4-year PhD Projects

Supervisors

Dr Huajie Jin
Department of Health Service & Population Research, Institute of Psychiatry, Psychology and Neuroscience   
Email: huajie.jin@kcl.ac.uk 
Website: https://www.kcl.ac.uk/people/huajie-jin 

Professor Sarah Byford
Department of Health Service & Population Research, Institute of Psychiatry, Psychology and Neuroscience
Email: sarah.byford@kcl.ac.uk 
Website: 1. https://www.kcl.ac.uk/people/sarah-byford   2. https://www.kcl.ac.uk/research/khe

Project Details

Background:  The schizophrenia whole disease model (WDM) developed by the applicants (HJ and SB) is the first health economic model which covers the entire schizophrenia care pathway in the UK, including prevention, diagnosis, and first-line and subsequent lines of treatment [4, 5]. However, due to a lack of RCT assessing different treatment sequences of antipsychotic medication, the effectiveness and safety data of subsequent lines of antipsychotic medications used in the WDM were obtained from network meta-analysis conducted for ‘general’ population with schizophrenia, rather than schizophrenia patients who have failed one or more different antipsychotic medications [6]. To address this limitation, we plan to use the real-world evidence (RWE) included in the Clinical Record Interactive Search (CRIS) to derive the health and cost impacts of different treatment sequences of antipsychotic medications and update the WDM.

Novelty and Importance:  To our knowledge, our study will be one of the first studies to use RWE to estimate the health and cost impacts of different antipsychotic sequence in people with schizophrenia. Our findings can help to improve the outcomes for people with schizophrenia by optimising the treatment sequence of antipsychotic medications and minimise the risk/severity of adverse effects.

Primary aim(s):  To use the CRIS database to estimate the health and cost impacts of different treatment sequences of antipsychotic medication and use the derived data to update the schizophrenia WDM.

Planned research methods and training provided:  A protocol will be drafted for the design and analysis of RWE emulation, including the detailed inclusion/exclusion criteria and outcome measures. The following data will be extracted from CRIS for the included patients: age, sex, ethnicity, comorbidities, treatment sequence of antipsychotic medication, history of relapse, and use of healthcare services. Treatment effectiveness, safety and cost will be estimated in the propensity score-matched cohorts using Cox regression, and then used to update the schizophrenia WDM.

The student will receive training about how to (i) use the CRIS database; (2) use RWE to emulate a clinical trial; and (3) use/adapt the schizophrenia WDM.

Objectives / project plan

Year 1: Background reading, training, and apply access to CRIS.

Year 2: Use the data obtained from the CRIS database to estimate the health and cost impacts of different treatment sequence of antipsychotic medications.

Year 3: Use the derived data to update the schizophrenia WDM.

Year 4: Write up of the thesis and prepare papers for publication. 

Two representative publications from supervisors

Publication 1:  Jin H, Tappenden P, MacCabe JH, Robinson S, Byford S. Evaluation of the cost-effectiveness of services for schizophrenia across the entire care pathway within a single whole disease model. Jama Network Open, 2020; 3(5):e205888.

Publication 2:  Jin H, Tappenden P, MacCabe JH, Robinson S, Byford S. Cost and health impacts of adherence to the NICE schizophrenia guideline recommendations. The British Journal of Psychiatry, 1-6. doi:10.1192/bjp.2020.241, 2020.

Keywords:  Schizophrenia; Antipsychotic medication; Adverse event; Economic model; Cost-effectiveness analysis.

Maudsley BRC research themes

• Psychosis & Mood Disorders
• Informatics
• Trials, Genomics and Prediction

Supervisors

Dr Sharon Stevelink
Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience
Email: Sharon.stevelink@kcl.ac.uk Website: https://kclpure.kcl.ac.uk/portal/sharon.stevelink.html

Dr Ioannis Bakolis
Department of Biostatistics & Health Informatics; Department of Health Service & Population Research, Institute of Psychiatry, Psychology & Neuroscience
Email: Ioannis.bakolis@kcl.ac.uk Website: https://kclpure.kcl.ac.uk/portal/en/persons/ioannis-bakolis(b460eb06-4530-47a6-a611-105902375217).html

Project Details

Background: Mental disorders are the most common cause of sickness absence, long-term occupational disability and the receipt of out-of-work benefits. In 2008, IAPT was introduced to enable more people diagnosed with CMDs to access psychological therapies and to find, remain or return to work.

Novelty and importance

• Government is investing an additional £122 million in IAPT to grow the economy
• It is unclear if accessing IAPT leads to improved work participation and what impact

Universal Credit[1] has on the mental health of those with CMDs

• A first ever UK linkage between benefits and medical records was established to address these pressing questions.
  

Primary aims

• To explore the mental health, treatment and benefits profile of patients accessing IAPT
• To explore the impact of benefit reforms (e.g. Universal Credit) on mental health, treatment utilization and benefit receipt among patients accessing IAPT
• To explore the impact of IAPT on patients’ work participation.

Planned research methods and training provided:  A retrospective longitudinal cohort using a readily available dataset of patients who accessed IAPT. Their medical records were linked with benefits records from the Department for Work and Pensions (n=190,000). Benefits data covers 2005-2020 and clinical data covers 2007-present. The student will attend courses in advanced statistical techniques (Cox regression analysis, time to event analysis, Bayesian modelling, quasi-experimental analytical methods (e.g. propensity score matching, regression discontinuity and differences in differences)).

Project plan

Year 1: Literature review on the impact of welfare reforms on people’s mental health, consult public co-researchers and advisory group for refinement of research questions, develop study protocol and data specification, data cleaning, submit literature review for publication, undertake training in advanced statistics.

Year 2: Start quantitative analysis, secondment to Cordell Health (a social enterprise and occupational health (OH) service provider).

Year 3: Finalise quantitative data analysis, consult public co-researchers and advisory group to assist with interpretation of findings. Write and submit publications to peer-reviewed journals. Disseminate findings at conferences and stakeholder events.

Year 4: Consult public co-researchers and advisory group to develop policy and clinical recommendations and optimal dissemination channels. Write up PhD thesis and submit publications to peer-reviewed journals. Disseminate findings at conferences and stakeholder events.

This PhD represents a unique opportunity for the student to spend time with an OH provider to understand how their research findings can be implemented into clinical practice and policy.

[1] a new benefit which has replaced all working-age benefits

Two representative publications from supervisors

Publication 1:  Stevelink SAM* Phillips A*, Broadbent M, Boyd A, Leal R, Dorrington S, Jewell A, Bakolis I, Madan I, Hotopf M, Fear NT, Downs J (2022). Linking electronic mental healthcare and benefits records in South London: design, procedure and descriptive outcomes. BMJ Open. Resubmitted after revisions.  *Joint first authors

Publication 2:  Stevelink SAM, Pollitt A, Madan I (2019). Mental health and work. What’s next? Occupational and Environmental Medicine, 76, 703-704. Doi: 10.1136/oemed-2019-105820.

Keywords:  Data linkage; Common mental disorders; Benefits; Improving Access to Psychological Therapies (IAPT) services; Work participation.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Eating Disorders and Obesity
• Psychosis and Mood Disorders
• Pain and Addictions
• Informatics

Supervisors

Dr Joseph Chilcot
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience   
Email: Joseph.chilcot@kcl.ac.uk
Website:  https://kclpure.kcl.ac.uk/portal/en/persons/joseph-chilcot(a237755a-6aa1-44c5-90f5-a6bfb4fea0ed).html

Dr Joanna Hudson
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
Email: Joanna.Hudson@kcl.ac.uk 
Website:https://www.kcl.ac.uk/people/joanna-hudson

Project Details

Background: Depression and anxiety are common in CKD [2, 4], however few randomized controlled trials of CBT, have been conducted in kidney patients [3]. Commencing dialysis is a particular time of clinical vulnerability, with approximately 20% having moderate depression which gets more severe over the first year of dialysis [5]. Tailored treatments to support people at this stage of care are needed.

Novelty and Importance: Testing a novel treatment to support people commencing dialysis in their emotional health, using implementation science hybrid designs, is needed to improve whole person care and reduce the research translation gap. The project will identify clusters of depressive and anxiety symptoms over time which serve as risk factors of poorer clinical outcomes.

Primary aim(s): To conduct an individually randomized feasibility implementation study to test a psychological intervention to reduce symptoms of depression and/or anxiety after commencing dialysis. To also examine clusters of depressive and anxiety symptoms over the first year of dialysis and evaluate their association with clinical outcomes.

Planned research methods and training provided: The project will use  a hybrid type 1 feasibility study design [1] informed by the RE-AIM implementation framework [6]. This will be a quantitative PhD with one nested qualitative study and intensive PPIE using a person based approach to intervention development [7]. Training needs will be supported by resources within the health psychology section, KCL and externally.

Objectives / project plan:

Year 1: We have an intervention “iAdjust” developed from research in kidney patients (https://pubmed.ncbi.nlm.nih.gov/27458126/) and informed by other transdiagnostic work. The intervention will be updated and further refined using a person-based approach. Qualitative interviews with health care professionals will inform implementation strategies to embed the intervention as part of routine care.

Year 2: Recruitment to the randomized feasibility implementation study and follow-up in the cohort study. Feasibility will be focused on exploring the intervention context using the RE-AIM framework (reach, effectiveness, adoption, implementation and maintenance of the intervention), testing core features of the design for a future trial. The cohort study will also be developed which will assess depressive and anxiety symptoms in all incident dialysis patients across KHP.

Year 3: Continue study recruitment, conduct trial analysis, and define implementation strategies needed to embed the intervention. 

Year 4: Analysis of the cohort data and evaluation of symptom clusters linking this data with local renal medical records to access the associations between mental health and clinical outcomes. Write-up and submission.

Two representative publications from supervisors

Publication 1:  Chilcot, J., Hudson, J. L., Moss-Morris, R., Carroll, A., Game, D., Simpson, A., & Hotopf, M. (2018). Screening for psychological distress using the Patient Health Questionnaire Anxiety and Depression Scale (PHQ-ADS): Initial validation of structural validity in dialysis patients. General Hospital Psychiatry, 50, 15-19.

Publication 2:  Hudson, J. L., Moss-Morris, R., Norton, S., Picariello, F., Game, D., Carroll, A., . . . Yardley, L. (2017). Tailored online cognitive behavioural therapy with or without therapist support calls to target psychological distress in adults receiving haemodialysis: A feasibility randomised controlled trial. Journal of Psychosomatic Research, 102, 61-70.

Keywords:  Chronic kidney disease; Depression; Anxiety; Cognitive-behavioural therapy; Randomized controlled trial.

Maudsley BRC research themes

• Psychosis & Mood Disorders
• Trials, Genomics and Prediction

Supervisors

Dr Mariana Pinto da Costa
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: mariana.pintodacosta@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/mariana.pintodacosta.html

Dr Rina Dutta
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: Rina.dutta@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/rina.dutta.html

Professor Robert Stewart
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience

Project Details

Background: Coinciding with growing internet and social media use, rates of suicide, attempted suicide, and self-harm have increased.

Novelty and importance: This project will bring together data from Google, Twitter, and electronic health records (EHRs) to investigate the relationship between searches and mentions of suicidal terminology in online platforms, and real-world adverse outcomes.

Primary aim: To investigate the temporal relationships between searches and tweets that focus on suicidal-related behaviour, and outcome fluctuations in a large mental health service covering an urban catchment area.

Planned research methods and training provided:

1. Umbrella review will be conducted to synthesise the global impact of online content relating to suicide-related terms on suicide-related outcomes. Pubmed, Scopus, Cochrane, Web of Science and PsycNet Databases will be screened for systematic reviews and meta-analyses.
2. Google Trends searches will be conducted for suicide-related behaviour terms. A downloadable datasheet of the relative search volumes will be extracted for each term.
3. Twitter data that refer to suicide-related behaviour will be analysed. All public tweets that include the selected keywords will be collated. The tweet text, the date and international timestamp of when it was published, and the number of retweets and likes generated will be extracted.
4. Natural language processing (NLP) will be used to identify suicidality-related concepts in EHRs of patients accessing secondary mental healthcare services using SLaM’s Clinical Record Interactive Search (CRIS). Crisis admissions, and suicidality-related occurrences of patients with any clinical diagnosis that are in contact with SLAM will be studied.

This PhD project will provide a broad range of training opportunities in the: 1) conduct of an umbrella review, 2) extraction and management of big data, 3) techniques for natural language processing applied to health records data, 4) statistical skills and data analysis, 5) patient and public involvement and engagement.

Objectives / project plan:

Year 1: Umbrella review

Years 2-3: Analysis of Google Trends searches / Twitter data / CRIS data 

Year 4: Thesis preparation/ findings dissemination and publications

This project will start with investigating general associations between Google Searches, Twitter data and EHRs, to then focus on more specific exposure-outcome relationships. For example, if suicide-related discussions in people with depression are associated particularly with outcomes in people with that diagnosis.

The analysis strategies established in previous research using Google Trends searches, Twitter and EHRs data by our group will be followed (Kolliakou et al 2016, 2020; Dutta et al 2021; de la Rosa et al 2022).

Two representative publications from supervisors

Publication 1:  de la Rosa P, … Pinto da Costa M et al. Associations of lockdown stringency and duration with Google searches for mental health terms during the COVID-19 pandemic: A nine-country study, Journal of Psychiatric Research, Volume 150, 2022, Pages 237-245, ISSN 0022-3956, https://doi.org/10.1016/j.jpsychires.2022.03.026.

Publication 2:  Kolliakou A, Bakolis I, Chandran D, Derczynski L, Werbeloff N, Osborn DPJ, Bontcheva K, Stewart R. Mental health-related conversations on social media and crisis episodes: a time-series regression analysis. Scientific Reports 2020; 10: 1342.

Keywords:  Suicide; Social media; Electronic health records; DRIS; Google searches.

Maudsley BRC research themes

• Informatics

Supervisors

Dr Rina Dutta
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience   
Email:  Rina.dutta@kcl.ac.uk
Website:  https://kclpure.kcl.ac.uk/portal/rina.dutta.html

Dr Daniel Leightley
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience   
Email:  Daniel.leightley@kcl.ac.uk
Website:  https://kclpure.kcl.ac.uk/portal/en/persons/daniel-leightley(c8799268-3e1f-492f-91fe-9e82ec5cd2cd)/publications.html

Professor Robert Stewart
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience

Project Details

Background:  The supervisors have conducted the 3S-YP study (https://3syp.com) exploring the association between social media and self-harm for n=380 13-25 year olds recruited from within SLaM services and followed up for 6 months.   

This studentship will focus on investigating the relationship between social media data (which participants consented to upload to the study) and the mental health diagnoses, mental health symptoms and signs, and self-harm behaviours identified from the monthly psychological wellbeing questionnaire data and by linkage to electronic health record data through the Clinical Record Interactive Search (CRIS) system.

Novelty and Importance:  Social media use and self-harm behaviour has been the focus of much recent media attention especially given there has been no accountability for social media platforms to be transparent about potential harms or research they have done internally about mental health. 

There is a clear need for well-validated natural language processing (NLP) algorithms to analyse the linked data and study the interplay with different mental health diagnoses / symptoms with service users playing a key role in interpretation, and how robust evidence of the influence of social media on mental health is determined and disseminated.

Primary aim(s):  The primary aim of this studentship is to contextualise the effect of social media use on self-harm behaviour amongst patients with (1) different primary diagnoses and (2) different pre-determined symptom clusters and relevant clinical findings.

The secondary aim is to contextualise self-harm and social media use via semi-structured interviews from a purposive sample of young people who have self-harmed.

Planned research methods and training provided

• Informatics:  Data curation, data processing and NLP
• Quantitative: Epidemiological and statistical methods
• Qualitative: Semi structured interviews; Qualitative data analysis.

There will be flexibility for the student to choose which area of training and therefore research methodology to focus on.

Objectives / project plan

Year 1:

• Systematic review on use of NLP to analyse mental health symptoms in electronic health records and their link with social media.
• Scoping, evaluation and validation of existing CRIS tools;
• Youth stakeholder engagement through YoungMinds and the Samaritans
• 3S-YP data familiarisation
• Scoping of cross-site collaboration with other UK CRIS centres, e.g. Camden and Islington

Year 2:

• Data annotation
• NLP tool development / refining
• Qualitative interviews

Year 3:

• Data analysis
• Replication in other CRIS style datasets
• Write-up

Year 4: 

• Final thesis preparation, results dissemination, publications

Two representative publications from supervisors

Publication 1:  Overview of self-harm and suicide related work in CRIS – Bittar, A, Velupillai, S, Downs, J, Sedgwick, R & Dutta, R 2020, 'Reviewing a decade of research into suicide and related behaviour using the South London and Maudsley NHS Foundation Trust Clinical Record Interactive Search (CRIS) system', Frontiers in Psychiatry. https://doi.org/10.3389/fpsyt.2020.553463

Publication 2:  Sedgwick R, Epstein S, Dutta, R, Ougrin D 2019, Social media, internet use and suicide attempts in adolescents. Current Opinion in Psychiatry. Vol. 32, No. 6. pp. 534-541.

Keywords:  Self-harm; Social media; Electronic health records; Mental health; Risk.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Informatics

Supervisors

Professor Carmine Pariante
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience   
Email:  Carmine.pariante@kcl.ac.uk
Website:  https://kclpure.kcl.ac.uk/portal/carmine.pariante.html

Dr Vaheshta Sethna
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience   
Email:  Vaheshta.sethna@kcl.ac.uk
Website:  Vaheshta Sethna - Research Portal, King's College, London (kcl.ac.uk)

Dr Rebecca Bind
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience

Project Details

Background:  Postnatal depression affects more than 1 in 10 women,  with even higher rates in women who are at high risk because of depression in pregnancy or difficult psychosocial circumstances. This condition is also particularly important because it impacts offspring psychological development, with longitudinal studies describing an increased risk of both internalising and externalising disorders in these children when they reach adolescence and young adulthood, likely through a disturbance in the early interactions between the affected mother and her child.

Novelty and Importance:  Singing groups for women with postnatal depression have already been found effective in decreasing depressive symptoms, but we do not know if this ‘art and health’ intervention affects the relationship between the mother and the child, and if so, how. Identifying such an effect, and discovering the underpinning mechanisms, will bring new, refined interventions in which singing is enhanced by psychological approaches (for example, mentalisation-based therapy) or biological approaches (for example, oxytocin activation) in order to maximise the benefits for both the mothers and the children.

Primary aim(s):  We will build on an ongoing, Wellcome-funded, randomized clinical trial, comparing singing interventions with routine attendance of mother and baby groups in mothers with postnatal depression. For this PhD, we will examine mechanistic data through already collected questionnaires, videos of mother and infants interacting together, and biological (saliva) samples (n=50 women in the singing groups and n=50 in the control group, before and after the intervention). The analysis of these mechanistic data is unfunded, and it is an excellent opportunity for a PhD student interested in multidisciplinary training.

Aim 1): Examine the videos to rate the quality of mother-infant interactions (using the Crittenden CARE-Index, a method for evaluating the quality of the parent-infant relationship) and mothers’ mentalisation abilities (The Parental Cognitive Attributions and Mentalizing Scale).

Aim 2): Examine maternal saliva samples to measure oxytocin, cortisol and inflammatory biomarkers (ELISA for biomarkers analysis).

Aim 3) Examine the relationship between changes in these psychological and biological variables, and changes in: depressive symptoms (EPDS); wellbeing; Maternal Postpartum Attachment Scale; and social support (UCLA Loneliness Scale, Multidimensional Scale of Perceived Social Support).

Aim 4) To spend three months as a placement with our industrial partner delivering the art intervention, Breath Arts Health Research, and to have hands-on experience of delivering the singing group intervention.

Objectives / project plan

Year 1: Training in the CARE-Index and the mentalization scale; score 100 videos.

Year 2: Training in ELISA for biological assessments; score the remaining 100 videos.

Year 3: Analyses of biological samples; integration of data with other clinical variables and scales.

Year 4: placement with Breath Health Arts research; writing up.

Any other notable aspects of the project: The student will have the opportunity to join the multidisciplinary research meeting of the supervisors’ laboratories, and will be exposed to research in a variety of settings, from cellular and molecular biology to youth mental health, from experimental medicine to epidemiological studies, including projects in neurodevelopment.

Two representative publications from supervisors

Publication 1:  Bind, R. H., Biaggi, A., Bairead, A., Du Preez, A., Hazelgrove, K., Waites, F., ... & Pariante, C. M. (2021). Mother–infant interaction in women with depression in pregnancy and in women with a history of depression: the Psychiatry Research and Motherhood–Depression (PRAM-D) study. BJPsych open, 7(3).

Publication 2:  Craig MC, Sethna V, Gudbrandsen M, Pariante CM, Seneviratne T, Stoencheva V, Sethi A, Catani M, Brammer M, Murphy DGM, Daly E. Birth of the blues: emotional sound processing in infants exposed to prenatal maternal depression. Psychol Med. 2022 Aug;52(11):2017-2023. doi: 10.1017/S0033291720002688. Epub 2022 Jul 4. PMID: 35786785; PMCID: PMC9386434.

Keywords:  Postnatal depression; Singing intervention; Mother-infant relationship; Biopsychosocial mechanisms; RCT.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Experimental Medicine and Novel Therapeutics

Supervisors

Dr Helena Zavos
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience   
Email:  Helena.zavos@kcl.ac.uk
Website:  https://www.kcl.ac.uk/people/helena-zavos

Dr Moritz Herle
Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience   
Email:  Moritz.1.herle@kcl.ac.uk
Website:  https://kclpure.kcl.ac.uk/portal/moritz.1.herle.html

Project Details

Background:  Physical and mental health problems influence each other.  Adults over 60 years contribute substantially to society, be it in a professional or family life context, and so it is important to investigate and identify protective factors and mechanisms improving health outcomes in this period of life. 

Novelty and Importance: This research will explore protective factors and mechanisms to improve health outcomes in this period of life.

Primary aim(s): To explore the longitudinal relationship between mental health and cardio-vascular disease, and their risk factors, and identify protective mediation factors as targets for intervention development. 

Planned research methods and training provided:  The student will be trained in longitudinal structural equation modelling (fixed effects, random effects and mediation). The student will learn causal inference methods within the counterfactual framework.

Objectives / project plan

Year 1: Understanding the needs and priorities of older adults with lived experience of chronic illness and its impact on mental health. 

Participants will consist of individuals with lived experience of CVD and mental health conditions, and we will discuss the impact of chronic illness on mental health, and protective factors in focus groups and 1-1 interview.   Qualitative analyses will be used to characterize core themes, which will then be used to generate specific hypotheses that can be studied using secondary data analyses of pre-existing cohort studies informing the priorities for the quantitative analyses in studies 2 and 3.

Year 2: Investigating the longitudinal associations between CVD, their risk factors and anxiety and depression.

Using secondary data analyses, the student will: i) explore the longitudinal relationship between anxiety, depression, and the risk factors associated with CVDs. ii)  to investigate the development of anxiety and depression after onset of established CVD whilst controlling for earlier depression and anxiety diagnoses

Year 3: Identifying social and behavioural factors (including participation, loneliness, physical activity) that reduce the impact of CVD on mental health.

Understanding social and behavioural factors which help to reduce depression and anxiety after a cardio-vascular event is of importance for recovery and quality of life. Using secondary data analyses, the student will explore factors that may mediate this association.  The specific mediators included will be informed by research conducted as part of aim 1. 

Year 4: Write up studies for publication, explore fellowship opportunities and focus on dissemination activities.

Samples: Data from the 1958 National Child Development Study, the 1970 British Cohort Study, and the English Longitudinal Study of Ageing (ELSA) will be utilized.

Two representative publications from supervisors

Publication 1:  Triantafyllou, Nas, Zavos et al. (2022).  The aetiological relationship between depressive symptoms and health-related quality of life: A population-based twin study in Sri Lanka, 17, e0265421

Publication 2:  Herle M, Micali N, Abdulkadir M, et al. Identifying typical trajectories in longitudinal data: modelling strategies and interpretations. Eur J Epidemiol. 2020;35(3):205-222. doi:10.1007/s10654-020-00615-6

Keywords:  Depression; Cardiovascular disease; Health Psychology; Mental health; Anxiety.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Informatics
• Trials, Genomics and Prediction

Supervisors

Professor James MacCabe
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience   
Email:  James.maccabe@kcl.ac.uk
Website:  https://kclpure.kcl.ac.uk/portal/james.maccabe.html

Professor David Taylor
Institute of Pharmaceutcal Science, Faculty of Life Sciences and Medicine  
Email:  David.taylor@slam.nhs.uk
Website:  https://kclpure.kcl.ac.uk/portal/david.taylor.html

Project Details

Background:

Clozapine is the only effective antipsychotic in treatment resistant psychosis, and is taken by approximately 1,000 patients in SLAM. However, owing to a risk of agranulocytosis (1), it requires regular blood tests involving venepuncture which must be carried out in clinical settings, and can be stigmatising, inconvenient and uncomfortable. In SLAM, we have been exploring the possibility of conducting these tests using portable devices that use a drop of blood from a fingerprick, and give results immediately which can be fed back to the patient. Recently, several technical innovations in microfluidics technology, especially viscoelastic processing, in combination with advanced AI analysis, have made portable and accurate machines possible for the first time. In 2022, we piloted the HemoScreen device (PixCell Medical Technologies Ltd), and demonstrated excellent accuracy, with a Spearmans R of 0.99 for WBC between venous and capillary samples (2). Furthermore, a pilot service user evaluation found that patients much preferred the capillary testing, and found the ability to obtain blood test results immediately very empowering. We have therefore negotiated with the manufacturers to provide us with 20 machines, and will evaluate these in collaboration with Leyden Delta BV, our clozapine supplier and monitoring service.

Novelty and Importance:  We believe that pioneering this device in SLAM will revolutionise clozapine monitoring for the 1,000 patients in SLAM, and lead to its adoption throughout the UK, and eventually Worldwide.

Primary aim(s):  To study the safety, acceptability and patient experience of adopting the HemoScreen capillary testing device across SLAM and to develop protocols for its use and an automated informatics pipeline for integration into ePJS. The aims are itemised under the “BRC Remit” section above.

Planned research methods and training provided

1. Quantitative statistical analysis including ROC curve analysis (for determining the best cutoff below which patients should have a confirmatory venous sample taken) and regression and Bland-Altman plots to measure agreement between capillary and venous samples.
2. Qualitative thematic analysis to evaluate service user and staff feedback regarding the use of the devices
3. Informatics training (to establish an automated data flow between the devices and ePJS)
4. Health Service Research training (to develop protocols for using the devices in SLAM
5. Diversity training
   

Objectives / project plan

Year 1:  Setup, ethical approvals, presentation to service user groups, training, start of data collection, preparing PhD upgrade.

Year 2:  Data collection (1.5 years total) ±industry placement.

Year 3:  Statistical analysis and writing up, submission, dissemination (by publication and to service user groups) Viva voce examination.

Year 4:  We will aim to complete in 3 years but if the student chooses to undertake an industry placement, the consequent delays to the project will be made up in year 4.

Two representative publications from supervisors

Publication 1:  Taylor D, Vallianatou K, Whiskey E, Dzahini O, MacCabe J. Distinctive pattern of neutrophil count change in clozapine-associated, life-threatening agranulocytosis. NPJ Schizophr. 2022;8:21.

Publication 2:  Atkins M, McGuire P, Balgobin B, Desouza N, Taylor D. Haematological point of care testing for clozapine monitoring. J Psychiatr Res. 2022;157:66-71.

Keywords:  Clozapine; Psychosis; Haematological Monitoring; Bengin Ethnic Neutropenia; informatics.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• informatics
• Trials, Genomics and Prediction
• Experimental Medicine and Novel Therapeutics

Supervisors

Professor Emmanuelle Peters
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience   
Email:  emmanuelle.peters@kcl.ac.uk
Website: 1. https://www.kcl.ac.uk/people/emmanuelle-peters  2. https://www.startherapytrial.co.uk/ 

Dr Amy Hardy
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience   
Email:  amy.hardy@kcl.ac.uk
Website:  https://www.kcl.ac.uk/people/amy-hardy

Dr Liam Mason
University College London, Max Plank Centre for Computational Psychiatry

Project Details

Background:  There is evidence that traumatic experiences play a causal role in development of psychosis as well as PTSD. Four neural/psychological mechanisms have been identified as underlying PTSD symptoms: abnormal encoding of trauma memories; dysfunctional cognitions; hypervigilance for threat; dissociation.

Novelty and Importance:  This project will provide much needed information about mechanisms underlying PTSD and psychosis and how they respond to therapeutic intervention, which is vital for the future development of psychological therapies for such patients.

Primary aim(s):  Primary objectives are to determine relationships between PTSD and psychosis symptoms and hypothesized psychological mechanisms, and whether improvement in symptoms in the therapy group is associated with changes in these mechanisms.

Planned research methods and training provided:  The project will be based mainly on a sub-sample (N=150 at baseline) of STAR trial participants who completed ESM (smartphone-based electronic diaries to record experiences and psychological functioning at regular intervals in everyday life over 6 consecutive days), pre- and post-therapy/TAU. Variables assessed include psychosis and PTSD symptoms as well as the four mechanisms hypothesized to underlie such symptoms. Each study will test a separate research question: e.g., psychotic symptoms will be mediated by the mechanisms associated with PTSD symptoms (baseline data); people in the therapy group will show greater reductions in the identified mechanisms than those in the TAU group, and changes in those mechanisms will predict reductions in PTSD symptoms (pre-post data).

Depending on student interest, there will be the opportunity to look at the relationship between ESM variables and neural responses measured by fMRI in the smaller sample who completed both ESM and fMRI (~50 people). There will also be opportunities to conduct 2-year follow-up clinical assessments for the STAR trial, to gain a deeper understanding of the measures used in the trial and the participants’ clinical presentation.

Training will be provided on ESM, and if required, fMRI analyses and STAR assessments.

Objectives/project plan

Year 1: training in ESM, and, if required, in fMRI and STAR assessments; literature review; conduct clinical assessments; Study 1 analysis.

Year 2: clinical assessments; studies 2 & 3 analyses, write-up studies 1 & 2.

Year 3: write-up study 3, main thesis write-up.

Year 4: unplanned extensions, potential opportunities for internship/placement and/or support for transition into post-doctoral phase.

The student will be embedded within the STAR team consisting of PhD students, Research Workers, therapists and clinical academics at 5 UK sites.

Two representative publications from supervisors

Publication 1:  Peters, E.R., Hardy, A., Dudley, R., Varese, F., Greenwood, K.E., Steel, C., Emsley, R., Keen, N., Bowe, S., Swan, S., Underwood, R., Longden, E., Byford, S., Potts, L., Heslin, M., Grey, N., Turkington, D., Fowler, D., Kuipers, E., & Morrison, A. (2022) Multisite randomised controlled trial of trauma-focused cognitive behaviour therapy for psychosis to reduce post-traumatic stress symptoms in people with co-morbid PTSD and psychosis, compared to treatment as usual: study protocol for the STAR (Study of Trauma And Recovery) trial. Trials, 23:429. https://doi.org/10.1186/s13063-022-06215-x

Publication 2:  Hardy, A. (2017) Pathways from trauma to psychotic experiences: A theoretically informed model of post-traumatic stress in psychosis. Frontiers in Psychology, 8: 697. https://doi.org/10.3389/fpsyg.2017.00697   

Keywords:  Psychosis; Post-Traumatic Stress Disorder (PTSD); Psychological therapy; Experience Sampling Methodology; fMRI.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Trials, Genomic and Prediction
• Neuroimaging
• Experimental Medicine and Novel Therapeutics

Supervisors

Dr Latha Velayudhan
Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email: Latha.velayudhan@kcl.ac.uk
Website: Latha Velayudhan - Research Portal, King's College, London (kcl.ac.uk)

Dr Christoph Mueller
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: christoph.mueller@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/christoph.mueller.html

Project Details

Background:  People with dementia are known to have increased use of physical and mental health care due to both medical comorbidities and neuropsychiatric symptoms. Besides affecting the individual with dementia, this has a substantial impact on caregivers, social care, and health services. People with dementia are frequently admitted to hospitals and often discharged into residential care settings.  While a proportion of risk factors for developing dementia is modifiable and thereby a target for intervention, little is known about what influences physical and mental health care resource use after receiving a diagnosis of dementia. Knowing these risk factors could feed into the development of support tools similar to the Lester adaption of the cardiometabolic health resource available for people with schizophrenia. Using a large primary care and mental health care data source, we aim to investigate the trajectories and the predictors for physical and mental healthcare use after dementia diagnosis and develop a high-risk for health care use checklist.   

Novelty and Importance:  Modifiable risk factors associated with increased physical and mental health care use in people with dementia will be identified, and the potential for early intervention explored.

Primary aims

1. To investigate the trajectories of medical and psychiatric problems following a dementia diagnosis, health care resources accessed, and predictors of these presentations.
2. To devise a high-risk for health care use checklist, embed this in the dementia dashboard currently in development, and test its feasibility and acceptability.
   

Planned research methods and training provided:  Data will be acquired from the Clinical Record Interactive Search (CRIS) system, which is linked to primary care (via Lambeth Data Net) and hospital-use data (via hospital episode statistics). More than 200 papers in the field of clinical informatics in mental health have been supported by the CRIS team, which will help the student to become proficient in using big data resources.

Objectives / project plan

Years 1 and 2: Data acquisition from CRIS and linked data sources. Analyses on health service use in people with dementia and its predictors.

Years 2 and 3: Development of the high-risk of health care us checklist, embedding it in the dementia dashboard, and evaluation of its feasibility and acceptability.

Year 3: Finishing data analyses; dissemination; write-up and submission of PhD thesis.

Year 4: Submit/revise publications arising from the project; apply for further research funding building on the PhD; allow time for unplanned extensions

Two representative publications from supervisors

Publication 1:  Mueller, C, Perera, G, Broadbent, M, Stewart, R & Velayudhan, L 2022, 'A retrospective analysis of patient flow in mental health services for older adults in South London during the COVID-19 pandemic', International Psychogeriatrics, vol. 34, no. 3, pp. 297-298. https://doi.org/10.1017/S1041610221002775

Publication 2:  Couch, E, Mueller, C, Perera, G, Lawrence, V & Prina, M 2021, 'The association between an early diagnosis of dementia and secondary health service use', Age and Ageing, vol. 50, no. 4, pp. 1277-12

Keywords:  Dementia; Health care trajectories; Multimorbidity; Electronic health records; Applied clinical informatics.

Maudsley BRC research themes

• Informatics

Supervisors

Dr Nicholas Cummins
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience
Email:  nick.cummins@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/nick.cummins.html

Dr Sara Simblett
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
Email: sara.simblett@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/sara.simblett.html

Professor Dame Til Wykes
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience

Project Details

Background:  Each year, 1.4 million people attend emergency departments in England and Wales with a recent head injury. Of these, one-fifth have features suggesting skull fracture or evidence of Traumatic brain injury (TBI). Depression is commonly experienced by up to a third of individuals but is sometimes difficult to accurately detect due to reliance on self-report measures and clinical interviews that are confounded by difficulties with retrospective recall due to memory impairments and difficulties with self-awareness and may delay an accurate diagnosis.

Novelty and Importance:  Novel research from our group in the field of digital phenotyping has been working towards validating the early detection of depression through acoustic and prosodic voice markers. People with depression tend to have reduced pitch variability and intonation and increased phonation and articulation errors. For people whose self-report and recall are problematic, for example following a TBI, this information may be particularly useful. However, to date, no studies have investigated the acceptability and feasibility of such an approach.

Primary aims

1. To assess the feasibility and acceptability of remote data collection of auditory speech samples and symptoms of depression (‘mood data’) with people who have experienced a TBI.
   

2. To investigate whether feasibility and acceptability is different for people from different sociodemographic backgrounds and with different levels of disability, for example, with and without speech impairments after TBI.
   

Planned research methods and training provided:  Training in the design of ecological momentary assessment (EMA) of speech and AI in healthcare. There will also be a strong emphasis on opportunities to learn about patient and public involvement (PPI) in research, which will involve qualitative methods and an opportunity for a secondment to learn about user experience and hands on speech and mood data collection from a digital technology company. 

Objectives / project plan

Year 1: Systematic review on a topic relevant to the research aims, alongside PPI work (e.g., focus groups) to understand the needs of this clinical group when gathering speech and mood data, with an FTE 0.2 secondment in industry over six months.

Year 2: Data collection for a feasibility and acceptability study gathering speech and mood data using EMA from a diverse sample of people with TBI.

Year 3: Analysis of feasibility and acceptability of gathered speech samples and mood data, stratifying by sociodemographic factors and severity of disability, including speech impairments.

Year 4: To complete write up of a PhD thesis.

Other notable aspects of the project:  The aim would be to work towards the development of a clinical trial following this PhD to analyse how depressed mood relates to speech signals in everyday life and evaluate how speech interventions alter depression trajectories.

Two representative publications from supervisors

Publication 1:  Cummins N, Dineley J, Conde P, et al. Multilingual markers of depression in remotely collected speech samples. Research Square; 2022. DOI: 10.21203/rs.3.rs-2183980/v1.

Publication 2:  Simblett, S., Matcham, F., Siddi, S., Bulgari, V., di San Pietro, C. B., López, J. H., Ferrão, J., Polhemus, A., Haro, J. M., & de Girolamo, G. (2019). Barriers to and facilitators of engagement with mHealth technology for remote measurement and management of depression: qualitative analysis. JMIR mHealth and uHealth, 7(1), e11325.

Keywords:  Speech; Depression; mHealth; Traumatic brain injury; Neuropsychology.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Informatics

Supervisors

Professor Daniel Stahl
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience
Email: Daniel.r.stahl@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/daniel.r.stahl.html

Professor Paolo Fusar-Poli
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
Email: paolo.fusar-poli@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/en/persons/paolo-fusarpoli(ced1ec59-cb83-4efe-b9f6-5c41302558a7).html

Dr Dominic Oliver
University of Oxford, Warneford Hospital

Project Details

Background:  Approximately 1% of the UK population suffers from psychotic disorders, such as schizophrenia. The onset of psychotic illness in young people can profoundly impact the life course of a young individual and current treatments offer minimal help. It is important to develop clinical prediction models (CPMs) to predict the risk of developing psychoses in individuals with mental health problems to enhance early intervention efforts. This project will focus on the development and validation of individualized CPMs and risk stratification in patients at high risk for psychosis using machine-learning methods

Novelty and Importance:  Current CPMs to predict the risk of developing psychoses are static using only potential predictors collected at the first visit to the mental health hospital. This project aims to develop a dynamic prediction model that automatically updates with the availability of new information. This decision tool would allow monitoring persons at risk and to offer them early intervention.

Primary aim(s):  This project aims to transform our static first episode psychoses risk CPM into a dynamic prediction model that automatically updates risk when new patient information (e.g., treatments, side effects, symptoms) becomes available using modern machine learning methods.

Planned research methods and training provided:  The candidate will establish a pipeline to extract and process electronic patient health records data and then develop a prediction model using machine learning methods to predict the risk of developing psychoses in a dynamic way. Two approaches will be compared: i) a statistical modelling approach (regularized cox landmark model), which is easily interpreted and performs automatic variable selection and ii) dynamic survival random forest models, which allow for the implementation of more complex models at the expense of both interpretability and built-in variable selection. The final model will be implemented in a web-based clinical application. Acceptance among clinicians and service users will be assessed in a feasibility study.

The student will gain relevant training in statistical methods, machine learning, and health informatics from the Department of Biostatistics and Health Informatics established education program.

Objectives/project plan

Year 1: Literature review about dynamic prediction model and early onset of psychoses, preprocessing of data, the establishment of service user group to guide planning of modelling approach and implementation.

Year 2: Development of prediction models using machine learning methods.

Year 3: External validation of models using other mental health national datasets.

Year 4: Implementation into web-based application, small implementation and acceptability study, thesis write up.

Two representative publications from supervisors

Publication 1:  Irving J, Patel R, Oliver D, Colling C, Pritchard M, Broadbent M, Baldwin H, Stahl D, Stewart R, Fusar-Poli P. Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk. Schizophr Bull. 2021 Mar 16;47(2):405-414.

Publication 2:  Fusar-Poli, P., Rutigliano, G., Stahl, D., Davies, C., Bonoldi, I., Reilly, T., & McGuire, P. (2017). Development and validation of a clinically based risk calculator for the transdiagnostic prediction of psychosis. JAMA Psychiatry, 74(5), 493-500. https://doi.org/10.1001/jamapsychiatry.2017.0284

Keywords:  Precision medicine; Dynamic prediction model; Machine learning; Psychosis; Risk prediction.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Trials, Genomics and Prediction

Supervisors

Dr Charlotte Tye
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience 
Email: charlotte.tye@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/charlotte.tye.html

Dr Michael Absoud
Department of Women's and Children's Health, Faculty of Life Sciences and Medicine
Email: Michael.absoud@kcl.ac.uk
Website: https://www.kcl.ac.uk/lsm/research/divisions/wh/groups/reproductive%20biology

Project Details

Background:  Epilepsy is commonly associated with conditions affecting children’s development, which can impact on learning, social and everyday life skills, leading to poor life outcomes and long-term mental health problems with high health service utilization. While research has indicated potential risk factors for early-onset epilepsy and its neurodevelopmental outcomes, most studies have been from primary care databases with limited data quality and uncontrolled retrospective investigations, after diagnosis of neurodevelopmental conditions has been established.

Novelty and Importance:  eLIXIR combines maternal and child electronic health records across south London boroughs, providing a unique opportunity to explore the maternal factors associated with early epilepsy and to determine infant health and developmental outcomes. Linkage to objective neurophysiological (EEG) reports and parent-reported behaviour has not been performed. It is critical that prospective studies are performed to assess early-life risk factors and predictors prior to the emergence of developmental difficulties to target intervention and improve longer-term outlook.

Primary aim(s):  To investigate:

1. the association between maternal and perinatal factors and early-onset epilepsy
2. health outcomes for mothers of children with early-onset epilepsy
3. child health and neurodevelopmental outcomes for early-onset epilepsy and link with identified risk factors.
   

Planned research methods and training provided:  Using eLIXIR infrastructure (approval granted), the student will combine maternity and neonatal data (BadgerNet and eREDBOOK/health visitor records), primary healthcare data (Lambeth DataNet), mental health (CRIS) and link with case ascertainment via EEG from the Evelina London Children’s Hospital, as well as questionnaire and video data securely shared via the vCreate platform (see Section 7). This data will be further linked with participants enrolled in the BEE Study who undergo a range of prospective assessments at multiple timepoints in the first two years of life, capturing information on developmental ability, neurocognition and emerging neurodevelopmental difficulties. This deep-phenotyping will be combined with linkage of maternity and neonatal health records to test findings from the eLIXIR cohort.

The student will be trained in collecting infant behavioural, clinical and neurocognitive measures in BEE, in addition to large-scale data linkage and integration of digital technologies into healthcare records with longitudinal data analysis, to provide a unique interdisciplinary skillset.

Project plan:

Year 1: Training, literature review, vCreate Neuro training/secondment, Aim 1 analysis.

Year 2: Data collection, Aim 2 analysis.

Year 3: Complete infant data collection, Aim 3 analysis.

Year 4: Dissemination of findings, thesis completion.

Two representative publications from supervisors

Publication 1:  Tye, C., Runicles, A., Whitehouse, A., & Alvares, G. (2019). Characterising the interplay between autism spectrum disorder and comorbid medical conditions: an integrative review. Frontiers in Psychiatry.

Publication 2:  Tye, C., McEwen, F., Liang, H., Underwood, L., Woodhouse, E., Barked, E.D., Sheerin, F., Higgins, N., Yates, J.R.W., TS 2000 Study Group, Bolton, P. (2020). Long-term cognitive outcomes in tuberous sclerosis complex. Developmental Medicine and Child Neurology, 62(3), 322-329.

Keywords:  Epilepsy; Infants; Development; Autism; Data linkage.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Informatics
• Neuroimaging

Supervisors

Dr Chloe Wong
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience 
Email: Chloe.wong@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/chloe-wong

Professor Gerome Breen
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience 
Email: Gerome.breen@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/gerome-breen

Project Details

Background:  Eating disorders (ED) affect ~8% of the global population (Galmiche et al., 2019). ED are often chronic and cause substantial costs. ED are complex with both genetic and environmental causes. Recent efforts have identified eight genome-wide significant loci to date (Watson et al., 2019) and multiple environmental factors (Larsen et al., 2021). Epigenetics, biological mechanisms that underlie the interaction between genes and the environment, might play significant roles in the aetiology and manifestation of ED, are currently understudied (Hübel et al., 2019). To address this research gap, we propose to study the epigenetic basis of ED using nanopore DNA methylation sequencing data in 4,000 participants from the Eating Disorders Genetics Initiative United Kingdom UK dataset (EDGI UK; edgiuk.org), recently funded by NIHR (a £4 million grant). The project will be supervised by Dr Chloe Wong, an expert in epigenetics and methods, and Prof Breen, and international psychiatric genetics expert and chief investigator of EDGI UK.

Novelty and Importance:  This will be, by 50 times, the largest epigenetic study (i.e. differential DNA methylation) across multiple types of ED disorder diagnoses (see Figure 1 for an UPSET plot of the description of the lifetime diagnoses from EDGI UK participants).

Primary Aim(s):  The overarching aim of this project is to identify differential epigenetic, i.e. DNA methylation, signatures associated with different types of Eating Disorders and related phenotypes using next-generation Nanopore epigenetic sequencing data from 4000 individuals.

Objectives / project plan:  The team has extensive links with ED charities and Lived Experience; you will also work with them, coproducing the research wherever possible, as part of ongoing participant and public engagement for EDGI UK research.

Objective 1) Nanopore DNA methylation sequencing data of 4000 EDGI will be generated by the BRC BioResource team at the SGPD, IoPPN. The student will be involved in performing fundamental data processing and QC, and pipeline establishment.

Objective 2) Conduct epigenome-wide association study (EWAS) to detect epigenetic signatures of eating disorders.

Objective 3) Conduct epigenome-wide association study (EWAS) to detect epigenetic signatures of extreme BMI phenotypes and lifetime minimum and maximum BMI, as well as other extreme behaviours such as purging.

Planned research methods and training provided: Epigenome-wide DNA methylation data will be generated by the BRC BioResource lab technicians and data processing plus QC will be performed using established pipeline in R. Relevant data analyses training will be provided by the first and second supervisors’ teams.

Year 1: Perform background reading and write a literature review on “Epigenetics in Eating Disorders”. Undertake relevant data analytic trainings and perform data QC and processing on Nanopore DNA methylation sequencing data. 

Year 2: Conduct epigenome-wide association study (EWAS) to detect epigenetic signatures of eating disorder diagnoses (Objective 2).

Year 3: Analyse extreme BMI phenotypes and weights (Objective 3) and to perform sensitivity analyses using single diagnostic groups of no other eating disorders/BMI-related comorbidities.

Year 4: Complete analyses and writing up of papers and thesis.

Two representative publications from supervisors

Publication 1:  Alameda L., Trotta G., Quigley H., Rodriguez V., Gadlrab R., Dwir D., Dempster D., Wong C.C.Y.*, Forti M.D.* (2022) Can epigenetics shine a light on the biological pathways underlying major mental disorders? Psychological Medicine. *Joint senior authorship.

Publication 2:  Preprint: Dina Monssen, Helena L Davies, Shannon Bristow, Saakshi Kakar, Susannah C B Curzons, Molly R Davies, Zain Ahmad, John R Bradley, Steven Bright, Jonathan R I Coleman, Kiran Glen, Matthew Hotopf, Emily J Kelly, Abigail R Ter Kuile, Chelsea Mika Malouf, Gursharan Kalsi, Nathalie Kingston, Monika McAtarsney-Kovacs, Jessica Mundy, Alicia J Peel, Alish B Palmos, Henry C Rogers, Megan Skelton, Brett N Adey, Sang Hyuck Lee, Hope Virgo, Tom Quinn, Tom Price, Johan Zvrskovec, Thalia C Eley, Janet Treasure, Christopher Hübel, Gerome Breen. The Eating Disorders Genetics Initiative (EDGI) United Kingdom medRxiv 2022.11.11.22282083; doi: https://doi.org/10.1101/2022.11.11.22282083 

Keywords:  Epigenetics; Eating Disorders; Nanopore Sequencing; BMI; Whole genome sequencing.

Maudsley BRC research themes

• Eating Disorders and Obesity
• Trials, Genomics and Prediction

Supervisors

Professor Gerome Breen
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience 
Email: Gerome.breen@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/gerome-breen

Dr Jonathan Coleman
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience 
Email: jonathan.coleman@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/jonathan.coleman.html

Project Details

Background: There is an urgent need for new drugs in psychiatry, with new modes of action and fewer side effects. Genome-wide association studies (GWAS) in psychiatry have been enormously successful. They have the potential to restart largely paused psychiatric drug development pipelines. Industry does not currently have methods to directly translate and analyse (poly)genetic data to identify potential new therapeutic drugs. Remarkably, very expensive and highly error prone laborious manual assessment by biologists, chemists, and geneticists is still required/preferred for each GWAS locus.

The new Psychiatric Genomics Consortium MDD GWAS (unpublished) has identified >500 genetic variants associated with depression in nearly 500,000 depression cases, significantly enriched for the targets of approved antidepressants. Notably, while the additional power provided by broadly defined depression cases is valuable, antidepressant target enrichment was primarily found in cases meeting full MDD criteria.

Novelty and importance: This studentship will use the largest genetic datasets available for broad depression and narrowly defined MDD to directly identify drug repositioning opportunities and small molecules, leading, with functional and therapeutic validation to new clinical trials in MDD.

Primary aim(s) and Methods:

Aim 1: To conducted sophisticated GWAS of a homogenously phenotyped mega-cohort of individuals with major depressive disorder (estimated 100K affected, >300K unaffected individuals), comprising several cohorts with highly consistent phenotyping.

Aim 2: Using data mining and machine learning frameworks, the student will extend our existing drug targetor pipeline, focusing on MDD and related psychiatric disorders.

Aim 3: To make this into a systems biology and neuroscience-informed AI for drug repurposing of approved drugs and discovery of small molecules.

Planned research methods and training provided:

In aim 1, this studentship will uniquely be able to make use the Genetic Link to Anxiety and Depression (GLAD study) and new data from UK Biobank, Dutch and Australian collaborators to (a) work to conduct a genome-wide association study (GWAS) of major depressive disorder (MDD) adjusted for specific confounders; (b) use these results to re-weight MDD GWAS meta-analyses from international consortia and (c) use these reweighted meta-analyses to identify drugs and small molecules for MDD. The next aims will develop one layer for hypothesis generation (the AI) and one layer for experimental validation, which will be subsequently iteratively used by the AI. In this project we will work with neurobiologist Prof Robert Hindges (MRC Centre for Neurodevelopmental Disorders), who is separately funded to carry out screening of MDD associated genes in zebrafish models. The first supervisor, Prof Breen, is the PI of the GLAD study and an internationally-recognised expert in psychiatric genetics, with a strong interest in leveraging GWAS for drug discovery. The second supervisor, Dr Coleman, is a statistical geneticist with extensive experience in the conduct of GWAS, and leads an emerging research group using GWAS to develop empirically-testable hypotheses in neurobiology.

Anticipated timeline:

Year 1: Training for Aim 1 and begin to undertake GWAS; training in machine learning and AI frameworks for Aims 2 and 3.

Year 2: Complete and publish Aim 1 GWAS. Reweight GWAS from international consortium, apply to existing pipeline as part of Aim 2.

Year 3: Complete and publish Aim 2. Begin to develop pipeline for Aim 3, incorporating published empirical data on systems biology and the brain epigenome.

Year 4: Complete Aim 3, integrating empirical data from collaboration with Prof Hindges. Write thesis.

Two representative publications from supervisors

Publication 1:  Coleman JRI, Gaspar HA, Bryois J; Bipolar Disorder Working Group of the Psychiatric Genomics Consortium; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Breen G. The Genetics of the Mood Disorder Spectrum: Genome-wide Association Analyses of More Than 185,000 Cases and 439,000 Controls. Biol Psychiatry. 2020 Jul 15;88(2):169-184. doi: 10.1016/j.biopsych.2019.10.015. Epub 2019 Nov 1. PMID: 31926635; PMCID: PMC8136147.

Publication 2:  Gaspar HA, Gerring Z, Hübel C; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Middeldorp CM, Derks EM, Breen G. Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder. Transl Psychiatry. 2019 Mar 15;9(1):117. doi: 10.1038/s41398-019-0451-4. PMID: 30877270; PMCID: PMC6420656.

Keywords:  MDD; Depression; Drug discovery; Statistical genetics; Bioinformatics.

Maudsley BRC research themes

• Eating Disorders and Obesity
• Trials, Genomics and Prediction

Supervisors

Professor James Teo
Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience
Email: jamesteo@nhs.net
Website: https://kclpure.kcl.ac.uk/portal/james.teo.html

Professor Mark Edwards
Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience
Email: mark.j.edwards@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/mark-edwards

Project Details

Background:  Over 50% of people with disease or damage affecting the nervous system have psychiatric symptoms. These symptoms are rated by patients as their most difficult problems, and they predict higher healthcare costs, morbidity and mortality. In addition, functional neurological disorder, a neuropsychiatric condition, is one of the commonest diagnoses made in neurology outpatients.  Despite this clear need, access to treatment and research activity in neuropsychiatric disorders are limited. In the latest Neurological Alliance survey of over 6000 patients with neurological illness, nearly 70% indicated that their mental health needs were not met.

Novelty and Importance:  Progress in developing improved pathophysiological understanding, treatments and services for people with neuropsychiatric disorders is hampered by key knowledge gaps: prevalence estimation, impact, determinants of outcome and service use, and patterns of comorbidity.  We need to identify patients, their common comorbidities, and to identify areas of high need within a particular neurological diagnosis and for specific psychiatric diagnoses in a transdiagnostic fashion.

With the unique possibilities offered by the informatics and AI engine behind Cogstack, and the integration of Cogstack within Kings College Hospital, the Maudsley Hospital Guys and St Thomas’ Hospitals and General Practitioner records, we have the opportunity for the first time to track and correlate psychiatric symptoms in those with neurological illness and Functional Neurological Disorder.

Primary aim(s):  To determine the prevalence of psychiatric disorders, their associated health outcomes and influence on patterns of healthcare utilization and costs in people with neurological disease and those with functional neurological disorder.

Planned research methods and training provided:  Training in clinical informatics, data querying in Python, population health measurement, health economics. A unique opportunity to use the latest machine learning and clinical informatics tools in the NHS.

Objectives / project plan

Year 1: Determine the prevalence of neuropsychiatric disorders using NHS informatics systems at Kings College Hospital and Guys & St Thomas Hospitals.

Year 2: Perform comparative studies of healthcare utilization, healthcare costs, medication and receipt of illness-related financial benefits in populations with specific neurological diagnoses with and without psychiatric symptoms.

Year 3: Informatics-based cohorting of FND patients through co-occurrence of related clinical codes of functional somatic symptoms suggestive of undiagnosed or early FND; experimental approaches include graph-based detection relative to previous psychiatric and neurodevelopmental diagnoses.

Year 4: Work with patient groups and other stakeholders to generate research priorities for service and treatment development in people with neuropsychiatric disorders.

Two representative publications from supervisors

Publication 1:  Hospital-wide Natural Language Processing summarising the health data of 1 million patients; Daniel Bean, Zeljko Kraljevic, Anthony Shek, James Teo,  Richard Dobson; medrxiv preprint. https://doi.org/10.1101/2022.09.15.22279981

Publication 2:  O'Keeffe, S., Chowdhury, I., Sinanaj, A., Ewang, I., Blain, C., Teodoro, T., Edwards MJ,  Yogarajah, M. (2021). A Service Evaluation of the Experiences of Patients With Functional Neurological Disorders Within the NHS.. Front Neurol, 12, 656466. doi:10.3389/fneur.2021.656466

Keywords:  Neuropsychiatry; Functional Neurological Disorder; Informatics; Phenotyping; Healthcare Utilization.

Maudsley BRC research themes

• Informatics

Supervisors

Dr Ewan Carr
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience 
Email: ewan.carr@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/ewan-carr

Professor Kimberley Goldsmith
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience
Email: kimberley.goldsmith@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/kimberley-goldsmith

Project Details

Background:  30% of people in the UK have multiple long-term conditions, accounting for over half of hospital admissions. Long-term conditions are characterised by fluctuating symptoms with periods of remission followed by relapse. Conventional techniques cannot reflect the complex, longitudinal dynamics of symptom trajectories featuring non-linear progression through successive disease stages or treatments.

This project will investigate alternative techniques that leverage rich clinical data to build a detailed picture of symptom fluctuations during the management of long-term conditions. This includes (1) Generalised additive models, a flexible approach to uncovering hidden longitudinal patterns; (2) Gaussian process modelling, a probabilistic model for complex processes; and (3) multistate models describing progression through disease states.

Novelty and Importance:  The digital transformation of healthcare is generating ever larger amounts of data that are high dimensional, high frequency and multimodal. This includes routine assessments in electronic patient records and remotely-collected information from smartphones and wearable devices.

New data streams offer tremendous potential to understand and thereby improve patient outcomes, but only if paired with appropriate statistical methodology. This PhD will bring new insights into prognostic trajectories to inform treatment adaptations and referrals.

Primary aim(s):  To evaluate, apply, and develop methods to uncover the dynamics of patient outcomes during treatment and management of long-term conditions.

Planned research methods and training provided:  The student will receive advanced training, including:

• Joint models for longitudinal data(Netherlands Institute for Health Sciences)
• Prediction modelling(King’s College London)
• Statistical methods for prognostic models (University of Birmingham)

Objectives / project plan:  This project exploits existing infrastructure and data, including:

• Integrating Mental & Physical healthcare: Research, Training & Services(IMPARTS) has, since 2012, developed infrastructure to monitor patient-reported mental/physical health at Guy’s and St Thomas’ Hospital (GSTT) and King’s College Hospital (KCH).
• Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD), a longitudinal cohort study (n=623) with information collected via smartphones and wearable devices over 24 months (e.g., physical activity, sleep).

Year 1 Objectives:

Systematic review; simulation study comparing chosen methods.

Also:

Data access and cleaning; launch event; pre-registration; training.

Year 2 Objectives:

Application in real-world data; publish simulation study.

Also:

Training; present to patient groups.

Year 3 Objectives:

Publish applied studies; write thesis.

Also:

Patient dissemination event.

Year 4:  Write thesis; seek postdoctoral funding.

Any other notable aspects of the project: This project builds upon existing BRC infrastructure and provides a platform for future translation with patient-centred digital tools.

Two representative publications from supervisors

Publication 1:  Skelton, Carr et al. (2022) “Trajectories of depression and anxiety symptoms during psychological therapy for common mental health problems” Psychological Medicine (forthcoming). https://psyarxiv.com/8scpx/

Publication 2:  Matcham, Carr, et al., (2022) “Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder”. Journal of Affective Disorders 310 (1). doi: 10.1016/j.jad.2022.05.005

Keywords:  Logitudinal; Trajectories; High-dimensional; Routine data; Dynamic.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Informatics
• Trials, Genomics and Prediction

Supervisors

Dr Angus Roberts
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience
Email: angus.roberts@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/angus-roberts

Dr Tao Wang
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience
Email: tao.wang@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/tao.wang.html

Professor Fiona Gaughran
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience

Project Details

Background:  Identifying eligible participants for a clinical trial requires matching a person’s detailed medical information with the complex criteria of the trial. Although electronic health records (EHR) have facilitated access to patient data, most information for accessing trial eligibility (e.g. family history) is recorded as free text and cannot easily be retrieved through a database query. Clinicians/researchers often need to manually examine clinical text to identify potential participants, which is time-consuming and expensive.

Natural language processing (NLP) techniques have been used to identify a trial cohort from EHR text. However, existing systems often rely on document-based information extraction, without summarized patient-level information across multiple documents. Thus, complex trial criteria (e.g., “young females who are not trying to become pregnant”) cannot be queried seamlessly, which limits their usability. Also, without integrating multimodal data (both structured and unstructured information) to form a holistic and unified representation space for patients, these systems cannot provide insights into the quality of a cohort and the impacts of various trial criteria.

Novelty and Importance:  Leveraging ongoing research on document-based information extraction in the BRC and established C4C infrastructure at SLaM, this project will develop a clinical knowledge graph (CKG) that links medical entities (e.g., patient, diagnosis and treatment) extracted from both structured and unstructured EHR data with existing medical ontologies (e.g., SNOMED), to enable reasoning over complex trial criteria, and assessment of the representativeness of a cohort. Moreover, candidates who had shown early symptoms of a disease but had not been diagnosed can be identified, given their high similarities to diagnosed peers, which would improve trial safety when the disease is a key exclusion criterion. This project will help address the long-standing recruitment challenge in mental health clinical trials and provide an important basis for further development in this understudied area.

Primary aim(s):  Develop a multimodal, automated cohort identification method and evaluate against existing methods.

Clinical use cases will align with BRC priorities, including treatment-resistant schizophrenia and clozapine; and mental health trials identified from the Centre for Innovative Therapeutics.

Planned research methods and training provided:

• Natural language processing
• Network science
• Deep learning

Training:  Training on NLP, Clinical Informatics, Deep Learning, Network Modeling and Statistical Methods through KCL courses.

Objectives / project plan

Year 1: Review literature; define data schema for patient-trial knowledge graphs.

Year 2: Develop prototype for a trial cohort identification and retrieval system.

Year 3: Finalize prototype development; evaluations with stakeholders.

Year 4: Dissemination, model sharing.

Two representative publications from supervisors

Publication 1:  Wang, T., Bendayan, R., Msosa, Y., Pritchard, M., Roberts, A., Stewart, R. and Dobson, R., 2022. Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach. Journal of biomedical informatics, 127, p.104010.

Publication 2:  Kraljevic, Z., Searle, T., Shek, A., Roguski, L., Noor, K., Bean, D., Mascio, A., Zhu, L., Folarin, A.A., Roberts, A. and Bendayan, R., 2021. Multi-domain clinical natural language processing with MedCAT: the medical concept annotation toolkit. Artificial Intelligence in Medicine, 117, p.102083.

Keywords:  Clinical informatics; Data science; Natural Language Processing; Clinical knowledge graph; Trial cohort identification.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Informatics
• Trials, Genomics and Prediction
• Experimental Medicine and Novel Therapeutics

Supervisors

Professor Richard Dobson
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience 
Email: richard.j.dobson@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/richard-dobson

Dr Nicholas Cummins
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience 
Email: nick.cummins@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/nicholas-cummins

Project Details

Background:  Speech is uniquely placed in health marker; no other signal contains its singular combination of cognitive, neuromuscular and physiological information. Models developed in speech studies have the real potential to provide unique preventive and predictive information about health to provide opportunities for enhanced self-management or screening services. These advantages aside, the potential of speech as a digital phenotype is yet to be realised.

Novelty and Importance:  A core reason behind lack of translation of speech phenotypes is a lack of replication analysis and robust generalised testing of the predictive power of potential speech phenotypes across health conditions. Existing speech toolboxes (E.g., openSMILE¹ and Praat²) are not suitable for enabling this need on a wide scale. Neither were designed for clinical applications and do not contain extraction methodologies relating to a basic standard speech parameter set specific for capturing health information. Both require considerable computer science knowledge to reliably operate. Therefore, to realise the potential of speech phenotypes there is an urgent need for an open-source speech features extraction toolbox specifically designed for clinical applications.

1. www.audeering.com/research/opensmile/
2. https://www.fon.hum.uva.nl/praat/

Primary aim(s):  This thesis will have two main aims:

• Develop a basic standard speech parameter set, informed by Patient and Public Involvement (PPI), specifically for capturing health information.
• Package all developed code as open-source (Python/R) libraries, which researchers and clinicians can easily import to enable the analysis of similar data.

Planned research methods and training provided:  The student would undertake the Core Research Skills training programme offered through KCL. They would also be able to undertake relevant modules as part of the Applied Statistical Modelling and Health Informatics programme offered by the department of Biostatistics and Health Informatics. There will also be a strong emphasis on opportunities to learn about PPI in research.

Objectives / project plan

Year 1: Conduct a systematic review of speech parameters typically used in health analysis alongside PPI work to understand what feedback clinical groups want from speech signals.

Year 2: Develop code to extract identified key speech parameters. Conduct a 6-month 0.4 FTE secondment with industry to further develop coding skills.

Year 3: Utilise existing speech database to conduct statistical analysis over a range of typical speech-health problems to highlight the strength of the developed features.

Year 4: Package code into open-source format and write up of PhD.

Two representative publications from supervisors

Publication 1:  Ranjan Y, Rashid Z, Stewart C, Conde P, Begale M, Verbeeck D, Boettcher S, Dobson R, Folarin A, RADAR-CNS Consortium. RADAR-base: open source mobile health platform for collecting, monitoring, and analyzing data using sensors, wearables, and mobile devices. JMIR mHealth and uHealth. 2019 Aug 1;7(8):e11734.

Publication 2:  Cummins N, Dineley J, Conde P, et al. Multilingual markers of depression in remotely collected speech samples. Research Square; 2022. DOI: 10.21203/rs.3.rs-2183980/v1.

Keywords:  Speech; Digital Phenotyping; mHealth; Signal Processing; Feature Extraction Techniques.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Psychosis and Mood Disorders
• Informatics

Supervisors

Dr Alfredo Iacoangeli
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience 
Email: Alfredo.iacoangeli@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/alfredo-iacoangeli

Professor Ammar Al-Chalabi
Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience
Email: ammar.al-chalabi@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/ammar-al-chalabi

Project Details

Background:  Patient’s heterogeneity, both in terms of clinical presentation and of biological causes of the disease, are factors that greatly affect the effectiveness and design of clinical trials. For example, longer trials are necessary to evaluate the impact of a therapy on disease progression for people with a slow-progressing form of the disease with respect to people with a fast-progressing form, and people with the same clinically defined disease, but different underlying mechanisms, might respond differently to the same treatment. This scenario is well exemplified in people affected by neurological and neuropsychiatric disorders such as Schizophrenia (SCZ) and Motor Neurone Disease (MND/ALS) which are characterised by highly variable clinical manifestations and a plethora of pathogenic mechanisms. In our laboratory, we have analysed biological (omics) and clinical data of thousands of SCZ and MND patients using machine learning and statistical approaches and identified subtypes of patients with distinct clinical outcomes and underlying candidate mechanisms. This PhD will apply such models to classify patients into homogenous classes and evaluate the differences in the progression of the disease in longitudinal datasets and their potential for the stratification of patients in clinical trials.

Novelty and Importance:  Patient heterogeneity is a factor that affects the effectiveness of all clinical trials and therapy development. Multi-omics machine learning (ML) approaches have shown great potential to provide a higher degree of resolution into the complexity of MND and SCZ. However, interpretability and reproducibility have limited their use. We will apply, for the first time, robust, validated and replicated methods that we have developed in the recent years, to large longitudinal datasets and for the re-analysis of trial data.

Primary aim(s):  To evaluate the usability of ML approaches based on multi-omics data for the prediction of diseases progression in longitudinal studies and for the stratification of patients in clinical trials.

Planned research methods and training provided:  Training in Machine Learning, Bioinformatics, omics techniques and cluster computing will be provided by the supervisors’ groups and via the attendance of specific courses from the postgrad programme in Applied Statistical modelling and Health Informatics of the Department of Biostatistics and Health Informatics.

Objectives / project plan

Year 1: In the first year the student will undergo training in machine learning, high performing computing and bioinformatics. The student will then gain access to the multi-omics datasets currently stored on KCL facilities and use the analysis framework and methodology developed in the laboratory for the subclassification of patients in these datasets. Currently available datasets include RNA-sequencing, DNA sequencing, Methylation, Proteomics and clinical data of MND, SCZ and controls cohorts (approximately 800 individuals) from Project Mine, KCL BrainBank and CommonMind Consortium.  

Year 2: In the second year the student will get access to longitudinal multi-omics datasets and use the analysis framework from year 1 to identify patients subgroups in these cohorts. They will then investigate whether the modelled subgroups present distinct progression patterns. Longitudinal data of MND and SCZ patients with matching omics, are available for a subset of the Project MinE dataset and from the PsyCourse study via collaboration.

Year 3: The third year will be dedicated the re-analysis of clinical trial data using the outputs from year 1 and 2. The student will test whether the different patient subtypes responded differently to treatment and whether this information can add value to the evaluation of the trial results. Multi-omics and trial data from are currently available on KCL facilities from the Mirocals study.

Year 4: This final year will be dedicated to the optimization of the classification model using feedbacks from the results obtained in year 2 and 3, the release of the method as open-access software on Github, and to the thesis and article writing.

Two representative publications from supervisors

Publication 1:  Tam, Oliver H., et al. "Postmortem cortex samples identify distinct molecular subtypes of ALS: retrotransposon activation, oxidative stress, and activated glia." Cell reports 29.5 (2019): 1164-1177.

Publication 2:  McLaughlin, Russell L., et al. "Genetic correlation between amyotrophic lateral sclerosis and schizophrenia." Nature communications 8.1 (2017): 1-12.

Keywords:  Neurological disorders; Psychiatric disorders; Clinical trial; Machine learning; Patient stratification.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Informatics
• Trials, Genomics and Prediction

Supervisors

Professor Rosalyn Moran
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience
Email: rosalyn.moran@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/rosalyn-moran

Dr Toby Wise
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience
Email: toby.wise@kcl.ac.uk
Website: 1. https://kclpure.kcl.ac.uk/portal/toby.wise.html 2. www.tobywise.com

Project Details

Background:  Contemporary computational theories of anxiety and depression emphasise the role of core neurocognitive processes that enable us to make decisions in everyday life. In particular, a bias in decision-making that leads to an excessive focus on negative outcomes is thought to be particularly important in worry and rumination. As a result, these neurocognitive processes represent a promising target for intervention if they can be modulated successfully using pharmacological agents. This project will use EEG to determine the extent to which manipulations targeting core neuromodulators influence these processes.

Novelty and Importance:  It is currently unclear to what extent these fundamental neurocognitive processes can be modulated by pharmacological manipulations of core neuromodulators. Determining the action of these manipulations on neural decision-making processes is a crucial step towards clinical trials of pharmacological interventions that target them specifically. It is also vital to characterise the fundamental neural profiles that predict response to these manipulations, so that future trials and interventions can be targeted at those who will benefit most.   

Primary aim(s):  This project will use EEG to determine the extent to which neural processes that support decision-making, and which are affected in anxiety and depression, can be beneficially modulated by pharmacological manipulations. These will target serotonergic, dopaminergic, and cholinergic neuromodulation and will be evaluated in individuals experiencing symptoms of generalised anxiety disorder. A secondary aim will be to determine which features of baseline EEG activity predict response to these different manipulations.

Planned research methods and training provided:  The research will primarily use EEG and computational modelling of behaviour in decision-making tasks, and training will be provided in these techniques. At a computational level, decision-making processes will be characterised using tree search models of goal-directed planning, with different components of this planning process being mapped on to specific electrophysiological signals. There will also be the opportunity to gain experience in pharmacology and machine learning methods.

Objectives / project plan

Year 1:  Training in EEG analysis and computational modelling. There will also the possibility of writing a systematic review on pharmacological manipulation of disorder-relevant cognitive processes.

Year 2:  Participate in data collection for an ongoing study administering pharmacological manipulations, with a focus on EEG.

Year 3:  Analyse EEG data and write up projects.

Year 4:  Perform additional exploratory analyses.

Two representative publications from supervisors

Publication 1:  Moran, Rosalyn J., Kenneth T. Kishida, Terry Lohrenz, Ignacio Saez, Adrian W. Laxton, Mark R. Witcher, Stephen B. Tatter et al. (2018) The protective action encoding of serotonin transients in the human brain. Neuropsychopharmacology

Publication 2:  Wise, T., Robinson, O., & Gillan, C. (2022). Identifying transdiagnostic mechanisms in mental health using computational factor modeling. Biological Psychiatry.

Keywords:  Anxiety; Worry; Pharmacology; EEG; Decision-making.

Maudsley BRC research themes

• Psychosis & Mood Disorders
• Neuroimaging
• Experimental Medicine and Novel Therapeutics

Supervisors

Professor Anthony Cleare
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: Anthony.cleare@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/en/persons/anthony-cleare(81cc4c4a-d4fd-4315-9426-63efa3b565ce).html

Dr Rebecca Strawbridge
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: Becci.strawbridge@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/becci.strawbridge.html

Project Details

Background:  Major Depressive Disorder (MDD) is considered to have the leading disability burden of all health conditions. Treatment-resistant depression (TRD) is common, severe and long-lasting, thus contributing substantially to the overall depression burdens. MDD/TRD treatments are still prescribed via trial-and-error, despite accumulating evidence that certain factors can predict response to different interventions. Although there is more evidence of response prediction for MDD than TRD, the literature is plagued by inconsistent findings. This reflects the extensive clinical heterogeneity between depressed patients. The greater severity and homogeneity of TRD illness indicate this as a high-priority focus for identifying meaningful predictors of clinical outcome. Evidence of TRD outcome prediction is so-far limited by small samples, short-term outcomes and artificial treatment settings.

Novelty/Importance:  We have conducted a large, long-term randomized controlled trial (RCT) of first-line TRD augmentation treatments. Unlike most RCTs, the LQD study was pragmatic in nature and therefore better reflects real-world treatment outcomes; we also followed participants up for 12months, permitting a more valid prospective assessment of clinical outcomes. Focusing on the currently recommended TRD therapies also contrasts with most existing research which assesses lesser used therapies. Finally, we emphasise employing a clinical outcome that is meaningful to patients and clinicians.

Primary aim(s):  Our overarching aim is to develop a model comprising factors that are feasible to assess in routine practice and predict outcomes to the recommended augmenters for TRD. Specific objectives comprise:

1. Define a priority outcome for patients, through literature review and patient/public involvement & engagement (PPIE) consultation.
2. Develop a prediction model of pre-treatment factors to identify a model predicting long-term outcomes from the LQD study.
3. Validation of the model in other studies of emerging therapies for TRD e.g., psilocybin, ketamine.

 Planned research methods and training provided:

The project uses the following research methods. Additional to trans-project training (e.g., coding), we have identified high-quality training provision for each method:

1. Systematic review (objective1),
2. Qualitative research & PPIE research (objective1),
3. Prediction modelling (objective 2&3; this is emphasised).

Objectives / project plan

Year 1:  Systematic review & PPIE consultation/consensus resulting in a definitive outcome variable for subsequent objectives (objective1 met).

Year 2:  Hypothesis generation and prediction model development (including model/variable selection). All data cleaning/analyses for objective2 completed.

Year 3:  Obtain access to additional dataset(s), to observe whether the model can be applied to other novel TRD therapies. Data cleaning/analysis for objective3 complete.

Year 4:  Interpretation and thesis write-up.

Two representative publications from supervisors

Publication 1: Taylor RW, Coleman JR, Lawrence AJ, Strawbridge R, Zahn R, Cleare AJ. Predicting clinical outcome to specialist multimodal inpatient treatment in patients with treatment resistant depression. Journal of affective disorders. 2021 Aug 1;291:188-97.   

Publication 2:  Taylor RW, Coleman JR, Lawrence AJ, Strawbridge R, Zahn R, Cleare AJ. Predicting clinical outcome to specialist multimodal inpatient treatment in patients with treatment resistant depression. Journal of affective disorders. 2021 Aug 1;291:188-97.

Keywords:  Prediction modelling; Treatment-resistant depression; Augmentation; Precision psychiatry; Treatment.

Maudsley BRC research themes

• Psychosis & Mood Disorders
• Trials, Genomics and Prediction
• Experimental Medicine and Novel Therapeutics

Supervisors

Dr Cedric Ginestet
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience
Email: Cedric.ginestet@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/cedric.ginestet.html

Dr Grainne McLoughlin
Social, Genetic and Developmental Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email: grainne.mcloughlin@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/grainne.mcloughlin.html

Project Details

Background:  Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental conditions with childhood onset that frequently co-occur. ADHD and ASD, or autism, are typically described as childhood disorders; yet, the transition from childhood to adulthood can highlight particular challenges in those with these diagnoses with a higher risk of developing a range of behavioural and cognitive problems, poorer psychosocial outcomes and lower employment levels. There is also considerable overlap between autism and ADHD. For example, a recent meta-analysis estimated a very high current (38.5%) and lifetime (40.2%) prevalence of ADHD among autistic individuals. This overlap can also be seen between continuous traits of autism and ADHD throughout the lifespan, which seems in part due to shared genetic influences.

The heterogeneous phenotypes associated with autism and ADHD has stymied development of a principled pathophysiological understanding of the conditions, particularly during and after developmental phases of transition, such as childhood to adulthood. It is possible that consideration of the biological and cognitive profile of patients in addition to symptoms enables more precisely targeted therapeutic interventions that potentially have more value in improving the quality of life and general functioning of individuals with autism and ADHD in adulthood. Furthermore, better understanding of cognitive and neurobiological abnormalities has the potential to advance understanding of the shared causality between the conditions, as the symptoms are often rooted in similar psychological and biological traits.

Novelty and Importance:  The novelty of this project lies in its application of recently developed multivariate statistical methods to data sets combining EEG data with behavioural and social functioning measurements. These regularized methods---sparse partial least squares (SPLS) and kernel canonical correlation analysis (KCCA)---will enable us to identify latent dimensions combining neurobiological markers with measures of social functioning.

This project benefits from access to two exceptionally large longitudinal data sets that combine cognitive-EEG data alongside several psychometric measures in twin samples of patients that have received structured clinical diagnoses of autism and/or ADHD. These two data sets are the Individual Differences in EEG in young Adults Study (IDEAS), a subset of the Twins Early Development Study (TEDS), which describes a total of 556 participants aged between 20-25 years; and another sample also consisting of twin pairs that have participated in a longitudinal study conducted at the Washington University in St. Louis  (WSL) comprising 781 participants initially assessed at the age of 12 and followed-up every two years until the age of 18 (with 65% retention rate).

Primary aim(s):

1. Evaluate the use of SPLS and KCCA on EEG data to identify latent dimensions of neurobiological correlates of ADHD and autism in young adulthood.
2. Evaluate the predictive power of EEG biomarkers of autism and ADHD for functional impairment and quality of life, beyond the predictive power of the diagnostic categories.
3. Confirm the validity of these EEG biomarkers in independent data sets, by making out-of-sample predictions, exploiting similarities in the two data sets under study.

Planned research methods and training provided:  The aforementioned two data sets will be analysed using regularized multivariate techniques introduced by Witten, Tibshirani and Hastie in 2009, which include SPLS and KCCA. These methods have successfully been used in psychiatry to identify latent dimensions in patients with clinical depression, linking behavioural measures with neuroimaging biomarkers.

The two prospective supervisors have a combined expertise in statistical regularization and the analysis of neuroimaging data. They have authored several publications on the specific data sets of interest. Training in the relevant statistical methods will be provided within the university, via attendance to some of the courses in the Department of Biostatistics and Health Informatics, and outside the university via attendance to international statistical workshops. In addition, research visits to our collaborators in Washington University will also be arranged.

Objectives / project plan

Year 1: Training in statistical methods both within and outside the university. Gaining experience in using SPLS and KCCA on simulated data. Analysis of the London twin sample.

Year 2: Analysis of the WSL twin sample. Further extensions of the methods, making use of twin status, and adaptation of the behavioural genetics approach to the regularized framework.

Year 3: Dissemination of the results to stakeholders. Final write-up of the PhD thesis.  

Year 4: Preparation of final publications and development of post-doctoral research plans.   

Two representative publications from supervisors

Publication 1:  Aydin, Ü., Capp, S.J., Tye, C., Lau-Zhu, A., Rijsdijk, F., Palmer, J., McLoughlin, G. Quality of life, functional impairment and continuous performance task event-related potentials (ERPs) in young adults with ADHD and autism: a twin study. (2022) JCPP-Advances. doi: 10.1002/jcv2.12090 

Publication 2:  Lau-Zhu, A., Fritz, A., McLoughlin, G. Overlaps and distinctions between attention deficit/hyperactivity disorder and autism spectrum disorder in young adulthood: Systematic review and guiding framework for EEG-imaging research. (2019) Neuroscience & Biobehavioral Reviews. 96, 93-115. doi: 10.1016/j.neubiorev.2018.10.009.

 
Keywords:  Precision medicine; EEG; Neurodevelopmental disorders; Statistical regularization; Prediction modelling.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Trials, Genomics and Prediction
• Neuroimaging

Supervisors

Professor Valeria Mondelli
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience 
Email: valeria.mondelli@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/valeria.mondelli.html

Professor Carmine Pariante
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: Carmine.pariante@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/carmine.pariante.html

Dr Alessandra Borsini
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience

Project Details

Background:  Minocycline is a cheap, off-patent anti-inflammatory drug, widely available as an antibiotic for acne and with established antidepressant properties. Of relevance to this PhD proposal, our RCT (1) has shown that only patients with elevated levels of inflammatory markers (C-reactive protein, CRP,≥ 3mg/L) benefit from the antidepressant effect of add-on minocycline treatment.

Novelty and Importance:  We will deliver new insights into the mechanisms of action of minocycline and, in doing so, we will identify targetable substrates for new antidepressant strategies, especially considering that the antibiotic property of minocycline makes it unsuitable for long-term use. In addition, we will work with people with lived experience of depression to develop material that can explain the ‘inflammation’ hypothesis of depression and the use of anti-inflammatories.

Primary aim(s):

Aim 1): we will use an already established human cellular model of microglia (HMC3 cell line from ATCC®), treated in vitro with ‘depressogenic cytokines’ with or without minocycline, and examine cellular changes, and production of kynurenine metabolites.

Aim 2): we will run a pilot randomised (2:1 minocycline/placebo) placebo-controlled, parallel group trial in n=30 subjects with obesity and treatment-resistant depression selected for CRP≥ 3mg/L, to investigate antidepressant response of minocycline in immune-metabolic depression and explore biological markers (e.g. cytokines and kynurenine metabolites) associated with changes in depressive symptoms. We will gather proof-of-concept evidence and estimate of effect sizes, in preparation for future larger studies.

Aim 3) we will conduct patient and public involvement (PPI) research, co-led by a person with lived experience of depression in the context of inflammation, to develop informational materials for patients on the inflammatory model of depression and the use of anti-inflammatories in depression.

Planned research methods and training provided: Aims 1 and 2: high performance liquid chromatography, MSD, immunohistochemistry, cellular imaging. Aim 3: PPI, focus groups.

Objectives / project plan

Year 1: training in cell culture; recruitment and assessment for pilot trial; identification of participants for focus groups.

Year 2: cellular experiments; MSD and HPLC in human and cellular samples; PPI focus groups.

Year 3: data analysis for the molecular and cellular data; production of the educational materials; writing up.

Year 4: submission, viva and completion; writing up of papers; public dissemination of findings.

Other notable aspects of the project:  The student will have the opportunity to join the multidisciplinary research meeting of the supervisors’ laboratories, the PIXIE Lab and the SPILAB, and will be exposed to research in a variety of settings, from cellular and molecular biology to youth mental health, from perinatal psychiatry to social prescribing.

1) Neuropsychopharmacology. 2021 Apr;46(5):939-948. doi: 10.1038/s41386-020-00948-6. Epub 2021 Jan 28. PMID: 33504955.

Two representative publications from supervisors

Publication 1:  Nettis MA, Lombardo G, Hastings C, Zajkowska Z, Mariani N, Nikkheslat N, Worrell C, Enache D, McLaughlin A, Kose M, Sforzini L, Bogdanova A, Cleare A, Young AH, Pariante CM, Mondelli V. Augmentation therapy with minocycline in treatment-resistant depression patients with low-grade peripheral inflammation: results from a double-blind randomised clinical trial. Neuropsychopharmacology. 2021 Apr;46(5):939-948. doi: 10.1038/s41386-020-00948-6. Epub 2021 Jan 28. PMID: 33504955; PMCID: PMC8096832. 

Publication 2:  Borsini A, Merrick B, Edgeworth J, Mandal G, Srivastava DP, Vernon AC, Nebbia G, Thuret S, Pariante CM. Neurogenesis is disrupted in human hippocampal progenitor cells upon exposure to serum samples from hospitalized COVID-19 patients with neurological symptoms. Mol Psychiatry. 2022 Oct 5. doi: 10.1038/s41380-022-01741-1. Epub ahead of print. PMID: 36195636.  

Keywords:  Depression; Inflammation; Microglia; mRNA; Antidepressants.

Maudsley BRC research themes

• Psychosis & Mood Disorders
• Experimental Medicine and Novel Therapeutics

Supervisors

Professor Declan Murphy
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: declan.murphy@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/declan.murphy.html

Dr Luke Mason
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: luke.mason@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/luke.mason.html

Project Details

Background:  There are currently no licensed treatments for the core symptoms of autism. Clinical trials to develop such treatments are limited by a lack of objective biomarkers able to stratify autistic people into biologically and clinically meaningful subgroups, and to measure progression of the condition over time. We have identified a novel EEG biomarker of social functioning in autism, but little is known about the neural mechanisms responsible for it.

Novelty and Importance: There are no extant biomarkers in autism that have received regulatory approval. We are the first group to receive a positive Qualification Opinion from the European Medicines Agency on a novel biomarker for the condition. The work proposed in this project will gather the mechanistic evidence required to bring this biomarker “from bench to bedside” and see it in used for the first time in clinical trials.

Primary aim(s):  Validate the novel EEG biomarker by 1) performing “shiftability” drug challenge studies using GABAergic and glutamatergic compounds; 2) conducting mechanistic experiments to probe the cognitive mechanisms underlying the marker; and 3) analyse existing EEG clinical trial data from Janssen Pharmaceuticals.

Planned research methods and training provided: Successful candidates will be trained on EEG acquisition, QC, preprocessing and analysis. They will work on the cutting edge of precision psychiatry, with options to train on and conduct multimodal data analysis using MRI, DTI, clinical, neurocognitive, genetic and eye tracking data.

Objectives / project plan

Year 1: Design and conduct pilot cognitive mechanistic EEG experiments to shift N170L in a sample of neurotypical people. Training on EEG data analysis of existing drug challenge datasets.

Years 2 & 3: Begin recruitment and data acquisition on main study to: a) validate N170L differences in autism; and b) conduct cognitive mechanistic and drug challenge experiments to shift N170L in both neurotypical and autistic people. Analyse clinical trial data from Janssen Pharmaceuticals (if available) and the LEAP dataset.  

Year 4: Complete data new data collection and analyse. Consolidate analyses on new and existing data (including multimodal analyses with MRI/DTi/genetics/eye tracking) and refine context of use of the N170L biomarker.

Two representative publications from supervisors

Publication 1:  Mason, L., Moessnang, C., Chatham, C., Ham, L., Tillmann, J., Dumas, G., ... & Jones, E. J. (2022). Stratifying the autistic phenotype using electrophysiological indices of social perception. Science translational medicine, 14(658), eabf8987. 

Publication 2:  Loth, E., Charman, T., Mason, L. et al. The EU-AIMS Longitudinal European Autism Project (LEAP): design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders. Molecular Autism 8, 24 (2017). https://doi.org/10.1186/s13229-017-0146-8 

Keywords:  EEG; Biomarker; Clinical Trials; Autism.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Trials, Genomics and Prediction
• Experimental Medicine and Novel Therapeutics

Supervisors

Professor Grainne McAlonan
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience 
Email: grainne.mcalonan@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/en/persons/grainne-mcalonan(7bbd53e5-4eab-48c8-9b6f-466f2553759b).html

Dr Dafnis Batalle
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: dafnis.batalle@kcl.ac.uk
Website: 1. https://kclpure.kcl.ac.uk/portal/dafnis.batalle.html  2. www.code-neuro.com

Project Details

Background:  There are currently no licensed treatments for the core symptoms of autism. Clinical trials to develop such treatments are limited by a lack of objective biomarkers able to stratify autistic people into biologically and clinically meaningful subgroups, and to measure progression of the condition over time. Observational studies can identify putative biomarkers but little is known about the neurochemical mechanisms that underly them. We will use EEG and neuroimaging data to connect findings from the largest observational study of autism in the world (AIMS-2-TRIALS LEAP) to serotonergic, GABAergic and glutamatergic function by conducting pharmacological challenge studies.

Novelty and Importance:  The economic (0.8%-2% of US GDP in 2015) and human cost of autism is significant and development of treatments for core symptoms and co-occurring conditions is a priority. Many observational studies have sought biomarkers for use in clinical trials of new treatments but there is little to no research on how putative biomarkers respond to pharmacological challenge in humans. This project exists at the cutting edge of applied neuropharmacology and represents an opportunity to significantly accelerate the discovery and validation of new treatments.

Primary aim(s):  Use pharmacological challenge with serotonergic, GABAergic and glutamatergic compounds to identify neurochemical and signaling mechanisms underlying putative EEG biomarkers of sensory and social information processing.

Planned research methods and training provided:  Training on EEG theory, acquisition, QC, preprocessing and statistical analysis. Options to work with imaging (MRI and DTI), clinical, genetic and eye tracking data. Training on neuropharmacology and its interaction with functional EEG markers.

Objectives / project plan

Year 1: Training in EEG acquisition, QC and analysis. Contribute to data collection on PSILAUT (COMP360, synthetic psilocybin) study.

Years 2 & 3: Develop unified EEG analytic pipeline for harmonising data between LEAP and shiftability studies. Replicate LEAP candidate biomarkers in baseline shiftability data, then examine the ability to “shift” the markers by dosage in drug challenge data. Acquire, QC and analyse data from PSILAUT and new shiftability studies.

Year 4: Explore multimodal analyses (e.g. MRI, DTI, clinical, genetics, eye tracking) in LEAP dataset to further triangulate evidence from observational and shiftability data on candidate biomarkers.

Two representative publications from supervisors

Publication 1:  Huang, Q., Pereira, A. C., Velthuis, H., Wong, N. M., Ellis, C. L., Ponteduro, F. M., ... & McAlonan, G. M. (2022). GABAB receptor modulation of visual sensory processing in adults with and without autism spectrum disorder. Science translational medicine, 14(626), eabg7859. 

Publication 2:  Taoudi-Benchekroun, Y., Christiaens, D., Grigorescu, I., Gale-Grant, O., Schuh, A., Pietsch, M., ... & Batalle, D. (2022). Predicting age and clinical risk from the neonatal connectome. NeuroImage, 119319.  

Keywords:  EEG; Neuropharmacology; GABA; Glutamate; Autism.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Trials, Genomics and Prediction
• Neuroimaging
• Experimental Medicine and Novel Therapeutics

Supervisors

Dr Owen O'Daly
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience
Email: o.o'daly@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/en/persons/owen-odaly(d75c5721-42e3-4313-a695-6337177d8773).html

Professor Robert Leech
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience
Email: robertleech6@gmail.com
Website: https://kclpure.kcl.ac.uk/portal/robert.leech.html

Project Details

Background:  Ketamine is recognized as a rapid acting antidepressant, with clinical effects emerging just hours are administration and peaking 24-48 hours later. A major feature of those with Major Depressive Disorder (MDD) is altered reward processing, and more specifically the enduring reduction in the ability to modify one’s behaviour in relation to reward, even during remission. We have shown that ketamine increases activity in the brains reward processing circuitry in remitted patients 2 hours after administration (Kotoula et al. 2022), and but little evidence exists for effects in symptomatic patients. There are also changes in connectivity during reward tasks and the resting state, but as yet no connectivity effects have been replicated. It is difficult to know why this is the case: are the effects small and thus there is overfitting of the noise, are the effects dynamic and thus changing during the scane. Intersubject correlation analysis (ISC) is a potential solution to understanding what is shared across subjected in terms of changes in activity which may be dynamic in nature.   

To date ISC has not been used to study the effects of drug action on the brain. 

Novelty and Importance:  Understanding the link between ketamine’s individuated therapeutic efficacy and changes in the activity of subcomponents of the classic reward circuitry would yield important insights into both antidepressant mechanisms of action which are dynamic in nature. In addition the method would allow us to explore what is shared and differs between responders and non-responders by using shared timeseries responses. The findings would drive the development of robust markers of ketamine’s effects which are consistent across patients and sub-groups, such as responsders.

Primary aim(s):  To characterize shared and distinct responses to ketamine during reward tasks in patients with depression.

Planned research methods and training provided:  The data for this independent project is partly collected (Kotoula et al. 2022) and partly being collected as part of a larger study (n=50), to be completed by Q4 2023. Training will be provided in the instruments to measure symptoms, neuropsychopharmacology, fMRI imaging methods, data analysis, open science methods.

Objectives / project plan

Year 1: Training and data collection for a small number of participants; systematic review of the use of intersubject correlation analyses and its application to drug studies; training in quality control and preprocessing of data.

Year 2: Training in ISC imaging data analysis; fMRI activation and connectivity and ISC analysis for the reward task; analysis for main effects of drug and analysis split by responder status.

Year 3: Integration of other data (e.g. physiological, subjective), analyses to examine relationships with blood and clinical markers; extension of work into a separate group; conference presentation.

Year 4: exploration of ISC methods for a different task; conference presentation, write up.

Two representative publications from supervisors

Publication 1:  Alexander L, Jelen LA, Mehta MA, Young AH. The anterior cingulate cortex as a key locus of ketamine's antidepressant action. Neurosci Biobehav Rev. 2021 Aug;127:531-554. doi: 10.1016/j.neubiorev.2021.05.003.  

Publication 2: Kotoula V, Stringaris A, Mackes N, Mazibuko N, Hawkins PCT, Furey M, Curran HV, Mehta MA. Ketamine Modulates the Neural Correlates of Reward Processing in Unmedicated Patients in Remission From Depression. Biol Psychiatry Cogn Neurosci Neuroimaging. 2022 Mar;7(3):285-292. doi: 10.1016/j.bpsc.2021.05.009.   

Keywords:  Ketamine; Neuroimaging; Treatment Resistant Depression; Dissociative; Biomarkers.

Maudsley BRC research themes

• Psychosis & Mood Disorders
• Neuroimaging
• Experimental Medicine and Novel Therapeutics

Supervisors

Professor Mitul Mehta
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience
Email: Mitul.mehta@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/mitul.mehta.html

Dr Fernando Zelaya
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience
Email: fernando.zelaya@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/fernando.zelaya.html

Dr Peter Hawkins
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience

Project Details

Background: Ketamine is recognized as a rapid acting antidepressant, with clinical effects emerging just hours after administration and peaking 24-48 hours later. A major feature of those with Major Depressive Disorder (MDD) is altered reward processing, and more specifically the reduced ability to modify one’s behaviour in relation to reward. Reward deficits and anhedonia endure during remission and ketamine alters reward processing which may in turn be related to changes in anhedonia related symptoms. We have shown that ketamine increases activity in the brains reward processing circuitry in remitted patients 2 hours after administration (Kotoula et al. 2022). In symptomatic patients, ketamine rapidly increases cerebral metabolism in several areas associated with reward processing. Recently a small study (n=14) showed reduction in sgACC hyperactivity to positive feedback five days post ketamine infusion in patients with depression. Apart from this, little is known about the effects of ketamine in reward circuitry; and how it relates to both metabolic changes and to symptomatic changes, in those regions. 

Novelty and Importance:  Compounds that target reward processing and specifically decrease anhedonia are considered promising candidates for development. Understanding exactly how the effects on reward manifest with ketamine is important to understand its antidepressant mechanisms, as well as distinguishing responders from non-responders; and enhance our knowlwedge to better develop assays that can evaluate (and monitor) future treatment approaches.

Primary aim(s):  To characterize the neural basis of decreased anhedonia following ketamine treatment in those with MDD, employing functional and physiological markers and predictors of response.

Planned research methods and training provided:  The data to be used for this independent project is being collected as part of a larger study (n=50), to be completed by Q4 2023. Training will be provided in the instruments to measure anhedonia, neuropsychopharmacology, fMRI and physiological imaging methods, data analysis, open science methods.

Objectives / project plan

Year 1: Training and data collection for a small number of participants; systematic review of the effects of depression treatment on reward processing; training in quality control and preprocessing of data.

Year 2: Training in quantitative physiological imaging data analysis; fMRI activation and connectivity analysis for the reward task; analysis for main effects of drug and analysis split by responder status.

Year 3: Integration of function and physiology using regression and multivariate approaches; ancillary analyses to examine relationships with blood and clinical markers; conference presentation.

Year 4: Analysis of predictors of treatment response using baseline imaging data, conference presentation, write up.

Two representative publications from supervisors

Publication 1:  Alexander L, Jelen LA, Mehta MA, Young AH. The anterior cingulate cortex as a key locus of ketamine's antidepressant action. Neurosci Biobehav Rev. 2021 Aug;127:531-554. doi: 10.1016/j.neubiorev.2021.05.003.   

Publication 2:  Kotoula V, Stringaris A, Mackes N, Mazibuko N, Hawkins PCT, Furey M, Curran HV, Mehta MA. Ketamine Modulates the Neural Correlates of Reward Processing in Unmedicated Patients in Remission From Depression. Biol Psychiatry Cogn Neurosci Neuroimaging. 2022 Mar;7(3):285-292. doi: 10.1016/j.bpsc.2021.05.009.   

Keywords:  Ketamine; Functional Neuroimaging; Treatment Resistant Depression; Metabolism; Biomarkers.

Maudsley BRC research themes

• Psychosis & Mood Disorders
• Neuroimaging
• Experimental Medicine and Novel Therapeutics

Supervisors

Dr Sridhar Natesan
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
Email: sridhar.natesan@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/sridhar.natesan.html

Professor Oliver Howes
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
Email: oliver.howes@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/oliver.howes.html

Project Details

Background:  Psychotic disorders are extremely distressing, and a significant number of patients fail to respond to current medications that target abnormal neurotransmitter levels. It is believed that changes to neurotransmitter levels are a result of abnormal neurodevelopmental trajectory. At the core of the neurodevelopmental trajectory is neuronal synaptic pathology – connections between neurons. Synaptic pathology is thought to cause abnormal functional connectivity of spatially separate brain regions in the brain and is commonly reported as a hallmark feature of psychotic disorders.

Novelty and Importance: S V2A is a presynaptic protein in the human brain and plays an important role in neurotransmitter release. Our group was the first to find that patients with schizophrenia have reduced SV2A density. By further pharmacologically challenging SV2A protein function using a single dose of leveritaceam (which inhibits SV2A function) and recording functional brain changes with MRI and Magnetoencephalography (MEG), we will be able to address the hypothesis of whether functional dysconnectivity plays an important role in psychotic disorders. If proven, then this will lead to the discovery of novel interventions to treat psychotic disorders. This will be the first of its kind experiment in humans.

Primary aim(s):  The aim of this study is to test whether the reduction of SV2A activity by a single dose of LEV will reduce functional connectivity as measured by fMRI, as well as reduce the level of coordination of brain activity, as measured by MEG. We will also aim to test whether SV2A density, as measured by [¹¹C]UCB-J PET imaging will correlate with fMRI and MEG measures.

Planned research methods and training provided:  The research will involve psychometric evaluations and neuroimaging involving MRI, PET and MEG modalities. Training will be provided so that the PhD student will be able to independently perform and analyse the data.

Objectives / project plan

Year 1: Training and dose determination of LEV (pilot phase) that will be suitable for the study in healthy volunteers and patients will be done along with standardization of scanning protocols.

Year 2:  Using the results of the pilot study the main study where LEV intervention will be evaluated in healthy volunteers and patients.

Year 3: The aim will be to complete data collection by the end of the 3^rd year and to start analyzing the data.

Year 4: Submission of PhD thesis.

Two representative publications from supervisors

Publication 1: Onwordi, E.C., Halff, E.F., Whitehurst, T. et al. Synaptic density marker SV2A is reduced in schizophrenia patients and unaffected by antipsychotics in rats. Nat Commun 11, 246 (2020). https://doi.org/10.1038/s41467-019-14122-0  

Publication 2: Onwordi, E.C., Whitehurst, T., Mansur, A. et al. The relationship between synaptic density marker SV2A, glutamate and N-acetyl aspartate levels in healthy volunteers and schizophrenia: a multimodal PET and magnetic resonance spectroscopy brain imaging study. Transl Psychiatry 11, 393 (2021). https://doi.org/10.1038/s41398-021-01515-3.

Keywords:  schizophrenia; Synaptic vesicle glycoprotein 2A; Neuroimaging; Levetiracetam; Functional connectivity.

Maudsley BRC research themes

• Psychosis & Mood Disorders
• Neuroimaging
• Experimental Medicine and Novel Therapeutics

Supervisors

Professor Chiara Nosarti
Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email: chiara.nosarti@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/chiara.nosarti.html

Dr Emma Robinson
Department of Biomedical Engineering, Faculty of Life Sciences and Medicine
Email: emma.robinson@kcl.ac.uk
Website: 1. https://kclpure.kcl.ac.uk/portal/emma.robinson.html  2. https://metrics-lab.github.io/

Project Details

Background:  THRIVE will increase our understanding of the origins of mental illness by elucidating cortical organisation and dynamics associated with psychiatric risk and resilience in typically and atypically developing children.

Novelty and Importance:  Cortical surface analysis of developmental data has improved understanding of cortical folding mechnisms (Garcia K PNAS 2018), and the maturation of cortical microstructure (Dimitrova R Neuroimage 2021). In this project, we will harmonise cortical surface processing pipelines, for analysis of multiple cohorts enriched for individuals at risk of developing neuropsychiatric disorders and use this to develop new insights into the biological and environmental mechanism driving these conditions.

Primary aim(s):  The project will develop longitudinal normative models of brain development from birth to middle childhood and use these to study the heterogenous origins of neuropsychiatric disorders.

Planned research methods and training provided:  This project will require careful harmonisation of image processing pipelines. The successful student will receive an unparalleled training combining knowledge about childhood neurodevelopment with neuroimaging, neuroinformatics and the linkage of imaging and behavioural outcome analysis. Training will be provided both (i) directly by the project, and (ii) by wider participation in the research group. Specifically, the student will develop skills in pipeline scripting, detailed neuroanatomical quality control and utilisation of image processing and machine learning tools. Training will be provided through attending: courses on Neuroanatomy, the FSL course, and the introduction to machine learning course (led by Dr Robinson).

Objectives / project plan

Year 1: The student will train in image processing and neuroanatomy. They will gain experience in running cortical surface processing pipelines using neonatal data from the dHCP and will adapt these pipelines to work for historical datasets that are lower resolution.

Year 2: The student will develop different biomarkers of neurodevelopmental outcome, using different techniques for feature extraction from imaging data, for example independent component analysis and non-negative matrix factorisation. These will be validated through building regression models predicting cognitive and behavioural outcomes.

Year 3: The student will select the best performing features and use these as the basis of longitudinal normative models of neurodevelopmental patterns associated with psychiatric risk and resilience. Unlike the deterministic models developed in Y2, these will be sufficiently flexible to account for heterogeneity in neurodevelopmental trajectories and to incorporate environmental data. These models will be used to develop tailored predictions of individual outcomes.

Year 4: Write up time, preparation of fellowships to transition into the post-doctoral phase.

Two representative publications from supervisors

Publication: Laila Hadaya, Konstantina Dimitrakopoulou, Lucy Vanes, Dana Kanel, Sunniva Fenn-Moltu, Oliver Gale-Grant, Serena J Counsell, A David Edwards, Mansoor Saqi, Dafnis Batalle, Chiara Nosarti. Parsing brain-behavior heterogeneity in very preterm born children using integrated similarity networks. bioRxiv 2022.10.20.513074; doi: https://doi.org/10.1101/2022.10.20.513074  

Publication 2: Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., ... & Van Essen, D. C. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171-178.

Keywords:  Brain development; Precision psychiatry; Mental health; Environment.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Neuroimaging

Supervisors

Professor Oliver Howes
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
Email: oliver.howes@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/oliver.howes.html

Dr Katherine Beck
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
Email: Katherine.beck@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/katherine.beck.html

Project Details

Background, Novelty and Importance:  About 30% of patients with schizophrenia do not have an adequate response to standard medication (all D2 blockers). This is termed treatment-resistant schizophrenia (TRS). There is only one licensed treatment for TRS but it is ineffective in 60% of patients, and poorly tolerated. A novel treatment approach is thus needed.

Studies find that patients with TRS do not show dopamine dysregulation (likely why D2 blockers fail). Instead, they show higher glutamate levels in the frontal cortex. Whilst these studies show associations between glutamatergic elevation and TRS, they are cross-sectional so they cannot show causality. This is a crucial limitation. We need to test causality by determining if reducing glutamatergic levels in patients with TRS improves symptoms. This will determine whether glutamatergic abnormalities are clinically relevant in TRS, and if reducing glutamate offers a new treatment approach.

Our recent study found that acute treatment (48 hours) with riluzole, (which blocks glutamate release) can reduce glutamatergic measures in TRS. This provides proof-of-concept that riluzole reduces glutamate in TRS.  However, this study was too short to determine if reducing glutamate leads to clinical improvement.  Now we will evaluate riluzole over a longer duration to determine if reducing glutamate leads to clinical improvement.

We will answer two fundamental questions:

Question 1: does lowering cortical glutamate levels lead to clinical improvement in TRS?

Question 2: is riluzole a potential novel therapeutic agent for TRS?

Planned research methods and training provided:  Design: double-blind, placebo-controlled study.  Patients with TRS will be recruited and randomized to placebo or riluzole for up to 2 months (40 patients/group). All participants will have baseline and follow up MRIs (including magnetic resonance spectroscopy (¹HMRS) to measure glutamate levels) and clinical assessments.

There will be training in recruitment, data collection, running an experimental medicine trial, clinical measures and data analysis including statistical methods and packages, and neuroimaging analysis.

Objectives:  To determine:

1. if higher cortical glutamate levels at baseline ¹HMRS are associated with greater symptom scores in TRS at baseline;
2. if riluzole reduces symptom scores in TRS;
3. if riluzole treatment reduces cortical glutamate levels.

Project Plan

Year 1: Recruit and collect data. Start literature review on brain glutamate alterations in TRS (publication and potentially part of PhD introduction).

Year 2: Recruit and collect data. Complete literature review. Training in statistics and neuroimaging analysis. Start to analyse data set.

Year 3: Complete data collection and analysis. Prepare PhD chapters/publications.

Year 4: If unforeseen delays occur complete outstanding analysis/ write up (e.g if data collection delayed due to poor recruitment or scanning problems). Develop imaging analysis and review skills to prepare for post-doc applications.

Two representative publications from supervisors

Publication 1: Pillinger T, Rogdaki M, McCutcheon RA, Hathway P, Egerton A, Howes OD. Altered glutamatergic response and functional connectivity in treatment resistant schizophrenia: the effect of riluzole and therapeutic implications. Psychopharmacology (Berl). 2019 Jul;236(7):1985-1997. doi: 10.1007/s00213-019-5188-5. Epub 2019 Feb 28. PMID: 30820633; PMCID: PMC6642056   

Publication 2: McCutcheon RA, Krystal JH, Howes OD. Dopamine and glutamate in schizophrenia: biology, symptoms and treatment. World Psychiatry. 2020 Feb;19(1):15-33. doi: 10.1002/wps.20693. PMID: 31922684; PMCID: PMC6953551.  

Keywords:  schizophrenia; biology; treatment; imaging; psychosis.

Maudsley BRC research themes

• Psychosis & Mood Disorders
• Neuroimaging
• Experimental Medicine and Novel Therapeutics

Supervisors

Professor Mark Edwards
Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology and Neuroscience 
Email: Mark.j.edwards@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/mark-edwards

Dr Joel Winston
Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology and Neuroscience
Email: joel.winston@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/joel.winston.html

Project Details

Background:  Functional neurological disorder (FND) causes quality of life impairment and disability levels similar to those seen in chronic neurological disease such as Parkinson’s Disease and Multiple Sclerosis. Diagnosis is ideally made on the basis of positive aspects of history and examination, rather than diagnosis by exclusion.

Although significant progress has been made in developing positive diagnostic tests (clinical and electrophysiological) for functional movement disorders producing tremor and weakness, there remain specific common motor syndromes that present a diagnostic challenge, including myoclonic-type jerks, functional tic-like movements and fixed functional dystonia. In addition, somatic and cognitive symptoms are very common in FND (for example over 90% of people complain of pain), but often there is clinical uncertainty if such symptoms are part of the FND or are represent different disorder.

Novelty and Importance:  Without positive diagnostic techniques, patients will continue to experience delays in moving into treatment and may be subjected to unnecessary tests and treatment. Additionally, our ability to accurately include people in clinical trials will be hampered. We plan, for the first time, to perform a comprehensive assessment of the diagnostic performance of a range of clinically applicable electrophysiological measures in people with functional myoclonus, functional tic-like movements and functional fixed dystonia in comparison to people with organic causes of similar abnormal movements. We will also compare premovement neural activity and sensory attenuation in a population of patients with FND and prominent pain with a  population with chronic primary pain.

Primary aim(s):  To determine the sensitivity and specificity of clinically applicable electrophysiological tests of pre-movement neural activity and sensory attenuation in the diagnosis of functional movement disorders and chronic pain.

Planned research methods and training provided:  The primary research methods will be adapted versions of clinical neurophysiological techniques. Training will be provided in EEG/evoked-potential recording and analysis, cognitive neuroscience experimental design and data analysis.

Objectives / project plan

Year 1: Establish paradigms for assessment of pre-movement potentials and sensory attenuation in healthy participants.

Year 2: Study sensitivity and specificity of pre-movement potentials and sensory attenuation in the diagnosis of functional myoclonus, tic-like behaviours and fixed dystonia.

Year 3: Study sensitivity and specificity of the reported lack of pre-movement potentials and impaired sensory attenuation prior to voluntary movement in people with functional movement disorders.

Year 4: Study the pre-movement potentials and sensory attenuation in people with chronic primary pain compared to people with FND and prominent pain. 

Two representative publications from supervisors

Publication 1:  Teodoro T, Meppelink AM, Little S, Grant R, Nielsen G, Macerollo A, Pareés I, Edwards MJ. Abnormal beta power is a hallmark of explicit movement control in functional movement disorders. Neurology. 2018 Jan 16;90(3):e247-e253. 

Publication 2:  Schwingenschuh P, Saifee TA, Katschnig-Winter P, Macerollo A, Koegl-Wallner M, Culea V, Ghadery C, Hofer E, Pendl T, Seiler S, Werner U, Franthal S, Maurits NM, Tijssen MA, Schmidt R, Rothwell JC, Bhatia KP, Edwards MJ. Validation of "laboratory-supported" criteria for functional (psychogenic) tremor. Mov Disord. 2016 Apr;31(4):555-62.

Keywords:  Functional neurological disorders; Chronic pain; Neurophysiology; Diagnostic medicine; Cognitive neuroscience.

Maudsley BRC research themes

• Pain and Addictions
• Experimental Medicine and Novel Therapeutics

Supervisors

Professor Kimberley Goldsmith
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience 
Email: kimberley.goldsmith@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/kimberley-goldsmith

Dr Ewan Carr
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience
Email: ewan.carr@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/ewan-carr

Project Details

Background:  To improve patient outcomes, we must understand how interventions work (mechanisms) and for whom (subgroups). Mediation and moderation analysis can answer such questions.

The growing availability of large, multimodal datasets comprising digital phenotyping and genomics brings new opportunities, but also new challenges. Existing techniques for mediation and moderation are poorly suited to high-dimensional contexts.

Recent advances combining traditional methods (e.g., structural equation modelling; SEM) with machine learning algorithms (e.g., LASSO) offer an alternative approach. These methods could be vital in early-phase studies and trials to identify novel mechanistic variables for subsequent evaluation.

Novelty and Importance:  Mediation and moderation can help target interventions, but traditional techniques need adapting to take advantage of new data streams. This project will apply state-of-the-art methods to uncover mechanistic variables in high-dimensional, multimodal datasets.

Primary aim(s):

• To deliver new insights into mechanisms underpinning interventions for anxiety and depression.
• To share code and training, increasing application and translation in early phase studies.

Planned research methods and training provided:

Data  The student will use:

• GLAD(Genetic Links to Depression and Anxiety), to identify mediators/moderators of outcomes following psychological therapy. Participants with lifetime experience of anxiety or depression were recruited since 2018 (≈32k, ≈20k with genome-wide genotyping) with linked medical records.
• TEDS (Twins Early Development Study), to identify mediators/moderators of psychological outcomes in early life. Twins born 1994-1996 (≈14k; ≈7k withgenotyping) completed regular assessments (ages 1-26).

Methods and training  The student will apply traditional approaches (SEM, causal mediation) alongside new machine learning methods (e.g., LASSO). They will identify potential mechanistic variables and assess the performance of competing methods.

The student will receive comprehensive training spanning traditional and modern approaches:

• Causal inferenceand SEM (King’s College London).
• Advanced SEM (Utrecht University).
• Prediction Modellingand Introduction to R (King’s College London).
• Clinical prediction models & Machine Learning (Maastricht University).

Objectives / project plan

Year 1

• Systematic review of techniques for high-dimensional mediation and moderation.
• Data access; launch event; pre-registration; training.

Year 2

• Simulation study investigating statistical properties of chosen methods.
• Training; meet patient groups; conferences.

Year 3

• Apply chosen methods to identify mechanistic variables in real-world datasets.
• Dissemination event.

Year 4

• Finish thesis write-up; seek postdoctoral funding via NIHR Advanced Fellowship call.

Other notable aspects of the project:  This study will link exploratory machine learning techniques with confirmatory inferential approaches from causal mediation, bridging an important methodological gap.

Two representative publications from supervisors

Publication 1:  Goldsmith, Hudson, Chalder, Dennison, Moss-Morris. (2020) “How and for whom does supportive adjustment to multiple sclerosis cognitive behavioural therapy work? A mediated moderation analysis” Behaviour Research and Therapy; 128. doi: 10.1016/j.brat.2020.103594 

Publication 2: Carr et al. (2022) Trajectories of mental health among UK university staff and postgraduate students during the pandemic” Occupational and Environmental Medicine. 79 (8). doi: 0.1136/oemed-2021-108097

Keywords:  Mediation; High-dimensional; Moderation; Genomics; Psychological therapy.

Maudsley BRC research themes

• Psychosis & Mood Disorders
• Trials, Genomics and Prediction

Supervisors

Dr Matthew Hollocks
Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email: Matthew.hollocks@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/matthew-hollocks

Professor Kate Tchanturia
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: Kate.Tchanturia@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/kate.tchanturia.html

Project Details

Background: Autistic young people are at disproportionate risk for experiencing co-occurring anxiety, with 52.5% of adolescents having at least one DSM-5 anxiety diagnosis (Hollocks et al, 2022). Cognitive Inflexibility (CI) is a key mechanism associated with anxiety in autism (Lei et al, 2022) and has been identified as a barrier to the effectiveness of existing psychological interventions and is therefore an important target for intervention. Cognitive Remediation Therapy (CRT) is an established intervention targeting cognitive processes strongly associated with autism, including cognitive inflexibility. CRT has shown promise in improving these cognitive difficulties in those with anorexia nervosa and autistic features (Dandil et al, 2020) but has yet to be adapted as a treatment for mental health difficulties in autism. 

Novelty and Importance: The development of interventions which target specific cognitive mechanisms underlying anxiety in autism is of vital importance to improve treatment efficacy and outcomes for this complex population. CRT also shows low dropout rates from treatment (Tchanturia et al 2017). This project will be an important step to establishing personalized interventions for cases where CI represents a significant barrier for successfully engaging in existing treatments such as cognitive behavioral therapy (CBT). We would anticipate this project would lead to further research, including a full trial of the intervention.

Primary aim(s)

• To co-design with autistic young people, parents, and clinicians a novel intervention targeting CI in autism.
• To establish the acceptability and feasibility of the intervention by conducting a pilot randomized controlled trial (RCT) of the novel intervention, compared to a suitable comparison group. This would likely include 40 autistic young people across the two groups.
• To gather preliminary outcome data on CI, anxiety, and other key mental health symptoms.

Planned training and development opportunities: Students will receive training in co-design principles and patient and public involvement & engagement (PPIE). Training in qualitative research, trial design, statistical analysis and writing for publication will be provided. Multiple opportunities to develop presentation skills, including attending the annual CRT in Psychiatry conference in NYC and an International Autism Conference. 

Project plan

Year 1: Systematic review of CRT to guide intervention development; conduct qualitative study of experience of CI and young person engagement/ co-design workshops. Training in PPIE, CRT and trial design, attending qualitative methods summer school. Ethical approvals; project protocol pre-registration.

Year 2: Data collection.

Year 3: Complete data collection; analysis; write-up and submission.

Year 4: Dissemination of the research findings to public, creating social media platform, organizing public engagement event with supervisors.

Two representative publications from supervisors

Publication 1:  Lei, J., Charman, T., Leigh, E., Russell, A., Mohamed, Z., & Hollocks, M. J. (2022). Examining the relationship between cognitive inflexibility and internalizing and externalizing symptoms in autistic children and adolescents: A systematic review and meta‐analysis. Autism Research. 

Publication 2:  Dandil Y, Smith K, Kinnaird E, Toloza C, Tchanturia K (2020) Cognitive Remediation Interventions in Autism Spectrum Condition: A Systematic Review Frontiers in Psychiatry; doi:10.3389/fpsyt.2020.00722

Keywords:  Autism; Anxiety; Cognitive; Intervention; RCT.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Trials, Genomics and Prediction

Supervisors

Dr Devin Terhune
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
Email: devin.terhune@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/devin-terhune

Dr Susannah Pick
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: susannah.pick@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/susannah-pick

Professor Mitul Mehta
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience

Project Details

Background:  Dissociative states include symptoms characterized by a disruption between normally integrated systems supporting awareness, memory, and perception. Hallmark dissociative states include depersonalisation/derealisation (feeling detached from one’s sense of self or environment) and identity alteration (experiencing a fragmented sense of identity). Dissociative states are increasingly recognized as transdiagnostic symptoms present in a range of conditions, including the dissociative disorders, psychosis, affective disorders, post-traumatic stress disorder, and functional neurological disorder. Dissociative states are associated with higher comorbidity, more severe symptom profiles, and poorer outcomes, with significant economic impact.

The neurobiological basis of dissociative states is poorly understood and there are no evidence-based biomarkers to aid diagnosis or guide treatment of dissociative psychopathology. Preliminary work suggests that electroencephalography (EEG) is a valuable method for identifying a biomarker of dissociation. EEG is also non-invasive, has minimal contraindications, and is compatible with pharmacological (nitrous oxide [N₂O]) and psychological (mirror-gazing) methods for inducing dissociation.

Novelty and Importance:  This will be the first attempt to identify the shared neurophysiological markers of induced and clinical dissociative states. The research will fill a significant gap in current understanding of the neural basis of dissociation and will highlight potential transdiagnostic biomarkers of dissociation that may facilitate identification, and guide treatment, of pathological dissociation. This work will have further implications for the possible use of these pharmacological agents and psychological interventions across diagnostic boundaries.

Primary aim(s):

• Identify neurophysiological markers of induced and clinical dissociation.
• Examine relevance of biomarkers to clinical outcomes.
  

Planned research methods and training provided:  The research will induce mild dissociative states using N₂O, an NMDA receptor antagonist, and mirror-gazing in non-clinical controls and individuals with depersonalisation-derealisation disorder. These techniques have been found to be safe and well-tolerated in these populations in our previous work. EEG will be recorded during resting state and dissociative symptom capture windows. Signal complexity (Lempel-Ziv complexity) and effective connectivity (Granger causality) measures will be computed in each condition/group, and multivariate pattern classification analysis, a form of machine learning, will be used to identify the overlapping neurophysiological features across methods and samples. The supervisory team will provide training in induction methods and EEG application and data analysis.

Objectives / project plan

Year 1:

• Conduct/publish a systematic review of dissociation induction methods.
• Research training/design/piloting/ethics.
• Patient/public involvement.
  

Year 2:

• Dissociation induction study in controls (N=30).
• EEG analysis (complexity, connectivity, and multivariate pattern classification analyses).
  

Year 3:

• Dissociation induction study in patients (N=30) and controls (N=30).
• EEG analysis.
• Examining predictive utility of biomarker for patient outcomes.

Year 4:

• Complete analyses and publish results.
• Research dissemination.
• Patient/public involvement.
• Submit thesis.

Two representative publications from supervisors

Publication 1:  Polychroni, N., Herrojo Ruiz, M., & Terhune, D. B. (2022). Introspection confidence predicts EEG decoding of self-generated thoughts and meta-awareness. Human Brain Mapping, 43, 2311-2327. https://tinyurl.com/24hrmth5 

Publication 2:  Pick, S., Rojas-Aguiluz, M., Butler, M., Mulrenan, H., Nicholson, T.R., & Goldstein, L.H. (2020). Dissociation and interoception in functional neurological disorder. Cognitive Neuropsychiatry 25, 294-311. https://tinyurl.com/2e3sy554

Keywords:  Dissociation; N-methyl-D-aspartate (NMDA) receptor; EEG; Multivariate pattern classification analysis; Depersonalisation.

Maudsley BRC research themes

• Psychosis & Mood Disorders
• Trials, Genomics and Prediction
• Neuroimaging
• Experimental Medicine and Novel Therapeutics

Supervisors

Dr Gemma Modinos
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
Email: gemma.modinos@kcl.ac.uk
Website: 1. https://modinoslab.com/  2. https://www.kcl.ac.uk/people/dr-gemma-modinos

Professor Mitul Mehta
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience
Email: mitul.mehta@kcl.ac.uk 
Website: https://www.kcl.ac.uk/people/mitul-mehta

Project Details

Background:  Abnormalities of the neurotransmitter system GABA are consistent findings in post-mortem and preclinical studies in psychosis. GABAergic abnormalities have been linked to not only the development of psychotic symptoms but also cognitive deficits that are not treated effectively by current antipsychotics. However, linking these molecular deficits to in vivo observations in clinical populations – a critical goal to evaluate novel interventions that would target GABAergic abnormalities – has been a challenge. This project aims to address this issue through three interrelated aims:

1. Meta-analysis of studies combining pharmacologic exposure to GABAergic drugs with task-based fMRI activity and connectivity
2. Investigation of the acute effects of diazepam (a GABA-enhancing drug) on task-based fMRI activity and connectivity during cognitive processing (working memory) in people at clinical high-risk for psychosis (CHR)
3. Application of a novel, multimodal analysis method informed by PET to characterise the molecular basis of the brain’s pharmacodynamic response under diazepam (Receptor-Enriched Analysis of functional Connectivity by Targets - REACT).

The results may provide important proof-of-concept evidence to support the future development of novel interventions for psychosis.

Novelty and Importance:  This will be the first study to investigate whether modulating GABAergic dysfunction in people at the putative premorbid stage of psychosis can improve fMRI activity and connectivity during cognitive processing. This will be achieved by combining a state-of-the-art pharmacological fMRI approach with a novel multimodal analysis method to link these non-invasive findings to specific neuroreceptor systems.

Primary aim(s):  To determine whether the acute administration of diazepam normalizes neural activity and functional connectivity during cognitive processing in people at CHR for psychosis, and characterise the molecular pathways involved in these pharmacological effects.

Planned research methods and training provided: The data to be used for this independent project is being collected as part of a larger study, to be completed by early 2023. Training will be provided in the clinical and cognitive instruments used to characterise the CHR state, neuropsychopharmacology, fMRI imaging methods, including REACT, data analysis dissemination.

Objectives / project plan

Year 1: Training, meta-analysis study, preliminary fMRI data analysis.

Year 2: fMRI data analysis, conference presentation, journal publications.

Year 3: Final data analysis, thesis write-up, conference presentation, journal publications.

Year 4: This additional time would allow unplanned extensions in case of any delays in participant recruitment and/or offer a period of time following thesis submission for the student to support their transition into the post-doctoral phase.

Two representative publications from supervisors

Publication 1:  Modinos G, Allen P, Grace AA, McGuire P. (2015) Translating the MAM model of psychosis to humans. Trends in Neurosciences; 38:129-38.

Publication 2:  Dipasquale O, Selvaggi P, Veronese M, Gabay AS, Turkheimer F, Mehta MA. (2019). Receptor-Enriched Analysis of functional connectivity by targets (REACT): A novel, multimodal analytical approach informed by PET to study the pharmacodynamic response of the brain under MDMA. Neuroimage; 195:252-260.

Keywords:  Psychosis; GABA; Brain imaging; Clinical High-Risk for Psychosis; fMRI.

Maudsley BRC research themes

• Psychosis & Mood Disorders
• Neuroimaging
• Experimental Medicine and Novel Therapeutics

Supervisors

Dr Will Lawn
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
Email: will.lawn@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/will-lawn

Professor Sir John Strang
Department of Addictions, Institute of Psychiatry, Psychology and Neuroscience
Email: john.strang@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/john-strang

Project Details

Background: In the UK, cannabis leads to 77% of treatment admissions for <18 year-olds (NDTMS 2019) and is the third most common drug associated with adult treatment (NDTMS 2019). No pharmacological treatments for CUD are approved for adults or adolescents (Sherman & McRae-Clark 2016). However, Freeman and colleagues recently reported that four weeks of daily CBD is efficacious in reducing cannabis use (Freeman et al. 2020, figure 3). CBD has also been shown to disrupt attentional bias to cigarette (Hindocha et al., 2018), cannabis (Morgan et al., 2010), and heroin cues (Hurd et al., 2019) and reduce craving and anxiety in people with heroin dependence (Hurd et al., 2019). Importantly, CBD at high doses, is safe in children and teenagers (Devinsky et al. 2016; Devinsky et al. 2018; Sands et al., 2019), is non-intoxicating (Haney et al. 2016) and has no abuse liability (Babalonis et al. 2017). Dronabinol (i.e. oral medical THC) reduces cannabis withdrawal and improves treatment retention in adults (Bahji et al., 2021) and reduces cannabis self-administration in the laboratory (Schlienz et al., 2018). Oral and vaporized THC has been safely given to teenagers (Mokrysz et al., 2016; Skumlien et al., 2022; Wong & Wilens, 2017). However, dronabinol’s therapeutic effects in adolescent CUD have not been investigated. Indeed, there are no pharmacological treatments available for CUD in adolescents.

Data from this experimental medicine study would provide possible therapeutic signals, illuminate CBD’s and/or dronabinol’s potential mechanisms and, if successful, would be used as proof-of-principle for a full clinical trial.

Novelty and Importance:  Given the rising toll of adolescent CUD, there is an urgent need for pharmacotherapies for this condition.

Primary aim(s):  Test the effect of cannabidiol and dronabinol on cue-induced craving for cannabis and cannabis withdrawal in late adolescents (16-18 year olds) following three days of cannabis abstinence.

Planned research methods and training provided:

Methods

Between-subjects experimental medicine, non-CTIMP study. Participants will be 90 teenagers (aged 16-18 year olds) who use cannabis ≥three days/week and have ≥mild CUD. Participants will be randomised to receive one dose of oral CBD (1,000mg) (n=30), oral dronabinol (20mg) (n=30) or matched placebo (n=30).

Participants will attend two in-lab sessions: (1) baseline (following no cannabis abstinence); and (2) drug administration session (after ≥72 hours of abstinence from cannabis). Participants will complete cue-induced craving tasks and measures of cannabis withdrawal, report subjective effects and give blood samples on both sessions. The primary outcome will be craving.

Training

• Systematic review
• Meta-analysis
• Randomized, placebo-controlled, double-blinded experiment
• Clinical trial secondary data analysis
• PPI

Objectives / project plan

Year 1: (1) Systematic review of treatment for cannabis use disorder in <18 year olds, (2) Meta-analysis of the subjective effects of THC and CBD on <18 year olds, (3) Public and patient involvement and engagement (4) Gain approvals for experiment and publish protocol and analysis plans.

Year 2: (1) Conduct experiment; (2) secondary analyses of existing dataset, investigating young adults (<24 year olds) receiving daily CBD for CUD (Freeman et al., 2020).

Year 3: (1) data analysis of experimental data; (2) publication; (3) write up thesis.

Year 4: contingency time.

Two representative publications from supervisors

Publication 1:  Lawn, W., Mokrysz, C., Lees, R., Trinci, K., Petrilli, K., Skumlien, M., ... & Curran, H. V. (2022). The CannTeen Study: Cannabis use disorder, depression, anxiety, and psychotic-like symptoms in adolescent and adult cannabis users and age-matched controls. Journal of psychopharmacology, 02698811221108956.

Publication 2:  Skumlien, M., Freeman, T. P., Mokrysz, C., Wall, M. B., Ofori, S., Petrilli, K., ... & Lawn, W. (2022). The effects of acute cannabis with and without cannabidiol on neural reward anticipation in adults and adolescents. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.

Keywords:  Cannabis; Cannabidiol; THC; Cannabis use disorder.

Maudsley BRC research themes

• Pain and Addictions
• Experimental Medicine and Novel Therapeutics

Supervisors

Professor Katya Rubia
Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email: katya.rubia@kcl.ac.uk
Website: Professor Katya Rubia (kcl.ac.uk)

Professor Paramala Santosh
Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email: paramala.1.santosh@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/paramala-santosh

Project Details

Background:  Stimulant medication is the prevalent treatment for ADHD but is not preferred due to side effects, is not suitable for all patients, and compliance is poor in adolescence. External trigeminal nerve stimulation (eTNS) is the first non-pharmacological ADHD treatment approved in 2019 by the USA Food and Drug administration (FDA), although efficacy data do not exist beyond a pilot study on 62 subjects and mechanisms of action are not understood. eTNS is a minimal risk, non-invasive neuromodulation that sends low electrical pulses under the skin on the forehead targeting the trigeminal system and can be applied during sleep. eTNS is thought to activate the locus-coeruleus arousal system and its connections to front-thalamic brain regions, that are typically underfunctioning in ADHD. A proof of concept randomised controlled trial (RCT) in 62 children with ADHD showed medium effect size improvement of ADHD symptoms after 4 weeks of real compared to sham eTNS administered every night for 8 hours by parents, with minimal side effects.

Novelty and Importance:  The study will confirm in a well powered multi-center RCT whether eTNS is an effective non-drug treatment for ADHD with minimal side effects that can be administered in-house and is hence likely preferred by users. Such a treatment would improve the healthcare and disease burden for patients.

Primary aim(s):  To conduct a confirmatory phase IIB, sham-controlled, parallel-arm, blinded, multicenter RCT in 150 ADHD children and adolescents to test whether 4 weeks of eTNS improves investigator-scored ADHD symptoms, other associated clinical problems, executive function performance, whether effects persist 6 months later, whether it is safe, and to understand the underlying mechanisms of action on ADHD fMRI brain function and how it relates to symptom improvement.

Planned research methods and training provided:  We will randomise 150 children and adolescents, 8-18 years, with a clinical ADHD diagnosis to real or sham eTNS over 4 weeks across 2 sites, London and Southampton, to test whether eTNS improves researcher-scored parent-rated ADHD symptoms (primary outcome), measured at baseline, weekly and 6 months follow-up. Secondary outcomes measured at baseline, 4 weeks and 6 months follow-up are cognitive performance on executive function tasks and other associated clinical symptoms. Safety will also be assessed. The underlying effects of eTNS on brain function will be tested in a subgroup of 56 ADHD children with fMRI, measured before and after 4 treatment weeks. Analyses will test treatment effects on outcome measures using general linear model adjusted for baseline ADHD scores, site, age, gender, and medication, in an intention to treat analysis and explore mediators, moderators and predictors of treatment response. 

Training: Clinical assessments including the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS), Good Clinical Practice (GCP), Safeguarding children level 1-3, Wechsler Abbreviated Scale of Intelligence (WASI-II), Neurocognitive assessments, vital signs, Empatica E4- objective hyperactivity meter, Tobii Pro eye tracker, fMRI acquisition and analyses.

Objectives / project plan

Year 1: Training in research assessments, recruitment and assessments.

Year 2: Recruitment and assessment, data checking and cleaning.

Year 3: Analyses and writing final report and primary paper.

Year 4: Writing papers and dissemination.

Two representative publications from supervisors

Publication 1:  Westwood SJ, Bozhilova N, Criaud M, Lam SL, Lukito S, Wallace-Hanlon S, Kowalczyk OS, Kostara A, Mathew J, Wexler BE, Kadosh RC, Asherson P, Rubia K. (2021) The effect of transcranial direct current stimulation (tDCS) combined with cognitive training on EEG spectral power in adolescent boys with ADHD: A double-blind, randomized, sham-controlled trial. IBRO Neurosci Rep. 12:55-64.

Publication 2:  Rubia, K., Westwood, S, Aggensteiner, P.-M., Brandeis, D. (2021) Neurotherapeutics for Attention Deficit/Hyperactivity Disorder (ADHD): A Review. Cells 10(8):2156

Keywords:  ADHD; Trigeminal Nerve Stimulation (TNS); Brain stimulation; Randomised Controlled Trial (RCT); fMRI.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Trials, Genomics and Prediction
• Neuroimaging
• Experimental Medicine and Novel Therapeutics

Supervisors

Dr Eleanor Dommett
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
Email: Eleanor.dommett@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/ellie-dommett

Dr Caroline Catmur
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
Email: caroline.catmur@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/caroline-catmur

Project Details

Background:  ADHD is characterized by inattention, impulsivity, and hyperactivity. It affects 6% of children and 3% of adults, resulting in significant impairment and reduced quality of life. Treatments are limited, particularly for adults. Psychostimulants are most effective but must be taken continuously and have side effects, creating a need for alternative treatments. The midbrain superior colliculus (SC) is hypothesized to be important in ADHD. It controls eye movements, including microsaccades, and SC activity is modulated by anti-saccadic training. Studies in healthy adults show microsaccade rate correlates with ADHD traits but no work has examined this in ADHD.

Novelty and Importance:  This project is the first to examine microsaccades in ADHD and assess an anti-saccade training intervention. We focus on adults who are under-represented in ADHD research.

Primary aim(s):

1. Assess whether microsaccades are a biomarker of ADHD (A1).
2. Establish whether anti-saccade training can improve ADHD symptoms (A2).
   

Planned research methods and training provided:  This project includes a systematic review examining eye movements and ADHD symptoms, allowing familiarization with literature, and production of a publication. This will be supported by both supervisors and university training. The experiments require online screening using clinical scales and laboratory testing of attention and impulsivity (e.g., Test of Variables of Attention), self-report ADHD measures (e.g., CAARS) and eye-tracking (Sustained Fixation Task). The first supervisor will provide training on screening and cognitive testing. Both supervisors will support eye-tracking with the second providing programming support. The student will work with a Research Advisory Panel (RAP) with the first supervisor supporting this.

Objectives / project plan

Year 1: Conduct and publish systematic review. Recruit 6-10 adults with ADHD for the RAP and work with them to develop the A1 protocol and obtain ethical approval. Power calculation (f²=0.15, power=0.8) for regression (predictors: ADHD traits, demographics and medication) indicates N=97, a sample size we achieved in our previous research.

Year 2: Complete A1 experiment including write-up. Work with RAP to disseminate A1 and develop A2 protocol and approvals. This will likely be weekly training (6 weeks) with 4-week follow-up. A proportion of lab visits will assess ADHD symptoms, whilst the remainder require only training; pro-saccade (control) or anti-saccade (experimental), with allocation through stratified randomization. Power calculation for an ANOVA requires N=56. However, to mitigate against attrition we will over-sample by 20%.

Year 3: Complete A2; disseminate findings; write thesis.

Year 4: Thesis submission; additional time for internship/placement, post-doctoral fellowship applications.

Two representative publications from supervisors

Publication 1:  Dinu, L. M., Phattharakulnij, N., & Dommett, E. J. (2022). Tryptophan modulation in individuals with attention deficit hyperactivity disorder: a systematic review. Journal of Neural Transmission, 1-17. (Example systematic review into alternative ADHD treatments by Supervisor 1)

Publication 2:  Cuve, H.C., Stojanov, J., Catmur, C., Bird, G. & Roberts-Gaal, M. (2022). Validation of Gazepoint low-cost eye-tracking and psychophysiology bundle. Behavior Research Methods, 54(2): 1027-1049. (Example eye tracking paper by Supervisor 2)

Keywords:  Attention Deficit Hyperactivity Disorder (ADHD); Eye movements; Saccades; Neurodiversity; Mental Health.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Experimental Medicine and Novel Therapeutics

Supervisors

Dr Dafnis Batalle
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: dafnis.batalle@kcl.ac.uk
Website: 1. https://kclpure.kcl.ac.uk/portal/dafnis.batalle.html  2. www.code-neuro.com

Professor Grainne McAlonan
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: grainne.mcalonan@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/en/persons/grainne-mcalonan(7bbd53e5-4eab-48c8-9b6f-466f2553759b).html

Project Details

Background:  Autism Spectrum Disorder (ASD) is a highly diverse and heterogeneous condition. Histopathology and neuroimaging studies in children and adults with a diagnosis of ASD have shown subtle disruptions in the organisation of their neural systems. Understanding underlying mechanisms that lead to ASD and establishing potential biomarkers for use in trials targeting brain responses to pharmacological challenges is paramount in order to accelerate the discovery of new therapeutic strategies for ASD.

Functional connectivity is often analysed assuming a condition of stationarity. While this is a useful approach to ascertain ‘on average’ characteristics of brain activity, connectivity of the brain is intrinsically dynamic, i.e., non-stationary. Addressing this problem, dynamic functional connectivity (dFC) measures the constant neural adjustments needed to control different brain states, adapt to transient situations, and integrate information. Measures of dFC have been associated with cognitive flexibility and information processing capacity, whilst altered transitions between dFC brain states have been reported in ASD. This novel approach is perfectly positioned to characterize the complex neuronal adaptations that follow brain dynamic ‘disruption’ after a pharmacological challenge, and the distinct dynamic responses between ASD and TD.

Novelty and Importance:  This project uses cutting-edge applied neuropharmacology, neuroimaging, and computational neuroscience to accelerate the discovery and validation of new therapeutic strategies for ASD.

Primary aim(s):

• Map the functional dynamic landscape of brain states in a population of ASD participants and matched typically developing (TD) individuals,
• Map typical and atypical brain dynamic responses of placebo-controlled pharmacological challenge studies of 5HT, GABAergic and glutamatergic compounds in ASD and TD participants.
• Establish the association of ‘shifted’ dynamics with population-based symptom networks.
  

Planned research methods and training provided:  We will use data from the KCL-lead EU-AIMS LEAP project, including 437 children and adults with ASD and 300 TD matched participants. We will also use data available from pharmacological studies taking place in our department (PI McAlonan; data available from Citalopram, Tianeptine, Arbaclofan, CDB). The student will also contribute to an ongoing trial of psilocybin in autism (PI McAlonan, in collaboration with Compass Pathways).

The student in this project will learn advanced data analysis techniques in order to characterise and assess brain dynamics (e.g. neuroimaging, graph theory, supervised/unsupervised machine learning, gradient projection analyses).

Objectives / project plan

Year 1: Characterisation of baseline brain dynamics in TD vs ASD (LEAP dataset)

Year 2: Characterisation of brain dynamic landscape in pharmacological challenges

Year 3: Characterisation of data-driven symptom networks and their association with brain dynamic responses

Year 4: Integration of results and writing up.

Two representative publications from supervisors

Publication 1:  Taoudi-Benchekroun, Y., Christiaens, D., Grigorescu, I., Gale-Grant, O., Schuh, A., Pietsch, M., ... & Batalle, D. (2022). Predicting age and clinical risk from the neonatal connectome. NeuroImage, 119319.

Publication 2:  Huang, Q., Pereira, A. C., Velthuis, H., Wong, N. M., Ellis, C. L., Ponteduro, F. M., ... & McAlonan, G. M. (2022). GABAB receptor modulation of visual sensory processing in adults with and without autism spectrum disorder. Science translational medicine, 14(626), eabg7859.

Keywords:  autism; fMRI; neuropharmacology; brain networks; psilocybin.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Informatics
• Trials, Genomics and Prediction
• Neuroimaging
• Experimental Medicine and Novel Therapeutics

Supervisors

Professor Sagnik Bhattacharyya
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
Email: Sagnik.2.bhattacharyya@kcl.ac.uk
Website: Sagnik Bhattacharyya - Research Portal, King's College, London (kcl.ac.uk)

Dr David Lythgoe
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience
Email: david.lythgoe@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/david.lythgoe.html

Dr Latha Velayudhan
Department of Old Age Psychiatry,  Institute of Psychiatry, Psychology and Neuroscience

Project Details

Background:  No approved treatments are available for psychosis in patients with Parkinson’s disease (PDP), which are particularly disabling, and cause much suffering. Existing antipsychotics have only modest benefits and significant side-effects.

Randomised clinical trials (RCTs) in people with psychosis indicate that cannabidiol has antipsychotic effects and is well-tolerated. We have shown that a single dose of cannabidiol partially normalises brain function abnormalities in the striatum and medial temporal cortex in early psychosis. Whether cannabidiol may have a similar effect in people with PDP remains unclear. The present proposal aims to investigate this.

Novelty and Importance: Cannabidiol is one of the most promising novel treatments in mental health, and its mechanism of action especially in psychosis associated with neurodegenerative disorders remains unclear.

Primary aim(s): To investigate-

1. the neurophysiological and neurochemical mechanisms underlying the antipsychotic effects of cannabidiol.
2. the relationship between effects of cannabidiol on brain signals and symptoms.
   

Planned research methods and training provided:

• Acquisition & analysis of neuroimaging and behavioural data
• Experimental medicine; industrial collaboration

Objectives / project plan:  The proposed PhD will be nested within a feasibility RCT in PDP to investigate the safety, tolerability and efficacy of Cannabidiol.

Design: Double-blind, parallel-arm, placebo-controlled RCT in 120 patients with PDP.

Participants will be randomised to one of two treatment arms (n=60 per arm) and patients will receive:

1. Treatment as usual (TAU) + Cannabidiol or
2. TAU + Placebo.

TAU will involve routine treatment in PD. 

The neuroimaging sub-study will involve recruiting 40 patients (n=20 from each treatment arm) taking part in the larger RCT, 20 PD patients without psychosis and 20 healthy individuals.

Experimental Treatment: Oral cannabidiol, 800mg/day or matched placebo for 12 weeks.

Participants: Adults (50-85 years) meeting the diagnostic criteria for PDP, PD without psychosis and healthy controls.

Assessments: Neuroimaging data (a range of imaging sequences including fMRI- verbal learning, MRS- glutamate (caudate and hippocampus)) will be acquired on a 3T MRI scanner using established protocols twice: at baseline and at the end of 12-week treatment. Psychopathology and cognition will be assessed at same timepoints. The PhD student will investigate the effect of CBD treatment on the brain signal and will have flexibility in choosing the specific neuroimaging approaches.

Analyses: Neuroimaging data will be analysed using established software.

Project plan

Year 1: Liaison with RCT team; subject enrolment; systematic review of background literature; data collection.

Year 2: Data collection; training in neuroimaging data preprocessing/ analysis; Industrial secondment; drafting review manuscript/s.

Year 3: Data collection; finalising analysis pipelines.

Year 4: Data analyses; Dissemination; Write-up of PhD.

Two representative publications from supervisors

Publication 1:  Bhattacharyya S, Wilson R, Appiah-Kusi E, et al. Effect of Cannabidiol on Medial Temporal, Midbrain, and Striatal Dysfunction in People at Clinical High Risk of Psychosis: A Randomized Clinical Trial. JAMA Psychiatry. 2018;75(11):1107-1117.

Publication 2:  Velayudhan L, McGoohan K, Bhattacharyya S. Safety and tolerability of natural and synthetic cannabinoids in adults aged over 50 years: A systematic review and meta-analysis. PLoS Med. 2021;18(3):e1003524.

Keywords:  Parkinson's disease; Psychosis; Cannabidiol; Randomised Controlled Trial (RCT); Neuroimaging fMRI MRS.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Neuroimaging
• Experimental Medicine and Novel Therapeutics

Supervisors

Professor Sir John Strang
Department of Addictions, Institute of Psychiatry, Psychology and Neuroscience
Email: John.strang@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/john-strang h

Dr Will Lawn
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
Email: will.lawn@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/will-lawn

Dr Basak Tas
Department of Addictions, Institute of Psychiatry, Psychology and Neuroscience

Project Details

Background:  Opioid overdose is now the leading cause of accidental death for people under the age of 50 in the USA (CDC,2022). In Scotland, opioid overdose deaths per capita are now greater than in the USA (Van Amsterdam et al.,2021). Many people are alone when they overdose (Davidson et al.,2003;Wojcicki et al.,2018) and any intervention requires accurate detection of overdose and the presence of another person. Furthermore, there is a significantly increased risk of overdose within the weeks following periods of cessation of opioid use because of suspected loss of tolerance (Strang et al. 2003;Merrall et al.,2010).

Novelty and Importance:  Remote, real-time measurement of respiratory depression (a hallmark of opioid overdose) is possible but little research exists on the mechanistic underpinnings, and comparison between remote and traditional measures. This project will uniquely study recreational heroin users who are the most vulnerable to the risk of heroin overdose and provide an important contribution to the ways in which we improve responses to overdose crises.

Primary aim(s):  1) To develop an experimental model of overdose with varying doses of opioid agonist (diamorphine) to investigate accuracy of wearable technology in detecting opioid overdose;

2) To understand whether there are differences in physiological responses between ex and current opioid users by examining hypercapnic drive.

Planned research methods and training:  Research methods: proof-of-concept, dose-escalation, experimental study among 20 volunteers who use heroin illicitly. Participants will attend ~4-6 sessions at the Clinical Research Facility and administer varying doses of prescribed diamorphine. Participants will be monitored whilst wearing bench-based devices and wearable sensors (chest sensor and wrist-watch) to detect physiological changes post-dose (Study 2a). A subgroup (n=10) from Study 2a will act as a control group to ex-users (n=10) in a separate, 1-session study using inhaled CO₂ (Study 2b).

Training: Statistical analyses; clinical study/commercial product development; respiratory physiology techniques; wearable/mobile technologies; controlled human drug administration experiments.

Objectives/project plan

Year 1:

• Conduct Study 1: National Programme of Substance Abuse Deaths analysis into geographic location and bystander presence at death related to opioid use;
• Approvals for Study 2.
  

Year 2:

• Conduct Study 2a using previously developed techniques (Jolley et al,2015; Tas et al.,2022).
  

Year 3:

• Conduct Study 2b;
• 3-month secondment with PneumoWave;
• Analysis of Study 2

Year 4: Write-up

Other notable aspects of the project:  Collaborative links with colleagues in KCL Respiratory Medicine exist with previous work. This connection will continue through reviewing of research methods and data.

Two representative publications from supervisors

Publication 1:  Heroin-induced respiratory depression and the influence of dose variation: within-subject between-session changes following dose reduction.  Tas, B., Jolley, C., Kalk, N., Bell, J., van der Waal, R. & Strang, J. 2020, In: Addiction. 115, 10, p. 1954-1959

Publication 2:  Undetected Respiratory Depression in People with Opioid Use Disorder  Tas, B., Kalk, N., Lozano- García, M. Rafferty, GF., Cho, P.S.P., Kelleher, M., Moxham, J., Strang, J. & Jolley, C. J. 2022, In: Drug and alcohol dependence. 234, 109401.

Keywords:  Opioid; Opioid overdose; Respiratory physiology; Wearable technology; Drug-related death.

Maudsley BRC research themes

• Pain and Addictions
• Informatics
• Experimental Medicine and Novel Therapeutics
• Digital Therapies

Supervisors

Professor Dame Til Wykes
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
Email: Til.wykes@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/til.wykes.html

Dr Matteo Cella
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
Email: Matteo.cella@kcl.ac.uk
Website: https://www.kcl.ac.uk/people/matteo-cella

Project Details

Background:  KCL researchers developed a successful cognitive remediation therapy approach called CIRCuiTS and evaluated its acceptability and benefits. The intervention is widely used across clinical settings, in different countries and has been translated into eight languages. CIRCuiTS is a therapist-supported therapy for people with psychosis to help achieve their recovery goals. The software uses cognitive computer tasks to teach strategy use and metacognitive skills and embeds learned skills in real life situations.

Novelty and Importance:  Cognitive remediation plus vocational or community rehabilitation is recommended to improve functioning, but these are often not available or difficult to access and may not be linked with the skills learnt during therapy. This study will develop and evaluate a virtual reality add-on for CIRCuiTS to create opportunities for clients to practice cognitive skills and strategies learned during cognitive remediation in easily accessible virtual environments. This novel approach will support skill generalization, confidence building and successful performance in everyday life situations that benefits recovery.

Primary aim(s):  To co-develop, with service users’ input, and evaluate the acceptability of a CIRCuiTS VR booster.

Planned research methods and training provided: Mixed methods including co-design and development with service users, and feedback evaluation with qualitative methods. Case series evaluation. Training in qualitative methods, service user involvement best practice from the BRC PPI theme. Basic VR programming and development skills. Cognitive and clinical assessment training. Quantitative research methods to assess indication of benefits.

Objectives / project plan:  To develop and evaluate the VR booster add-on for CIRCuiTS and assess it acceptability, feasibility, and indication of usefulness. The initial phase will focus on co-development and adaptation of VR environments. The second phase is an evaluation of the VR augmented CIRCuiTS in 20 participants with psychosis and gather acceptability information alongside an evaluation of recovery goal progress.

Year 1:  VR software co-development and feedback with the service users’ advisory panel. Qualitative research methods training. Studies ethics and R&D completed.

Year 2:  Completing VR environments development alpha and beta testing. Incorporate all feedback and finalize VR environments to test. Begin recruitment for case series.

Year 3:  Complete case series recruitment. Analyses of case series outcomes and acceptability interviews.

Year 4:  Thesis write-up and publications preparation.

Other notable aspects of the project:  Collaboration with the VR lab and with the CIRCuiTS potential spin out company incubated at KCL.

Two representative publications from supervisors

Publication 1:  VITA A, BARLATI S, CERASO A, NIBBIO G, ARIU C, DESTE G, WYKES T. (2021) Effectiveness, core elements, and moderators of response of cognitive remediation for schizophrenia: a systematic review and meta-analysis of randomized clinical trials. JAMA psychiatry, Aug 1;78(8):848-58

Publication 2:  Bowie CR, Bell MD, Fiszdon JM, Johannesen JK, Lindenmayer JP, McGurk SR, Medalia AA, Penadés R, Saperstein AM, Twamley EW, Ueland T, Wykes T.  Cognitive remediation for schizophrenia: An expert working group white paper on core techniques.  Schizophr Res. 2020 Jan;215:49-53.

Keywords:  Cognition; Cognitive remediation therapy; Psychosis; Virtual reality; Functional outcome.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Digital Therapies

Supervisors

Dr Paul Shotbolt
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: paul.shotbolt@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/paul.shotbolt.html

Professor Sukhi Shergill
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
Email: sukhi.shergill@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/sukhi.shergill.html

Professor Mark Edwards
Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience

Project Details

Background:  Functional Neurological Disorder (FND) is the second commonest diagnosis in neurology clinics and causes significant disability (Carson & Stone, 2015). Motor FND symptoms are subjectively reported by patients as involuntary (Edwards, 2012). This may be mediated by altered sense of body ownership and agency, also found in schizophrenia (Shergill 2014).

Previous studies of these constructs, using experimental paradigms such as the rubber hand illusion, have led to conflicting results. In this project, novel VR environments will be used. We anticipate that their immersive nature plus the ease of manipulation to change experimental conditions will allow more valid investigation.    

Novelty and Importance:  This project represents a novel use VR to examine key constructs in the pathophysiology of FND, specifically sense of body ownership and agency. These constructs will also be assessed in patients with schizophrenia and compared with healthy controls. Increased understanding of FND will lead to more effective treatments.

Primary aim(s):  The hypotheses are that, compared to controls, patients with FND and schizophrenia will; 1. be more susceptible to manipulation of sense of body ownership. 2. show reduced agency over the movements of an avatar.

25 individuals diagnosed with FND, 25 with schizophrenia and 25 healthy controls recruited. Body ownership and agency assessed in two VR environments; a ‘virtual mirror’ avatar (participants see an avatar in front of them that follows their movements), and a ‘virtual body illusion’ (participants see a projected true image of their body from the back).

Planned research methods and training provided:  1. assessment of FND and schizophrenia patients 2. VR application design for clinical and non-clinical applications in secondment with Mesmerise 3. all aspects of relevant research methods and data analysis. 

Outline of risks and mitigating activities

1. Ethics/ regulatory approval. We will obtain the necessary regulatory approvals ahead of the student’s start date.
2. We have an exisiting database of patients with motor FND to ensure we have sufficient eligible patients at study start date.
3. The duration of the secondment will be flexible to allow more time for data collection if necessary.

VR environment design. The VR environments are currently being used in a MSc project and will only require minimal changes.

Objectives / project plan

Year 1:  Systematic review of agency / body ownership in FND and other clinical populations. Finalise design and VR environments, start recruitment.

Year 2:  Run and complete study, secondment with Mesmerise

Year 3:  Write up thesis and publications, disseminate results at conferences (e.g. British Neuropsychiatry Association, UK Functional Neurological Symptoms meetings).

Year 4:  Further industry secondments / internships. Support for applications for post-doctoral phase. Next steps with funding – fellowships / further collaborative grants with industry sponsor (Mesmerise Global).

Two representative publications from supervisors

Publication 1:  Increased suicide attempt risk in people with epilepsy in the presence of concurrent psychogenic nonepileptic seizures. Faiman I, Smith S, Hodsoll J, Young AH, Shotbolt P. Faiman I, et al. J Neurol Neurosurg Psychiatry 2022;0:1–7. doi:10.1136/jnnp-2022-329093

Publication 2:  Functional magnetic resonance imaging of impaired sensory prediction in schizophrenia. Shergill SS, White TP, Joyce DW, Bays PM, Wolpert DM, Frith CD. JAMA Psychiatry 2014 Jan;71(1):28-35.

Keywords:  Neuropsychiatry / Neurology; Functional Neurological Disorder; Psychopathology; Virtual reality; Novel therapeutics.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Experimental Medicine and Novel Therapeutics
• Digital Therapies

Supervisors

Dr Nicolaas Puts
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: Nicolaas.puts@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/nicolaas.puts.html

Dr Eileen Daly
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: Eileen.daly@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/eileen.daly.html

Project Details

Background:  Despite decades of study, the neurophysiological basis of ASD remains poorly understood. Multiple lines of evidence suggest that GABA, the main inhibitory neurotransmitter in the brain, plays a role in the pathophysiology of autism. Identifying the neurophysiologic and clinical correlates of altered GABAergic inhibition in autism will provide insight into the pathophysiology and provide a foundation crucial to pursuing innovative interventions, linked to GABAergic function. Differences in touch perception are often reported in autism and are a high-priority area for support. Cellular studies have shown that GABA_A and GABA_B receptors play distinct roles in encoding touch. Our work has shown that tactile differences in autism are suggestive of both altered GABA_A and GABA_B receptor function with distinct associations with autistic traits in both spatial discrimination, and detection of threshold stimuli. However, a direct connection between GABA_A and GABA_B mechanisms, GABA levels, tactile differences, and ASD symptomatology, has not been shown. It is well established that GABAergic circuitry within somatosensory cortex is crucial for encoding tactile stimuli.  GABA_A-receptors play an important role in shaping spatiotemporal patterns of activation as a response to tactile input, such as lateral inhibition. GABA_B-receptors play a role in modulating excitability of pyramidal neuron and have been linked to feedforward inhibition mechanisms key in regulating near-threshold stimuli.  Although differences in tactile perception in autism are thought to be regulated by both GABA_A and GABA_B neurotransmission, a direct connection between GABA_A and GABA_B-mechanisms, and autism symptomatology, has not been shown.

Figure 1. Potential pathways from mechanisms to intervention

Novelty and Importance:  Our “shiftability studies” allow for directly testing whether GABA_A and GABA_B are affected in autism. By linking these to distinct sensory processes (GABA_A - lateral inhibition; GABA_B - sensory gating) which can be measured both behaviorally using psychophysics and physiologically using EEG, we can directly test the role of distinct GABAergic mechanisms in these measures core to the autism phenotype, test whether these can be shifted, and assess impact of these shifts on clinical phenotype. 

Primary aim(s):  To test whether pharmacological intervention targeting different cortical mechanisms allows for separating distinct perceptual differences in autism, with unique impacts on clinical outcomes.

Planned research methods and training provided:  Using our shiftability approach, we will examine psychophysical and EEG markers of tactile perception shiftability approach, we will examine psychophysical and EEG markers of tactile perception before and after administration of GABA_A and/or GABA_B targeted drugs. We will train in all methods including setting up a pharmacological study, robust and reproducible data acquisition, and advanced statistical analysis, in addition to professional development and career mentorship.

Objectives / project plan

Year 1: Study set-up, ethics, piloting.

Year 2: Data acquisition and analysis. Dissemination of protocol/pilot data.

Year 3: Data acquisition and analysis. Dissemination of first results (paper, conference).

Year 4: data analysis and write up. Dissemination of project.

Two representative publications from supervisors

Publication 1:  He, J. L., Wodka, E., Tommerdahl, M., Edden, R. A. E. E., Mikkelsen, M., Mostofsky, S. H., & Puts, N. A. J. . (2021). Disorder-specific alterations of tactile sensitivity in neurodevelopmental disorders. Communications Biology, 4(1), 97. https://doi.org/10.1038/s42003-020-01592-y

Publication 2:  Huang, Q., Pereira, A. C., Velthuis, H., Wong, N. M. L., Ellis, C. L., Ponteduro, F. M., Dimitrov, M., Kowalewski, L., Lythgoe, D. J., Rotaru, D., Edden, R. A. E., Leonard, A., Ivin, G., Ahmad, J., Pretzsch, C. M., Daly, E., Murphy, D. G. M., & McAlonan, G. M. (2022). GABAB receptor modulation of visual sensory processing in adults with and without autism spectrum disorder. Science Translational Medicine, 14(626). https://doi.org/10.1126/SCITRANSLMED.ABG7859

Keywords:  Autism; GABA; Sensory; Pharmacological.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Neuroimaging
• Experimental Medicine and Novel Therapeutics

Supervisors

Dr Eileen Daly
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: eileen.daly@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/eileen.daly.html

Dr Bethany Oakley
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: bethany.oakley@kcl.ac.uk
Website: Beth Oakley - Research Portal, King's College, London (kcl.ac.uk)

Professor Emily Simonoff
Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience

Project Details

Background:  At least 50% of autistic people experience anxiety, however there are very few effective, evidence-based therapies available to autistic people that target anxiety (Hollocks et al., 2019; Benevides et al., 2020). This is partly due to lack of evidence for mechanisms underpinning anxiety features in autism that represent targets for intervention, and acceptable and effective autism-adapted anxiety interventions (Oakley, Loth and Murphy, 2021).

Novelty and Importance:  This PhD project will support the development and implementation of digital tools to elucidate underpinning mechanisms for anxiety in autism and provide proof-of-concept for the use of active app-based anxiety intervention strategies specially designed for autistic people.

Primary aim(s)

• To identify mechanistic pathways to anxiety in autism, using objective behavioural, cognitive, and physiological indices captured by digital tools.
• To assess the acceptability and effectiveness of a novel, app-based anxiety intervention for autistic people that targets mechanisms investigated in Aim 1.

Planned research methods and training provided

• Systematic review, meta-analysis.
• Quantitative research methods and analytics –behavioural, cognitive, physiological (e.g., heart rate variability), neurobiological (EEG, MRI), biochemical (e.g., serotonin, inflammation) and/or genetic data, and app usage data.
• Qualitative research methods and analytics: semi-structured interviews, thematic analysis.
• Clinical trial design.

Objectives / project plan

Year 1:  The student will perform a systematic review/meta-analysis of existing research on digital wearables and therapeutics targeting anxiety in autism, forming the introductory materials for the PhD.

The student will also contribute to some of the data collection and database curation for two ongoing research projects, from which the datasets will form the core components of the PhD. The first is the ‘clinical endpoints’ component of AIMS-2-TRIALS (Developing wearable technology with involvement from autistic people (aims-2-trials.eu)) - closely linked to the Longitudinal European Autism Project (LEAP) of >700 individuals characterised by their clinical, cognitive/behavioural profiles, brain structure/function and genomics.

The second dataset is drawn from the ‘Molehill Mountain Feasibility Study’ of an app-based Cognitive Behavioural therapeutic approach for anxiety - developed with, and adapted for, autistic people (Molehill Mountain Feasibility Study. - Full Text View - ClinicalTrials.gov).

Year 2:  The focus of year two will be the analysis of available AIMS-2-TRIALS and Molehill Mountain Feasibility Study datasets, for which the student was involved in the data collection during Year 1.

The student will conduct analyses to investigate associations between anxiety and its candidate mechanistic pathways in the context of autism, with data available pertaining to cognitive/behavioural and social factors, neurobiology, and biochemical markers/ genetics (AIMS-2-TRIALS dataset). Specific variables for analysis will be selected depending on the results of the literature review (Year 1) and the student’s own interests. This analysis will also incorporate data collected using digital tools/ wearables in everyday life (e.g., sleep, physical activity, sensory/stressor exposure, autonomic function).

The student will additionally analyse data on the acceptability/ feasibility of a novel app-based anxiety intervention for autistic people (targeting mechanisms investigated above) and its effectiveness for reducing anxiety in autistic adults (Molehill Mountain feasibility dataset).

Year 3:  The third year of the PhD project will allow for completion of the work/ analyses carried out in Years 1-3, including completing the write up of the thesis.

Year 4:  The results from this project will inform app optimisation and the design/launch of a future clinical trial (beyond the scope of this proposal). Thus, the final year of the PhD will support the student in their transition to the postdoctoral phase through, for example, opportunities for consolidation of the work/ analyses (including following placement in industry), publication, and insight on the translation of scientific research to real-world clinical trials.

Two representative publications from supervisors

Publication 1:  Oakley, Jones, Crawley, Charman, Buitelaar, Tillmann, Murphy & Loth. (2020). Alexithymia in autism: cross-sectional and longitudinal associations with social-communication difficulties, anxiety and depression symptoms. Psychological Medicine, 52, https://doi.org/10.1017/s0033291720003244

Publication 2:  Oakley, Loth & Murphy. (2021). Autism and mood disorders. International Review of Psychiatry, 33(3), https://doi.org/10.1080/09540261.2021.1872506

Keywords:  Autism; Anxiety; Mental health; Neurodevelopmental; Digital therapies.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Digital Therapies

Supervisors

Professor Ulrike Schmidt
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: Ulrike.schmidt@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/ulrike.schmidt.html

Professor Iain Campbell
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: Iain.campbell@kcl.ac.uk
Website: https://kclpure.kcl.ac.uk/portal/iain.campbell.html

Dr Karina Allen
Principal Clinical Psychologist and Honorary Senior Lecturer
South London and MAudsley NHS Foundation Trust, Eatings Disorders Outpatients Department

Project Details

Background:  Anorexia nervosa (AN) is a disabling and deadly disorder. Available treatments are only moderately effective, thus novel approaches are needed. We have previously shown in proof of concept and feasibility studies that different types of non-invasive brain stimulation (repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS)) applied to the dorsolateral pre-frontal cortex can be administered safely, are highly acceptable to users and positively impact clinical symptoms and mood in people with eating disorders, including those with AN. tDCS is being increasingly across different psychiatric disorders, in part because it is well-tolerated and can be combined with experimental paradigms facilitating new learning (such as exposure treatments or different forms of cognitive training). Use of immersive virtual reality is also increasingly being explored as a potential treatment for eating disorders, using illness relevant stimuli such as virtual exposure to avoided (high calorie) food items/eating or social eating or to changes in the person’s body size, i.e. from underweight to normal.

Novelty and Importance:  We are not aware of any studies in eating disorders that have augmented tDCS with virtual reality (or any other form of) exposure treatment. Thus, the approach taken is highly novel and it is hoped that synergistic effects may make the combination treatment particularly potent.

Primary aim(s):

• To assess the feasibility and acceptability of combining tDCS with virtual exposure therapy (VET) as a potential adjunct to the treatment of anorexia nervosa;
• To explore the use of different potential exposure paradigms (body, food and social) in this context;
• To obtain preliminary effect sizes for outcome measures;
• To qualitatively assess the acceptability of the combination intervention. 

Planned research methods and training provided:  The student would be expected to conduct a feasibility RCT of real/sham TDCS with virtual exposure therapy as a key part of their thesis. It is estimated that the RCT would include ~ 80 participants to be able to detect medium between group effect sizes and to allow for a degree of drop out.

Project plan

Year 1:  Familiarisation with/training in tDCS and VR methodologies and in conduct of a feasibility trial (e.g. good clinical practice, regulatory approvals etc). 

Writing a relevant systematic review, e.g on the use of tDCS and VET  in psychiatric disorders

Writing the protocol for the feasibility trial.

Year 2:  Participant recruitment and conduct of the main part of the proposed study with quantitative and qualitative data.  

Year 3:  Completing participant follow-ups and writing up. It is anticipated that the feasibility trial will yield  experimental (e.g. neurocognitive), questionnaire and qualitative data, that will be presented in different thesis chapters.

Year 4:  If needed, some time could be used to ensure all data are written up as papers and as time preparing for e.g. a fellow-ship or follow-on funding. A protocol for a large scale study could also be included in the PhD write-up.

Two representative publications from supervisors

Publication 1:  Gallop L, Flynn M, Campbell IC, Schmidt U. Neuromodulation and Eating Disorders. Curr Psychiatry Rep. 2022 Jan;24(1):61-69.

Publication 2:  Flynn M, Campbell I, Schmidt U. Does concurrent self-administered transcranial direct current stimulation and attention bias modification training improve symptoms of binge eating disorder? Protocol for the TANDEM feasibility randomized controlled trial. Front Psychiatry. 2022 Aug 3;13:949246.

Keywords:  Anorexia nervosa; Eating disorders; Neuromodulation; Virtual reality; Exposure treatment.

Maudsley BRC research themes

• Eating Disorders and Obesity
• Experimental Medicine and Novel Therapeutics