Maria Jelen

PhD thesis: Examining whole-brain functional connectivity in relation to normative neurodevelopment and transdiagnostic dimensions of psychopathology

My doctoral work aims to enhance understanding of neuro- and psychopathology in large developmental cohorts using neuroimaging and machine learning methods. To achieve this, we will apply a variety of computational techniques to assess the normative range of functional connectivity development, which appreciates the high interpersonal diversity in neurocognition and the vastly different experiences young people have. We aim to take a transdiagnostic approach, looking at continuous dimensions of symptoms rather than strict diagnostic categories, which often overlap and do not sufficiently capture individual differences. We also aim to investigate whether deviation from normative brain development can be used to predict symptoms of psychopathology or neurodiversity, in an attempt to find what promotes vulnerability and resilience and better support developing youth.

Who or what inspired you to pursue your research interests?

Psychopathology affects many of us, regardless of age, but it often emerges in childhood and adolescence. The experience of myself and my loved ones, as well as practice working with young people experiencing mental health, neurodevelopmental and learning difficulties has shown me how much more there is to be done to understand and support young people’s neural and emotional development. My interests in machine learning and data science stem, in turn, from my inspiring research experiences at UCL and Yale University, where I was able to discover the technology and creativity researchers can use to help understand the developing brain.