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.
PhD Position in Computational Neurosciences / Translational Clinical Neurosciences
PhD Position in Computational Neurosciences / Translational Clinical Neurosciences
We have an open PhD position @kambeitzlab in cooperation with the Forschungszentrum Jülich, Institute of Neuroscience and Medicine - Cognitive Neuroscience (INM-3) in the area Computational Neurosciences / Translational Clinical Neurosciences. The scope of the research is the investigation of neurobiological mechanisms underlying brain dynamics using multimodal neuroimaging data and computational neurosciences approaches, more specifically the changes of brain dynamics in the context of psychological or neurological disorders, as well as the relationship to risk- and resilience-factors. More information can be found here.
Dr. Martin Hebart
Revealing Interpretable Representational Dimensions Shared Between Humans and Deep Neural Networks
Dr. Nils Opel
Challenges for Translational Psychiatry in the Age of Big Data
Dr. Urs Braun
Network Neuroscience in Psychiatry
Dr. Stephanie Forkel
White matter tractography & its application in health and disease
Dr. Diana Prata
Neuroimaging Genetics in Psychiatry
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.
Antonucci LA, Penzel N, Pigoni A, Dominke C, Kambeitz J, Pergola G (2020): Flexible and specific contributions of thalamic subdivisions to human cognition. Neurosci Biobehav Rev. 2021 May;124:35-53. DOI: 10.1016/j.neubiorev.2021.01.014. Epub 2021 Jan 23.
Wenzel J, Haas SS, Dwyer DB, Ruef A, Oeztuerk OF, Antonucci LA, von Saldern S, Bonivento C, Garzitto M, Ferro A, Paolini M, Blautzik J, Borgwardt S, Brambilla P, Meisenzahl E, Salokangas RKR, Upthegrove R, Wood SJ, Kambeitz J, Koutsouleris N, Kambeitz-Ilankovic L, PRONIA consortium (2021): Cognitive subtypes in recent onset psychosis: distinct neurobiological fingerprints. Neuropsychopharmacology. DOI:
Penzel N, Antonucci LA, Betz LT, Sanfelici R, Weiske J, Pogarell O, Cumming P, Quednow BB, 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 RRK, Pantelis C, Wood SJ, Koutsouleris N, Kambeitz J, PRONIA Consortium (2021): Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis. Neuropsychopharmacology. DOI: 10.1038/s41386-021-00977-9.
Rosen M, Betz LT, Schultze-Lutter F, Chisholm K, Haidl TK, Kambeitz-Ilankovic L, Bertolino A, Borgwardt S, Brambilla P, Lencer R, Meisenzahl E, Ruhrmann S, Salokangas RKR, Upthegrove R, Wood SJ, 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. 2021 Feb 23;S0149-7634(21)00086-5. DOI: 10.1016/j.neubiorev.2021.02.03
Betz LT, Penzel N, Kambeitz-Ilankovic L, Rosen M, Chisholm K, Stainton A, Haidl TK, Wenzel J, Bertolino A, Borgwardt S, Brambilla P, Lencer R, Meisenzahl E, Ruhrmann S, Salokangas RKR, Schultze-Lutter F, Wood SJ, Upthegrove R, Koutsouleris N, Kambeitz J, PRONIA consortium (2020): General psychopathology links burden of recent life events and psychotic symptoms in a network approach. npj Schizophrenia volume 6, Article number: 40. DOI:
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
For a complete list of publications, please click here.