Our brain develops in a complex interplay between genetic and environmental factors. Improved insight into this developmental process can help us understand why we develop mental health problems.
Many mental disorders emerge in childhood and adolescence. These are also periods of life when substantial changes in the brain's structure and functioning take place. In this research group we study the developmental processes of the brain and how these interact with genes and the environment. We are particularly interested in three factors important to the lives of children and youth:
- The socioeconomic and environmental conditions in which they are raised.
- New demands and challenges associated with adolescence, such as increasing independence from parents and complexity in relationships with peers.
- Subtle sex differences in brain development.
Increased insight into the neurocognitive developmental processes can provide us with a better understanding of the mechanisms behind the development of mental health, and thereby contribute to improve effective preventive measures and interventions. This can improve the mental health of children and adolescents.
Our research questions:
- How is brain and cognitive development connected to the development of mental health and wellbeing?
- How do genetic, socioeconomic, environmental and psychosocial factors affect our neurocognitive development and lead to increased risk or resilience towards mental health problems?
- Can subtle sex differences in brain and brain development help us understand sex differences in the prevalence, clinical presentation and course of mental health disorders?
How we work:
We study the development of the brain’s structure and functioning through brain imaging techniques, mainly magnetic resonance imaging (MRI) and electroencephalography (EEG). These are used both in our own longitudinal studies where the same individuals are followed over time as well as in large, open international datasets that we make use of. The brain imaging data is analysed using advanced software and combined with computational methods to identify patterns in data and structural equation modeling.