manual of each test.

Cognitive testing
The cognitive tests that were applied will be used to assess the cognitive phenotype of depression, and whether genetic risk variants are related to impairment in specific cognitive domains.As with the questionnaire data, some cognitive tests were also repetitions of the GS:SFHS baseline assessment (Table 1).We included "cold" (emotion-independent) and "hot" (emotion-laden) cognitive tests, given growing evidence for distinct and interacting relationships between depression and measures of hot and cold cognition 27 .
The cold cognitive test battery included validated and widely used cognitive tests that measure crystallised-and fluid-type cognitive tasks.The Mill Hill Vocabulary test 28 was used as a measure of acquired verbal intelligence, and is an estimate of 'crystallised intelligence' and peak cognitive ability.The Controlled Oral Word Association task 29 was used as a measure of phonemic verbal fluency using three letters (C, F, and L).The Digit Symbol Coding subtest from the Wechsler Adult Intelligence Scale-III 30 was used to measure information processing speed.A United Kingdom version of the Logical Memory subtest from the Wechsler Memory Scale-III 31 was used to assess verbal memory and provided a measure of immediate and delayed verbal declarative recall.Total scores were created for each cognitive test by adding the number of correct responses; higher scores indicate better performance.The Matrix Reasoning test, a paper adaptation of the computerised version from the COGNITO psychometric examination 32 , was used to measure perceptual organisation and visuospatial logic.A summary of all mood, personality, and cognitive data and their completeness is shown in Table 3.
Three 'hot' cognitive measures were administered on a touchscreen laptop computer.The first task -the Bristol Emotion Recognition Test -consisted of 96 trials (16 of each emotion) that assessed recognition of six basic human facial emotions (happiness, anger, sadness, disgust, surprise, and fear), and biases in the attribution of emotion.The Affective Go/No-Go task comprised 120 trials that assessed behavioural inhibition using facial emotional stimuli (happy, sad, and neutral expressions).Finally, given evidence for impairments in reward processing in depression 33 , we also included a modified version of the Cambridge Gambling Task, which assesses decision-making, risk-taking behaviour, and reward processing (30 trials).These three tests are described in detail elsewhere 34,35 .

Brain magnetic resonance imaging
The neuroimaging protocol will allow analysis of potential risk factor relationships with brain structure and function, and test neurobiological mechanisms that are associated with depressive symptoms and resilience.In Aberdeen, participants were imaged on a 3T Philips Achieva TX-series MRI system (Philips Healthcare, Best, Netherlands) with a 32 channel phasedarray head coil and a back facing mirror (software version 5.1.7;gradients with maximum amplitude 80 mT/m and maximum slew rate 100 T/m/s).A projector and "Presentation" (Neurobehavioural Systems Inc, Berkeley, CA, USA) version 18.1 were used for the presentation of task-based fMRI.In Dundee, participants were scanned using a Siemens 3T Prisma-FIT (Siemens, Erlangen, Germany) with 20 channel head and neck phased array coil and a back facing mirror (Syngo E11, gradient with max amplitude 80 mT/m and maximum slew rate 200 T/m/s).A magnetic resonance compatible LCD screen was used to display fMRI (NordicNeuroLab, Bergen, Norway) task stimuli using "Presentation" version 20.0.
Both centres used the same protocol including structural and functional sequences.The structural sequences collected were as follows: 3D T1-weighted fast gradient echo with magnetisation preparation; 3D T2-weighted fast spin echo; 3D Fluid Attenuation Inversion Recovery (FLAIR); Diffusion Tensor Imaging (DTI); and Susceptibility Weighted Imaging (SWI) or T2*-weighted gradient echo.The functional sequences comprised of two task-based fMRI tasks and a resting state fMRI sequence.The sequence parameters, as well as the order of acquisitions, are presented in Table 4.
T1-weighted images of the brain were used to assess brain regional volumes, cortical thickness, gyrification index, voxelbased morphometry analysis, certain lesions such as lacunes, cortical and larger subcortical infarcts, and will also serve as the basis for co-registration with other sequences.A 3D T2-weighted sequence was used to detect lacunes, perivascular spaces, cortical and subcortical infarcts, and other morphological measurements, such as hippocampal subfield extraction.A 3D FLAIR was used to detect white matter hyperintensities.SWI data, for the determination of brain microbleeds, basal ganglia mineralisation, and cortical superficial siderosis, were acquired using a 3D multi-echo gradient-echo sequence in Aberdeen and a single-echo protocol in Dundee.Phase and magnitude data were saved for the calculation of T2* relaxation.All vascular lesions listed above are defined in the Standards for Reporting Vascular changes on Neuroimaging standards 36 .All structural images were reviewed by a neuroradiologist for visual analysis of vascular changes and incidental findings.Whole-brain DTI were recorded to allow assessment of microstructural integrity of white matter including fibre direction and structural connectivity.This protocol reflects established neuroimaging approaches as used in several large cohort studies of ageing and of cerebrovascular diseases 37,38 .
There were two task-based fMRI sequences: an implicit emotional processing task (fearful versus neutral faces), and a modified version of an instrumental reward task with an additional choice value component.Both of these, as well as resting state fMRI, were acquired at 30 degrees away from the anterior commisure-posterior commisure (AC-PC), towards the coronal plane.The fearful faces from NimStim 39 facial stimuli set assessed emotional-limbic circuitry through a block fMRI design, and measures the brain's neural responses to the viewing of fearful faces in the absence of learning.In order to avoid a gender bias of the images, two versions of the tasks were used, counterbalanced across participants.The Reward task measured reward-related brain activity using an event-related fMRI design in a reinforcement learning context.The resting state fMRI was used to investigate functional connectivity and brain networks.

Results
What are the key findings?Here, we report findings for the complete data set including 1,188 participants.Table 5 shows some demographic similarities and differences between the current STRADL cohort and existing samples.More specifically, the median age of the STRADL sample was 62 years, which is older compared to both STRADL remote follow-up and wider GS:SFHS populations.Gender distributions were comparable to existing data, with 59% being female, and our sample had higher levels of education (university-level education = 40%), compared to existing data.Furthermore, based on SCID interviews, a higher proportion of STRADL participants were diagnosed with a lifetime history of mood disorder (30.7%), compared to GS:SFHS (13.2%).
Out of the total sample, 28.8% received a diagnosis of MDD, and a further 1.9% of bipolar disorder.Recurrent mood disorder was present in 72.7%, and melancholic features (56%) were dominant in the group.Of those with a diagnosis, 71% were female.Overall, however, the cohort was of good psychological health at the time of assessment, as indicated by mean scores on the GHQ, HADS, and QIDS (Table 6), which fell below the thresholds for the presence of psychological distress.
Cognitive scores across all measures were normally distributed.Psychological health, personality, and cognitive scores are presented in Table 6.

Discussion
Strengths and limitations STRADL data have been robustly collected on a wide range of key phenotypes that allow epidemiological study of depression and resilience in a population-based cohort.The MRI and detailed depression phenotyping protocol described here was cross-sectional; however, STRADL can provide longitudinal measures of cognition, personality, and psychological health.This is because many of the cognitive tests applied in STRADL are deliberately the same as those used at the GS:SFHS baseline assessment, as well as some personality and mood measures, as shown in Table 1.The availability of repeated cognitive and questionnaire testing allows us to assess potential determinants of change in cognition and psychological health.Similarly, routine NHS data, and ACONF and Walker cohorts' early-life variables, are linked to STRADL data, providing further opportunities for longitudinal predictors on depression and resilience across the full life-course.Limitations of this cohort are that, as with many longitudinal population studies, participants were more likely to be of good health, and come from more advantaged backgrounds such as higher education and better socioeconomic circumstances than the population in general -findings that are similar to GS:SFHS and STRADL remote follow-up cohort profiles 8,12 , and UK Biobank 17 .Notably, however, participants from a range of health and demographic backgrounds were represented in this group.

Ethical approval and consent
Ethical approval for the study was obtained from the Scotland A Research Ethics Committee (REC reference number 14/55/0039) and the local Research and Development offices.All participants provided written informed consent prior to the collection of any data or samples.
/doi.org/10.12688/wellcomeopenres.15538.1 v1 tina.habota@abdn.ac.uk : Formal Analysis, Investigation, Project Administration, Writing -Original Draft Preparation, Writing -Review & Editing; Author roles: Habota T : Formal Analysis, Investigation, Methodology, Writing -Original Draft Preparation, Writing -Review & Editing; Methodology, Writing -Review & Editing; : Conceptualization, Methodology, Writing -Penton-Voak IS Munafò MR Review & Editing; : Conceptualization, Funding Acquisition, Methodology, Writing -Review & Editing; : Conceptualization, Evans KL Seckl JR Funding Acquisition, Methodology, Writing -Review & Editing; : Conceptualization, Funding Acquisition, Methodology, Writing -Wardlaw JM Review & Editing; : Conceptualization, Funding Acquisition, Writing -Review & Editing; : Conceptualization, Funding Lawrie SM Haley CS Acquisition, Methodology, Writing -Review & Editing; : Conceptualization, Funding Acquisition, Methodology, Writing -Review & Porteous DJ Editing; : Conceptualization, Funding Acquisition, Methodology, Writing -Review & Editing; : Conceptualization, Funding Deary IJ Murray AD Acquisition, Methodology, Supervision, Writing -Review & Editing; : Conceptualization, Funding Acquisition, Methodology, McIntosh AM Supervision, Writing -Review & Editing ISP-V and MRM are co-directors of Jericoe Ltd, a company that designs software for the assessment and modification of Competing interests: emotion recognition.STRADL is supported by the Wellcome Trust through a Strategic Award (104036/Z/14/Z).GS:SFHS received core support Grant information: from the CSO of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006).ADM is supported by Innovate UK, the European Commission, the Scottish Funding Council via the Scottish Imaging Network SINAPSE, and the CSO.HCW is supported by a JMAS SIM Fellowship from the Royal College of Physicians of Edinburgh, by an ESAT College Fellowship from the University of Edinburgh, and has received previous funding from the Sackler Trust.LR has previously received financial support from Pfizer (formerly Wyeth) in relation to imaging studies of people with schizophrenia and bipolar disorder.JDH is supported by the MRC.DJM is an NRS Clinician, funded by the CSO.RMR is supported by the British Heart Foundation.ISP-V and MRM are supported by the NIHR Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol.The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health; and MRM is also supported by the MRC (MC_UU_00011/7

The Sackler Trust, and has previously received research funding from Pfizer, Eli Lilly, and Janssen. Both AMM and IJD are members of The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (MR/K026992/1); funding from the BBSRC and MRC is gratefully acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
). JMW is supported by MRC UK Dementia Research Institute and MRC Centre and project grants, EPSRC, Fondation

Table 1 . List of novel and repeated variables collected in STRADL face-to-face assessment, compared to STRADL remote follow-up and GS:SFHS baseline assessment.
site.RNA and blood DNA samples were stored at -80°C, and all others at -20°C, before being sent to the Edinburgh Clinical Research Facility at the University of Edinburgh for analysis and long-term storage.A summary of the completeness of these clinical data is shown in Table2and Table3.FBC and CRP analysis are complete, and other blood and hair samples are in the process of being analysed.