2024 PhD Projects

Supervisors

Professor Alice Egerton
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience   
Email: Alice.Egerton@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/alice.egerton

Dr Nicolaas Puts
Department of Forensic & Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: Nicolaas.puts@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/nick-puts

Dr Cathy Davies
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
Email: cathy.davies@kcl.ac.uk 

Project Details

Background:  This project uses advanced brain imaging techniques to characterise glutamatergic (excitatory, E) and GABAergic (inhibitory, I) synaptic function in schizophrenia. E/I synaptic function is fundamental to schizophrenia and thus a major target for novel drug development. Progress in this area is impeded by a lack of suitable measures of E/I function in the living human brain, against which novel compounds could be tested during early clinical development.

This project will use ultra-high field (7T) MRI and functional magnetic resonance spectroscopy (fMRS) to measure brain glutamatergic and GABAergic dynamics in people with a diagnosis of schizophrenia and healthy volunteers. It will go beyond collecting single static measures of glutamate or GABA concentration, as has been done previously, and examine the dynamic changes in glutamate / GABA which occur during visual stimulation, and the orderliness / complexity of glutamate and GABA signals, which may respectively better reflect E/I synaptic activity and synaptic dynamics. The project focuses on people with treatment resistant schizophrenia (TRS), and cognitive symptoms – which are the areas in greatest need for new therapeutics.

Together with the research team, the student will recruit and assess study participants and acquire MRS data. They will use and adapt an existing analytical platform and large healthy volunteer dataset for glutamate / GABA dynamics at 3T to the study data at 7T for their analyses.

Novelty and importance:  Application of fMRS to study E/I synaptic function in schizophrenia is novel, and only recently possible since installation of the KCL 7T scanner. The PhD should deliver a neuroimaging platform against which potential interventions can be tested in future experimental medicine studies.  

Primary aim:  To characterise E/I synaptic function and dynamics in relation to TRS and cognition.  

Study design and sample size:  Case control neuroimaging study; 3 groups (healthy volunteers, TRS, remitted schizophrenia), n = 25/group.

Planned research methods and training provided:

• Application of case-control, neuroimaging, and translational approaches to mental health.
• Recruitment and assessment of study participants
• 7T fMRS data acquisition and analyses
• Academic writing, presentation and publication, transferable skills

Objectives / project plan:

Year 1: Completion of compulsory and bespoke training; participant recruitment, data acquisition; adaptation of fMRS dynamics platform at 3T to 7T and publication of this work; introduction to PhD thesis.  

Year 2: Participant recruitment, data acquisition; data analyses.

Year 3: Complete data analyses, prepare publications, finalise thesis including overall discussion.

Two representative publications from supervisors

Publication 1:  Variability and magnitude of brain glutamate levels in schizophrenia: a meta and mega-analysis. Merritt, K., et al., Egerton A., 2023. Molecular Psychiatry doi: 10.1038/s41380-023-01991-7.

Publication 2:  Pasanta, D., He, J. L., Ford, T., Oeltzschner, G., Lythgoe, D. J., & Puts, N. A. (2022). Functional MRS studies of GABA and glutamate/Glx - A systematic review and meta-analysis. Neuroscience and Biobehavioral Reviews, 144, 104940. https://doi.org/10.1016/J.NEUBIOREV.2022.104940

Maudsley BRC research themes

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

Supervisors

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

Dr Jonathan Coleman
Department of Social, Genetic & Developmental Psychiatry, 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 psychiatric drugs, particularly for major depressive disorder (MDD) and eating disorders (ED). Current MDD therapies only work for a minority, and few drugs are approved for ED.

Information from genome-wide association studies (GWAS) could identified prioritized targets within disorder-associated regions, reducing drug development time and costs. The Psychiatric Genomics Consortium (PGC) has identified >550 genetic variants associated with major depression, significantly enriched for the targets of approved antidepressants. ED GWAS has identified associations with anorexia nervosa and with binge eating.

Novelty and importance:  This studentship will use the largest genetic datasets available for MDD and ED to identify drug repositioning opportunities. This will provide direct hypotheses for functional validation, leading to new clinical trials addressing key therapeutic needs.

Primary aims: 

Aim 1: Expand an existing bioinformatic pipeline for identifying drugs and small molecules to integrate new approaches, and to include information about uncommon, rare, and structural variants.

Aim 2: Use an MDD GWAS adjusted for specific confounders to re-weight MDD GWAS meta-analyses from international consortia and use these reweighted meta-analyses to identify drugs and small molecules for MDD.

Aim 3: Use data on rare and uncommon variants from a large long-read sequencing study of 4000 ED participants to enrich common variant ED GWAS data and conduct analyses to identify drugs and small molecules for ED.

Study design and sample size:  The project will expand the bioinformatic tools used by our existing drug repositioning pipeline (Drug Targetor) with existing and emerging tools. Methodological innovation is possible, particularly in integrating rare and structural variation.

MDD data from PGC-MDD will be supplemented by a GWAS adjusted for specific confounders (such as BMI) using data from 120K+ severe MDD cases from the Genetic Link to Anxiety and Depression (GLAD) study, UK Biobank, and from Dutch and Australian collaborators. Together these form the largest extant collection of MDD GWAS data, well powered to detect drug-related phenotypes (as demonstrated in the forthcoming PGC MDD paper).

ED data from PGC-ED will be supplemented with long-read sequencing from 4000 individuals with ED. This is the most powerful dataset available in ED, with deep coverage of different types of genetic variation.

Planned research methods and training provided: The student will develop a Snakemake pipeline applicable to GWAS summary statistics, flexibly incorporating suitable languages (R, Python). Training will be provided by the supervisors, supplemented by local collaborations and specialist training courses.

Prof Breen is the co-PI of the GLAD study and an internationally recognised expert in psychiatric genetics, with a strong interest in leveraging GWAS for drug discovery.

Dr Coleman is a statistical geneticist with extensive experience in the conduct of GWAS and its use in developing empirically testable hypotheses in neurobiology.

Objectives / project plan:

Year 1: Training in genetic methods for Aims 2 and 3. Begin pipeline expansion, and train in programming as needed.

Year 2: Apply pipeline to MDD GWAS (Aim 2) and continue pipeline development for Aim 3.

Year 3: Complete and publish Aim 3. Publish pipeline as Aim 1.

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.

Maudsley BRC research themes

• Eating Disorders and Obesity
• Psychosis and Mood Disorders
• Trials, Genomics and Prediction

Supervisors

Dr Johnny Downs
Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience   
Email: johnny.downs@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/johnny-downs

Dr Alice Wickersham
Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email: alice.wickersham@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/alice-wickersham

Professor Edmund Sonuga-Barke
Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience

Project Details

Background:  Randomized controlled trials (RCT) are the most rigorous method to examine causal relationships between an intervention and outcome. They are fundamental to the development of evidence-based treatments in children and young people, but are difficult to conduct. Conventional RCTs carry additional burden on participants and researchers in the form of in-person recruitment and appointments, leading to reduced participation and greater loss to follow-up. These factors disproportionately affect young people and caregivers with mental illnesses.

Novelty and importance:  One approach to tackling this issue is through the adoption of pragmatic, virtual clinical trials, where assessment and data collection do not occur in traditional clinical settings, but are facilitated via remote interaction. These can confer benefits by facilitating faster recruitment across a greater geographic area while retaining the investigative team at one site. By developing such a platform in collaboration with users, this project will enable us to conduct virtual trials at scale, strengthening our capacity to accelerate translation of innovative mental health interventions into practice following rigorous trial testing.

Primary aims:  This project will investigate how a virtual data collection platform called myHealthE could be adapted to provide rapid triage, e-recruitment and delivery of digital mental health therapeutic trials in young people.

Study design and sample size:  A secondary data analysis of myHealthE users will comprise over 10,000 families who have signed up to the platform. The subsequent development and testing of the virtual trial platform will involve stakeholder engagement with at least 30 different stakeholders, including young people, caregivers, researchers, software engineers, and clinicians.

Planned research methods and training provided:  The student will receive training on:

• Conducting systematic review
• Advanced epidemiological approaches
• Rapid synthesis of qualitative data from co-design workshops and interviews
• Inclusive design methodologies
• Clinical governance frameworks for implementing digital products within NHS services
• Clinical trial methodologies

Objectives / project plan:

Year 1: Identify the core requirements for developing a virtual trial platform in child and adolescent mental health settings through a systematic review of academic and grey literature.

Year 2: Identify sociodemographic and clinical factors potentially associated with low engagement with a virtual trial platform, by quantifying the differential uptake of myHealthE.

Year 3: Use a co-design approach with clinicians, researchers, young people and caregivers to develop and test a versatile, virtual trial platform that can be used in clinical populations of young people with mental health disorders. 

Two representative publications from supervisors

Publication 1:  Kostyrka-Allchorne, K., Chu, P., Ballard, C., Lean, N., French, B., Hedstrom, E., Byford, S., Cortese, S., Daley, D., Downs, J., Glazebrook, C., Goldsmith, K., Hall, C.L., Kovshoff, H., Kreppner, J., Sayal, K., Shearer, J., Simonoff, E., Thompson, M.,& Sonuga-Barke, E.J. (2023). Remote Recruitment Strategy and Structured E-Parenting Support (STEPS) App: Feasibility and Usability Study. JMIR Pediatrics and Parenting, 6(1), e47035.

Publication 2:  Morris, A.C., Ibrahim, Z., Heslin, M., Moghraby, O.S., Stringaris, A., Grant, I.M., Zalewski, L., Pritchard, M., Stewart, R., Hotopf, M., Pickles, A., Dobson, R.J.B., Simonoff, E. and Downs, J. (2023). Assessing the feasibility of a web‐based outcome measurement system in child and adolescent mental health services–myHealthE a randomised controlled feasibility pilot study. Child and Adolescent Mental Health, 28(1), 128-147.

Maudsley BRC research themes

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

Supervisors

Professor Katya Rubia
Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience   
Email: katya.rubia@kcl.ac.uk    Website: Professor Katya Rubia (kcl.ac.uk)  &  https://www.kcl.ac.uk/research/attens-project

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/matthewhollocks

Professor Mitul Mehta
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience
Email: mitul.mehta@kcl.ac.uk

Project Details

Background:  Trigeminal nerve stimulation (TNS) is the first non-pharmacological ADHD treatment approved in 2019 by the FDA, although efficacy data do not exist beyond a pilot study on 62 children with ADHD which showed improvement of symptoms after 4 weeks of nightly treatment with medium effect size; furthermore, mechanisms of action are not understood. TNS is a safe, non-invasive neuromodulation that sends low electrical pulses under the skin on the forehead targeting the trigeminal system that activates the locus-coeruleus arousal system and its fronto-thalamic connections, typically under-functioning in ADHD.

Novelty and importance:  The study will confirm in a multi-center RCT whether TNS is an effective novel non-drug treatment for ADHD. The PHD specifically will elucidate the so far unknown mechanistic aspects of TNS.

Primary aims:  To test effects of TNS on 1) fMRI brain function 2) objective hyperactivity, heart rate variability and electrodermal activity 3) to test mediators/predictors of treatment response.

Study design and sample size:  A confirmatory phase IIB, sham-controlled, parallel-arm, blinded, multicenter RCT on the effects of 4 weeks of real versus sham TNS in 150 ADHD children on ADHD symptoms, other clinical problems, executive functions, fMRI brain function and objective physiological data. We will randomise 150 children and adolescents, 8-18 years, with ADHD to real or sham TNS over 4 weeks across London and Southampton, to test whether TNS improves parent-rated ADHD symptoms (primary outcome), cognitive performance and other clinical, fMRI and physiological measures measured at baseline and after 4 weeks. This PhD will focus on the mechanistic fMRI brain function and physiological measures.

Planned research methods and training provided:  The PhD will analyse 1) effects of real versus sham TNS on fMRI data in a subgroup of 56 ADHD children, measured in 3 fMRI tasks and a resting scan. (S)he will be trained in fMRI activation and functional connectivity analyses (SPM, FSL). 2) underlying effects of TNS on heart rate variability, electrodermal activity and objective hyperactivity measured on an E4 Empatica actimeter device. The student will be trained in actimeter data analysis (SPSS). 3) mediators, moderators and predictors of treatment response on the main outcome measure, the ADHD-RS, based on all baseline data. The student will be trained in structured mediation analyses to explore mediating and moderating effects and logistic regression models to test response predictors based on all baseline measures using a reduction > 20% in ADHD-RS symptoms.

Objectives / project plan:

Year 1: fMRI data analysis and write-up of ~ 7 papers (3 papers on brain activation and 4 papers on functional connectivity measures for resting state and tasks).

Year 2: Analysis of the actimeter data and paper write-up.

Year 3: Analysis of predictors, mediators and moderators of treatment response and paper write-up. Write-up of PHD thesis.

Two representative publications from supervisors

Publication 1:  Westwood SJ, Criaud M, Lam SL, Lukito S, Wallace-Hanlon S, Kowalczyk OS, Kostara A, Mathew J, Agbedjro D, Wexler BE, Cohen Kadosh R, Asherson P, Rubia K (2023) Transcranial direct current stimulation (tDCS) combined with cognitive training in adolescent boys with ADHD: a double-blind, randomised, sham-controlled trial. Psychol Med. 53(2):497-512

Publication 2:  Westwood SJ, Conti AA, Tang W, Xue S, Cortese S, Rubia K.  (2023) Clinical and cognitive effects of external trigeminal nerve stimulation (eTNS) in neurological and psychiatric disorders: a systematic review and meta-analysis. Mol Psychiatry. doi: 10.1038/s41380-023-02227-4.  Online ahead of print

Maudsley BRC research themes

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

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/en/persons/ulrike.schmidt

Dr Maria Livanou
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
Email: Maria.livanou@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/maria.livanou

Dr Karina Allen
South London & Maudsley NHS Foundation Trust and King's College London
Email:  Karina.allen@slam.nhs.uk

Dr Ruth Knight
Department of Psychology, York St John University

Project Details

Background:  Body image concerns are a key risk factor for eating disorders (EDs). The prevalence of EDs is rising, especially in young people. Prevention and early intervention are essential to reduce disease burden. Immersive virtual reality (VR) programmes are increasingly being explored as potential preventative or treatment interventions for EDs. Soulpaint is a novel VR platform that has translated an arts-based intervention, body mapping, into a novel VR format. Body mapping has been used as a tool to facilitate emotion regulation, reduce body dissatisfaction and increase body acceptance (Boydell, 2021).

All of this is highly relevant to EDs, where maladaptive emotion regulation, high body dissatisfaction and low body acceptance go hand in hand. Improvements in emotion regulation and body acceptance are key factors in ED recovery.

Novelty and importance:  The approach taken is highly novel and has great potential for prevention and treatment in EDs.

Primary aims:  To assess the feasibility and acceptability of virtual body mapping via Soulpaint as a potential (a) single session prevention for young people with body dissatisfaction and (b) adjunct to the treatment of EDs.

Study design and sample size: This PhD will include a feasibility RCT testing a single session of Soulpaint Body Mapping vs waiting list control in ~80 young people with high body dissatisfaction, to assess uptake and acceptability and effects on body image, a range of other psychosocial outcomes and food consumption.

A second project will include intensive PPI work to develop the Soulpaint prototype further for ED patients with different body sizes (underweight to obese), using a 3-4 session format and trialing the adapted intervention(s) in a clinical cohort of ~60-80 ED patients across the weight spectrum, assessing BMI, ED symptoms, body image, and other psychopathology. Qualitative interviews will be conducted. 

Planned research methods and training provided:  Multi-method project including quantitative and qualitative methods.  

Objectives / project plan:

Year 1:

• Familiarisation with/training in VR methodologies, training in conduct and analysis of clinical trials and qualitative methodologies.
• Writing a systematic review, e.g body mapping in psychiatric disorders.
• PPI focus groups with different ED patient populations to develop the Soulpaint prototype further.
• Writing study protocols & obtaining regulatory approvals.
• Internship
  

Year 2: Participant recruitment and conduct of the proposed studies.

Year 3: Completing participant follow-ups and writing up.

Other notable aspects of the project:  This is a unique opportunity for a creative student to assist with prototype development of the VR intervention.

Two representative publications from supervisors

Publication 1: Treasure J, Duarte TA, Schmidt U. Eating disorders. Lancet. 2020 Mar 14; 395(10227):899-911. doi: 10.1016/S0140-6736(20)30059-3. PMID: 32171414.

Publication 2: Oldershaw A, Lavender T, Sallis H, Stahl D, Schmidt U. Emotion generation and regulation in anorexia nervosa: a systematic review and meta-analysis of self-report data.
Clin Psychol Rev. 2015 Jul;39:83-95. doi:10.1016/j.cpr.2015.04.005. Epub 2015 May 2. PMID: 26043394.

Maudsley BRC research themes

• Eating Disorders and Obesity
• Experimental Medicine and Novel Therapeutics
• Digital Therapies

Supervisors

Dr Will Lawn
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience   
Email: Will.lawn@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/will.lawn

Dr James Rucker
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: james.rucker@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/james-rucker

Project Details

Background:  The anxiolytic effects of cannabis based medicinal products (CBMPs) have been touted for decades. Medical cannabis was legalised in the UK in November 2018 and observational, prospective studies of private prescriptions have demonstrated that patients taking CBMPs show significant reductions in anxiety. CBMPs vary in cannabinoid content, including their cannabidiol (CBD) & delta-9-tetrahydrocannabinol (THC) content and formulation (e.g. oil, oral capsule, vaporised flower). There is promising evidence that CBD may reduce anxiety following a single dose in laboratory-models and following four weeks of daily treatment. However, more research is needed to unpack the anxiolytic effects of varying doses of CBD and THC. Furthermore, research into CBMP’s benefits as adjunctive treatments and in those with treatment-resistant anxiety is needed.

Novelty and importance:  No controlled study has compared the effects of a single oral dose of THC and CBD on anxiety levels using an anxiety-promoting laboratory procedure in participants receiving treatment for anxiety. We need to determine the anxiolytic impact of various CBMP doses and formulations in different groups to develop future clinical trial applications.

Primary aims: 

(1) To test the acute effects of a single dose of CBD and THC on a laboratory measure of induced anxiety;

(2a) To capitalize on existing data to explore the effects of different doses of THC and CBD on laboratory measures of anxiety;
(2b) and associations between CBMP usage and anxiolytic responses from real-world data in different patient groups;

(3) explore acceptability of CBMP treatment in people with treatment resistant GAD. 

Study design and sample size:  

Study 1: meta-analysis. Study 2: secondary data analysis of longitudinal data (n~1,000).

Study 3 (main study): non-CTIMP randomized, placebo-controlled, double-blind, crossover experiment, in people receiving treatment for GAD (n=24). ‘Study’ 4/PPI: Patient and public involvement (PPI).

Planned research methods and training provided: (1) Meta-analysis: training at KCL; (2) longitudinal analysis using linear mixed models: training by Will Lawn & KCL; (3) controlled drug administration experiments: training by Will Lawn & James Rucker.

Objectives / project plan:

Study 1 (Year 1): meta-analysis of CBD’s anxiolytic effects in the lab, moderated by dose; and meta-analysis of THC’s effects on anxiety, moderated by dose; systematic review of CBMP-drug interactions.

Study 2 (Year 1 & 2): mega-analysis of combined prospective, observational studies tracking CBMP patients’ anxiety levels, moderated by CBMP type and participant characteristics, specifically their concurrent medication use and treatment history. Collaboration with Sapphire & T21.

Study 3 (Years 2 & 3): a randomised, placebo-controlled, double-blind, crossover, non-CTIMP experiment comparing the one-off effects of: placebo, oral CBD (800mg), and dronabinol (oral THC, 5mg) on laboratory measures of anxiety (including a social stress test) in participants (n=24) receiving treatment for anxiety. Approvals required: KCL or NHS REC, HRA, pharmacy, KCL R&D, KCL RAC. Not MHRA. Study 4/PPI – conduct PPI to assess the feasibility of a pilot clinical trial to test a CBMP for anxiety in patients with treatment resistant anxiety.

Two representative publications from supervisors

Publication 1:  Lawn, W., Trinci, K., Mokrysz, C., Borissova, A., Ofori, S., Petrilli, K., ... & Curran, H. V. (2023). The acute effects of cannabis with and without cannabidiol in adults and adolescents: A randomised, double‐blind, placebo‐controlled, crossover experiment. Addiction, 118(7), 1282-1294.

Publication 2:  Ergisi, M., Erridge, S., Harris, M., Kawka, M., Nimalan, D., Salazar, O., Rucker, J. & Sodergren, M. H. (2022). UK Medical Cannabis Registry: an analysis of clinical outcomes of medicinal cannabis therapy for generalized anxiety disorder. Expert review of clinical pharmacology, 15(4), 487-495.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Pain and Addictions
• Experimental Medicine and Novel Therapeutics

Supervisors

Professor Trudie Chalder
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience   
Email: trudie.chalder@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/trudie.chalder

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

Project Details

Background:  Functional neurological disorder refers to a range of neurological presentations which are not explained by specific, identifiable neuropathology (APA, 2013). The disorder is associated with marked distress, disability, reduced quality of life, and poor daily functioning. Treatment options in the UK are often limited, non-specific, or non-existent, depending on geographical location.

Novelty and importance:  Our experimental mechanistic studies indicate that awareness, integration and regulation of autonomic and affective responsivity might represent important treatment targets in FND. Interventions aimed at these processes have the potential to facilitate improvements in FND symptoms, emotional functioning and quality of life more broadly. However, they are currently lacking. There is a need for a readily accessible, cost-effective intervention to address these mechanisms in patients with FND, at scale.

Primary aims:  This project aims to develop a novel digital intervention that will enhance patients’ understanding of the links between their autonomic/affective responses and changes in their FND and other physical symptoms, and to demonstrate the value of daily affect/arousal regulation exercises.

Specific objectives:

1. Obtain patient and other stakeholder views on the proposed intervention.
2. Develop the smartphone intervention and select a suitable wearable device.
3. Conduct a feasibility RCT in a sample of patients with mixed FND symptoms.

Study design and sample size:  This is a mixed-methods study. Qualitative interviews and/or focus groups will be conducted with up to 20 patients with FND and other relevant stakeholders. Twenty patients with FND will be recruited to a feasibility RCT.

Planned research methods and training provided: Qualitative methods; clinical trials; experimental task design/programming.  Training on these methods will be provided by the supervisors and/or members of their teams.

Objectives / project plan:

Year 1:

• Systematic review of existing digital interventions in FND and other relevant clinical populations (e.g., chronic fatigue/pain).
• Development of the new intervention in collaboration with relevant experts by lived experience and digital health technologies.
• Obtain regulatory approvals (sponsorship, research ethics).

Year 2:

• Feasibility RCT - Patients with FND will be randomly allocated to the active intervention or a control intervention. Outcome measures will be completed at baseline, end of treatment, and at a 3-month follow-up assessment.
• Pre- and post-intervention experimental tasks (e.g., emotional reactivity/regulation, interoception).

Year 3:

• Feasibility study and experimental task data analysis.
• Completion of thesis.
• Peer-reviewed journal article(s) and conference presentations.

Two representative publications from supervisors

Publication 1:  Pick, S., Millman, L. M., Ward, E., Short, E., Stanton, B., Reinders, A. S., Winston, J. S., Nicholson, T. R., Edwards, M. J., Goldstein, L. H., David, A. S., Chalder, T., Hotopf, M., & Mehta, M. A. (2023). Unravelling the influence of affective stimulation on functional neurological symptoms: a pilot experiment examining potential mechanisms. J Neurol. Neurosurg. Psychiatry, jnnp-2023-332364. Advance online publication. https://doi.org/10.1136/jnnp-2023-332364

Publication 2:  Yasaman Emad, Nicola Dalbeth, John Weinman, Trudie Chalder, Keith J. Petrie (2023) Can Smartphone Notifications Help With Gout Management? A Feasibility Study. The Journal of Rheumatology Nov, jrheum.2023-0711; DOI: 10.3899/jrheum.2023-0711.

Maudsley BRC research themes

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

Supervisors

Dr Gemma Modinos
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience   
Email: gemma.modinos@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/dr-gemma-modinos

Dr Edward Chesney
Department of Addictions, Institute of Psychiatry, Psychology and Neuroscience
Email: edward.chesney@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/edward.4.chesney

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:  The development of adverse clinical outcomes in people with psychosis and people at clinical high-risk for psychosis (CHR-P) has been associated with abnormalities in a corticolimbic brain circuitry involved in how people process and regulate emotions. The basis of these abnormalities is thought to be an imbalance in excitatory (glutamatergic) and inhibitory (GABAergic) signalling between brain cells. Recent research has shown that administration of the anticonvulsant drug levetiracetam to adult rats rescued stress-induced behavioural (anxiety-like responses, impaired sociability) and neurophysiological features (increased subcortical dopamine neuron firing and hippocampal activity) relevant to psychosis. However, levetiracetam’s effect on emotion processing and its underlying neural circuitry in CHR-P individuals remains unknown. This project will address this aim by testing if levetiracetam can help improve emotion processing and resting-state functional MRI connectivity within a corticolimbic network in people at CHR-P.

Novelty and importance:  This will be the first study to investigate whether acute pharmacological modulation of excitation-inhibition balance in people at CHR-P can improve emotion processing and functional connectivity of underlying brain circuitry. The results of this study may provide important proof-of-concept evidence about whether interventions that regulate emotion responsivity / excitation-inhibition balance could be a new way for understanding and treating psychosis early.

Primary aims:  To determine whether the acute administration of levetiracetam normalises emotion processing in people at CHR-P, and characterise the functional connectivity of brain circuits involved in these pharmacological effects.

Study design and sample size:  Basic science study that examines the effects of a single dose of levetiracetam on behavioural and neuroimaging measures. The study uses a randomised, double-blind, placebo-paired, within-subject crossover design, and will involve 36 participants who meet CHR-P criteria, to allow for potential dropout rate of 20%, aiming for at least 29 subjects with the emotion processing task and both scans complete.

Planned research methods and training provided:  The neuroimaging data to be used for this independent project is being collected as part of a larger study, for which ethics application is currently being submitted to the REC. The emotion processing task will be added to the protocol for this project. Training will be provided in the clinical, cognitive/emotion processing assessments, neuropsychopharmacology, fMRI imaging methods, data analysis, dissemination.

Objectives / project plan:

Year 1: Ethics amendment to include emotion processing task, participant recruitment and data collection.

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

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

Two representative publications from supervisors

Publication 1:  Modinos G, Kempton MJ, Tognin S, et al. Association of Adverse Outcomes With Emotion Processing and Its Neural Substrate in Individuals at Clinical High Risk for Psychosis. JAMA Psychiatry. 2020;77(2):190–200. doi:10.1001/jamapsychiatry.2019.3501

Publication 2:  Burrows M, Kotoula V, Dipasquale O, Stringaris A, Mehta MA. Ketamine-induced changes in resting state connectivity, 2 h after the drug administration in patients with remitted depression. J Psychopharmacol. 2023 Aug;37(8):784-794. doi: 10.1177/02698811231189432. 

Maudsley BRC research themes

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

Supervisors

Dr Victoria Pile
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience   
Email: victoria.pile@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/victoria.pile

Professor Patrick Smith
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
Email: Patrick.smith@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/patrick.smith

Dr Thomas Ward
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
Email:  thomas.ward@kcl.ac.uk    Website:  https://kclpure.kcl.ac.uk/portal/en/persons/thomas-ward

Project Details

Background:  Current interventions for adolescent depression are suboptimal and difficult to access. Intrusive images of aversive autobiographical memories (e.g. bullying) are strongly implicated in the development and maintenance of depression. Why certain images continue to haunt young people and define their sense of self is less clear. Our work suggests that a key mechanism is the young person’s inner dialogue/cognitive response to these images, e.g. self-criticism/self-compassion. We have co-developed and positively evaluated novel imagery-based protocols for young people, which are ripe for augmentation through innovative technologies. Using the AVATAR therapy approach to interacting, in real-time, with an individually tailored digital embodiment of their inner dialogue (the avatar), we will powerfully target these mechanisms and increase confidence, compassion and self-awareness. 

Novelty and importance:  Developing science-driven early interventions is crucial to improve effectiveness and offer young people more choice. AVATAR therapy has demonstrated large treatment effects in adults with psychosis and has recently shown promise for other disorders. It offers a powerful and engaging experiential approach that could be particularly suited to young people. A randomized controlled trial would naturally follow this project.

Primary aims: 

• To evaluate the proposed cognitive mechanisms using qualitative and experimental approaches.
• To co-design with young people, parents, and clinicians a novel digitally enhanced intervention targeting intrusive images. 
• To explore the intervention’s acceptability, feasibility, and safety.

Study design and sample size:  The project would consist of robust co-design methodology (workshops and individual interviews) alongside three studies:

1. Qualitative study (n=10-15) exploring why certain images become intrusive and distressing.
2. Experimental study (two groups, n=60) assessing the mechanisms identified in study 1 (e.g., self-criticism vs. self-compassion) on future intrusions and mood.
3. Case series (n=10-15) to initially evaluatethe intervention in young people scoring above clinical cut-off for depression.

Planned research methods and training provided:  Methods: framework analysis and semi-structured interviews; single session manipulation with ecological momentary assessment for symptom and intrusion data; case series in secondary schools.

Training: co-design principles, patient and public involvement, qualitative research, experimental design, clinical training in AVATAR and imagery approaches, statistical analysis and writing for publication, presentation skills.

Objectives / project plan:

Year 1: complete study 1 (ethics obtained prior to start date); KCL ethical approval for studies 2 and 3; co-design workshops with young people, parents, and clinicians.

Year 2: studies 2 and 3

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

Dissemination: publications, conference presentations, public engagement event and conceptual video.

Two representative publications from supervisors

Publication 1:  Pile V, Williamson G, Saunders A, Holmes EA, Lau JY (2021). Harnessing emotional mental imagery to reduce anxiety and depression in young people: an integrative review of progress and promise. The Lancet Psychiatry. 1;8(9):836-52.

Publication 2:  Pile V, Smith P, Leamy M, Oliver A, Bennett E, Blackwell SE, Meiser-Stedman R, Stringer D, Dunn BD, Holmes EA, Lau JYF (2021). A Feasibility Randomised Controlled Trial of a Brief Early Intervention for Adolescent Depression that Targets Emotional Mental Images and Memory Specificity (IMAGINE). Behaviour Research and Therapy. 24:103876.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Psychosis and Mood Disorders
• Experimental Medicine and Novel Therapeutics
• Digital Therapies

Supervisors

Professor Colette Hirsch
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience   
Email: Colette.hirsch@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/colette-hirsch

Dr Frances Meeten
Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience
Email: Frances.2.meeten@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/frances.2.meeten.html

Project Details

Background:  One in six adults experience high anxiety each week, reducing their ability to work, maintain relationships and quality of life. We need more effective, scalable, interventions for anxiety. This can come in the form of a digital therapeutic delivered via mobile apps, to be used by anxious people when and where they wish. We will use the same approach we have successfully employed in the development of our other novel digital therapeutics. We will assess the causal role of candidate mechanisms that maintain a transdiagnostic process -  intolerance of uncertainty (IoU) – which itself predicts and maintains anxiety. These mechanisms will form the basis of targets for a future digital intervention to reduce IoU and anxiety.

Novelty and importance:  Drawing on expertise from the Experimental Psychology section of the Digital Therapeutic theme of the BRC, this research will use an evidenced based approach to understand the causal role of candidate mechanisms in the maintenance of IoU. It will then test whether an approach that targets one key mechanism or a combination of mechanism is warranted for future digital therapeutic interventions for anxious people.

Primary aims: 

Aim:  To develop an understanding of the causal role of cognitive mechanisms that may maintain IoU. To then determine whether targeting a combination of mechanisms, or a single mechanism, should be considered for future digital interventions to reduce intolerance of uncertainty in anxious people.

Objectives

1. To work with people with lived experience of IoU and anxiety with different ethnic identities to develop materials for new approaches to reduce IoU, ensuring that the materials are tailored to their day-to-day lives.
2. To use experimental psychology approaches to modify candidate mechanisms and assess their impact on IoU.
3. To determine whether a single mechanism approach or a combined mechanisms approach leads to the greatest reductions in IoU and anxiety, paving the way for new digital therapeutics.

Planned research methods and training provided:  Across all stages of the PhD the student will learn a wide range of psychological research techniques including acquisition of specialist skills in experimental psychopathology, understanding of the process of new digital therapeutics development, co-design of research with people with lived experience of anxiety, using open science practices. Training via BRC Digital Therapies Theme training will be focused on a range of pertinent processes included effective intervention development, an understanding of implementation challenges, developing a sustainable model and regulatory approval processes. This will pave the way for a Post Doctoral fellowship application to develop and test a full scale digital therapeutic. 

Objectives / project plan:

Year 1

• Months 1 Convene PPIE group
• Month 2 Apply for KCL ethics
• Month 3-4 adapt & develop materials for Study 1
• Month 5-6 Programme & pilot Study 1 modification training on experimental platform in Gorilla
• Month 7- 10 Run Study 1 online recruiting from across the UK via established social media routes
• Study 1: Modify interpretation bias to assess its causal role in maintaining IoU.
• Month 11-13 Write up Study 1 for publication
• Month 12 Apply for ethics

Year 2 

• Month 13-14 develop materials for Study 2
• Month 14- 15 Programme & pilot modification training
• Month 16-19 Run Study 2 via online recruiting from across the UK via established social media routes
• Study 2: Modify cognitive reappraisals to assess its causal role in maintaining IoU.
• Month 20-23: Write up study 2 for publication
• Month 21 Apply for ethics
• Month 22 develop extra materials for Study 3
• Month 23-24 Programme & pilot modification training on experimental platform in Gorilla

Year 3

• Month 25-30 Run Study 3 via online recruiting from across the UK via established social media routes
• Study 3:  Investigate whether a combined mechanisms approach is superior to a single mechanism approach to reduce intolerance of uncertainty
• Month 31-36 Write up PhD

Two representative publications from supervisors

Publication 1:  Hirsch, C. R., Krahé, C., Whyte, J., Krzyzanowski, H., Meeten, F., Norton, S., & Mathews, A. (2021). Internet-delivered interpretation training reduces worry and anxiety in individuals with generalized anxiety disorder: A randomized controlled experiment. Journal of Consulting and Clinical Psychology, 89(7), 575–589. https://doi.org/10.1037/ccp0000660

Publication 2:  Meeten, F., Dash, S. R., Scarlet, A., & Davey, G. C. L. (2012). Investigating the effect of intolerance of uncertainty on catastrophic worrying and mood. Behaviour Research and Therapy, 50, 690-698. https://10.1016/j.brat.2012.08.003.

Maudsley BRC research themes

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

Supervisors

Professor Sandrine Thuret
Department of Basic & Clinical Neurosciene, Institute of Psychiatry, Psychology and Neuroscience   
Email: sandrine.1.thuret@kcl.ac.uk    Website: https://www.thuretlab.com/   &   https://www.kcl.ac.uk/people/sandrine-thuret

Professor Allan Young
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: allan.young@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/allan-young

Dr Andrea Du Preez
Department of Basic & Clinical Neurosciene, Institute of Psychiatry, Psychology and Neuroscience 
Email: andrea.du_preez@kcl.ac.uk    Website:  https://kclpure.kcl.ac.uk/portal/en/persons/andrea.du_preez

Project Details

Background:  There is a need to tailor treatment for major depressive disorder (MDD). A challenge in personalised MDD treatment is the lack of predictive biomarkers for guiding therapy selection.

Novelty and importance:  One potential biomarker is hippocampal neurogenesis (HN), a well-established neurobiological process linked to MDD. However, assessing HN in live humans is impossible, which led the Thuret lab to develop an innovative in vitro HN assay using a hippocampal stem cell line and a few drops of serum from patients. Using this assay, we have endorsed HN as a biomarker for depression and have demonstrated the assay’s potential to inform treatment strategies.

Primary aims:  Considering (a) the ability of the serum from patients to influence HN according to depression status and the impact of antidepressants on HN, and (b) that electroconvulsive therapy (ECT) is an efficacious MDD treatment associated with enhanced hippocampal neurogenesis, we hypothesize that positive response to ECT treatment is associated with, and potentially even driven by, HN. Therefore, the aims of this PhD project are to (1) validate the HN assay as a biomarker for predicting response to ECT treatment in MDD and (2) test an alternative biomarker - based on the same HN neurobiological evidence – that is a 10-min neurogenesis-dependent pattern separation task as part of the ongoing ECT intervention to determine low pattern separation at baseline can predict response to ECT treatment. Finally, the third aim will be to (3) optimize for clinical use the best biomarker.

Study design and sample size:  Baseline serum samples (n=88-150) and pattern separation will be assessed from a planned ECT study by the Young group.

Planned research methods and training provided:  The PhD student will employ and be trained for:

• Hippocampal stem cell culture, immunocytochemistry, high-content cellular imaging and all the steps of the NH assay.
• Administering and analysing the pattern separation Mnemonic Similarity Task (MST).
• All statistical analyses and machine learning/ prediction modelling.

Objectives / project plan:

Objective 1: Determine how HN at baseline can predict ECT response.

Objective 2: Determine how pattern separation at baseline can predict ECT response.

Objective 3: Optimize the best biomarker.

Year 1:

• Review existing predictive biomarkers for MDD treatment (paper1).
• Generate neurogenesis data from HN assay with baseline serum.
• Collect baseline MST pattern separation data.

 Year 2:

• Data analyses for HN assay and MST pattern separation.
• Generation of treatment response prediction models (paper2).

 Year 3:

• Optimize best biomarker (paper3).
• Thesis writing.

Two representative publications from supervisors

Publication 1: The role of circulatory systemic environment in predicting interferon-alpha-induced depression: The neurogenic process as a potential mechanism.
Borsini A, Pariante CM, Zunszain PA, Hepgul N, Russell A, Zajkowska Z, Mondelli V, Thuret S. Brain Behav Immun. 2019 Oct;81:220-227. doi: 10.1016/j.bbi.2019.06.018. Epub 2019 Jun 14. PMID: 31207337

Publication 2:  Comparative efficacy and acceptability of non-surgical brain stimulation for the acute treatment of major depressive episodes in adults: systematic review and network meta-analysis. Mutz J, Vipulananthan V, Carter B, Hurlemann R, Fu CHY, Young AH. BMJ. 2019 Mar 27;364:l1079. doi: 10.1136/bmj.l1079. PMID: 30917990

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Trials, Genomics and Prediction

Supervisors

Professor Chiara Nosarti
Department of Child & 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 Dafnis Batalle
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: dafnis.batalle@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/dafnis-batalle

Project Details

Background:  Alterations in functional brain dynamics have been associated with psychiatric disorders in adults. This project will probe functional brain dynamics in children who were born very preterm (<33 weeks’ gestation), as they experience multiple comorbid mental health concerns (Yates et al 2020) and are at increased risk of receiving a psychiatric diagnosis compared to term-born controls (25% vs 10% by the age of 18, respectively; Johnson and Wolke, 2013). Our previous work showed global atypical brain dynamics in preterm compared to term-born infants, which were associated with autism traits in toddlerhood (França et al. 2023).

Novelty and importance:  Little is known about the development of functional brain dynamics following very preterm birth and whether this is linked to specific constellations of behaviors. This study will increase the current understanding of the origins of mental illness by explicating neurodevelopmental mechanisms. This novel information could be used to develop targeted interventions to reduce psychiatric risk in vulnerable children.

Primary aims:  To characterize functional brain dynamics cross-sectionally and longitudinally, from term equivalent age to childhood, in relation to transdiagnostic behavioral outcomes in children who were born very preterm.

Study design and sample size:  Longitudinal birth cohort study: 511 children who were born very preterm from birth up to age 11 (22 months, n=489; 4-7 years, n=251; 8-11 years, n=240 and 120 controls).

Planned research methods and training provided:  We will characterize global dynamics using Kuramoto Order Parameter based measures and modular dynamics using Leading Eigenvector Analysis. To probe transdiagnostic behavioral outcomes we will use an integrative clustering approach we have previously developed (Hadaya et al. 2023): Similarity Network Fusion, combining childhood behavioral and socio-emotional measures, socio-demographic, and clinical factors. Cross-sectional and longitudinal functional brain dynamics will be compared between resultant behavioral subgroups.

The successful student will receive an unparalleled training combining knowledge about childhood neurodevelopment with neuroimaging, neuro-informatics and advanced statistics. 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, image processing and machine learning tools. Training will be provided through attending courses on neuroanatomy, introduction to machine learning and FSL.

Objectives / project plan:

Year 1: Training in image processing and neuroanatomy.

Year 2: Developing different biomarkers of neurodevelopmental outcome based on features of dynamic functional connectivity, which will be validated through regression models predicting behavioral outcomes.

Year 3: The models developed in Y2 will be used to develop tailored predictions of individual outcomes. Write up time, preparation of fellowships to transition into the post-doctoral phase.

Other notable aspects of the project:  Our team has extensive experience analysing paediatric neuroimaging data and training PhD candidates from diverse backgrounds. No previous experience with neuroimaging and complex data analysis is required.

Two representative publications from supervisors

Publication 1:  Hadaya L, Dimitrakopoulou K, Vanes LD, Kanel D, Fenn-Moltu S, Gale-Grant O, Counsell SJ, Edwards AD, Saqi M, Batalle D, Nosarti C (2023). Parsing brain-behavior heterogeneity in very preterm born children using integrated similarity networks. Transl Psychiatry. 13(1):108

Publication 2:  França LGF, Ciarrusta J, Gale-Grant O, Fenn Moltu S, Fitzgibbon S, Chew A, Falconer S, Dimitrova R, Cordero-Grande L, Price AN, Hughes E, O’Muircheartaigh  J, Duff E, Tuulari JJ, Deco G, Counsell SJ, Hajnal JV, Nosarti C, Arichi A, Edwards AD, McAlonan G, Batalle D (2024). Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment. BioRxiv https://doi.org/10.1101/2022.11.16.516610 (In Press at Nature Communications #NCOMMS-23-08626B)

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Neuroimaging

Supervisors

Dr David Mark Howard
Department of Social, Genetic & Developmental Psychiatry, Institute of Psychiatry, Psychology and Neuroscience   
Email: david.howard@kcl.ac.uk    Website:  https://kclpure.kcl.ac.uk/portal/en/persons/david.howard  &  https://www.drhoward.co.uk/

Dr Evangelos Vassos
Department of Social, Genetic & Developmental Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email: Evangelos.vassos@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/evangelos.vassos

Project Details

Background:  Standard epidemiological approaches for analysing depression simply split data into those with and without the disorder and compare the risk factor profiles between the two groups. However, this assumes that everyone has the same underlying risk of developing depression. Incorporating polygenic scores, which capture the genetic liability for depression, will improve the discrimination of the non-genetic factors (such as ill health or adverse life events) associated with depression.

Novelty and importance:  Depression is the leading cause of global disability with 1 in 6 people developing the disorder. The proposed research will be both impactful and novel by incorporating genetic data alongside the extensive health, medical and lifestyle data from the UK Biobank. This project will advance our understanding of the factors associated with depression and will potentially deliver clinical benefits through enabling earlier intervention and treatment.

Primary aims: 

1. Improve our understanding of the non-genetic risk factors associated with depression
2. Determine the risk factors associated with alternative definitions of depression
3. Identify risk factors with differential association between depression in males and depression in females

Study design and sample size:  A 2x2 factorial design will be used to examine the effects of polygenic score for depression and depression status for associations with non-genetic risk factors.

• Primary dataset: UK Biobank (n ≈ 500,000)
• Replication dataset: Generation Scotland (n = 18,773)
• Polygenic scores derived from Psychiatric Genomics Consortium data (n ≈ 3.5 million)

Planned research methods and training provided:  An initial literature review will provide the student with knowledge and awareness of the subject matter. This review will help determine the non-genetic risk factors used in the research and form the basis for the student’s upgrade report at the end of year 1. Genetic and statistical analysis approaches will be used in years 2 and 3 to address the proposed research aims.

At the outset we will work with the student to create a personal development plan (PDP), identifying the skills and training required, including epidemiological and genetic methods and computer programming. The PDP will be reviewed quarterly to ensure learning and development goals are being met and that any additional training requirements are added. The student will be based in the Statistical Genetics Unit which will provide the ideal environment and level of support to undertake the research.

Objectives / project plan:

Year 1: Literature review; gain access to data; undertake training in genetic analysis; calculate polygenic scores for depression.

Year 2: Undertake training in epidemiology and statistical analysis; conduct analysis on depression; analyse the data using alternative measures of depression (subtypes, severity, and symptoms).

Year 3: Test for differences in risk factors by sex; write up and submit thesis.

Two representative publications from supervisors

Publication 1:  https://doi.org/10.1038/s41593-018-0326-7

Publication 2:  https://doi.org/10.1016/j.bpsc.2023.12.001

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Trials, Genomics and Prediction

Supervisors

Dr Raquel Iniesta
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience   
Email: raquel.iniesta@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/raquel-iniesta

Dr Nicholas Cummins
Department of Biostatistics & 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:  Treatment interventions in psychiatry are far from being effective. In depression, only 30­50% of individuals achieve remission even in the context of a well­conducted clinical trial. Available research show that Artificial Intelligence (AI) makes prescribing of antidepressant drugs more accurate but the incorporation of AI has been slower than hoped given important ethical challenges like the accuracy, bias, fairness and transparency of the tools. The present PhD project will facilitate tools to guide in the design of studies that ensure a fair, unbiased and transparent development of machine learning (ML) models for antidepressant treatment selection.

Novelty and importance:  The present research has a big potential to guide on the design of studies that can lead to fair and unbiased ML models building that can safely be used to personalize antidepressant treatment allocation. Current tools are designed to audit already built ML models. Our approach is to develop a toolkit that can help identify and prevent the bias/fairness/transparency problematic before the model is built: at the design stage. In addition, the research will provide insightful information on the quality of available ML models of antidepressant outcomes.

Primary aims:  (1) Developing and validating a toolkit to guide in the study design that ensures the development of unbiased, fair and transparent AI to predict antidepressant treatment outcomes. (2) Assessing the study design of existing publicly available studies developing ML models to predict antidepressant response, and the resulting bias, fairness and transparency of the developed ML.

Study design and sample size:  This PhD project will develop a toolkit to contribute designing studies that lead to accurate ML to predict antidepressant response and assess the performance of public ML models developed on studies of antidepressant response. Objectives / project plan: (Year 1) The candidate will run a systematic literature review to identify available ML models predictive of antidepressant outcomes. The student will create a public data base of available studies and models. (Year 2) The student will develop and validate a tool to guide on the design of unbiased, fair and transparent models for antidepressant outcomes prediction. (Year 3) The developed tool and existing ML audit tools will assess a selection of existing studies and models, including (but not be restricted to): COMED (N= 665), STAR*D (N=4041), MARS (N=604) and GENDEP (N=811).

Planned research methods and training provided: This PhD includes a training plan in ML, ethics of AI and relevant programming languages/software.

Objectives / project plan:

Year 1: The candidate will run a systematic literature review to identify available ML models predictive of antidepressant outcomes. The student will create a public data base of available studies and models.

Year 2: The student will develop and validate a tool to guide on the design of unbiased, fair and transparent models for antidepressant outcomes prediction.

Year 3: The developed tool and existing ML audit tools will assess a selection of existing studies and models, including (but not be restricted to): COMED (N= 665), STAR*D (N=4041), MARS (N=604) and GENDEP (N=811).

Two representative publications from supervisors

Publication 1:  Iniesta R, Stahl D, McGuffin P. Machine learning, statistical learning and the future of biological research in psychiatry. Psychol Med. 2016 Sep;46(12):2455-65. doi: 10.1017/S0033291716001367

Publication 2:  Iniesta R, Malki K, Maier W, Rietschel M, Mors O, Hauser J, Henigsberg N, Dernovsek MZ, Souery D, Stahl D, Dobson R, Aitchison KJ, Farmer A, Lewis CM, McGuffin P, Uher R. Combining clinical variables to optimize prediction of antidepressant treatment outcomes. J Psychiatr Res. 2016 Jul;78:94-102. doi: 10.1016/j.jpsychires.2016.03.016

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Trials, Genomics and Prediction

Supervisors

Dr Moritz Herle
Department of Social, Genetic & Developmental Psychiatry, Institute of Psychiatry, Psychology and Neuroscience   
Email: Moritz.1.herle@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/moritz-herle

Professor Cathryn Lewis
Department of Social, Genetic & Developmental Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email: Cathryn.lewis@kcl.ac.uk    Website:  https://www.kcl.ac.uk/research/sgu

Project Details

Background:  Change in appetite and weight are core symptoms of depression, but we know little about their relationship and their biological underpinnings. The genetic contributions to mental health disorders and human behaviour are polygenic, with many genetic variants contributing, each having a small impact. This genetic component of disorders and behaviours can be measured in a polygenic score, giving a single number that captures genetic liability for each trait.  These polygenic scores give insights into the relationship between different and their biological underpinnings. 

Novelty and importance:  In this PhD, you will dissect the relationship between eating behaviours, low mood, and major depression, using rich clinical, diet and mental health data from existing studies. 

Primary aims:  Identify the genetic and phenotypic relationships between depression symptoms, appetite and weight in a trans-diagnostic approach.

Study design and sample size:  Secondary statistical analysis of existing clinical, epidemiological and genetic studies from studies such as UK Biobank, Twins Early Development study (TEDS).

Planned research methods and training provided:  R statistical analysis, latent class analysis, genetic methods, polygenic scores, structural equation modelling.  Training will be through online courses (DataCamp), in person training courses (e.g. Boulder genetics course), peer-to-peer learning with research team colleagues. 

Objectives / project plan:

Year 1: Are depression symptoms associated with eating behaviours?  Using data resources from TEDS and UK Biobank, you will harmonise phenotypic measures, then establish the relationships between variables, using tools such as phenotypic and genetic correlations, and polygenic scores.

Year 2: Do eating behaviours and depressive symptoms share their genetic architecture? Integrating genetics with multivariate measures of mood and eating behaviours to identify the core genetic relationships.

Year 3: What are the impacts of eating behaviours and depressive symptoms on weight change? Assessing the relationships between depression as a psychiatric disorder, eating behaviours and weight change considering mediation, interactions, and causal associations. 

Other notable aspects of the project: You will be able to focus the PhD according to your interests within the BRC remit, enabling you to develop into an interdisciplinary researcher with skills in mental health, statistics, and genetics. These are skills shortage areas, with many postdoctoral research opportunities available, inside and outside academia.

Two representative publications from supervisors

Publication 1:  Lewis CM, Vassos E:  Polygenic Scores in Psychiatry: On the Road From Discovery to Implementation.  Am. J Psychiatry. 2022. 179(11):800-806 (Review and Overview). doi: 10.1176/appi.ajp.20220795 .

Publication 2:  Herle, M., Abdulkadir, M., Hübel, C., Ferreira, D. S., Bryant-Waugh, R., Loos, R. J. F., Bulik, C. M., De Stavola, B., & Micali, N. (2021). The genomics of childhood eating behaviours. Nature human behaviour, 5(5), 625–630. https://doi.org/10.1038/s41562-020-01019-y

Maudsley BRC research themes

• Eating Disorders and Obesity
• Trials, Genomics and Prediction

Supervisors

Professor Andre Strydom
Department of Forensic & Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience   
Email: Andre.strydom@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/andre.strydom

Dr Olivia Kowalczyk
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience
Email: olivia.kowalczyk@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/olivia.kowalczyk  &  https://oliviakowalczyk.co.uk/

Project Details

Background:  People with Down syndrome (DS) are genetically predisposed to develop Alzheimer’s disease (AD) because of having three copies of the amyloid precursor protein gene (APP). AD is now the main health concern for older DS adults and their families. New anti-amyloid therapies hold much promise and DS is a key population in which to test the hypothesis that these therapies can prevent or delay AD. However, there are safety concerns for these treatments, including risk for brain swelling and bleeds potentially impacting on  brain function, particularly in those at risk of cerebral amyloid angiopathy (CAA). It is therefore critical to have easily measured imaging markers of risk and treatment response. This project will explore features of resting-state functional connectivity in DS using fMRI, and how these relate to AD at clinical, neuroimaging, and plasma biomarker levels.

Novelty and importance:  fMRI markers could help to select those suitable for early treatment of AD, and monitor response and risk. This proposal includes collaboration with an industry partner to inform future treatment trials using anti-amyloid therapies.

Primary aims: 

Aim 1: To identify fMRI correlates of DS in comparison to age and sex-matched controls, in young DS individuals before the onset of AD.

Aim 2: Determine the cross-sectional relationship between functional connectivity (measured with fMRI) and MRI markers of CAA (microbleeds, etc.), plasma Aβ auto-antibodies and AD biomarkers in older people with DS.

Aim 3: Determine the longitudinal relationship between changes in fMRI and the development of AD in older DS adults.

Study design and sample size:  Longitudinal cohort study of DS adults  aged 18 and older – n=60 with useable data at baseline; those >age 35 are followed longitudinally to track development of features of AD.

Planned research methods and training provided:  Longitudinal cohort and neuroimaging research methods.  Training in regulatory, recruitment, and data management aspects will be provided in year 1; in year 2, analysis methods and coding; year 3 - advanced fMRI analysis and writing skills.

Objectives / project plan:

Year 1:

• Training (M0-5)
• Data collection (follow-up data) (M6-12)

Year 2:

• Ongoing data collection (M13-18)
• Training in analysis and coding (throughout year)
• Industry placement (M21-24)
• Analysis of baseline data (M19-24)

 Year 3:

• Advanced MR analysis training, thesis writing etc. (M25-30)
• Longitudinal data analysis (M25-32)
• Complete thesis write-up (M32-M36)

Other notable aspects of the project:

• Access to existing data
• Existing industry collaborations

Two representative publications from supervisors

Publication 1:  https://doi.org/10.1002/alz.13097

Publication 2:  https://doi.org/10.1002/alz.12799

Maudsley BRC research themes

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

Supervisors

Dr Nicolaas Puts
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience   
Email: nicolaas.puts@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/nick-puts

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

Professor Emily Jones
Centre for Brain & Cognitive Development; Birkbeck, University of London
 
 

Project Details

Background:  Sensory difficulties are a central facet of autism, substantially impacting daily life and well-being. Emerging evidence suggests an imbalance in excitation/inhibition (E/I) as a potential source of these difficulties, leading to investigation of GABAergic modulators like arbaclofen as promising therapeutics. However, past clinical trials in ASD have faced hurdles due to both inherent heterogeneity and a focus on complex behaviours.

The link between E/I balance and sensory processing manifests more directly compared to other complex ASD features. Our prior work demonstrates that a granular approach to studying sensory processing, focusing on specific neural mechanisms like adaptation, allows for enhanced precision in understanding the E/I-behaviour relationship. Additionally, sensory processing difficulties rank as a priority area for autistic individuals and demonstrably influence other core and associated features like social interaction and anxiety. This makes the sensory system ideally suited for mechanism-based stratification in arbaclofen response studies.

We will leverage existing and ongoing studies to identify reliable neurophysiological (e.g., EEG) and behavioural (e.g., psychophysical) biomarkers of arbaclofen response within the context of sensory processing. Subsequently, we can investigate the development and variability of these biological markers in large developmental cohorts to gain deeper insights into E/I differences in autism.

Novelty and importance:  Prior clinical trials in ASD have fallen short of expectations. This project adopts a novel approach by focusing on a critical area of autism – sensory processing – and employing robust experimental techniques to identify biologically-informed markers of arbaclofen response. Establishing who may or may not respond to arbaclofen is a crucial step in developing more effective interventions for individuals with ASD.

Primary aims:  Our primary objective is to determine whether individual variations in E/I function, as reflected in specific biomarkers, can predict treatment response to arbaclofen for managing sensory difficulties in autism.

Study design and sample size:  Multiple; We have access to data from the POND arbaclofen trial (16 weeks, autism only, pediatric, n = ~60) and arbaclofen shiftability study at KCL (placebo-controlled, two doses, case-control; n = ~50) and the proposed AIMS-2-TRIALS RCT follow-up (n = ~60), AIMS-2-TRIALS PIP and LEAP studies (developmental 3-35, n = ~600) and SFARI study (n = ~100)

Planned research methods and training provided: T

• Understanding of RCT, and shiftability studies
• Analysis of psychophysical and EEG data across developmental stages
• Potential analysis of MRI/MRS data of E/I markers including Magnetic Resonance Spectroscopy
• Advanced statistical prediction modeling and subgroup analyses
• Academic writing, presentation and publication.
• Transferable skills
• Participatory research

Objectives / project plan:

Year 1: Completion of compulsory and bespoke training (e.g. GCP, GDPR, translational research); understanding of data analytical procedures and data access to various databases.  Systematic review on sensory differences and E/I balance.

Year 2: Data analysis from RCT-follow-up and prediction modeling; methods chapter and first results and submission of abstract. Focus group.

Year 3: Complete data analyses, prepare publications, finalise thesis including overall discussion.

Other notable aspects of the project:  The primary supervisor is autistic and will also bring lived-experience to the project.

Two representative publications from supervisors

Publication 1:  He, J., Oeltzschner, G., Mikkelsen, M., Deronda, A., Harris, A., & Crocetti, D. (2023). Region-specific elevations of glutamate + glutamine correlate with the sensory symptoms of autism spectrum disorders. Translational Psychiatry, 11(1). https://doi.org/10.1038/s41398-021-01525-1

Publication 2:  Mason, L., Moessnang, C., Chatham, C., Ham, L., Tillmann, J., Dumas, G., Ellis, C., Leblond, C. S., Cliquet, F., Bourgeron, T., Beckmann, C., Charman, T., Oakley, B., Banaschewski, T., Meyer-Lindenberg, A., Baron-Cohen, S., Bölte, S., Buitelaar, J. K., Durston, S., … Jones, E. J. H. (2022). Stratifying the autistic phenotype using electrophysiological indices of social perception. Science Translational Medicine, 14(658), eabf8987. https://doi.org/10.1126/scitranslmed.abf8987

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental 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

Professor Daniel Stahl
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience 
Email: Daniel.r.stahl@kcl.ac.uk

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 and 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 aims:  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).

Objectives / project plan:

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

Year 2: Prediction model development (including model/variable selection). All data cleaning/analyses for objective2 completed. Obtain access to additional dataset(s), to observe whether the model can be applied to other novel TRD therapies.

Year 3: Data cleaning/analysis for objective3 complete. Interpretation and thesis write-up.

Two representative publications from supervisors

Publication 1: Taylor RW, Marwood L, Greer B, Strawbridge R, Cleare AJ. Predictors of response to augmentation treatment in patients with treatment-resistant depression: a systematic review. Journal of Psychopharmacology. 2019 Nov;33(11):1323-39.

Publication 2:  GTaylor 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.

Maudsley BRC research themes

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

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 Mark Edwards
Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience
Email: mark.edwards@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/mark.j.edwards

Professor Sukhi Shergill
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience 
Email: sukhi.shergill@kcl.ac.uk

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 aims:  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.

Study design and sample size:  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. 

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). 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:  Virtual reality in functional neurological disorder: A theoretical framework and research agenda for use in the real world. David Brouwer, Hamilton Morrin, Timothy Nicholson, Devin B. Terhune, Michelle Schrijnemaekers, Mark Edwards, Jeannette Gelauff, Paul Shotbolt. https://doi.org/10.31234/osf.io/xjurc. Submitted to Lancet Neurology Dec 23.

Publication 2:  Virtual reality in functional neurological disorder: A theoretical framework and research agenda for use in the real world. David Brouwer, Hamilton Morrin, Timothy Nicholson, Devin B. Terhune, Michelle Schrijnemaekers, Mark Edwards, Jeannette Gelauff, Paul Shotbolt. https://doi.org/10.31234/osf.io/xjurc. Submitted to Lancet Neurology Dec 23.

Maudsley BRC research themes

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

Supervisors

Professor Jenny Yiend
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience   
Email: Jenny.Yiend@kcl.ac.uk    Website:  https://kclpure.kcl.ac.uk/portal/en/persons/jenny.yiend & http://www.csilab.org/

Dr Panayiota Michalopoulou
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
Email: panayiota.michalopoulou@kcl.ac.uk    Website: http://www.csilab.org/

Professor Sukhi Shergill
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience; Kent and Medway Medical School
Website: http://www.csilab.org/

Project Details

Background:  Despite optimal antipsychotic treatment, 20-45% of people with psychosis continue to experience significant psychotic symptoms. Non-response occurs early in the illness and longitudinal studies in First Episode of Psychosis (FEP) have shown that over 70% of clinically classified non-responders later in the course of the illness, do not respond to antipsychotics at the time of FEP. Antipsychotic non-response is associated with more hospital admissions, poorer social functioning, and worse vocational and academic achievements. Neurobiological predictors of non-response are lacking, and its clinical identification is often delayed resulting in turn in delays in optimal treatment initiation and worse outcomes. Cognitive impairment is almost ubiquitous in FEP and has devastating effects on functioning, while antipsychotics are not effective. Cognitive control (CC) and glutamate (excitatory) and GABA (inhibitory) neurotransmission have been implicated in the response to antipsychotics.  

Novelty and Importance:  This is a novel application of combined brain stimulation (TMS) with electroencephalography (EEG), with inter-disciplinary cognitive neuroscience tests to investigate the role of cortical excitation and inhibition and CC in treatment response in FEP. The study will develop our understanding on pathophysiological mechanisms underlying antipsychotic response and has the potential to: a) identify neurophysiological and cognitive predictive biomarkers, which will permit the introduction of optimal treatments much earlier than currently implemented and effectively reduce the duration of untreated psychosis, which has been strongly linked with worse outcomes b) contribute to the development of preclinical models to assess efficacy of novel treatments modulating the GABA and glutamatergic systems Notably, the lack of preclinical models is often a bottleneck in drug development c) refine parameters of TMS treatments in schizophrenia.

Primary aims:  We will use a safe and non-invasive combination of TMS-EEG and CC tests to detect differences in cortical excitability and inhibition between FEP responders and non-responders and study the role of CC in AP response.

Study design and sample size:  We will recruit 40 FEP patients (20 responders and 20 non-responders) and 20 healthy controls in a cross-sectional design, consisting of 3 visits (screening visit, cognitive visit and TMS-EEG visit).

Planned research methods and training provided:  TMS-EEG administration and TMS-EEG data analysis, highly specialised and transferable set of skills for the student to further and advance their research career.

Training in administration of cognitive tests highly specialised for cognition in psychosis and administration training in clinical scales relevant to psychosis and questionnaires relevant to neurophysiological and cognitive research in psychosis and healthy controls.

Secondment to pharma to understand commercial development of preclinical and clinical biomarkers for use in neuroscience and psychiatry and biomarker strategies in early clinical trials in psychiatric disorders.

Objectives / project plan:

Year 1: Training, literature revies, start recruitment.

Year 2: Continue recruitment, 6-week pharma industry secondment, preprocessing of TMS-EEG data.

Year 3: Recruitment completion, cleaning and data analysis, writing up completion and  viva preparation.

Other notable aspects of the project: Unique opportunity for pharma industry secondment and experience gained on biomarker development.   

Two representative publications from supervisors

Publication 1:  Thomas M, Szentgyorgyi T, Vanes LD, Mouchlianitis E, Barry EF, Patel K, Wong K, Joyce D, Shergill SS. Cognitive performance in early, treatment-resistant psychosis patients: Could cognitive control play a role in persistent symptoms? Psychiatry Res 2021;295: 113607

Publication 2:  di Hou M, Santoro V, Biondi A, Shergill SS, Premoli I. A systematic review of TMS and neurophysiological biometrics in patients with schizophrenia. J Psychiatry Neurosci 2021;46: E675-701

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Neuroimaging

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-pickl

Professor Mitul Mehta
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience
Email: mitul.mehta@kcl.ac.uk

Project Details

Background:  Dissociative states include symptoms characterised by a disruption between normally integrated systems supporting awareness and perception. Hallmark examples include depersonalisation and derealisation (feeling detached from one’s sense of self or environment, respectively). Dissociative states are increasingly recognised as transdiagnostic symptoms present in a range of conditions, including psychosis, affective disorders, and post-traumatic stress disorder. They are also associated with higher comorbidity and poorer outcomes, with significant economic impact.

The neurophysiological 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 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 neural 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 in different conditions.

Primary aims: 

1. Identify neural markers of induced and clinical dissociation.
2. Examine relevance of biomarkers to clinical outcomes.

Study design and sample size:  Experiment 1 (N=40) will use a repeated-measures design examining the impact of N₂O and mirror-gazing on dissociative states and their biomarkers in controls. Experiment 2 (N=60) will use a mixed-model design comparing these biomarkers in controls and patients with depersonalisation-derealisation disorder.

Planned research methods and training provided:  The research will induce mild dissociative states using N₂O, an NMDA receptor antagonist, and mirror-gazing in controls and patients. Our work demonstrates that these techniques are safe and well-tolerated. EEG will be recorded during resting state and dissociative symptom capture windows. Signal complexity and effective connectivity measures will be contrasted in conditions/groups and multivariate pattern classification analysis (machine learning), will be used to identify the overlapping neural features in conditions/groups. The supervisory team will provide training in induction methods and EEG application and data analysis.

Objectives / project plan:

Year 1:

• Conduct a systematic review of dissociation induction methods.
• Training/piloting/ethics.
• Patient/public involvement.
• Dissociation induction in controls.
• EEG analysis.

Year 2:

• Dissociation induction in patients and controls.
• EEG analysis.
  

Year 3:

• Examining predictive utility of biomarker for patient outcomes.
• 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

Maudsley BRC research themes

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

Supervisors

Professor Eva Loth
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience   
Email: eva.loth@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/eva.loth

Dr Jonathan O’Muircheartaigh
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: jonathanom@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/jonathanom

Dr Julie Nihouarn Sigurdardottir
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: julie.nihouarn@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/julie.nihouarn

Project Details

Background:  Neurodivergent people (including autistic people and ADHDers) have 40-70% risk of developing mental health problems (anxiety, depression, burn-out). This is likely due to a combination of biological characteristics (e.g., sensory sensitivities in autism) and social/ environmental factors (e.g., bullying, stigma). Early detection, personalised treatment and prevention of mental health problems are key research and clinical priorities but hindered by a lack od well-powered studies, notably over preschool years, that assess biological and social-environmental factors. 

Novelty and importance:  The Preschool Brain Imaging and Behaviour Project (PIP) is the world-wide first longitudinal multi-centre, multi-disciplinary brain-imaging, “cross-condition” study of 500 preschoolers with autism, ADHD, ID or typical development followed from 3 to 6 years. PIP provides a unique opportunity to identify the interaction between social and biological markers and mechanisms that are either specific for (subpopulations within) clinical conditions or transdiagnostic.

Primary aims: 

1. To explore ethical, scientific and practical considerations regarding risks/ benefits of biomarkers and early interventions;
2. To trace developmental trajectories of emotional and sensory profiles in neurodivergent preschoolers;
3. To identify social (risk and protective) and biological mechanisms that are linked to concurrent clinical profiles and predict clinical development. In particular, we will test the hypothesis that processing unpredictable information impacts both sensory sensitivities and emotional processes in anxiety-vulnerability.

Study design and sample size:  Longitudinal, multi-disciplinary, “cross-condition” design, with 500 children.

Planned research methods and training provided:  The project involves a range of advanced quantitative methods (latent growth curve modelling, normative modelling, clustering). Training will be provided by research supervisors and/ or leading research collaborators; and workshops conducted within the AIMS-2-TRIALS and/ or Respect4Neurodevelopment networks.

Objectives / project plan:

Year 1: The student will conduct a survey (based on an ongoing qualitative study) on ethical, scientific and practical considerations of biomarkers and early intervention with neurodivergent participants and the general population. The student will train in growth curve analyses to identify clinical trajectories in sensory profiles and anxiety.

Years 2-3 (including 6-months secondment at Roche): The student will examine a) social risk/ protective factors and b)  derive neurocognitive profiles based on the combination of brain imaging indices and behavioural tests. We will combine normative modelling of each measure with robust clustering to identify neurocognitive profiles, and then examine the relationship of neurocognitive subgroups to concurrent clinical features and their prognostic value in predicting clinical trajectories.

Other notable aspects of the project:  This PhD offers multi-disciplinary training in different brain imaging and analysis techniques, participatory research, bio-ethics, and gives the student the opportunity to gain competitive industry experience through the Roche  Internships for Scientific Exchange (RiSE) Programme.

Two representative publications from supervisors

Publication 1:  Loth, E., et al., Identification and validation of biomarkers for autism spectrum disorders. Nat Rev Drug Discov, 2016. 15(1): p. 70-3.

Publication 2:  Loth, E., Does the current state of biomarker discovery in autism reflect the limits of reductionism in precision medicine? Suggestions for an integrative approach that considers dynamic mechanisms between brain, body, and the social environment. Front Psychiatry, 2023. 14: p. 1085445

Maudsley BRC research themes

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

Supervisors

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

Dr Dominic Oliver
Institute of Psychiatry, Psychology and Neuroscience and Department of Psychiatry, University of Oxford
Email: dominic.a.oliver@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/dominic-oliver

Professor Paolo Fusar-Poli
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
Email: paolo.fusar-poli@kcl.ac.uk  Website: paolo.fusar-poli@kcl.ac.uk

Project Details

Background:  Severe mental disorders (SMD) include unipolar mood disorders, bipolar mood disorders and psychotic disorders and are characterized by high clinical, societal, familial and personal burdens. To date, preventive psychiatry has focused primarily on the clinical high risk for psychosis (CHR-P) state, identifying people with prodromal psychosis, however, there is a push to extend this to SMD more broadly as transdiagnostic prevention. It is important to develop clinical prediction models (CPMs) to predict the risk of developing SMDs in individuals with mental health problems to enhance early intervention efforts. This project will focus on the development and validation of individualized CPMs for predicting SMD onset using machine-learning methods building upon a current transdiagnostic risk calculator for psychoses.

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 and expands the risk predictions to other SMDs. This decision tool would allow monitoring persons at risk and offer them early intervention.

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

Study design and sample size:  Electronic Health Records from Maudsley BRC CRIS system (N=1,300,000).

Planned research methods and training provided:  The candidate will build a pipeline for extracting and processing patient health records to develop a dynamic machine learning prediction model for SMD risk. 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 interpretability. The final model will be integrated into a web-based clinical tool, and its acceptance among clinicians and users will be assessed in a feasibility study.

The student will receive training in statistical methods, machine learning and health informatics through the Department of Biostatistics and Health Informatics' 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: 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.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Trials, Genomics and Prediction

Supervisors

Dr Dafnis Batalle
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience   
Email: dafnis.batalle@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/dafnis-batalle

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

Project Details

Background:  Brain structural and functional connectivity provides the basis to understand the underpinning structure and neural basis of higher-order cognitive processes and holds promise to develop stratification markers of neurodevelopmental conditions (NDCs), such as autism and attention deficit hyperactivity disorder. While some studies have shown atypical patterns of brain connectivity in adults with NDCs, little is known about the development of structural and functional connectivity from infancy to adulthood, their relation to clinical change over time (i.e., in ‘core’ neurodevelopmental features, co-occurring mental health features, and functional/quality of life outcomes) and to genetics, or what insights can be gained by the fusion of multimodal connectivity metrics. 

Novelty and importance:  Multi-centre consortia led by KCL, such as the AIMS-2-TRIALS and the Developing Human Connectome Project (dHCP), have acquired large multi-modal datasets of neurotypical and neurodivergent participants through the lifespan. By leveraging multimodal cross-sectional and longitudinal data, this project aims to characterise typical and atypical trajectories of brain structural and functional connectivity, and their association with genetic, demographic, and clinical measures. We will do this from infancy to adulthood, with the aim to develop stratification biomarkers for NDCs, which will accelerate the provision of mechanism-informed intervention choices to advance personalised medicine.

Primary aims: 

1. Characterise multimodal SC and FC (from diffusion MRI, functional MRI, and EEG) through the lifespan.
2. Compare patterns of SC and FC between neurotypical and neurodivergent participants, investigate concurrent and prognostic associations with both cognitive (e.g., social processing sensitivity) and clinical neurodevelopmental features, co-occurring mental health symptom severity, and quality of life.
3. Investigate associations between SC and FC (multimodal data fusion), and associations with polygenic scores for autism and gene expression in the brain.

Study design and sample size:  We will analyse data already acquired from large cross-sectional and longitudinal cohorts. AIMS-2-TRIALS LEAP uses an accelerated longitudinal design to follow a cohort of ~700 neurotypical and neurodivergent participants between 6 and 30 years of age. The Developing Human Connectome Project includes cross-sectional multi-modal neonatal MRI (n~800), with follow-up data at 18 months.

Planned research methods and training provided: We will provide neuroimaging and data analysis training required for this project, including microstructure, tractography, and structural connectivity analyses (from diffusion MRI); fMRI and EEG analyses (e.g. RSNs, brain dynamics, microstates); graph theory; general data analysis skills (e.g. ICA/PCA); data-driven clustering; and predictive modelling (machine learning).

Objectives / project plan:

Year 1: Neuroimaging training, SC and FC inference. Exploration of multimodal data fusion.

Year 2: Statistical data analysis and association with neurotypical and neurodivergent development. Data-driven clusters, and association with clinical and cognitive features.

Year 3: SC-FC coupling and association with polygenic scores and gene expression. Write up time, transition into the post-doctoral phase.

Other notable aspects of the project:  We have experience training PhD candidates from diverse backgrounds without previous experience in neuroimaging and complex mathematical modelling.

Two representative publications from supervisors

Publication 1:  França LGS, Ciarrusta J, Gale-Grant O, Fenn-Moltu S, Fitzgibbon S, Chew A, …, & Batalle D (2024); Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment; Nature Communication; In Press.

Publication 2:  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.

Maudsley BRC research themes

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

Supervisors

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

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

Project Details

Background:  The digital transformation of healthcare is generating ever-larger amounts of high-dimensional, high-frequency, and multimodal data. This includes data from routine appointments as well as remotely collected data from smartphones and wearables.

These new data streams, combining multiple sources and addressing both physical and mental health, offer huge potential in understanding the progression and management of long-term conditions. One-third of people in the UK have multiple long-term conditions, accounting for half of hospital admissions. Long-term conditions are characterised by fluctuating symptoms, alternating between periods of remission and relapse.

Novelty and importance:  Remote and routine data collection enables novel insights into outcomes among people with long-term conditions, but only if paired with appropriate statistical techniques.

This project addresses the urgent need for modern statistical approaches that make effective use of 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.

Primary aims:  To evaluate, apply, and develop state-of-the-art statistical techniques to uncover the dynamics of patient outcomes during treatment and management of long-term conditions.

Study design and sample size:  This is a secondary analysis building on two existing datasets:

• RADAR-MDD (Remote assessment of disease and relapse in major depressive disorder) is the largest remote measurement study in depression conducted to date, with 623 participants providing real-time passive information (e.g., physical activity, sleep) and regular self-report assessments for two years.

• IMPARTS (Integrating Mental & Physical healthcare: Research, Training & Services) provides 10 years of data on mental and physical health symptoms collected at 32 clinics across GSTT and KCH hospitals.

Planned research methods and training provided:  The student will receive statistical training in joint modelling (Netherlands Institute for Health Sciences), prediction modelling (King’s College London), and prognostic modelling (University of Birmingham).

Objectives / project plan:

Year 1:

• Objectives:  Systematic review and simulation study comparing chosen methods.
• Other activities:  Data cleaning; pre-registration; methodological training.

Year 2:

• Objectives:  Application in real-world data.
• Other activities:  Training; presentation to patient groups.

Year 3

• Objectives:  Publish applied studies; write thesis; apply for postdoctoral funding.
• Other activities:  Patient dissemination event.

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 symptoms, anxiety symptoms and functional impairment during internet-enabled cognitive-behavioural therapy” Behaviour Research and Therapy 169. doi: 10.1016/j.brat.2023.104386

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

Maudsley BRC research themes

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

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 aims: 

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

Study design and sample size:  Secondary analysis of large observational cohorts (total ~45K records, with ~27K available with genotyping information). These large datasets should provide ample power for the analyses.

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 (~32k) with linked medical records.
• TEDS (Twins Early Development Study), to identify mediators/moderators of psychological outcomes in early life. Twins born 1994-1996 (~14k) 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 (KCL).
• Advanced SEM (Utrecht University).
• Prediction Modellingand Introduction to R (KCL).
• Clinical prediction models & Machine Learning (Maastricht University).

Objectives / project plan:

Year 1:

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

Year 2:

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

Year 3:

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

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.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Trials, Genomics and Prediction

Supervisors

Dr Matthew Kempton
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience   
Email: matthew.kempton@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/matthew-kempton

Dr Ashwin Venkataraman
Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email: ash.venkataraman@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/ash.venkataraman &  https://sites.google.com/view/brainregion

Project Details

Background:  Large-scale neuroimaging studies such as the ENIGMA consortium have established robust MRI biomarkers associated with psychosis and affective disorders. However, these findings are based on case-control samples with strict inclusion and exclusion criteria, and have not been tested in real-world clinical samples with higher comorbidities, lower resolution scanning protocols and greater ethnic diversity. If neuroimaging is to be used in clinical practice it is essential that these biomarkers show predictive validity in real-world clinical data.

Novelty and importance:  For the first time, this project brings together data from the ENIGMA VBM Tool (Dr Kempton) which has recently produced detailed 3D maps brain abnormalities in a number of disorders from over 10,000 patients https://sites.google.com/view/enigmavbm; and the SLaM Image Bank (Dr Venkataraman) which includes 13,950 MRI scans from patients from the South London and Maudsley NHS Trust https://sites.google.com/view/brainregion/slamimagebank.

Primary aims: 

1. To determine the diagnosis of psychosis, major depressive disorder and bipolar disorder from MRI scans of patients in the SLaM Image bank.
2. To determine key clinical measures such as age of onset and illness severity from MRI scans from the SLaM Image Bank.
3. To predict future clinical outcome in MRI scans from SLaM patients.

Study design and sample size:  Predictive validity of MRI biomarkers in a real-world clinical dataset. Sample: SLaM imaging bank (n=13,950), approximately 10,000 patients from the ENIGMA consortium.

Planned research methods and training provided: Full training in neuroimaging and analysis methods will be provided by both supervisors. Dr Kempton leads a Research Methods MSc module in research methods and lectures in neuroimaging and Dr Venkataraman teaches on the Therapeutic Research in Psychiatry MSc and Clinical Neuropsychiatry MSc, students would be able to attend relevant MSc lectures as well as one-to-one training.

Objectives / project plan:

Year 1: Training in structural MRI analysis techniques including VBM. Selecting patients with psychosis and affective disorders from the SLaM Imaging bank, processing data with the ENIGMA VBM tool. Determining diagnosis of psychosis in the imaging bank and age of onset.

Year 2: Determining the diagnosis of Bipolar Disorder and Major Depressive Disorder from MRI scans and linking these to clinical variables. Matching up data from SLaM Image bank to outcome clinical data on CRIS.

Year 3: Predicting clinical outcome from SLaM Image Bank data. Thesis write-up.

Other notable aspects of the project: This project presents the opportunity to work with neuroimaging data from 100+ sites across the world and develop a tool to predict clinical outcome in real-world patient data across the lifespan.

Two representative publications from supervisors

Publication 1:  Si S, Bi A, Yu Z, See C, Kelly S, Ambrogi S, ... Radua J, McGuire P, Thomopoulos S, Jahanshad N, Thompson PM, Barth C, Agartz I, James A, Kempton MJ Mapping gray and white matter volume abnormalities in early-onset psychosis - an ENIGMA multicenter voxel-based morphometry study. Molecular Psychiatry January 2024 DOI 10.1038/s41380-023-02343-1.

Publication 2:  Venkataraman, A. V., Marshall, C. & Rittman, T. Automated brain image analysis in dementia using artificial intelligence: a roadmap for the development of clinical tools. Available at: osf.io/myuq7. OSF Preprint. 2023.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Neuroimaging

Supervisors

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

Professor Thalia Eley
Department of Social, Genetic & Developmental Psychiatry, Institute of Psychiatry, Psychology and Neuroscience   
Email: Thalia.eley@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/thalia-eley

Project Details

Background:  Anxiety disorders and post-traumatic stress disorder (PTSD) are common, chronic, and costly. Their biology is poorly understood, and predicting their onset, prognosis, and appropriate treatment is challenging. Genome-wide association studies (GWAS) from the Psychiatric Genomics Consortium (PGC) have identified 94 (PTSD) and 59 (Anxiety) regions of the genome in which genetic variants are associated with these conditions.

Novelty and importance:  Focus groups in the Twins Early Development Study (TEDS) revealed that participants want a focus on genetic influences on anxiety-related conditions and on the effect of triggering environments. This studentship will address those aims using large local datasets with rich and deep phenotype information, and leveraging our analytical networks including PGC-Anxiety and PGC-PTSD.   

Primary aims: 

Aim 1: Conduct genome-wide association studies (GWAS) of anxiety in individuals with and without trauma exposure.

Aim 2: Conduct GWAS of PTSD versus anxiety in non-comorbid individuals. Collaborate with PGC-PTSD to undertake meta-analysis with other datasets and post-GWAS analyses, including prediction into local datasets.

Aim 3: Compare prediction of anxiety and PTSD from a genetics-only model versus a clinical/environmental model and versus a combined model.

Study design and sample size:  We will use local datasets (Genetics Links to Anxiety and Depression [GLAD] study, TEDS), UK Biobank, and the emerging Our Future Health study. International datasets from the PGC (including PGC-PTSD, and PGC-Anxiety) will also be available. These are the largest collections of genomic data for these conditions and were sufficiently powered to identify tens of associated genomic regions.  

Planned research methods and training provided: 

The main methods will be GWAS and secondary analyses using genome-wide genotype data. The student will audit a MSc-level statistical genetics module, with advanced training in these areas from Dr Coleman. The student will join the local statistical genomics community, including the supervisors’ groups as well as those of Profs Lewis and Breen. Training in specific advanced skills such as prediction modelling can be obtained through the wider IoPPN, or through external courses.

Dr Coleman is a statistical geneticist with extensive experience in the genomics of stress-related disorders. He is a key analytical contributor to the PGC-PTSD and to other PGC groups.

Prof Eley is an internationally renowned expert in the behavioural genetics of anxiety. She is principal investigator of TEDS, and co-leads both GLAD and PGC-Anxiety.   

Objectives / project plan:

Year 1: Training in GWAS methodology; begin aim 1.

Year 2: Complete aim 1. Conduct aim 2. Begin aim 3.

Year 3: Complete aim 3. Write thesis.

Other notable aspects of the project: Working with PGC-PTSD and PGC-Anxiety at the cutting-edge of genomics in these disorders will provide considerable opportunities for networking and international exposure.

Two representative publications from supervisors

Publication 1:  Mundy, J., Hübel, C., Gelernter, J., Levey, D., Murray, R. M., Skelton, M., ... & Coleman, J. R. I. (2022). Psychological trauma and the genetic overlap between posttraumatic stress disorder and major depressive disorder. Psychological medicine, 52(16), 3975-3984.

Publication 2:  Purves, K. L., Coleman, J. R., Meier, S. M., Rayner, C., Davis, K. A., Cheesman, R., ... & Eley, T. C. (2020). A major role for common genetic variation in anxiety disorders. Molecular psychiatry, 25(12), 3292-3303.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Trials, Genomics and Prediction

Supervisors

Professor Jonna Kuntsi
Department of Social, Genetic & Developmental Psychiatry, Institute of Psychiatry, Psychology and Neuroscience   
Email: jonna.kuntsi@kcl.ac.uk    Website: http://www.kcl.ac.uk/people/jonna-kuntsi

Dr Ewan Carr
Department of Biostatistics & Health Informatic, Institute of Psychiatry, Psychology and Neuroscience
Email: ewan.carr@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/ewan.carr

Professor Richard Dobson
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience
Email: richard.j.dobson@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/richard.j.dobson

Project Details

Background:  Late adolescence and the transition to adulthood is a highly challenging and potentially critical period for young people with ADHD that can lay the foundations for diverging adulthood trajectories. Many of the conditions that frequently co-occur with ADHD emerge in adolescence and major life transitions lead to multiple new demands and changes in available support networks. This vulnerable phase coincides with the clinical transition from child and adolescent mental health care to adult services, which itself is a focus of major current clinical concern: most youth with ADHD do not successfully transfer to adult services, despite significant needs for ongoing treatment. Opportunities for intervention are currently not fully realised due to both the young people’s disengagement from clinical services and our limited understanding of real-world targets for more holistic interventions.

Novelty and importance:  Using remote measurement technology (RMT) the team has developed for ADHD, the ADHD transition project aims to identify fluctuations in symptoms and the wider phenotype at a level of detail not previously possible, and to identify real-world targets for intervention that include environmental factors and health behaviours. The study data will subsequently inform the development of an app that aims to transform monitoring, self-management, personalised treatment and engagement with clinical services during ADHD transition.

Primary aims: 

Aim 1: To identify, with precision, the nature and timing of real-world changes that take place in the transition to adulthood for young people with ADHD (e.g. changes in clinical symptoms and functional impairment; healthy lifestyle behaviours (physical activity, sleep, daily structure, online lifestyle), social support, employment/studies).

Aim 2: Using the rich remote monitoring data to identify factors that predict such changes in the outcome measures.

Study design and sample size:  ART-transition is a prospective observational cohort study that involves remote monitoring of 250 young people with ADHD for up to 24 months.

Planned research methods and training provided:  The student will have an opportunity to analyse data during the ‘Discover’ phase of development of a prototype for a new ADHD-transition app. This involves addressing the above aims using the available data, which will inform the subsequent prototype app development by the team. Training will be provided by the supervisors and via specialised training courses in appropriate analysis methods.    

Objectives / project plan:

Year 1 will involve training in analytical approaches; literature review; development of analysis pipelines and preliminary analyses on data collected using the wearable device.

Year 2 will involve analyses on both active (e.g. Active App questionnaires) and passive (wearable device and Passive App) monitoring data to address research questions that form Aim 1.

Year 3 will involve analyses of both active and passive monitoring data to address research questions that form Aim 2.

Two representative publications from supervisors

Publication 1:  Denyer H, Ramos-Quiroga JA, Folarin A, Ramos C, Nemeth P, Bilbow A, Woodward E, Whitwell S, Müller-Sedgwick U, Larsson H, Dobson RJ, Kuntsi J. ADHD Remote Technology study of cardiometabolic risk factors and medication adherence (ART-CARMA): a multi-centre prospective cohort study protocol. BMC Psychiatry. 2022 Dec 20;22(1):813. doi: 10.1186/s12888-022-04429-6. PMID: 36539756; PMCID: PMC9764531.

Publication 2:  Denyer H, Deng Q, Adanijo A, Asherson P, Bilbow A, Folarin A, Groom MJ, Hollis C, Wykes T, Dobson RJ, Kuntsi J, Simblett S. Barriers to and Facilitators of Using Remote Measurement Technology in the Long-Term Monitoring of Individuals With ADHD: Interview Study. JMIR Form Res. 2023 Jun 30;7:e44126. doi: 10.2196/44126. PMID: 37389932; PMCID: PMC10365629.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Informatics
• Digital Therapies

Supervisors

Professor Sabine Landau
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience   
Email: sabine.landau@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/sabine.landau & https://www.kcl.ac.uk/people/sabine-landau

Dr Giouliana Kadra-Scalzo
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience 
Email: giouliana.kadra@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/dr-giouliana-kadra-scalzo  &  https://kclpure.kcl.ac.uk/portal/en/persons/giouliana.kadra

Professor Robert Stewart
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: robert.stewart@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/robert.stewart

Project Details

Background:  Routinely collected clinical data provide a powerful resource for evaluating interventions for real-world patient populations because they are large, naturalistic resources; however, internal validity is frequently impacted by confounding. Hernan and colleagues have proposed a trial emulation framework which applies causal inference concepts to mimic a target trial using observational data. In this context propensity scoring approaches are often used to handle multiple measured confounders.

Novelty and importance:  

• To provide clarity and guidance on using propensity scoring for trial emulation with observational data.
• To develop a tool that can be applied across different types of EHR data, advancing methodological capability and supporting decision-making for trial emulations.

Primary aims: 

1. To develop a decision support tool for principled propensity scoring in target trial emulation using EHRs.
2. To develop software tools to enable propensity scoring in practice, e.g. create wrappers to convert software output into clinically meaningful effect size estimates.
3. To develop exemplar target trial emulation studies using the Maudsley’s CRIS EHR data resource.

Study design and sample size:  In Year 1 the student will conduct a review of propensity scoring methods for trial emulation studies and relevant software. In Year  2 and 3 the student will use retrospective cohort design for Case study 1 and 2.

Based on previous work we have done, using CRIS, we will be able to access the deidentified clinical records of over 10,000 service user with a mental health diagnosis.

Planned research methods and training provided:  The Maudsley’s Clinical Record Interactive Search (CRIS) platform has supported extensive research since 2008 by allowing research access to de-identified mental health EHRs on over 500,000 cases from a diverse south London geographic catchment of 1.3m residents. The student will have an unique opportunity to develop applied data science skills using an internationally leading resource. Case studies might include antipsychotic polypharmacy and/or lithium discontinuation in late life, but these will be informed by both a review and patient/carer input.

Objectives / project plan:

Year 1:

• Review of propensity scoring methods for trial emulation studies and relevant software.
• Seek patient/carer input on case studies for application.
• Develop pipeline for extraction of relevant data for case studies.
• Undertake relevant training (CRIS system; Introduction to Target Trial Emulation; Causal Modelling and Evaluation).

Year 2:

• Develop a decision support tool to guide propensity scoring applications using EHRs.
• Develop software tools (wrappers) to facilitate underdeveloped steps in the propensity scoring process.
• Case Study 1 (e.g., polypharmacy versus monotherapy in the treatment of psychosis on mental health service use, general hospitalisation, mortality)

Year 3:

• Case Study 2 (e.g., late-life lithium discontinuation in bipolar disorder on relapse).

Other notable aspects of the project:  The student will work at the forefront of the exciting new field of target trial emulations using routinely collected data.

Two representative publications from supervisors

Publication 1:  Scola G., Chis-Ster A., Bean D., Pareek N., Emsley E. & Landau S. (2023) Implementation of the trial emulation approach in medical research: a scoping review, BMC Medical Research Methodology 23: 186; DOI: 10.1186/s12874-023-02000-9

Publication 2:  Kadra G, Stewart R, Shetty H, MacCabe JH, Chang CK, Kesserwani J, et al. (2017) Antipsychotic polypharmacy prescribing and risk of hospital readmission. Psychopharmacology 235: 281–289. DOI:  10.1007/s00213-017-4767-6

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Trials, Genomics and Prediction

Supervisors

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

Dr David Mark Howard
Department of Social, Genetic & Developmental Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email: david.howard@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/david.howard  &  https://www.drhoward.co.uk/

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

Project Details

Background:  Eating disorders (ED) affect ~8% of the global population (Galmiche et al., 2019). ED are often chronic and cause substantial costs. EDs 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 underlies the interaction between genes and the environment, might play significant roles in the aetiology and manifestation of ED, but are understudied (Hübel et al., 2019). To address this research gap, we propose to study the epigenetic and rare genetic variants basis of ED using nanopore long read DNA 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, an 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) and rare variant study across multiple types of ED disorder diagnoses.

Primary aims:  The overarching aim of this project is to identify differential epigenetic, i.e. DNA methylation, and rare variant signatures associated with different types of Eating Disorders and related phenotypes using long read Nanopore sequencing data from 4000 ED cases from EDGI-UK volunteers and >10,000 healthy volunteers.

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

Objectives / project plan:

The team has extensive links with ED charities and Lived Experience; you will also work with them, co-producing the research wherever possible, as part of ongoing participant and public engagement for EDGI UK research.

Year 1: The student will familarise with the genetics and epigenetics basis of ED and EDGI database. Nanopore DNA methylation and rare variant sequencing data of 4000 EDGI participants will be generated by the BRC BioResource team at the SGDP, IoPPN. The student will be involved in performing fundamental data processing and QC, and pipeline establishment.

Year 2: Conduct an epigenome-wide association study (EWAS) to detect epigenetic signatures of eating disorders and extreme eating behaviours. 

Year 3: Conduct rare genetic variant association study to detect structural variants (CNVs), repeats, and point mutations associated with extreme anorexia nervosa or binge eating disorder.

Other notable aspects of the project: We have unique involvement of people with lived experience in our eating disorders project and the student will have the opportunity to present their work to people with experience of an ED, their families, and to clinicians, as well as the research community.

Two representative publications from supervisors

Publication 1:  Alameda, L., Trotta, G., Quigley, H., Rodriguez, V., Gadelrab, R., Dwir, D., Dempster, E., Wong, C. C. Y.*, & Forti, M. D.* (2022) *Joint senior authorships; Can epigenetics shine a light on the biological pathways underlying major mental disorders? Psychological Medicine, 52(9), 1645-1665. https://doi.org/10.1017/S0033291721005559

Publication 2:  Monssen D, Davies HL, Kakar S, Bristow S, Curzons SCB, Davies MR, Kelly EJ, Ahmad Z, Bradley JR, Bright S, Coleman JRI, Glen K, Hotopf M, Ter Kuile AR, Malouf CM, Kalsi G, Kingston N, McAtarsney-Kovacs M, Mundy J, Peel AJ, Palmos AB, Rogers HC, Skelton M, Adey BN, Lee SH, Virgo H, Quinn T, Price T, Zvrskovec J, Eley TC, Treasure J, Hübel C, Breen G. (2023) The United Kingdom Eating Disorders Genetics Initiative. Int J Eat Disord. doi: 10.1002/eat.24037. Epub ahead of print. PMID: 37584261.

Maudsley BRC research themes

• Eating Disorders and Obesity
• 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
Email: robert.stewart@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/robert.stewart

Project Details

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

Novelty and importance:  Bringing together data from Google, social media, 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 aims:  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 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.

Year 2:  Analysis of Google Trends searches / Twitter data / CRIS data.

Year 3:  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.

Maudsley BRC research themes

• Informatics

Supervisors

Dr Margaret Heslin
Department of Health Service & Population Research, Institute of Psychiatry, Psychology and Neuroscience   
Email: Margaret.heslin@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/margaret.heslin

Professor Robert Stewart
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience
Email: robert.stewart@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/robert.stewart

Project Details

Background:  Health inequalities are avoidable, systematic disparities in health between different groups of people. Severe mental illness (long term and debilitating mental illness requiring secondary mental health care) is both a risk factor for, and an outcome of health inequalities, in that different groups of people have unequal risk of experiencing severe mental illness, and people with severe mental illness are more likely to experience inequalities in physical health. HIV is also an illness with health inequalities with unequal risk and unequal outcomes for different groups of people. However, there is very little research exploring HIV in people with severe mental illness.

Novelty and importance:  No study has examined the health inequalities in HIV for people with severe mental illness. This study will be the first. Identifying health inequalities are important for highlighting need, and then making changes in policy and clinical approaches to address that need, and to inform future interventions to improve health.

Primary aims:  To explore inequalities in HIV in people with severe mental illness in a UK population. 

Study design and sample size:  TRetrospective cohort study and retrospective case control study. People with HIV+SMI = n4,481; people with SMI only = n176,696; people with HIV only = n6,601

Planned research methods and training provided: 

• Evidence synthesis (MSc module)
• Big data (MSc module)
• Advanced statistics (MSc module)
• DataCamp training for R, SQL, Python
• Practical training in the use of big data from UK Health Security Agency and Biomedical Research Centre, KCL
• General research skills training (informed consent, clinical research)
• Transferrable skills courses (including project management, writing training, presenting skills)
• Regular PhD seminars

Objectives / project plan:

Year 1: Orientation to university, school, department. Collaboration building with PPI groups, relevant academics, UKHSA and fellow students. Training. Conduct systematic review on risk and outcomes for HIV in people with severe mental illness. Familiarisation with data/data systems at KCL/UKHSA.

Year 2: Use data to examine inequalities in the risk of HIV in people with severe mental illness, and inequalities in the HIV treatment cascade for people with severe mental illness compared to people without severe mental illness. Identify particular points at which inequalities arise in order to inform novel future interventions.

Year 3: Collaborate with PPI groups and relevant collaborators to interpret findings, write up thesis, disseminate to researchers, clinicians, practitioners, and policy makers, through publications in academic journals, presentation at conferences, stakeholder events, education and training, and social media.

Two representative publications from supervisors

Publication 1:  Heslin M et al. & Stewart R. Prevalence of HIV in mental health service users: a retrospective cohort study. BMJ open. 2023 Apr 1;13(4):e067337.

Publication 2:  Brown et al & Heslin. In submission. Barriers and Facilitators to Accessing Sexual and Reproductive Health Services for People with Severe Mental Illness: A Systematic Review.

Maudsley BRC research themes

• Informatics

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: https://www.kcl.ac.uk/people/sarah-byford  &  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 aims:  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.

Study design and sample size:  Cost-effectiveness analysis based on economic modelling. The sample size could range from a couple of hundred to a couple of thousand, depending on the number of patients in the CRIS database who meet the inclusion criteria for the decision question of interest.

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, and then use the derived data to update the schizophrenia WDM.

Year 3: 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.

Maudsley BRC research themes

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

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
Department of Social, Genetic & Developmental Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email: Moritz.1.herle@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/moritz-herle

Project Details

Background:  The overarching aim of this project is to understand the impact of cardiovascular disease and its risk factors on anxiety and depression and to identify intervention targets that sit in between them. We focus explicitly on cardiovascular diseases (CVDs) and their risk factors (including hypertension, hypercholesterolemia, and obesity) as these are the common chronic physical health conditions.   Depression has consistently been associated with incident cardiovascular disease, as well as poor outcomes in established coronary heart disease and stroke. 

Novelty and importance:  This research will explore the impact of CVD on anxiety and depression and identify protective factors and mechanisms to improve health outcomes. Understanding social and behavioural factors which help to reduce depression and anxiety after a cardiovascular event is of importance for recovery and quality of life.

Primary aims:  To explore the longitudinal relationship between mental health and CVD, and their risk factors, and identify protective mediation factors as targets for intervention development.

Study design and sample size:  Data from two large scale cohort studies will be utilized: 1) the 1958 National Child Development Study; the 1970 British Cohort Study, and 2) the English Longitudinal Study of Ageing (ELSA).

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

Objectives / project plan:

Year 1: Understanding the needs and priorities of individuals with lived experience of CVD, anxiety and depression.  Participants will consist of individuals with lived experience of CVD.  We will discuss the impact of CVD on symptoms of anxiety and depression and explore possible protective factors in focus groups and 1-1 interviews.   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 for the quantitative analyses in studies 2 and 3.

Year 2: Investigating impact of CVD, and their risk factors on trajectories of anxiety and depression.  Using secondary data analyses, the student will investigate how metabolic health conditions (e.g. diabetes or hypertension) and onset of established CVD affect trajectories of anxiety and depression.

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

Two representative publications from supervisors

Publication 1:  Nas Z, Zavos HMS, Sumathipala A, Jayaweera K, Siribaddana S, Hotopf M, Rijsdijk FV. Associations Between Anxiety Symptoms and Health-Related Quality of Life: A Population-Based Twin Study in Sri Lanka. Behav Genet. 2021 Jul;51(4):394-404. doi: 10.1007/s10519-021-10051-1.

Publication 2:  Herle, M., Pickles, A., Pain, O., Viner, R., Pingault, J. B., & De Stavola, B. L. (2023). Could interventions on physical activity mitigate genomic liability for obesity? Applying the health disparity framework in genetically informed studies. European journal of epidemiology, 38(4), 403–412. https://doi.org/10.1007/s10654-023-00980-y

Maudsley BRC research themes

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

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.f.oakley@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/bethany.f.oakley

Professor Emily Simonoff
Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email: emily.simonoff@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/emily.simonoff

Project Details

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

Novelty and importance:  This PhD project will implement digital wearables to elucidate underpinning mechanisms for anxiety in autism and provide proof-of-concept for the use of digital therapies for anxiety that are specially designed with and for autistic people.

Primary aims: 

1. To identify mechanisms underpinning anxiety in autism, using digital wearable technologies.
2. To assess the acceptability/feasibility of digital therapies that target mechanisms identified in Aim 1.

Study design and sample size:  The project includes data from two ongoing studies that involve joint working with multidisciplinary teams (academics, clinicians, lived experience experts, industry, non-profit organisations) across Europe:

1. The AIMS-2-TRIALS Longitudinal European Autism Project – the largest multidisciplinary study worldwide to identify variability in autism that includes >700 participants (Development in children and adults – LEAP (aims-2-trials.eu)).
2. A feasibility study of an app-based anxiety intervention for autistic people that includes 100 participants (Study Details | Molehill Mountain Feasibility Study. | ClinicalTrials.gov).

Planned research methods and training provided:  The student will gain skills in 1+ of the following (depending on their research interests):

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

Objectives / project plan:

Year 1: Systematic review/meta-analysis of research on digital wearables and therapeutics targeting anxiety in autism. Contribution to data collection/database curation for the two research projects defined above, forming the core datasets for the PhD.

Year 2: Analyses of associations between anxiety and underpinning mechanisms in the context of autism (AIMS-2-TRIALS dataset). Data are available pertaining to cognitive/behavioural and social factors, neurobiology, biochemical markers/genetics, and digital tools/wearables (e.g., sleep, autonomic function).  

Analyses of the acceptability/feasibility of a novel app-based anxiety intervention for autistic people (targeting mechanisms investigated above; Molehill Mountain feasibility dataset).

Year 3: Completion of work/analyses, including write up of the thesis.

Other notable aspects of the project:  The PhD will support the student in their transition to the postdoctoral phase through, for example, leading/co-leading on high impact publications, opportunities to present at national/international conferences, and translation of results to inform the design/launch of a future clinical trial (beyond the scope of this proposal). 

Two representative publications from supervisors

Publication 1:  Oakley B, Jones E, Crawley D, Charman T., Buitelaar J., … & Loth, E. (2020).  Alexithymia in autism: cross-sectional and longitudinal associations with social-communication difficulties, anxiety and depression symptoms. Psychological Medicine. 2022;52(8):1458-1470. doi:10.1017/S0033291720003244.

Publication 2:  Oakley B, Boatman C, Doswell S, Dittner A, Clarke A, Ozsivadjian A, et al. (2023) Molehill Mountain feasibility study: Protocol for a non-randomised pilot trial of a novel app-based anxiety intervention for autistic people. PLoS ONE 18(7): e0286792. https://doi.org/10.1371/journal.pone.0286792.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Digital Therapies

Supervisors

Dr Flavio Dell'Acqua
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience   
Email: flavio.dellacqua@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/flavio.dellacqua & https://www.scopus.com/authid/detail.uri?authorId=24757840500

Dr Daniel van Wamelen
Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience
Email: Daniel.van_Wamelen@kcl.ac.uk    Website: https://www.kcl.ac.uk/people/daniel-van-wamelen

Project Details

Background:  The reward circuit, a complex network of cortical and subcortical regions connected by white matter pathways, is affected in both Huntington's Disease (HD) and Major Depressive Disorder (MDD). Decision making in people with HD (PwHD) is impaired, with a bias towards immediate reward, overlapping with reward perceptions in people with MDD. Despite the symptom overlap, underlying neural changes in the reward system remain understudied, although common WM changes might contribute. As a clearly defined monogenic disorder, HD forms the perfect setting to validate any clinical imaging methodology related to reward systems.

Novelty and importance:  The project's novelty includes the use of advanced neuroimaging methods to look at structural connectivity and microstructural data from separate studies, the iMarkHD and BIODEP studies. Utilising advanced tractography, we will create an atlas of the reward circuit’s white matter pathways and compare it across conditions to identify common pathways. This mapping will allow for a comprehensive analysis of structural and microstructural white matter integrity, enhancing our understanding of the neurobiological underpinnings of neuropsychiatric symptoms in these disorders and develop new research tool and templates for future studies.

Primary aims:  1) Produce a structural connectivity atlas of the reward system; 2) Investigate cross-sectional and longitudinal changes in the reward network in PwHD; 3) Compare white matter alterations in the reward network of individuals with HD and MDD.

Study design and sample size:  We will use data from the iMarkHD study (76 PwHD from premanifest to manifest stages, 36 healthy volunteers), and the BIODEP study (130 people with MDD, 40 controls). Analyses consist of separate analysis for each condition and a comparison between conditions to elucidate shared and distinct aspects of reward system alterations.

Planned research methods and training provided:  Advanced spherical deconvolution tractography and microstructure imaging methods like free-water-elimination and diffusion kurtosis imaging, will be applied to characterise reward network connectivity and microstructure changes with particular attention for cortico-striatal and mesolimbic connectivity. Analytical methods will include linear mixed models, multivariate analysis, and machine learning. Training from the NatBrainLab and courses from IOPPN will provide all the essential skills for this project.

Objectives / project plan:

Year 1: Focus on acquiring foundational knowledge in reward circuit neuroanatomy and neurochemistry, along with initial mapping and analysis of datasets.

Year 2: Perform cross-sectional and longitudinal analysis of PwHD.

Year 3: Comparative and multivariate analyses to identify distinct white matter changes profiles in the reward circuit across HD and MDD.

Two representative publications from supervisors

Publication 1:  Dell’Acqua, F., & Tournier, J. D. (2019). Modelling white matter with spherical deconvolution: How and why? NMR in Biomedicine, 32(4), 1–18.

Publication 2:  van Wamelen, D.J., & Aziz, N.A., 2021. Hypothalamic pathology in Huntington disease. Handbook of clinical neurology, 182, 245-255.

Maudsley BRC research themes

• Psychosis and Mood Disorders
• Neuroimaging

Supervisors

Dr Antonio Valentin Huete
Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience   
Email: Antonio.valentin@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/antonio.valentin

Dr Verity McClelland
Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience
Email: verity.mcclelland@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/verity.mcclelland

Project Details

Background:  Dystonia/dyskinetic cerebral palsy (DCP) are complex, severely disabling movement disorders with no cure, in which individuals suffer painful involuntary muscle contractions, twisting movements and abnormal postures. Pharmacological and invasive therapies have varying levels of effectiveness and are limited by adverse effects, so there is an urgent clinical need to develop alternative, more personalised interventions.

Movement-related modulation of the specific brain rhythm “mu” is impaired in dystonia/DCP, indicating abnormal cortical sensorimotor processing¹.  Mu modulation also occurs with motor imagery, a cognitive strategy employed to improve motor function², raising the exciting possibility that enhancing mu modulation via neurofeedback could have therapeutic value in this population. EEG-based BCIs have shown emerging evidence of clinical benefit as non-invasive rehabilitation interventions in adult stroke over the last decade³, although research in other neurological motor disorders or in children is sparse³. There is a critical need for improving BCI technology, usability and accessibility so these systems can be harnessed for neurorehabilitation in paediatric patients with dystonia/DCP.

This PhD project is associated with an on-going case-control study investigating whether children with dystonia/DCP can enhance their mu modulation if provided with EEG-based neurofeedback. (Sample size: 50 patients; 40 controls. Age 5-25 years).

Primary aims:  To improve the BCI technology by advancing intuitive interfaces, employing gamification and refining EEG parameters for optimal and individualised neurofeedback. Additional aims include evaluating the impact of these alterations, as well as the role of cognitive strategies, on BCI user engagement and performance. This work will contribute to the above study, facilitating subsequent development of the paradigm for future clinical trials.

Planned research methods and training provided:  The candidate will receive training in recording and advanced analysis of scalp EEG, statistical analysis, scientific writing and presentation skills. They will work directly with children/young people with movement disorders and their families and will learn to deliver cognitive therapies including guided discovery and Cognitive Orientation to daily Occupational Performance.

Objectives / project plan:

Year 1: Training in EEG data collection/analysis; developing BCI gamification and performance measures; exploring and refining EEG parameters (e.g. peak frequency, topography, temporal characteristics); cognitive strategy training.

Year 2: Conduct EEG spectral, connectivity⁴ and statistical analyses; continue refining BCI paradigm and performance measures,.

Year 3: Continue data analysis; optimise EEG parameters for personalised neurofeedback.

Other notable aspects of the project:

• Collaborating with a multi-disciplinary clinical paediatric team, neurophysiologists, computational neuroscientists and biomedical engineers, both nationally and internationally.

References

1. McClelland VM, Fischer P, Foddai E, et al. EEG measures of sensorimotor processing and their development are abnormal in children with isolated dystonia and dystonic cerebral palsy. Neuroimage Clinical. 2021; doi.org/10.1016/j.nicl.2021.102569
2. Butchereit K, Manzini M, Polatajko HJ, Lin JP, McClelland VM, Gimeno H. Harnessing cognitive strategy use for functional problems and proposed underlying mechanisms in childhood-onset dystonia. Eur J Paediatr Neurol. 2022;41:1-7. doi:10.1016/j.ejpn.2022.08.007
3. Behboodi A, Lee WA, Hinchberger VS, Damiano DL. Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review. Journal of neuroengineering and rehabilitation. 2022;19(1):104. doi:10.1186/s12984-022-01081-9
4. Sakellariou DF, Dall'Orso S, Burdet E, Lin JP, Richardson MP, McClelland VM. Abnormal microscale neuronal connectivity triggered by a proprioceptive stimulus in dystonia. Scientific reports. 2020;10(1):20758. doi:10.1038/s41598-020-77533-w

Two representative publications from supervisors

Publication 1:  McClelland VM, Fischer P, Foddai E, Dall’Orso, S, Burdet E, Brown P, Lin JP.  EEG measures of sensorimotor processing and their development are abnormal in children with isolated dystonia and dystonic cerebral palsy. Neuroimage: Clinical 2021 30:102569. https://doi.org/10.1016/j.nicl.2021.102569

Publication 2:  McClelland VM, Valentin A, Rey HG, Lumsden DE, Elze MC, Selway R, Alarcon G, Lin JP.  Differences in globus pallidus neuronal firing rates and patterns relate to different disease biology in children with dystonia. Journal of Neurology, Neurosurgery and Psychiatry 2016; 87(9):958-67.

Maudsley BRC research themes

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

Supervisors

Dr Jonathan O’Muircheartaigh
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience   
Email: jonathanom@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/jonathanom

Dr Tomoki Arichi
School of Biomedical Engineering and Imaging Sciences, King's College London
Email: tomoki.arichi@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/tomoki.arichi

Dr Chiara Casella
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience
Email: chiara.casella@kcl.ac.uk    Website: https://kclpure.kcl.ac.uk/portal/en/persons/chiara.casella

Project Details

Background:  The highly choreographed program of prenatal brain development sets the stage for later life. Alterations in this process correlate with impaired cognitive and behavioural and neurological outcomes. Early adversities/insults to this process can include extrinsic factors to the fetus like [1] maternal immune activation, [2] direct viral infection of the fetus and intrinsic factors like [3] genetic or chromosomal anomalies (amongst many others). All have been associated with behavioural, cognitive or neurological neurodevelopmental conditions (NDCs). The occurrence of common NDCs with these varied risk factors may be underlined by similar abnormal development in cortical networks.

Novelty and importance:  Ultra-high field (7T) MR imaging offers greatly enhanced signal to noise ratio compared to standard MRI systems, enabling acquisition of high resolution and contrast data with significantly higher sensitivity. Our ongoing 7T epilepsy study and the newly-funded Gen2020 study (focusing on children born during the initial COVID-19 pandemic) use state-of-the-art advanced imaging at 7T, offering a unique way to interrogate distinct fetal risk factors for common outcomes. Crucially, the enhanced sensitivity allows study of subcortical structures like the thalamus are highly sensitive to changes in early development (implicated in preterm birth and epilepsies for example) but the structure itself is complex, with many small nuclei connected to diverse cortical networks.

Primary aims: 

1. Delineate subcortical/cortical brain networks using local and publicly available 7T MRI.
2. Identify the influence of prenatal cortical malformations on subcortical-cortical networks.
3. Explore the effect of prenatal maternal infection with COVID-19 on childhood brain network development, examining associations between inflammatory markers at birth (maternal and infant) with brain outcome.

Study design and sample size:  

Cross sectional, cross-disorder design involving two ongoing studies: childhood focal epilepsy (n=40, collection ongoing) and Gen2020 COVID-19 (n=50, to be collected).

Planned research methods and training provided:  Analysis of very high-resolution imaging data, advanced imaging statistics, data-driven and supervised methods to extract important features from neuroimaging data, Shell scripting (bash, python). Training will be provided by research supervisors, collaborators and their teams.

Objectives / project plan:

Year 1: MRI safety, child and adult consent and assent training. Image analysis pipeline development for thalamic network analysis.

Year 2: Collection of 7T MRI data in children with maternal COVID-19 exposure. They will investigate associations with birth inflammatory markers.

Year 3: Compare network connectivity in epilepsy and COVID-19-exposed children, correlating with developmental outcomes.

Two representative publications from supervisors

Publication 1:  Casella, C., Vecchiato, K., Cromb, D., Guo, Y., Winkler, A. M., Hughes, E., ... & O’Muircheartaigh, J. (2023). Widespread, depth‐dependent cortical microstructure alterations in paediatric focal epilepsy. Epilepsia.

Publication 2:  Bridgen, P., Tomi-Tricott, R., Uus, A., Cromb, D., Quirke, M., Almalbis, J., …& Arichi T. (2023). High resolution and contrast 7 Tesla MR brain imaging of the neonate. Frontiers in Radiology.

Maudsley BRC research themes

• Child Mental Health and Neurodevelopmental Disorders
• Neuroimaging