Refining the construct of resilience and identifying neural markers of resilience in young people

Child and adolescent mental health is a growing concern. Adolescence in particular is a period in which an individual goes through both rapid and extensive developmental change, and is often the point at which psychopathology can emerge (Dahl & Gunnar, 2009). Although childhood and adolescent mental health has been the subject of extensive scientific research, there is an emerging paradigm shift in psychiatry and clinical psychology, whereby researchers are now beginning to believe that it is just as important to understand resilience as mental illness.

Resilience is a very complex and multidimensional phenomenon, and there is a lack of consensus regarding how to operationalise or measure it. However, it is based on the observation that many people maintain positive mental health despite exposure to stressors or even traumatic events (Kessler et al., 1995). Despite the huge potential that resilience research has, its utility is currently limited by a number of challenges. For example, research within this field often fails to distinguish between different resilience mechanisms and often adopts very narrow definitions of resilience, such as not developing PTSD following trauma exposure. Additionally, there is also no consensus on how to detect or measure resilience. For example, the effect of the timing of negative life events (e.g. infancy vs. adolescence) and duration of exposure to negative life events is often not considered. Furthermore, assumptions are often made within the literature; e.g., there is a cumulative effect of negative life events and all negative life events can be treated as equivalent (e.g. physical abuse vs. bereavement).

The overarching aim of this PhD is to refine the construct of resilience, in order to move the field forward and maximise the usefulness of the construct, and to identify structural neural markers of resilience in young people. In order to achieve this, we will first aim to refine the construct of resilience by applying advanced computational methods to data collected in the FemNAT-CD study. Once these novel definitions and classes of resilience have been identified, we will aim to identify structural neural markers of resilience in young people, again using data from the FemNAT-CD study (our discovery sample). Finally, we will then aim to replicate and extend our earlier findings using a replication sample (such as from the ALSPAC study).

A More Comprehensive Outline of Study 1:

Advanced computational methods will be applied to a large European dataset containing clinical, environment and demographic data collected from children and adolescents who took part in the FemNAT-CD study, which investigated the neurobiology and treatment of female Conduct Disorder. Applying these methods to this dataset will allow us to use data-driven pattern recognition methods to identify boundaries on the continua of mental health through to mental disorder and adversity through to privileged conditions. This will enable us to identify the underlying dimensions of adversity and psychopathology that give rise to resilience. We will then apply constrained clustering approaches to identify classes of resilience, which we will relate to structural MRI data in the latter stages of the PhD.