Population Neuroscience

Lead: Gunter Schumann

The Population Neuroscience Research Group establishes and analyses big neuroimaging genetic datasets to precisely identify brain mechanisms underlying behaviour, and their genetic and environmental mediators. This enables prediction and stratification of mental disorders.

  • Brain network based stratification of reinforcement-related disorders

    The STRATIFY study is a European research project that aims to identify neural networks that underlie reinforcement-related disorders such as Major Depression, Alcohol Use Disorders, Schizophrenia and Eating Disorders. This project, including through brain imaging and genetics, aids in developing a neurobiological model for the classification of psychopathologies to develop improved interventions for mental health disorders.

  • 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.

    More information can be found under: www.environmental-project.org

  • Prof. Tianye Jia

    Prof. Xiao Chang

    Dr. Yunman Xia

    Zilin Li

Precision Medicine

Lead: Tristram Lett

Current diagnostic systems in psychiatry do not reflect the neurobiological mechanisms underlying mental illness, which hinders the development of therapies targeting the etiological mechanisms of mental illness.

Research confined to diagnostic boundaries yields heterogeneous results, whereas transdiagnostic studies often investigate individual symptoms in isolation. Thus, there is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, their relation to individual disorders, and the underlying neurobiological mechanisms. Our goal is to leverage quantitative neurobiological evidence from multimodal MRI and advanced brain simulation to address specific facets of psychopathology. Using machine learning and deep learning techniques, we aim to uncover concealed patterns of psychopathology that transcend diagnostic boundaries and are closely linked to multimodal brain features.

These patterns will be characterised in naturalistic, longitudinal samples across the age-span, cross-disorder patient samples, and with further dissection of brain mechanisms in the digital twin brain. Additionally, our latent features will be tailored to individuals using multilevel -omics data and deep characterization of environmental stressors employing remote sensing satellite data. While defined by precise quantitative neurobiological and environmental measures, we will develop novel frameworks of psychiatric nosology with direct application to both clinical research and individualised patient care.

  • The Human Brain Project was a European Future and Emerging Technologies (FET) Flagship project that ran from 2013 to 2023. It pioneered a new paradigm in brain research, at the interface of computing and technology.

  • Dr. Jean-Charles Roy

    Prof. Tianye Jia

    Prof. Xiao Chang

    Dr. Yunman Xia

    Zilin Li

Environment, Brain and Behavior

Lead: Elli Polemiti and Nilakshi Vaidya


The influence of the environment on brain and mental health is multifaceted, with potential to affect individuals negatively and positively. The environment can be broadly divided into the “macroenvironment”, which includes factors such as urbanisation, climate change, air pollution and regional socioeconomic status, and the “microenvironment”, pertaining to psychosocial experiences, such as childhood maltreatment and social isolation. The development of mental health conditions may be attributed to the cumulative impact of these environmental factors across a person’s life, reflecting a complex interplay between risk and protective factors of micro- and macroenvironment. 

PONS leverages large-scale big-data cohorts, deeply phenotyped neuroimaging studies and advanced mapping techniques with the goal to dissect and understand the individual and combined role of these environmental exposures, thereby offering a holistic view on their influence on mental health. We synthesise evidence from studies across different countries, cultures, ethnic groups and urban/ rural residencies, adopting cross-sectional and longitudinal approaches, aiming to enhance our understanding on how the environment shapes mental health on a global scale throughout different stages of life.

  • Identification of urbanicity-related environmental variables and risk and resilience profiles linked to mental illness; characterization of etiological pathways of urbanicity-related symptoms of depression, anxiety and substance use involving brain structure and function and genetics.

  • A targeted global online intervention for pandemic-related mental health problems

    Public health measures to combat epidemics and pandemics and its consequences in the short term and the long term globally depend on the development and implementation of successful strategies for behavioural modification. The efficacy of such measures is determined by psychological and environmental factors, which may differ between inhabitants of industrialized high-income countries (HIC) and low-and-middle-income countries (LMIC). Understanding the similarities and differences of behavioural and environmental moderators and their interactions in various countries will enable the identification and targeting of risk factors globally.

    Read more: https://gepris.dfg.de/gepris/projekt/458317126?language=en

  • Dr. Yanqun Zheng

    Yuzhu Li

    Zilin Li

Data Science

Lead: Yuxiang Dai / Hedi Kebir

The Data Science group in PONS specializes in combining epigenetic insights with brain mechanisms to deepen our understanding of mental health disorders. Utilizing state-of-the-art computational methods, we investigate the nuanced interactions between molecular alterations and brain functionality. Our focus is on crafting algorithms to decode the intricate connections between epigenetic signals and neuroimaging findings, aiming to enhance the precision of associating brain activity with behavioral outcomes. By applying these algorithms to broad clinical datasets, our research aims to innovate diagnostic and therapeutic strategies in mental health, highlighting the pivotal role of data science in uncovering complex biological networks.

  • Hedi Kebir

    Yuzhu Li