About the Project

Objectives

Objective 1 – Identification of adverse environmental signatures, their interaction with genetics and their relationship with brain and behaviour in citizens and patients (WP1, 2, 3, 4, 6, 12)
  • Development of a prediction model for environmental impact on brain and mental health by relating transdiagnostic symptoms of mental illness from federated big cohorts through geoposition with environmental measures of urbanicity, climate/atmosphere and psychosocial stress in response to the pandemic (Figure 5) using innovative techniques, such as remote sensing satellite data, climate modelling, and digital health applications.
  • Identification of structural and functional brain features related to the environmental signatures and behavioural symptom clusters identified.
  • Characterisation of at-risk individuals by analysing gene ´ environment interactions and other potential risk factors such as age, existing mental illness and social deprivation.
Objective 2 – Characterization of molecular, neurobiological and cognitive mechanisms underlying the brain and behavioural changes related to environmental adversity (WP4, 5)
  • Characterisation of molecular signatures and biochemical pathways associated with the structural and functional brain networks and behavioural changes associated with environmental adversity and resilience through large scale -omics analyses in deep phenotyping population-based and clinical cohorts (e.g. IMAGEN and STRATIFY). These analyses involve genetics, epigenetic methylation, gene expression and proteomics that are analysed using normative modelling and AI-derived deep learning prediction models.
  • Characterisation of perturbations of signalling pathways affected by the -omics signatures identified in peripheral blood to measure the effect on cell morphology, gene expression and translation, neurotransmitter release and electrophysiology in 3D brain organoids (‘assembloids’) that reflect the neuronal composition of the brain network nodes most affected by the environmental factors identified.
  • Development of a biologically-grounded model of the neurobehavioural response to environmental adversity by integrating environmental signatures and results of the 3D organoid experiments in virtual brain models to simulate the changes in brain network activity and behaviour induced by molecular signatures.
Objective 3 – Establishment of quantitative neurobiological biomarkers for prediction and stratification of environmentally-related mental illness (WP1, 2, 3, 4, 5, 6)
  • Development of stratification algorithms of individuals based on multimodal brain imaging biomarkers by estimating normative models integrating the identified environmental and molecular risk signatures across the life-span in the population.
  • Development of validated multivariate environmental risk and resilience biomarkers allowing for dissection of the heterogeneous underlying pathophysiological mechanisms within mental disorders.
  • Development of clinically useful predictive biomarkers for the risk, onset and progression of environmentally-related symptoms of depression, anxiety, stress and substance abuse in clinical populations using federated machine learning techniques that accommodate longitudinal data.
Objective 4 – Development of pharmacological, cognitive and educational interventions targeting molecular and neurobiological mechanisms of environmentally-sensitive symptoms of mental illness (WP 7, 8)
  • Screening for compounds reverting the neuronal phenotype by targeting signalling pathways related to the molecular signatures identified by large scale screening using a fully automated stem-cell based neuronal assay. Compounds that revert the phenotype will be back-tested for their molecular mechanism of action in 3D organoids.
  • Digital health interventions based upon virtual reality (VR) programmes designed to develop adaptive coping strategies to the environmental risk profiles identified. These VR programmes will be transformed into app-based prevention and intervention programmes for widespread dissemination.
  • Dissemination and communication of research outcomes to educate the wider public through apps and virtual media, as well as conferences and seminars targeted to different stakeholders ranging from citizens and patients to decision makers in public health and policy.
Objective 5 – Establishing a programme of responsible research and innovation (WP1,3,6,7,8,9)
  • Maintenance of the societal ‘licence to operate’ of the project by engaging in a two-way dialogue about objectives and design of the project with key stakeholders.
  • Stakeholder participation in research design and development of intervention through user focus groups, as well as s consultation on European and national public health priorities.
  • Critical reflection and consultation on ethical and societal consequences of our research programme through advisory boards, including of experts from social science and humanities.

Project plan and workpackages

Project plan and workpackages

Ambition

Identification of adverse environmental signatures and their effect on brain and behaviour

  • Developing an integrated AI-based platform for spatial epidemiology in mental health that enables the prediction and characterization of individual as well as aggregated impacts of urbanicity, climate/atmosphere and psychosocial stress on brain structure, function and behaviour, with the goal of identifying genetic, behavioural or social risk groups on a population level.

Characterization of molecular, neurobiological and cognitive mechanisms underlying the brain and behavioural changes related to environmental adversity.

  • Discovering the molecular basis of environmentally-related changes in functional and structural brain networks and their relations to symptoms of mental illness.
  • Developing a neurobiological model of mental illness considering the mediating effects of environmental factors using virtual brain simulations.

Establishment of quantitative neurobiological markers for prediction and stratification of environmentally-related mental illness

  • Stratifications of individuals by estimating innovative normative models based on structural and functional brain data with integrated environmental and genetic factors.

Development of pharmacological, cognitive and educational interventions.

  • Identification of pharmacological compounds targeting causal mechanisms of environmentally-induced symptoms of mental illness and characterising their molecular properties in 3D organoids.
  • Using VR to develop an intervention reinforcing adaptive coping to environmental adversity.

Implementing responsible research and innovation

  • Anticipation of possible outcomes, engagement with stakeholders, critical reflection
  • Application of participatory methods at various stages
  • Societal impact with humanities and social sciences

Identification of adverse environmental signatures and their effect on brain and behaviour

  • Developing an integrated AI-based platform for spatial epidemiology in mental health that enables the prediction and characterization of individual as well as aggregated impacts of urbanicity, climate/atmosphere and psychosocial stress on brain structure, function and behaviour, with the goal of identifying genetic, behavioural or social risk groups on a population level.

Characterization of molecular, neurobiological and cognitive mechanisms underlying the brain and behavioural changes related to environmental adversity.

  • Discovering the molecular basis of environmentally-related changes in functional and structural brain networks and their relations to symptoms of mental illness.
  • Developing a neurobiological model of mental illness considering the mediating effects of environmental factors using virtual brain simulations.

Establishment of quantitative neurobiological markers for prediction and stratification of environmentally-related mental illness

  • Stratifications of individuals by estimating innovative normative models based on structural and functional brain data with integrated environmental and genetic factors.

Development of pharmacological, cognitive and educational interventions.

  • Identification of pharmacological compounds targeting causal mechanisms of environmentally-induced symptoms of mental illness and characterising their molecular properties in 3D organoids.
  • Using VR to develop an intervention reinforcing adaptive coping to environmental adversity.

Implementing responsible research and innovation

  • Anticipation of possible outcomes, engagement with stakeholders, critical reflection
  • Application of participatory methods at various stages
  • Societal impact with humanities and social sciences

Project Phases and Timelines

Project Phases and Timelines

International Collaboration

This project aims to leverage the substantial investments made in the last 15 years to recruit and characterize large scale, population-based national cohorts and national registries, as well as comprehensively characterized deep phenotyping samples. These massive efforts have laid the groundwork for a transformation in the approach for big data science and the investigation of molecular and cognitive mechanisms underlying mental health, and their relation to environmental factors. Rather than recruiting new cohorts it is now possible to combine and enrich existing cohorts with pertinent data, thus augmenting interoperability, creating novel resources and generating research results at a fraction of the time and cost of de novo acquisitions. Based on these premises, we include several national and international R&I activities in this proposal: (i) The UKBB, the German NAKO and the Norwegian registry as core population-based cohorts to measure the relation of adverse environmental factors with mental health. These data, including exact geopositions, are accessible to the scientific community following a defined application procedure. Our group has used all pertinent UKBB data before and we have discussed and agreed upon our research approach with a representative German NAKO, Professor Tobias Pischon in Berlin. There are exiting, well established research links and several previous research projects our partner UO (Andreasen, Westlye) have established with Scandinavian cohorts. The German NAKO and Scandinavian registries allow for recontacting of participants for digital health assessments, something which is not usually possible in UKBB. (ii) The EC-funded COVIDMENT cohort is designed to significantly advance current knowledge of mental morbidity trajectories in the COVID-19 pandemic by utilizing large, data-rich population-based registry resources, biobanks, and ongoing questionnaire data with longitudinal follow-up (est. >23 million individuals; of which >800.000 with confirmed COVID-19 infection), biobanks (est. >500.000 individuals), and new COVID-19 cohorts with questionnaire data (est. > 250.000 individuals) from the Nordic countries and Estonia.  (iii) Data from these cohorts will be federated using ‘Trygge’ a software solution developed at UO as part of an EC- H2020 funded research project CoMorMent (https://www.comorment.uio.no). (iv) Deep phenotyping and molecular analyses will be carried out in the European IMAGEN cohort, initially funded by an FP7 Innovative Project grant and the European clinical STRATIFY study funded by an ERC advanced grant.

Complementing recruitment and assessments of patients and by harmonizing the STRATIFY protocol to the IMAGEN protocol, we are able to use IMAGEN to identify a group of individuals at the extreme end of a clinically-relevant phenotype in early adulthood and determine whether the biological signatures in those individuals correspond to signatures identified in  clinical samples across age groups and mental disorders. Moreover, backwards translation of the clinically-derived biological markers is also supported. This enables the development of interventions that target specific neurobehavioural phenotypes, thereby facilitating more efficient and cost-effective public health programs. (v) Our associate partner, Professor Paul Thompson leads the ENIGMA consortium (www. http://enigma.ini.usc.edu/), the largest imaging genomics initiative globally to understand brain structure, function, and disease. Several members of the project team are leading or participate in ENIGMA working groups. Analysis of the ENIGMA datasets will enable us to test for the amount of variance explained by the environmental brain features identified in our project in a wide range of psychiatric disorders, thus quantifying the amount of environmentally-related brain pathology. (vi) We collaborate with the Human Brain Project (HBP) in The Virtual Brain (TVB) and will link our -omics results to eBRAINS, the HBP’s digital research infrastructure. (vii) Our associate partners at Fudan University are leading the formation of the cohorts of the Zhangjiang International Biobank (ZIB), a component of the Chinese Brain Project (CBP). These cohorts will serve as independent validation samples of the European IMAGEN and STRATIFY cohorts. In case of acceptance of this proposal, additional funding from the Chinese government will be made available to complete -omics analyses of these cohorts and to provide personnel support for data analyses, including virtual brain experiments.