Priestley PhD Scholarship in Youth Mental Health: Resilience in adolescence following childhood mal

United Kingdom
Feb 13, 2018
Mar 16, 2018
Organization Type
University and College
Full Time

Project Supervisors:
Dr Stephane De Brito, School of Psychology, University of Birmingham
Professor Peter Tino, Computer Science, University of Birmingham
Professor Stephen Wood, Orygen, Melbourne

Project description:
Adolescence represents a transition period from childhood to adulthood, during which many changes are experienced concomitantly, including physical maturation, drive for independence, increased salience of social and peer interaction, cognitive and brain development(1). Some adolescents are prone to risky behaviours and development of mental health disorders (MHD). Adolescents who have experienced childhood maltreatment (CM), as compared to those who have not, are at an increased risk of MHD in adolescence and young adulthood(2). Given the dramatic increase and the chronic course of adolescent onset MHD(3), it is important to enhance our understanding of resilience to promote mental well-being in adolescents and young adults. Resilience is best conceptualized as a developmentally dynamic and multidimensional process(4). Within the framework of mental well-being, psychological resilience is defined as an individual’s attainment of positive adaptation and competent functioning within the context of significant adversity.

There are, however, two major gaps in our knowledge.
1. A consensus on how to define, detect and measure resilience is still lacking(5). Studies across the literature have imposed diverse strategies on a limited number of dimensions of competence and psychopathology, have largely failed to account for the timing and duration of exposure to CM, and implicitly assume that the impact of different forms of CM is cumulative.
2. There is limited knowledge of the brain structures and processes that characterise resilient youths(6,7)

The proposed PhD has two main aims:
Aim 1: Refining the boundaries of resilience. The student will exploit the FemNAT-CD dataset, an existing EU-funded study on 1,840 European children and adolescents, including >900 mentally healthy individuals, to explore the full range of effective degrees of freedom present in multi-domain measures of psychopathology, and exposure to CM. This will result in a higher-dimensional definition of resilience. First, detailed quantitative clinical and CM data (including timing and duration), age and gender, will be fed into data-driven (i.e., theoretically agnostic) pattern recognition methods to define new groupings/phenotypes in the continua of mental health (from normal through to disorder) and CM (from no CM to high CM). The student will then combine these newly found phenotypes into resilient and non-resilient classes that can be further operationalized in this and other studies. The student will generate both person-centred (categorical; how do resilient individuals differ from non-resilient?) and variable-centred (dimensional; very resilient through to low resilient) measures and approaches.

Aim 2: Applying the resilience construct to neuroimaging data from the FemNAT-CD sample. The novel classes of resilient phenotypes and resilience processes will be employed to analyse the neuroimaging data from FemNAT-CD (structural and functional; tasks: emotion regulation, reward processing). The student will employ state-of-the-art machine learning methods for analysing and modelling neuroimaging data to identify structural and functional neuroimaging markers and networks that accompany resilience processes and phenotypes. As both males and females are included in FemNAT-CD and the age range spans 9-18 years, the student will be able to test for similarities and differences in the mechanisms of resilience across gender and different developmental phases (childhood through to adulthood).

Interdisciplinarity is at the core of the project, which will combine methods from psychology, neuroimaging, and computer/data science. The project is now possible because of the recent and rapid technological advancements in the field of neuroimaging and computer/data science, which together will put this project at the forefront of resilience research. As such, the student will be trained in methods from developmental psychopathology, advanced structural/functional neuroimaging and computer science. Dr Stephane De Brito has extensive expertise in structural and functional magnetic resonance brain imaging techniques to better understand patterns of resilience and vulnerability in children and adults who have experienced early adversity. Prof. Peter Tino, who is a world expert on machine learning and its interdisciplinary applications, is currently co-supervising a MIBTP PhD student with Dr De Brito and has a proven track record of successful collaborations with other colleagues in Psychology). Professor Stephen Wood is a world expert on youth mental health and has already co-supervised a PhD student to completion with Dr De Brito.

Funding Notes

The IMH at the University of Birmingham was established in August 2017, with a focus on inter-disciplinary approaches to youth mental health. The IMH was developed in collaboration with colleagues in Melbourne, and we have four PhD scholarships, to be awarded 1/year for the next four years, commencing September 2018. We are awarding one scholarship this year, and advertising three projects. Each scholarship is for four years with the expectation that the student spends at least one year in Melbourne. These awards are part of the wider Priestley joint PhD programme between the Universities of Birmingham and Melbourne.


1. Crone and Dahl, (2012) Nat Rev Neurosci. 13: 636-50.
2. McCrory and Viding, (2015) Dev Psychopathol. 27: 493-505.
3. Costello, et al., (2016) Soc Psychiatry Psychiatr Epidemiol. 51: 639-46.
4. Cicchetti and Blender, (2006) Ann N Y Acad Sci. 1094: 248-58.
5. Davydov, et al., (2010) Clin Psychol Rev. 30: 479-95.
6. Teicher, et al., (2016) Nat Rev Neurosci. 17: 652-66.
7. Burt, et al., (2016) J Child Psychol Psychiatry. 57: 1287-1296.