PhD Studentship in Child Mental Health and Neurodevelopmental Disorders

Two projects are available to choose from.  When applying for this studentship, applicants should upload an A4 document with their application, describing their interests and why they wish to apply for their chosen project.

Project 1:  Towards precision mental health care: prediction of the lifetime course of major depressive disorder and ADHD in children and young people. 

Supervisors

Dr Gonzalo Salazar de Pablo
Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email:  gonzalo.salazar_de_pablo@kcl.ac.uk
Website:  https://kclpure.kcl.ac.uk/portal/en/persons/gonzalo.salazar_de_pablo

Professor Katya Rubia
Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email:  katya.rubia@kcl.ac.uk
Website: Katya Rubia - King's College London

Dr Johnny Downs,
Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email:  johnny.downs@kcl.ac.uk
Website:  https://kclpure.kcl.ac.uk/portal/en/persons/johnny.downs

Project Description

Background:  In C&A, MDD is often associated with long-term adverse outcomes. Treatment guidelines do not consider likely future trajectory, which stems from the current inability to predict whether a C&A person with MDD will have symptoms that persist, will show a relapsing-remitting course or experience a full recovery. 

This PhD aims to advance the field by answering two empirical questions. First, can we provide a clinically useful prognostic tool by leveraging clinical data linked to socio-environmental and educational databases?  Secondly, does the addition of either genomic features or data on childhood brain structure and function further boost the accuracy of prediction?  A clinically useful prognostic tool would represent a major step towards precision mental health care.

We will leverage new ‘child friendly’ approaches to mapping brain function that allow us to include children who often cannot tolerate conventional MRI, such as children with severe hyperactive symptoms.  These technologies, available at the new Clinical Research Centre of the Pears Maudsley Centre include optically-pumped magnetoencephalography (OPM-MEG) and functional near-infrared spectroscopy (fNIRS).  We will acquire maps of brain function and connectivity using these technologies and determine if they reveal similar predictors of the course of ADHD symptom as those revealed by conventional MRI.  

Aims:  To advance precision mental health for children and adolescents (C&A) with major depressive disorder (MDD) and ADHD by developing prognostic tools to predict the likely course C&A with MDD.

1) Identify socio-demographic and clinical features in C&A experiencing MDD that predict future trajectories of MDD.

2) Evaluate whether the integration of neurobiological predictors increases the accuracy of prediction models.

3)  Explore if novel methods of mapping brain function and connectivity that are more ‘child-friendly’ than conventional magnetic resonance imaging (MRI), can identify predictors of ADHD course.

Methods and project plan:  

WP1: Identify features that predict the course of MDD. We will use SLaM data for internal validation and Adolescent Brain and Cognitive Development (ABCD) cohort for external validations (including around 1,000 C&A with MDD and ≥4 observations). Risk prediction models will be developed.

WP2: Integrate neural features into predictive models and determine if the addition of genomic and/or childhood neural features boosts predictive power. Discovery cohort will be tABCD cohort and cohorts based in Canada, Brazil and the UK will be used for external validation.

WP3: Leverage novel imaging tools to map neural circuitry in those with ADHD, determining if we can detect similar predictors of outcome as those revealed by conventional MRI.  We will use technologies that allow some degree of movement, such as OPM- Magnetoencephalography and fNIRS.

Expected outputs:  

At least four publications:

A/WP1:  Manuscript 1: Systematic review/meta-analysis of putative risk factors for MDD trajectories in C&A.

B/WP1 & 2:  Prognostic models for outcomes of C&A with MDD (persistent, relapsing and remitting) based on deep clinical and contextual phenotyping.  A manuscript exploring the addition of genomic and neural data.

C/WP3:  Pilot data collected using novel multimodal methods on C&A, with ADHD and initial exploration of presence of predictors, drawing contrasts with those identified using conventional MRI.

Key references:

• American psychiatric association (2013). Diagnostic and statistical manual of mental disorders., washington dc,.
• Fergusson, d. M., boden, j. M. & horwood, l. J. (2007). Recurrence of major depression in adolescence and early adulthood, and later mental health, educational and economic outcomes. Br j psychiatry, 191, 335-342.
• Fernandes, b. S., williams, l. M., steiner, j., leboyer, m., carvalho, a. F. & berk, m. (2017). The new field of 'precision psychiatry'. Bmc med, 15, 80.
• Fusar-poli, p., hijazi, z., stahl, d. & steyerberg, e. W. (2018). The science of prognosis in psychiatry: a review. Jama psychiatry, 75, 1289-1297.
• Khanra, s., khess, c. R. J. & munda, s. K. (2018). "precision psychiatry": a promising direction so far. Indian j psychiatry, 60, 373-374.
• Lueken, u., straube, b., yang, y., hahn, t., beesdo-baum, k., wittchen, h. U., konrad, c., ströhle, a., wittmann, a., gerlach, a. L., pfleiderer, b., arolt, v. & kircher, t. (2015). Separating depressive comorbidity from panic disorder: a combined functional magnetic resonance imaging and machine learning approach. J affect disord, 184, 182-192.
• Mullen, s. (2018). Major depressive disorder in children and adolescents. Ment health clin, 8, 275-283.
• National institute for health and care excellence (nice) (2017). Depression in children and young people: identification and management. [nice guideline ng134]. Retrieved from https://www.nice.org.uk/guidance/ng134.
• Parker, g., mccraw, s. & hadzi-pavlovic, d. (2015). The utility of a classificatory decision tree approach to assist clinical differentiation of melancholic and non-melancholic depression. J affect disord, 180, 148-153.
• Redlich, r., almeida, j. J., grotegerd, d., opel, n., kugel, h., heindel, w., arolt, v., phillips, m. L. & dannlowski, u. (2014). Brain morphometric biomarkers distinguishing unipolar and bipolar depression. A voxel-based morphometry-pattern classification approach. Jama psychiatry, 71, 1222-1230.
• Salazar de pablo, g., studerus, e., vaquerizo-serrano, j., irving, j., catalan, a., oliver, d., baldwin, h., danese, a., fazel, s., steyerberg, e. W., stahl, d. & fusar-poli, p. (2021). Implementing precision psychiatry: a systematic review of individualized prediction models for clinical practice. Schizophr bull, 47, 284-297.
• Terry, s. F. (2015). Obama's precision medicine initiative. Genet test mol biomarkers, 19, 113-114.
• Wolff, r. F., moons, k. G. M., riley, r. D., whiting, p. F., westwood, m., collins, g. S., reitsma, j. B., kleijnen, j., mallett, s. & group†, p. (2019). Probast: a tool to assess the risk of bias and applicability of prediction model studies. Ann intern med, 170, 51-58.

Project 2:  Identifying ‘biotypes’ to guide treatment choice in ADHD

Supervisors

Dr Nicoletta Adamo
Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email:  nicoletta.adamo@kcl.ac.uk
Website:  https://kclpure.kcl.ac.uk/portal/en/persons/nicoletta.adamo

Professor Gustav Sudre
Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience
Email:  gustavo.sudre@kcl.ac.uk
Website:  https://kclpure.kcl.ac.uk/portal/en/persons/gustavo.sudre

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/en/persons/chiara.nosarti

Project Description

Background:  This project aims to identify distinct profiles ('biotypes') that predict treatment response in childhood ADHD, improving upon the current 'one size fits all' approach. The goal is to match each child to their optimal treatment based on clinical, cognitive, and neural features. Current ADHD treatment approaches have remained largely unchanged for 30 years, relying on trial and error to find effective interventions. Accurate prognostic models for ADHD which could assist clinicians in the treatment selection are urgently needed.

Aims:  

1. Analyse clinical and psychosocial predictors of ADHD treatment response using NHS mental health care data
2. Identify neural circuit patterns that predict response to different ADHD treatments
3. Evaluate new 'child-friendly' brain mapping methods as alternatives to conventional MRI to identify neural biotypes

Methods and project plan

The project comprises 3 work packages (WP):

WP1:  Analysis of NHS mental health records from South London to examine associations between treatment outcomes and various patient characteristics using regression models and statistical learning approaches.

WP2:  Use existing MRI data to detect distinct neural circuit alterations ('biotypes') in ADHD patients and test if these predict treatment response, utilizing AI/machine learning and clustering approaches. 

WP3:  Pilot study of approximately 130 youth with severe ADHD using newer, more child-friendly imaging technologies (OPM-MEG and fNIRS) to map neural circuitry and compare findings with conventional MRI results.

Expected outputs

WP1 (months 1-4): systematic review and meta-analysis of biotyping approaches used in child psychiatry.

WP1 (months 4-8): manuscript on the treatment response prediction analyses conducted on anonymized mental health records.

WP2 (months 9-12): a scoping literature review to identify the target neural circuits grounded in existing neurobiological models of ADHD. 

WP2 (months 13-16): manuscript on the biotypes identified within those diagnosed with ADHD.

WP2 (months 16-24): manuscript on the relationship between biotypes and treatment responsivity.

WP3 (months 24-36): pilot data collected using novel multimodal methods on children and adolescents with ADHD and initial exploration of the presence of biotypes, drawing contrasts with those identified using conventional MRI in WP2.

Key references:

• Brookes, M. J., Leggett, J., Rea, M., Hill, R. M., … Bowtell, R. (2022). Magnetoencephalography with optically pumped magnetometers (OPM-MEG): the next generation of functional neuroimaging. Trends Neurosci, 45(8), 621-634.
• del Campo, N., Chamberlain, S. R., Sahakian, B. J., & Robbins, T. W. (2011). The Roles of Dopamine and Noradrenaline in the Pathophysiology and Treatment of Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 69(12), e145-e157. https://doi.org/10.1016/j.biopsych.2011.02.036
• Faraone, S. V., Banaschewski, T., Coghill, D., Zheng, Y.,. . . Wang, Y. (2021). The World Federation of ADHD International Consensus Statement: 208 Evidence-based conclusions about the disorder. Neurosci Biobehav Rev, 128, 789-818. doi:10.1016/j.neubiorev.2021.01.022
• Gallagher, A., Wallois, F., & Obrig, H. (2023). Functional near-infrared spectroscopy in pediatric clinical research: Different pathophysiologies and promising clinical applications. Neurophotonics, 10(2), 023517. doi:10.1117/1.NPh.10.2.023517

Award

Funding is available for:

• One 3-year PhD studentship to commence October 2025.

The award covers:

• Full Home rate tuition fees;
• A stipend (currently £21,237 per year);
• A contribution towards training, conference attendance and research costs.

Overseas applicants may apply but will need to cover the difference in tuition fees.

Entry Requirements

The programme is very competitive and applicants must have, or be predicted to obtain at least a 2.1 or equivalent overseas qualification in a relevant science subject. 

Students who do not meet the above primary criteria may apply if they satisfy the following conditions:

• A 2.2 BSc degree and a MSc in a relevant subject with Merit or Distinction.
• A 2.1 or higher non-science first degree (BA) and a MSc in a relevant subject with Merit or Distinction.

English Language

Applicants for these studentships must meet the King's College London Band D English language requirements.  Any offer you are made will be conditional upon you meeting these requirements prior to enrolment.

Further information is available on the King's College London English language entry requirements page. 

Strengthening our commitment to equality, diversity and inclusion (EDI)

In line with the NIHR's national strategy we aim to:

• foster an inclusive environment
• engage the talents and energy of diverse people in all areas of our work
• improve the relevance and quality of our research

To do this we will widen access for greater diversity and inclusion of applicants across under-represented groups.  This includes, but is not restricted to gender and sexuality, diverse racial and ethnic groups, people from different geographical regions of varied socio-economic backgrounds, people with disabilities or lived experience of mental health issues.

People who have used mental health services are actively encouraged to apply.

You will benefit from the wide range of training and support offered by King's College London to its postgraduate research students.

Maudsley BRC students are members of the NIHR Academy and have access to resources and training opportunities provided nationally through the Academy and wider NIHR infrastructure.