Approved Research

An Integrative Approach to Predict Mental Health Outcomes: The Contribution of Genetics, Brain Architecture, and Behavior

Lay summary

Psychotic and mood disorders are complex and challenging to understand due to their varied symptoms and complex genetic architecture. However, large-scale datasets like the UK Biobank provide an excellent opportunity to study the genetic factors associated with these disorders. Previous research has shown that genetic risk in non-coding areas of the genome can influence gene expression across different brain regions, leading to observable effects on behavior and neurophysiological correlates.

This project, estimated to last three years, aims to build on that knowledge by assessing variability in structural and functional brain architecture using Magnetic Resonance Imaging (MRI)-derived estimates. The goal is to investigate how these estimates might predict mental health-related outcomes.

Considering the significant role of sociodemographic, psychosocial, cognitive, physical health, and lifestyle factors as risk factors for psychotic and mood disorders, this project aims to leverage all of these measures to develop a comprehensive understanding of the genetic contributions to these disorders.

Specifically, the project will focus on identifying gene co-expression pathways that underlie region-specific brain characteristics. By computing individual parsed polygenic co-expression indices, the project will explore the relationship between gene expression and structural and functional brain characteristics using multiple MRI modalities that capture interindividual brain variability. Additionally, the project will utilize supervised and unsupervised machine learning algorithms to define individual profiles based on the relationship between cognitive and gene-brain features. These profiles will then be used to predict mental health-related outcomes.

The findings of this project will be disseminated through open-access platforms, allowing for replication and dissemination of the results. The extensive and valuable data available in the UK Biobank makes it an ideal dataset for this project, which aims to contribute to the growing field of personalized medicine in mental health care.