environMENTAL

Reducing the impact of major environmental challenges on mental health

environMENTAL is an EU-funded project, studying the impact of climate, pollution, urbanicity, regional socioeconomic conditions, as well as the Covid19 pandemic on brain health, and characterize its underlying biological mechanisms. We will analyse data from more than one million European citizens and patients to uncover brain mechanisms linked to environmental adversity and leading to
symptoms of depression, anxiety, stress and substance abuse.

About the project

Participate in the project

Digital prevention and early intervention are important targets in non-pharmacological treatment. environMENTAL will pursue two parallel approaches, developing a mental health app and virtual reality interventions to reduce the impact of significant environmental challenges on human well-being.

How to participate

Seminars

The environMENTAL seminar series was established to inform our stakeholders about the environMENTAL project targets and to create a platform of active discussion and knowledge exchange.

Challenges and opportunities for drug discovery in mental disorders

There is an undisputed need for better therapies addressing mental illnesses. However, despite major advances in the understanding of the molecular basis of disorders such as depression, anxiety schizophrenia and autism in the past decades, efforts to discover and develop new drugs for neuropsychiatric disorders have remained relatively unsuccessful. The disappointments can be traced to failures in the target identification and target validation effort, as reflected by the poor ability of current cellular and animal models to predict efficacy and side-effects. Here will I discuss how patient-based disease modelling can be implemented in the early drug discovery process as a crucial component in addressing current problems.

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Implementation of a patient-derived disease model of Phelan McDermid Syndrome for the identification of SHANK3-specific chemical modifiers

Autism Spectrum Disorders (ASD) are a heterogeneous group of neurodevelopmental disorders which complicates the identification of an effective therapeutic solution. More than 75 % of people suffering from Phelan McDermid Syndrome (PMS) exhibits ASD symptoms. This rare disease is due to a deletion/alteration in chromosome chromosome 22 (22q13) leading to a haploinsufficiency of SHANK3 expression. SHANK3 encodes a scaffolding protein in the postsynaptic density of excitatory synapses. A reduced expression of SHANK3 leads to an alteration in neuron morphology and synapse connectivity via unknown mechanism.
After the identification of a developmental hyperdifferentiation phenotype in patient-derived iPS cells during the first 6-14 days of differentiation into glutamatergic neurons, a fully automated process was developed allowing us to perform a >7000 molecules screening to reverse hyperdifferentiation. Validation of identified hit compounds was performed in a synaptic imaging assay after 28 days of differentiation. In summary, we performed phenotypic screening using specific pathological hallmarks of a neurodevelopmental disease in patient-derived of cells.

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EBRAINS user engagement

Our twin goals:

the scientific work of the Human Brain Project(HBP)
the ongoing development of the state-of-the art Research Infrastructure for brain studie: France Nivelle, Chief Communications and Content Officer, EBRAINS

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environMENTAL Methodological approaches for MRI data integration biomarker discovery and validation within the environmental project

In this talk, I will give an overview of the analytical strategy we will employ for biomarker discovery in the environMENTAL project. To find generalisable biomarkers to predict and stratify mental disorders, we will need to solve many methodological challenges including how to meaningfully aligning data from heterogeneous cohorts spanning the whole lifespan, fusing data from modalities with very different characteristics, accounting for complex patterns of missing data and extracting generalisable and interpretable low dimensional representations from complex datasets.
Moreover, many of these analyses need to be performed in a decentralised and distributed manner. I will give an overview of some of the analytical techniques that we will employ to solve these challenges, including federated machine learning techniques, normative modelling, deep learning, transfer learning and classical penalised multivariate regression techniques. I will illustrate by discussing in detail how such techniques can be applied to neuroimaging data but it should be remembered that they are all more widely applicable.

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environMENTAL Making the MOSTest of genetics multivariate approaches to variant discovery and disorder prediction

Brain and behavioural measures related to mental illness have complex genetic architectures, involving many common polymorphisms with small individual effects, challenging psychiatric genetic research. Given the distributed nature of genetic signal across brain regions, and high levels of pleiotropy across mental disorders, joint analysis of sets of neuroimaging or mental health measures in a multivariate statistical framework provides a way to substantially enhance discovery of genetic markers with current sample sizes.
In this presentation, I will introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable permutation-based inference. I will provide an overview of findings from a series of studies applying MOSTest to data from tens of thousands of individuals from large population cohorts, enabling the discovery of thousands of genetic markers associated with a range of traits. I will further illustrate how the enhanced statistical power achieved through joint analysis of brain and behavioural measures can be leveraged to improve genetics-based prediction of mental disorders in clinical cohorts. As such, these multivariate approaches can significantly contribute to achieving the goals of environMENTAL.

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The environMENTAL Team

an interdisciplinary team of excellence

Driving a highly innovative and interdisciplinary approach, the project teams the ideas and expertise of neuroscientists, psychiatrists, geo-scientists, climatologists, psychologists, epidemiologists, anthropologists, computer scientists, experts in digital interventions as well as non-academic stakeholders such as patient associations.
The consortium will be supported by a Stakeholder Board, which will advise on ethical and societal questions to ensure a responsible research and innovation programme.