Research
Employing a deep phenotyping approach, we integrate polygenic predictors with an extensive battery of assessments through computational psychiatry. Our toolkit includes behavioral testing, psychometry, cutting-edge brain imaging, genetics and multi-OMICs profiling, allowing us to delve deep into the multifaceted dimensions of mental health.
Through longitudinal studies embracing diverse young individuals from both the general and neurodivergent populations, we aim to unravel the intricate connections between genetics, biology, and behavior within the context of psychopathology.
Risk Stratification
A primary objective is to showcase that layers of biological information can be utilized to stratify the general population according to mental health status.
Trait Specification
We dissect the polygenic risk architecture of diagnostic entities into biological sub-components, enabling the identification of trait-specific signatures by assessing associated phenotypic characteristics.
Psychiatric Prediction
Capitalizing on the multimodal aspect of our data through multilayer integration and network-based approaches, we are identifying complex signatures associated with risk of mental health conditions. Prospective mental health information is integrated into machine-learning models for psychiatric prediction.
Data Resources
Much of our research relies on a unique and extensively characterized cohort, the CA18106 cohort, which serves as a pivotal data resource. Originally established for consciousness research through a European collaboration, this cohort provides a representative cross-section of the general young population around the mean age of onset of prevalent mental disorders.
Comprising approximately 900 research volunteers aged 18-40 (mean age ~23) from Denmark (DK), Poland (PL), and the Czech Republic (CZ), each participant has undergone comprehensive psychometric evaluations, behavioral examinations, and detailed MRI brain imaging. Genetic profiles have been constructed from DNA donations, with over 90% participation. Additionally, a subset has provided fasting-state blood for metabolomics, immunomics, and proteomics profiling.
While individuals currently undergoing treatment for a mental disorder were excluded, past diagnoses were recorded by ~13% of participants, reflecting the population average in this age category. In the Danish subset, detailed data including diagnoses, socioeconomics, prescriptions, demographics, etc., are accessible through personal identifiers (CPR) in Danish registries.
We are currently preparing a 5-year follow-up study utilizing questionnaire-based assessments to further understand mental health trajectories within the CA18106 cohort.