Lab for Prediction and Prevention in Mental Health

German Version

In order to understand mental health, it is crucial to consider the complex interplay of an individual with its environment. This includes biological and psychological factors as well as social interactions or the use of digital media. In our lab, we conduct research on this exciting field with the aim to:

  • identify adverse influences for mental health as well as protective factors
  • provide modern tools to allow individualised diagnostics and preventive strategies
  • develop personalised interventions to support mental health

Our team consists of a mix of methodologically-oriented clinicians and clinically-oriented methodologists, which allows us to live a close interaction between research and clinical application. Together we aim at the clinical translation of novel tools inspired by data-science oriented approaches such as:

  • machine-learning and big data analytics to identify informative patterns in behavioural or biological data
  • network models to characterise complex relationships between symptoms, risk and protective factors
  • dynamic systems modelling to provide a mechanistic understanding of brain in health and disease
  • meta-analysis to address essential clinical questions based on a large body of evidence

We hope that by extending the focus to the development of behavioural, cognitive and neuro-imaging biomarkers, we can improve our understanding of mental health and generate models for individualised prediction in psychiatric patients or individuals at risk. Ultimately, we aim to personalise behavioural, pharmacological and psychotherapeutic interventions in the complex human-digital setting of psychiatry today using our expertise in machine learning and computational methods.

Key Publications

  • Betz LT, Penzel N, Rosen M, Kambeitz J (2020): Relationships between childhood trauma and perceived stress in the general population: a network perspective. Psychol Med 1–11. DOI: 10.1017/S003329172000135X
  • Kambeitz J, Goerigk S, Gattaz W, Falkai P, Benseñor IM, Lotufo PA, et al. (2020): Clinical patterns differentially predict response to transcranial direct current stimulation (tDCS) and escitalopram in major depression: A machine learning analysis of the ELECT-TDCS study. J Affect Disord 265: 460–467. DOI: 10.1016/j.jad.2020.01.118
  • Proebstl L, Kamp F, Manz K, Krause D, Adorjan K, Pogarell O, ..., Kambeitz, J. (2019): Effects of stimulant drug use on the dopaminergic system: A systematic review and meta-analysis of in vivo neuroimaging studies. Eur Psychiatry 59: 15–24. DOI: 10.1016/j.eurpsy.2019.03.003
  • Kambeitz-Ilankovic L, Betz LT, Dominke C, Haas SS, Subramaniam K, Fisher M, et al. (2019): Multi-outcome meta-analysis (MOMA) of cognitive remediation in schizophrenia: Revisiting the relevance of human coaching and elucidating interplay between multiple outcomes. Neurosci Biobehav Rev 107: 828–845. DOI: 10.1016/j.neubiorev.2019.09.031
  • Kamp F, Proebstl L, Penzel N, Adorjan K, Ilankovic A, Pogarell O, ..., Kambeitz, J. (2019): Effects of sedative drug use on the dopamine system: a systematic review and meta-analysis of in vivo neuroimaging studies. Neuropsychopharmacology 44: 660–667. DOI: 10.1038/s41386-018-0191-9
  • Betz LT, Brambilla P, Ilankovic A, Premkumar P, Kim M-S, Raffard S, et al. (2019): Deciphering reward-based decision-making in schizophrenia: A meta-analysis and behavioral modeling of the Iowa Gambling Task. Schizophr Res 204: 7–15. DOI: 10.1016/j.schres.2018.09.009
  • Koutsouleris N, Kambeitz-Ilankovic L, Ruhrmann S, Rosen M, Ruef A, Dwyer DB, et al.(2018): Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis. JAMA Psychiatry 75: 1156–1172. DOI: 10.1001/jamapsychiatry.2018.2165
  • Steffens M, Meyhöfer I, Fassbender K, Ettinger U, Kambeitz J (2018): Association of Schizotypy With Dimensions of Cognitive Control: A Meta-Analysis. Schizophr Bull 44: S512–S524. DOI: 10.1093/schbul/sby030
  • Kambeitz J, Cabral C, Sacchet MD, Gotlib IH, Zahn R, Serpa MH, et al. (2017): Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies. Biol Psychiatry 82: 330–338. DOI: 10.1016/j.biopsych.2016.10.028
  • Kambeitz J, la Fougère C, Werner N, Pogarell O, Riedel M, Falkai P, Ettinger U (2016): Nicotine-dopamine-transporter interactions during reward-based decision making. Eur Neuropsychopharmacol 26: 938–947. DOI: j.euroneuro.2016.03.011
  • Kambeitz J, Kambeitz-Ilankovic L, Cabral C, Dwyer DB, Calhoun VD, van den Heuvel MP, et al. (2016): Aberrant Functional Whole-Brain Network Architecture in Patients With Schizophrenia: A Meta-analysis. Schizophr Bull 42 Suppl 1: S13–21. DOI: 10.1093/schbul/sbv174

Team

CV's of the team
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