Periodic Reporting for period 2 - environMENTAL (Reducing the impact of major environmental challenges on mental health)
Reporting period: 2023-12-01 to 2025-05-31
environMENTAL investigates how global challenges—climate change, urbanization, and COVID-19 stress—affect mental health across the lifespan. Using comprehensive data from 1.5 million Europeans, the project aims to identify brain mechanisms, risk and resilience biomarkers, innovative interventions, and evidence-based strategies for preventing and treating environment-related mental illnesses.
WP01 – Gene-Environment Data Infrastructure and Discovery: Core infrastructures were established to investigate the mental health effects of COVID-19, revealing increased psychological distress in adolescents and adults. High-resolution geolocation data for MoBa participants were integrated with genetic and environmental data. A major innovation was the development of FEMA GWAS, a tool designed for longitudinal and family-based genetic data analysis, enabling sophisticated gene–environment interaction modelling.
WP02 – Multimodal Environmental Assessment Tool: Comprehensive environmental datasets characterizing urbanicity, rurality, climate, and atmospheric conditions were developed across all participating countries. These datasets have been fully processed and integrated into a spatial data infrastructure supporting multilayered environmental annotation. This infrastructure will support both mechanistic research and policy-relevant environmental health mapping.
WP03 – Digital Health Assessments: The project launched ‘StreetMind’ [www.streetmind.eu]) a digital health platform including a smartphone app and browser interface to collect behavioural, cognitive, and affective data in real-world environments. Ongoing assessments across multiple cohorts are capturing digital biomarkers using neuro-psycho-behavioural stratification methods and virtual reality paradigms. Data integration is underway, with initial analyses feeding into predictive models.
WP04 – Data Analysis and Biomarker Identification: Federated learning was implemented using the COINSTAC platform to enable secure, distributed data analysis across sites. Algorithms were developed to infer population-level genetic and environmental determinants of mental health. WP4 also contributed significantly to methodological advances in the field, including the development of a harmonisation framework and publication of key findings on adversity’s neurobiological effects.
WP05 – Deep Phenotyping and Omics Characterisation: All multi-omics data (genotyping, transcriptomics, methylation, and proteomics) from major cohorts were acquired, quality-checked, and centrally shared. Using an integrative analysis strategy, loci on Chromosomes 3 and 7 were identified as relevant for inflammation and neurobiological pathways. Machine learning was employed to generate predictive multimodal biomarker profiles for depression, eating disorders, and alcohol use, which will now undergo validation.
WP06 – Replication and Validation in Clinical Cohorts: Existing clinical cohorts covering diverse mental disorders (e.g. depression, AUD, schizophrenia) were harmonised through a Python-based framework aligned with RDoC constructs. Transdiagnostic analyses are underway, and multivariate Environmental Risk and Resilience Signatures (ERRS) are being derived using normative modelling. These signatures will inform personalised prevention and intervention approaches.
WP07 – Mechanistic Investigations and Drug Discovery: Validated 2D and 3D stem cell-derived brain models have been used to investigate cellular stress responses. WP7 selected a panel of atmospheric pollutants and partnered with WP5 to identify candidate genes (e.g. PCCB, CRHR1). These efforts are leading to high-content assay development for compound screening and deeper mechanistic insights into environment-gene interactions.
WP08 – Digital Intervention: A controlled virtual reality experiment was conducted to study how environmental change scenarios affect stress and pro-environmental behaviour. The Best-Worst group (exposed to a positive then deteriorated environment) showed greater psychological engagement and reduced carbon footprint. Findings demonstrate how immersive simulations can be used to influence real-world attitudes and inform scalable intervention strategies.
WP09 – Responsible Research and Innovation: A central achievement was the establishment of an *Experts by Experience* (EbE) Board, composed of individuals with lived mental health experience. The Ethics Advisory Board (EAB) was expanded with a cybersecurity expert to address risks related to AI and VR technologies. WP9 provided guidance for ethical review processes across partners, enhancing compliance and trust.
WP10 – Dissemination, Communication, and Exploitation: The project disseminated 61 peer-reviewed publications, launched the Earth, Brain, Health Commission (4,200 online attendees), and hosted a Summer School with 160 early-career participants. AI/mental health summits and policy engagement events have strengthened project visibility and stakeholder engagement.
WP11 – Project Coordination and Management: The Project Management Office ensured robust coordination, governance, and communication among partners. Deliverables including the 1st Periodic Report and updated Data Management Plan (DMP) were submitted on schedule.
WP12 – Data Management: An advanced data infrastructure was established at the Berlin Institute of Health, featuring high-performance computing and cloud components. The *Mellite* meta-database, aligned with FAIR data principles, is in final stages of deployment and will enhance transparency, data sharing, and cross-WP interoperability.
WP13 – Ethics Requirements: All ethics deliverables have been fulfilled, with reviews addressing AI, VR, and vulnerable populations, ensuring that human participant research complies with ethical standards.
WP02 – Multimodal Environmental Assessment Tool: Comprehensive environmental datasets characterizing urbanicity, rurality, climate, and atmospheric conditions were developed across all participating countries. These datasets have been fully processed and integrated into a spatial data infrastructure supporting multilayered environmental annotation. This infrastructure will support both mechanistic research and policy-relevant environmental health mapping.
WP03 – Digital Health Assessments: The project launched ‘StreetMind’ [www.streetmind.eu]) a digital health platform including a smartphone app and browser interface to collect behavioural, cognitive, and affective data in real-world environments. Ongoing assessments across multiple cohorts are capturing digital biomarkers using neuro-psycho-behavioural stratification methods and virtual reality paradigms. Data integration is underway, with initial analyses feeding into predictive models.
WP04 – Data Analysis and Biomarker Identification: Federated learning was implemented using the COINSTAC platform to enable secure, distributed data analysis across sites. Algorithms were developed to infer population-level genetic and environmental determinants of mental health. WP4 also contributed significantly to methodological advances in the field, including the development of a harmonisation framework and publication of key findings on adversity’s neurobiological effects.
WP05 – Deep Phenotyping and Omics Characterisation: All multi-omics data (genotyping, transcriptomics, methylation, and proteomics) from major cohorts were acquired, quality-checked, and centrally shared. Using an integrative analysis strategy, loci on Chromosomes 3 and 7 were identified as relevant for inflammation and neurobiological pathways. Machine learning was employed to generate predictive multimodal biomarker profiles for depression, eating disorders, and alcohol use, which will now undergo validation.
WP06 – Replication and Validation in Clinical Cohorts: Existing clinical cohorts covering diverse mental disorders (e.g. depression, AUD, schizophrenia) were harmonised through a Python-based framework aligned with RDoC constructs. Transdiagnostic analyses are underway, and multivariate Environmental Risk and Resilience Signatures (ERRS) are being derived using normative modelling. These signatures will inform personalised prevention and intervention approaches.
WP07 – Mechanistic Investigations and Drug Discovery: Validated 2D and 3D stem cell-derived brain models have been used to investigate cellular stress responses. WP7 selected a panel of atmospheric pollutants and partnered with WP5 to identify candidate genes (e.g. PCCB, CRHR1). These efforts are leading to high-content assay development for compound screening and deeper mechanistic insights into environment-gene interactions.
WP08 – Digital Intervention: A controlled virtual reality experiment was conducted to study how environmental change scenarios affect stress and pro-environmental behaviour. The Best-Worst group (exposed to a positive then deteriorated environment) showed greater psychological engagement and reduced carbon footprint. Findings demonstrate how immersive simulations can be used to influence real-world attitudes and inform scalable intervention strategies.
WP09 – Responsible Research and Innovation: A central achievement was the establishment of an *Experts by Experience* (EbE) Board, composed of individuals with lived mental health experience. The Ethics Advisory Board (EAB) was expanded with a cybersecurity expert to address risks related to AI and VR technologies. WP9 provided guidance for ethical review processes across partners, enhancing compliance and trust.
WP10 – Dissemination, Communication, and Exploitation: The project disseminated 61 peer-reviewed publications, launched the Earth, Brain, Health Commission (4,200 online attendees), and hosted a Summer School with 160 early-career participants. AI/mental health summits and policy engagement events have strengthened project visibility and stakeholder engagement.
WP11 – Project Coordination and Management: The Project Management Office ensured robust coordination, governance, and communication among partners. Deliverables including the 1st Periodic Report and updated Data Management Plan (DMP) were submitted on schedule.
WP12 – Data Management: An advanced data infrastructure was established at the Berlin Institute of Health, featuring high-performance computing and cloud components. The *Mellite* meta-database, aligned with FAIR data principles, is in final stages of deployment and will enhance transparency, data sharing, and cross-WP interoperability.
WP13 – Ethics Requirements: All ethics deliverables have been fulfilled, with reviews addressing AI, VR, and vulnerable populations, ensuring that human participant research complies with ethical standards.
The project sets a new benchmark in environmental mental health research. It pioneers integration of multi-omics and high-resolution environmental exposures using Gene–Environment Data Infrastructures (WP01/02) and the novel FEMA GWAS tool. The *StreetMind* platform (WP03) introduces scalable digital phenotyping. WP04’s federated learning and data harmonisation protocols enable secure, transnational analytics. WP05 links molecular pathways to behavioural outcomes. ERRS (WP06), 3D stem cell models (WP07), and immersive VR-based behaviour modification (WP08) represent breakthrough tools. The EbE Board (WP09) showcases ethical leadership and participatory governance.