FOCUS:
Understanding mental health requires more than looking at symptoms in isolation — it means tracing the dynamic interplay between biology, psychology, the social environment, and the digital world. Our lab combines clinical expertise with computational innovation to investigate how environmental exposures, individual vulnerabilities, and social interactions shape trajectories of mental health.
We aim to:
-
identify risk and protective factors across biology, behavior, and environment (including the exposome),
-
develop AI-driven tools for early detection and personalised prevention,
-
translate digital and clinical innovations into effective interventions in psychiatry.
Alongside our research, we run an outpatient service for early recognition and intervention, ensuring our insights are grounded in clinical practice and benefit patients directly.
METHODS:
Our team brings together methodologically-oriented clinicians and clinically-oriented methodologists. This dual perspective enables us to move seamlessly from theory to practice.
We apply and advance methods such as:
-
Artificial intelligence and machine learning to extract meaningful patterns from complex behavioural, clinical, and biological data,
-
Generative and network models to simulate interactions between symptoms, risk factors, and environments,
-
Digital markers and computational psychiatry approaches to personalise diagnosis and monitoring,
-
Exposome research to capture the cumulative impact of environmental influences,
-
Meta-analysis and evidence synthesis to address critical clinical questions with the broadest possible evidence base.
AIMS:
By integrating AI-driven analytics, digital psychiatry, and environmental perspectives, we aim to build models that predict individual risk and treatment response with greater accuracy.
Ultimately, our goal is to personalise behavioural, pharmacological, and psychotherapeutic interventions in psychiatry — bridging clinic and computation to deliver better mental health outcomes in today’s human–digital world.
NEWS
17.01.2025
New paper! Can AI minds help understand mental health? 🤔
We propose using generative agents to simulate how environment & social factors impact mental health. Think virtual cities with AI citizens to study what shapes wellbeing.
Read more: https://www.nature.com/articles/s41746-024-01422-z
#AI #MentalHealth
Can artificial minds help us understand determinants of mental health? 🤔
Excited to share our new paper in @npjdigitalmed with Andreas Meyer-Lindenberg!
🧠 Real World Example:
Picture a person navigating a busy city - their mental wellbeing shaped by countless factors from noise levels to social interactions. Traditional research can only scratch the surface of these complex dynamics.
🔍 Technical Innovation:
We propose using generative agents (Park et al: https://lnkd.in/emDH_sM8) - AI entities powered by Large Language Models (LLMs) - to simulate human behavior in virtual environments in order to investigate determinants of mental health.
💻 Technical Architecture:
The innovation lies in how these agents combine:- Memory systems that store past experiences- The ability to process environmental cues- Realistic social interactions- Capacity to report mental health symptoms
This allows researchers to study how different factors interact over time in ways that would be impossible in real-world studies.
⚡️ Why This Matters:
This could offer a new approach to how we study mental health by:
- Enabling controlled experiments impossible in real life- Accelerating intervention development
- Informing urban planning for better mental health
- Advancing precision psychiatry
📄 Read the full paper here: https://lnkd.in/ev7qKkiehashtag
28.11.2024
Lab at DGPPN conference in Berlin!
Every year a few weeks before the festive season, it is time to travel to Berlin for the DGPPN conference. We had a great time presenting, listening and discussing - and also celebrating a bit... ;)
4.10.2024
🚨New Preprint!🚨
🚨New Preprint!🚨 Our latest study reveals neurobiological contributions to both static and dynamic functional connectivity (FC) using computational brain modeling. Learn how specific regions shape sFC and dFC patterns! 🧠📊
Check it out: https://www.biorxiv.org/content/10.1101/2024.10.01.614888v1
25.09.2024
KambeitzLab@ENCP!
We had a great time visiting European College of Neuropsychopharmacology in Milano this year. Lot of great interactions around exciting topics: A.I., patient stratification and of course: better treatment for patients with psychosis and depression...
01.09.2024
Joseph joins editorial board of NPJ Digital Medicine
LAB MEMBERS
Joseph Kambeitz
Prof.
Lana Kambeitz-Ilankovic
PD Dr.
Jessica Hartmann
Dr.
Marlene Rosen
Dr.
Hannah Hacker
MSc.
Öznur Baştürk
MSc.
Luzie Badde
cand. med.
Lotta Pries
Dr.
Sophie Fengler
Dr.
Linnea Hoheisel
MSc.
Alina Zubair
cand. med.
Nina Walter
cand. med.
Theresa Lichtenstein
PD Dr.
Annkathrin Böke
MSc.
Millennia Chakraborty
MSc.
Laura Völkel
cand. med.
LAB ALUMNI
Alina Zubair
Dr.
Linnea Hoheisel (now at INSPIRE Lab at University of Maryland)
Dr.
Hanna Kadel
MSc.
Ana Muthesius
Dr.
Nina Walter
Dr.
Uma Rzayeva
MD
Julian Wenzel
Dr.
Carolin Doll
MSc.
Marlene Rosen
Dr.
Sophie Müller
MSc.
Susanne Inanloo
MSc.
Alessandra Raio
MSc.
RESEARCH PROJECTS
LAMBDA study
PRESCIENT / AMP SCZ
2Long4COVID BLUT
Individualised prediction in clinical high risk
Understanding Schizophrenia using Computational Modeling: Biological Simulations of the Brain’s Functional Connectome Copy
Symptom networks in psychotic and affective disorders
CARE Network
PsyLetics
Transdiagnostic phenotyping of psychopathology in a help-seeking population (PhenoNetz) - an experience sampling study
Longitudinal course of cannabis-induced psychosis (CIP)
Personalized cognitive training
TALKS
Date | Presenter | Title |
|---|---|---|
18.01.2023 | Dr. Kevin Hilbert | Prediction of response to psychotherapy in anxiety disorders |
15.02.2023 | Dr. Sandra Vieira | Precision psychotherapy: where are we and next steps |
30.03.2022 | Mallory Dobias | Single-session interventions for Mental Health |
27.04.2022 | Dr. Alex Stainton | Cognition & Resilience in Clinical High-Risk for Psychosis |
10.02.2021 | Dr. Martin Hebart | Revealing Interpretable Representational Dimensions Shared Between Humans and Deep Neural Networks |
24.02.2021 | Dr. Nils Opel | Challenges for Translational Psychiatry in the Age of Big Data |
24.03.2021 | Dr. Urs Braun | Network Neuroscience in Psychiatry |
14.04.2021 | Dr. Stephanie Forkel | White matter tractography & its application in health and disease |
16.06.2021 | Dr. Diana Prata | Neuroimaging Genetics in Psychiatry |
01.12.2021 | Dr. Gemma Modinos | Neurobiology of psychosis risk: From mechanistic to big data approaches |
08.12.2021 | Dr. Belinda Platt | Biological and cognitive vulnerability for youth depression: from laboratory research to preventive interventions. |
Check here for upcoming talks from invited speakers.
WORK WITH US
We frequently offer research projects in the area of mental health, machine learning & big data and neuroimaging. Feel free to get in contact if you are interested in collaborating or working with us.
SELECTED PUBLICATIONS
Bechdolf, A., Correll, C.U., Hellenschmidt, T., Holzner, L., von Hardenberg, L., Jäckel, D., Kambeitz, J., Koutsouleris, N., Kusserow, N., Leopold, K., Meisenzahl, E., Pfennig, A., Reif, A., Reininghaus, U., Schellong, M., Shmuilovich, O., Uhlhaas, P.J., Domanska, O.M., 2025. Integrated Youth Mental Health Services – niederschwellige, integrierte Angebote für junge Menschen in psychischen Krisen: Internationale Erfahrungen und die aktuelle Situation in Deutschland. Nervenarzt. https://doi.org/10.1007/s00115-025-01895-7
Chekroud, A.M., Hawrilenko, M., Loho, H., Bondar, J., Gueorguieva, R., Hasan, A., Kambeitz, J., Corlett, P.R., Koutsouleris, N., Krumholz, H.M., Krystal, J.H., Paulus, M., 2024. Illusory generalizability of clinical prediction models. Science 383, 164–167.
Hoheisel, L., Hacker, H., Fink, G.R., Daun, S., Kambeitz, J., 2025. Computational modelling reveals neurobiological contributions to static and dynamic functional connectivity patterns. Front. Comput. Neurosci. 19, 1525785.
Kambeitz, J., Cabral, C., Sacchet, M.D., Gotlib, I.H., Zahn, R., Serpa, M.H., Walter, M., Falkai, P., Koutsouleris, N., 2017. Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies. Biol. Psychiatry 82, 330–338.
Kambeitz, J., Meyer-Lindenberg, A., 2025. Modelling the impact of environmental and social determinants on mental health using generative agents. NPJ Digit. Med. 8. https://doi.org/10.1038/s41746-024-01422-z
Kambeitz-Ilankovic, L., Rzayeva, U., Völkel, L., Wenzel, J., Weiske, J., Jessen, F., Reininghaus, U., Uhlhaas, P.J., Alvarez-Jimenez, M., Kambeitz, J., 2022. A systematic review of digital and face-to-face cognitive behavioral therapy for depression. NPJ Digit Med 5, 144.
Klein, P.C., Ettinger, U., Schirner, M., Ritter, P., Rujescu, D., Falkai, P., Koutsouleris, N., Kambeitz-Ilankovic, L., Kambeitz, J., 2021. Brain Network Simulations Indicate Effects of Neuregulin-1 Genotype on Excitation-Inhibition Balance in Cortical Dynamics. Cereb. Cortex 31, 2013–2025.
Koutsouleris, N., Dwyer, D.B., Degenhardt, F., Maj, C., Urquijo-Castro, M.F., Sanfelici, R., Popovic, D., Oeztuerk, O., Haas, S.S., Weiske, J., Ruef, A., Kambeitz-Ilankovic, L., Antonucci, L.A., Neufang, S., Schmidt-Kraepelin, C., Ruhrmann, S., Penzel, N., Kambeitz, J., Haidl, T.K., Rosen, M., Chisholm, K., Riecher-Rössler, A., Egloff, L., Schmidt, A., Andreou, C., Hietala, J., Schirmer, T., Romer, G., Walger, P., Franscini, M., Traber-Walker, N., Schimmelmann, B.G., Flückiger, R., Michel, C., Rössler, W., Borisov, O., Krawitz, P.M., Heekeren, K., Buechler, R., Pantelis, C., Falkai, P., Salokangas, R.K.R., Lencer, R., Bertolino, A., Borgwardt, S., Noethen, M., Brambilla, P., Wood, S.J., Upthegrove, R., Schultze-Lutter, F., Theodoridou, A., Meisenzahl, E., PRONIA Consortium, 2021. Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression. JAMA Psychiatry 78, 195–209.
Penzel, N., Antonucci, L.A., Betz, L.T., Sanfelici, R., Weiske, J., Pogarell, O., Cumming, P., Quednow, B.B., Howes, O., Falkai, P., Upthegrove, R., Bertolino, A., Borgwardt, S., Brambilla, P., Lencer, R., Meisenzahl, E., Rosen, M., Haidl, T., Kambeitz-Ilankovic, L., Ruhrmann, S., Salokangas, R.R.K., Pantelis, C., Wood, S.J., Koutsouleris, N., Kambeitz, J., PRONIA Consortium, 2021. Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis. Neuropsychopharmacology 46, 1484–1493.
Rißmayer, M., Kambeitz, J., Javelle, F., Lichtenstein, T.K., 2024. Systematic Review and Meta-analysis of Exercise Interventions for Psychotic Disorders: The Impact of Exercise Intensity, Mindfulness Components, and Other Moderators on Symptoms, Functioning, and Cardiometabolic Health. Schizophr. Bull. https://doi.org/10.1093/schbul/sbae015
Rosen, M., Betz, L.T., Schultze-Lutter, F., Chisholm, K., Haidl, T.K., Kambeitz-Ilankovic, L., Bertolino, A., Borgwardt, S., Brambilla, P., Lencer, R., Meisenzahl, E., Ruhrmann, S., Salokangas, R.K.R., Upthegrove, R., Wood, S.J., Koutsouleris, N., Kambeitz, J., PRONIA Consortium, 2021. Towards clinical application of prediction models for transition to psychosis: A systematic review and external validation study in the PRONIA sample. Neurosci. Biobehav. Rev. 125, 478–492.
Şahin, D., Kambeitz-Ilankovic, L., Wood, S., Dwyer, D., Upthegrove, R., Salokangas, R., Borgwardt, S., Brambilla, P., Meisenzahl, E., Ruhrmann, S., Schultze-Lutter, F., Lencer, R., Bertolino, A., Pantelis, C., Koutsouleris, N., Kambeitz, J., PRONIA Study Group, 2024. Algorithmic fairness in precision psychiatry: analysis of prediction models in individuals at clinical high risk for psychosis. Br. J. Psychiatry 224, 55–65.
Wenzel, J., Badde, L., Haas, S.S., Bonivento, C., Van Rheenen, T.E., Antonucci, L.A., Ruef, A., Penzel, N., Rosen, M., Lichtenstein, T., Lalousis, P.A., Paolini, M., Stainton, A., Dannlowski, U., Romer, G., Brambilla, P., Wood, S.J., Upthegrove, R., Borgwardt, S., Meisenzahl, E., Salokangas, R.K.R., Pantelis, C., Lencer, R., Bertolino, A., Kambeitz, J., Koutsouleris, N., Dwyer, D.B., Kambeitz-Ilankovic, L., PRONIA consortium, 2023. Transdiagnostic subgroups of cognitive impairment in early affective and psychotic illness. Neuropsychopharmacology. https://doi.org/10.1038/s41386-023-01729-7
Wenzel, J., Dreschke, N., Hanssen, E., Rosen, M., Ilankovic, A., Kambeitz, J., Fett, A.-K., Kambeitz-Ilankovic, L., 2024. Ecological momentary assessment (EMA) combined with unsupervised machine learning shows sensitivity to identify individuals in potential need for psychiatric assessment. Eur. Arch. Psychiatry Clin. Neurosci. 274, 1639–1649.
ACKNOWLEDGEMENTS
CONTACT US
