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:
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identify risk and protective factors across biology, behavior, and environment (including the exposome),
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develop AI-driven tools for early detection and personalised prevention,
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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:
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Artificial intelligence and machine learning to extract meaningful patterns from complex behavioural, clinical, and biological data,
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Generative and network models to simulate interactions between symptoms, risk factors, and environments,
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Digital markers and computational psychiatry approaches to personalise diagnosis and monitoring,
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Exposome research to capture the cumulative impact of environmental influences,
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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
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LAB MEMBERS
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LAB ALUMNI
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RESEARCH PROJECTS
Cardio with Christina 9:00 am
Weight Lifting with Iron 10:00 am
Boxing with Jasmine 9:30 am
TALKS
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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
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